Jim Gillard, Operations Master Black Belt, Polar Semiconductor JMP User since 2003 Fun Facts: Jim is an amateur astronomer, has built a roll-off roof observatory at his home and attends the Texas Star Party each year. What is your favorite feature in JMP? My favorite feature about JMP is, by far, the JSL scripting. Being able to automate tasks and create dashboards for communication saves so much time Graph Builder is my favorite built-in feature because of its versatility. It’s easy to see what you are going to get! What was your first job (ever)? I grew up on a dairy cow farm, so my first job was completing all the chores that come with caring for dairy cows. This also included building and fixing equipment and buildings. My first paid job was working for a canning company, moving and labeling cans of food.  What is your proudest professional moment? I was involved in getting a third of the entire company’s annual capital budget approved with one of the toughest executives I’ve ever encountered. I was able to anticipate which questions he would ask and was always prepared with answers. How did you get interested in operations and process improvement? I was a young process engineer with little statistical knowledge, but I worked with a statistician who was running DOEs on a very tough issue. I was amazed how he was able to identify the important factors and assess where the setting should be to improve yield. I quickly understood the power of statistics and experimental design. Getting trained in Six Sigma was a natural progression.     What do you like most about the work you do? Making the invisible (data and information) visible to others. Providing scripts for others to use so that they can do their work more efficiently. Helping others understand and use statistical tests and methods.  How are you currently using JMP? Running scripts to summarize data and pushing results to a dashboard for others in the company to reference. I also provide scripts that others can use to run ad hoc reports, thus reducing the time to discover information by hours when it was done manually.    What is the first project you worked on using JMP? Summarizing yields for wafer manufacturing.   Is there anything you would like to say to JMP development or John Sall? Thanks for the interactive, visual tools. And keep listening to your customers for improvements!  How do you see the field of data science progressing in the next 20-50 years? Machine learning and dealing with messy and incomplete data to find the important factors are key. Building models will greatly help. We will continue to find ways to relate disconnected data. What advice would you give a beginning JMP user? Get the data into a table and start looking at it graphically. Everything you need is under the red arrow! ... View more

Michael Anderson, Senior Statistical & Metrology Development Engineer, Corning Fun fact: I have been a runner for 32 years,  but my first race ever was in 2012 at age 46! Currently, I log more than 2,500 miles per year. JMP User since 1991 "When JMP arrived, it was a revelation," says JMP Champion Michael Anderson of Corning. What is your favorite feature in JMP? That is like asking which child is my favorite! The reality is I use JMP in all phases of my work, so I have a favorite screening feature, experimental design feature and so on. It is funny, I was very late to the Graph Builder party and still use the Variability Chart quite often. Now Graph Builder is where I spend my time as soon as I get a new data set. On the experiment side, I would say that most of my experiments have been Definitive Screening Designs, as they are incredibly powerful and efficient for a development organization. What was your first job (ever)?  There are a couple of ways to answer that question. As a young person (before “officially” working at age 16), I had a paper route and lugged trays of sodas up and down the stairs at what is now Patriots Stadium. The first normal scheduled job I had was as a store clerk at a long defunct New England chain called “Bradlees”. I was given a lot of responsibility and learned a ton about business operations, inventory and people! What is your proudest professional moment? In ~2000 I was asked to give a talk at what turned out to be a one-time measurement technology conference. I was able to talk about the new field of spectroscopic scatterometry. That stands out for being the culmination of a lot of hard work in an area that was both theoretical and practical. I was using my engineering, optics and statistics skills in a way that would bring new capabilities to the industry. How did you get interested in statistics?  I took all of the available statistics classes during my engineering undergrad and really enjoyed them. But, it was during my first job discussing precision resistors with another engineer who didn’t understand distributions, truncation, segmentation and the calculation of likelihood that got me really energized. It became clear I wanted to further pursue statistics graduate classes and statistics as part of my career. What do you like most about the work you do? I love that I get to share my love of data analysis with people at all levels. I also enjoy getting to mentor younger engineers just starting out in statistics and JMP. How are you currently using JMP? I have JMP open all day, every day, and utilize the graphing, DOE, screening and modeling features extensively. In our product and process development area, we are constantly interacting with research and production groups. That means we do small sample analysis, exploratory experiments and use large and wide data sets. Functional Data Explorer is the most recent area that has seen a great increase in usage. What was the first project you worked on using JMP? I was new at a mid-sized company and was leading a high-visibility project and knew my reputation would be made from this one way or the other. I had recently started using JMP, and used it to look at some preliminary data and run a simple factorial experiment. Within two weeks, I was able to present, using three graphs, results that showed the problem and suggested a solution in a way that had been missed for nearly two years. The power of JMP and everything it enabled was firmly established for me and many others in that moment. Is there anything you would like to say to JMP development or John Sall? I would like to thank John Sall and all the incredible developers behind JMP for their vision and dedication. It is hard to think of what things were like in 1989 as an engineer and statistician. There were software packages that could do analysis, others that could do presentation graphics, and still others for specialized quality features. But each had significant downsides and learning curves. When JMP arrived, it was a revelation. How quick and easy it made getting past the early stages of analysis of our data. I was presenting at a conference in 1994 and using a Fit Y by X graph with some added fitting items. Afterwards someone asked if I had used JMP! We then got into a discussion about how much we loved JMP and how it had changed so much of our workflow from the moment we’d started using it.  How do you see the field of data science/analytics progressing in the next 20-50 years? The death of human-based statistical analysis is greatly exaggerated. I was educated right at the end of learning to use a slide rule. I learned how to calculate ANOVA, regression, matrix math and so on by hand. At the time, there were some who bemoaned the use of computers to do these things, saying it would replace thinking statisticians with cold machines. Now we hear that AI and ML will obsolete statisticians. I simply don't believe it. With each new leap in technology, we have seen the ability of people to make better use of data being a key to learning and innovation. ... View more

Sky Alibhai, Wildlife Biologist, Director and co-founder of WildTrack Zoe Jewell, Veterinarian, President and co-founder of WildTrack JMP Users since 1996   Sky Alibhai and Zoe Jewell have used JMP for decades in their work to monitor and help save endangered species. How long have you been JMP users? We started with version 3. Back then we were two young (ish) conservation biologists living in a remote part of Zimbabwe on the Botswana border. We lived in Hwange National Park, monitoring black rhino for the Zimbabwean government. We went out each day collecting data on rhino whereabouts, noting their behavior and collecting many other ecological data. Our old statistical software had expired, and we were looking for something that had better visualization capabilities. We used to subscribe to MacUser magazine, which we had to collect from the nearest post office an hour away on a terrible dirt road. One week, there was a feature about statistical software. JMP got an excellent review, and we decided to try it. What was your first job (ever)? Zoe: Picking potatoes for a farmer in West Cornwall. A group of school kids used to car pool out to the farm during the holidays, equipped with wellington boots and a packed lunch. We followed the plough all day up and down the furrows, loading potatoes into big paper bags. We were paid by the bag, and so if one worked hard, it paid quite well. We’d emerge at the end of the day literally covered in mud and dust, clutching our hard-earned cash and heading for the beach to wash off! Sky: Working in the family business selling secondhand clothes when I was 10 in Kampala, Uganda, where I was brought up. Times were hard with six kids in our family – everybody had to pitch in. My first paid job was when I was still in high school. I got a Saturday job in a tiny private school in Kampala teaching other high school students (mostly from even more deprived backgrounds). What is your favorite feature in JMP? Of course, we are inclined to say our Footprint Identification Technique (FIT) add-in! In many ways, FIT embodies many of the elements of JMP that make it such unique and user-friendly software. Using JSL in JMP, we are able to import and manipulate images, do feature extraction and then use several of the platforms (Fit Y by X, Fit Model, Discriminant, Cluster etc.) to do the analytics for FIT. Another favourite feature is JMP’s excellent quantitative mapping, such as the alpha hull approach for mapping animal distributions and density contour mapping, with the ability to overlay these on background topological maps. How did you get interested in the type of work you do? Zoe: One cold winter day in England, where I grew up, I remember being taken to visit my grandfather, a Ukrainian Jewish refugee whose family fled to England in the 1920s. He had a thriving furrier business in London’s Bond Street. I was about 5 and only knew that he made fur coats for rich people. He lifted me up and sat me on a huge pile of soft skins in his workshop, and as I felt the soft beautiful fur move through my fingers, I asked him where they had come from. He said the pile I was sitting on came from an animal called a snow leopard, and that he went to remote parts of Siberia each year to buy the skins from trappers. Slowly the penny sank in – these wonderful animals were being killed in huge numbers for their fur. That made a huge impression on me. Sky: When I was 6-8 years old, TV was first introduced to Uganda. My family didn’t have a TV so I remember going to a friend’s house to watch after school. There was a BBC program called Zoo Quest fronted by a very young and sprightly David Attenborough. One of the episodes showed the Komodo dragon, I was totally captivated and blown away by the notion that these "dragons" were actually alive and kicking on this remote island in this present day and age! It wasn’t one of those moments of epiphany but much more subliminal – it stayed with me. Then in my second year at the one and only University in Uganda, I was fortunate enough to be able to help with a research project on the famous tree-climbing lions in, what was then, the Queen Elizabeth National Park. I just knew it! I wanted to be a wildlife biologist. So now I have been able to follow my passion. And, I am glad to say that David Attenborough, some 60 years on, is still at it, albeit a trifle slower, making some of the most dramatic and captivating wildlife documentaries and without doubt continuing to inspire young kids all over the world.  What is your proudest professional moment? Zoe & Sky: Getting our paper on the effects of immobilization in female black rhino published in The Journal of Zoology.  Our unexpected finding, that anesthetizing the females caused them to abort, was driven by 10 years of data, collected by walking for 30 km each day in often dangerous conditions. The pieces of the data puzzle came together in JMP, and our finding was the foundation of what we've done since then, developing and implementing alternative and sustainable techniques for protecting wildlife. This was momentous for us in many ways, not least, the fact that we had a lot of pushback (and threats!) from the section of the scientific community who had a vested interest in continuing with the use of invasive tagging techniques. Sky: I am inclined to mention also the moment that I found out that I had been accepted to read for a doctorate at Oxford University with a full scholarship. It was a milestone, more for contextual reasons than anything else. There I was, from a very humble background, brought up in an outpost like Uganda, then attending the only university as an undergraduate in that tiny obscure country and this way back in 1971. To add to that, I had never been outside East Africa and never been on a plane before! What do you like most about the work you do? Apart from the obvious satisfaction of knowing that we are moving wildlife conservation towards sustainable and animal-friendly techniques, we love two other aspects of this work: First, the multidisciplinary engagement. Of course, we work with other conservation biologists across a wide spectrum of species and academic institutions. We also collaborate closely with statisticians, software developers, engineers, forensic scientists, agriculturalists, aeronautics companies, writers, artists, law-makers, the military and many others. Having conversations across these disciplines generates so many new ideas and perspectives. Secondly, we love the global reach that WildTrack brings. We have projects in more than 20 countries over five continents, and each area brings different socio-political challenges that directly impacts how we collaborate, and requires considerable flexibility. A conservation policy in India, for example, might be hugely different from China. Imagine the challenge of dealing with human-wildlife conflict between tigers and villagers. A solution in India might require many layers of complex bureaucracy and consultations with stakeholders to reduce human-tiger interactions. In China, where local and central government can act quickly, whole areas can be cleared of human settlements and set aside for wildlife in a very short time. Both systems have their challenges and opportunities, and that makes for very interesting work. Is there anything you would like to say to JMP development or John Sall? We've been so fortunate to be part of the JMP family for almost 24 years now, working both remotely from various field sites, and on campus. John Sall wrote our first JMP script allowing us to import images into JMP. Since then, JMP developers have made significant contributions to the power of the FIT add-in. Not only has JMP provided us with wonderful technical and logistical support for our wildlife conservation work, but we’ve always felt that our friends at JMP have supported us personally in what we do. They are with us through all our ups and downs, challenges and successes. JMP is truly a powerful and flexible community, and we are grateful to John Sall for creating the unique workspace ethos that powers JMP innovation and discovery. How do you see the field of data science progressing in the next 20-50 years? As data streams from almost every discipline in ever-increasing quantities, the ability to manage it will be central. While we believe that artificial intelligence will be able to deal with many routine time-consuming challenges, we think that the interface between AI and human skills will be the focus of the most interesting development. In our field, wildlife conservation data must inform policy, but that policy must be a part of a human-centered community-based strategy. Far from robbing humans of jobs, more sophisticated data-driven science, such as AI, could allow humanity and other species to co-exist in a stable global ecosystem. What advice would you give a beginning JMP user? Take the time to play with JMP without necessarily having an objective in mind – because that’s the process of discovery! But above all, get to grips with understanding the elements of statistics. All too often the erroneous use of analytical methods can lead to misleading conclusions. The old adage "Lies, damn lies and statistics" is as relevant today as it ever was! What is a fun or unusual fact about yourself? Zoe: I have a really hard time recognizing faces! I learned to recognize people by their hairstyles, or gaits, and often voices, but rarely faces. I am the worst person to watch a movie with (‘Who’s that?" repeatedly’). Animals, on the other hand, provided a much easier pattern-template which includes their footprints. Sky: I have a unique and somewhat dubious distinction of having had my B.Sc. degree in Uganda conferred upon me by none other than the self-styled murderous dictator Idi Amin! The year was 1971, and he had just installed himself as the new President of Uganda in a bloodless coup. However, laid down in the constitution was the requirement that the President of the country also act as the Chancellor of Makerere University (the only university in Uganda). So there was Idi Amin adorned in academic regalia seated on a dais dishing out the gongs to all the graduates – this from a man who was a complete illiterate and later went on to perpetrate some of the most heinous crimes in that beautiful, friendly and peaceful country. Zoe & Sky: We can't say it wasn’t for love, but we got married at the direction of the Zimbabwean government! Their permit office demanded a marriage certificate for our research permit renewal, and we didn't have one, so the deal was done! We flew back to the UK and booked the registry office in Cambridge. On a very cold December day, we were married to the irrepressible tones of The Bear Necessities from The Jungle Book movie. We forget the date every year but are faithfully reminded by our witnesses! ... View more

Trish Roth, Principal Scientist, Abbott Laboratories Fun fact: I grew up in New England, and my happy place is Vermont for skiing, hiking and horseback riding.  JMP User since 1998    What is your favorite feature in JMP? Hands down, I love and use the Tabulate and Join functions the most. The Table and Analyze menus are my most frequently used platforms. The ability to save an analysis script back to a data table is also a huge time saver. What was your first job (ever)? My first job was at the Carter's underwear factory typing invoices in the shipping department. What is your proudest professional moment? Being selected to receive Black Belt training. It was a great recognition of both technical and teamwork capabilities. That opportunity opened many doors to get higher profile assignments and advancement opportunities. How did you get interested in research and analytics? I transitioned from research science doing experimentation in a biochemistry laboratory to data analytics over a series of years. Going through Black Belt training really opened my eyes to the importance and power of analytics and data presentation. I have found it incredibly rewarding to be able to help people get insights through data and then become more self-sufficient with data exploration. What do you like most about the work you do? There is never a dull moment! Being able to do analytics has afforded me the opportunity to work on many different projects. I have gotten to work with a lot of different folks, travel to different facilities and be part of many different teams. I like the challenges and variety. How are you currently using JMP? I spend a lot of time in the Control Chart platform doing SPC. This takes me into regression and analysis of variance platforms as well. Most recently, I have heavily utilized the Graph Builder platform and means comparisons functions to evaluate changes over time and between different populations of data. What is the first project you worked on using JMP? I learned DOE and ran studies optimizing and evaluating manufacturing parameters for use of biochemical components of drugs of abuse tests. Is there anything you would like to say to JMP Development or John Sall? Don’t forget the little guy. The basic functions such as creating data visuals for data summarization and presentation make JMP useful to a broader audience. The latest statistical and machine learning techniques are used by a more limited audience, and with the spread of open source products like R, this pool of advanced statistics users may shrink. Continue to invest in usability, simple things like better page formatting on printing, integration with Microsoft Office products and business intelligence tools. Seamless integration across platforms is becoming a must, including cloud services and compatibility with AWS products. How do you see the field of data science progressing in the next 20-50 years? I see data science evolving to be a standard business tool across departments, not just reserved for the statisticians and a specialized role called data scientist. Much like use of spreadsheets and word processing is standard practice, today what is a data science project will be standard practice in the future.  What advice would you give a beginning JMP user? Do some of the online tutorials, and find a local area colleague to get you started. There is a learning curve, so be patient. Push yourself to STEP AWAY FROM EXCEL 😊 and use some of the table and graphing functions first. ... View more

"I have used JMP almost exclusively for DOE and analysis for the last 25 years," says Mark Chahl of ExxonMobil Chemical. Mark Chahl, Engineering Associate, ExxonMobil Chemical JMP user since 1994 Fun fact: Fluent in English, French, Dutch, Punjabi and currently working on mastering Spanish What is your favorite feature in JMP? JMP features are like my children: I love them all. But recently, I am in awe of Predictor Screening. It is great for manufacturing problem solving. What was your first job (ever)? I was a cook at McDonalds. The uniform pants didn’t have front pockets – so you couldn’t stand around with your hands in them. Due to this fact, the lesson we were taught was: “Time to lean? Time to clean.” How did you get interested in chemical engineering? I did a degree in chemistry and realized I really need to be an engineer instead. A great decision! Engineering had more hands-on, problem-solving work (and better job prospects)! What was your first experience with statistical quality and process control? In the summers of 1987 and 1988 I had the ultimate summer job for a young Canadian man: quality control at Labatt’s Brewery in my hometown of London, Ontario. Yes, I was working with beer and getting paid for it! It was this summer job where I was first introduced to specifications, testing, SPC and SQC. I did a lot of testing of incoming materials, beer ready for bottling/canning, beer pasteurization, packaging quality. One of the tests I had to perform daily was bottle cap torque testing. A case of 24 bottles with twist-off caps would be taken off the line and put in a refrigerator for 24 hours. Then, using an inch-pound ranged torque wrench with a socket that fit the bottle cap, I would open each bottle, record the peak opening torque and chart it. Now the sad part of the story, the whole case of 24 was then dumped down the sink! It took some skill to grip the case in a way such that none of the bottles fell out. What is your proudest professional moment? Getting a patent for my work and convincing Exxon Mobil to try JMP! US Patent 5990256 was for a process that improved melt strength for making film. I did the math and convinced a few PhDs that my idea would work. It did! What do you like most about the work you do? I like it because it is technically challenging, and I get to teach and mentor others. How are you currently using JMP? Polymer manufacturing troubleshooting and improvement. We analyze time-series sensor data and product quality data. What was the first project you worked on using JMP? It’s been too long ago to remember, although I am certain the work on US Patent 5990256 was done with JMP. I have used JMP almost exclusively for DOE and analysis for the last 25 years. Is there anything you would like to say to JMP development or John Sall? We adopted JMP because of its product excellence and your customer care. Keep up the great work! How do you see the field of data science/analytics progressing in the next 20-50 years? More people will be doing it. I do hope that it becomes a required course for engineering undergraduates. ... View more

"While statisticians may not always be in leadership positions, they can influence the people, projects and organizations they serve," says JMP Champion Kevin White. Kevin White, Applied Statistics Manager, Eastman Fun fact: Avid tennis player and plays doubles 100 times a year JMP user since 1991 What is your favorite feature in JMP? If forced to pick one, I would go with Custom Design given the nature of the work I’ve done over the years. Custom Design provides incredible flexibility to create and evaluate many types of experimental designs. Process Screening is a newer feature that has quickly become another favorite given its power to quickly evaluate the process stability and capability of many processes.    What was your first job (ever)? At 15, I began working at the Baskin-Robbins in the local mall where I learned the proper technique to scoop ice cream that has served me well over the years. When there were no customers, my manager would have us practice weighing our scoops so that we wouldn’t give away too much free ice cream. Knowing what I know today, this would have been some interesting data to collect and visualize using JMP to look at variability within and between scoopers.  To this day, I still have a strong affinity for their World Class® Chocolate flavor.  What is your proudest professional moment? I don’t know that I can point to a single moment. For my entire career, I’ve worked as a company internal statistical consultant. Over the hundreds of projects supported, I’ve been anywhere from a pair of hands to what I consider an equal collaborator with the scientists and engineers. The proudest or most satisfying aspects of my career have been on those projects where I was fully integrated with project teams where an idea has been taken from lab scale to production to ultimately selling a new product that our customers value. How did you get interested in statistics? I always loved math and started as a math major in college, but ultimately switched to statistics because my dad is a statistician. As I learned more about the work he did, I became convinced it would be a good career choice. I have a daughter who will be graduating from college soon with a degree in business analytics and a son who just started college with plans to major in statistics. I guess you could say it runs in the family.  What do you like most about the work you do? While it may sound cliché, it’s helping others gain insight from their data. I think it’s important for statisticians to be humble and take joy in serving others. One of my favorite definitions of leadership is from John Maxwell. He simply says that leadership is influence. While statisticians may not always be in leadership positions, they can influence the people, projects and organizations they serve.  How are you currently using JMP? In my current role, I lead a team of statisticians who provide experimental design and other statistical modeling solutions in support of top growth platforms and manufacturing productivity and quality initiatives. We use JMP Pro in about every way imaginable, from creating and analyzing experimental designs to cleaning and analyzing messy data from manufacturing. The interactive data visualization and analysis that JMP Pro provides makes us better and more efficient in the work we do.       What was the first project you worked on using JMP? The first project that comes to mind would be my independent project while working on my master’s degree in statistics at the University of Tennessee. My professor connected me with a company that made cake frosting, and the project involved looking at various sites and production lines with the primary goal being to identify differences in key quality characteristics. JMP was my tool of choice because of the ease of creating control charts, comparing process capabilities and analyzing sources of variation.          What advice would you give a beginning JMP user? I would suggest beginning users start with the New User Welcome Kit to get comfortable with the basics of JMP. To learn more about specific topics of interest, then consider the webinars whether they’re live, on-demand, or from the Mastering JMP series. Also, become part of the JMP Community. This is a great place to connect with other JMP users, get your questions answered and join the conversation. And, sign up for the JMP newsletters as this will keep you informed on new webinars, tips, and more. Finally, see if there is a JMP user group within your region or even within your company.  There are many resources available, so try them out and see what brings you value.        Is there anything you would like to say to JMP development or John Sall? First, I’m thankful that John Sall had the vision to create this dynamic software 30 years ago. It’s hard to imagine my career without JMP.  Secondly, keep listening to your customers. I was fortunate to serve on JMP’s customer advisory board a few years ago and the thing that impressed me the most was how much the JMP developers listened. This included John Sall who probably took more notes than anyone about potential opportunities to improve JMP. Lastly, keep innovating. Recent JMP releases have continued to improve old features and add new. Keep them coming. It’s always exciting to see what the next version will bring.       How do you see the field of data science/analytics progressing in the next 20-50 years? I expect the field to grow fueled by the amount of data available, research leading to improved methods and increased computing power. On the data front we have seen exponential growth in data, and it will only continue. I also expect to continue to see advancements in things like machine learning algorithms and software, making it easier for data scientists to use the methods. And, increases in computing power will allow the processing of large amounts of data faster than ever before. Given my career, I do have concerns about the future of the industrial statistician who is strong in quality methods and experimental design. With many university statistics departments changing their emphasis to data science or analytics, it’s already becoming harder to find people who want to be industrial statisticians. While I understand the allure of being a data scientist, I would not be surprised if somewhere down the road we reach a tipping point and recognize the need for more industrial statisticians. ... View more

The year was 1989. Computer scientist Sir Tim Berners-Lee proposed and successfully implemented the first communication between a Hypertext Transfer Protocol (HTTP) client and server via the internet. Faith by George Michael was the  No. 1 album. Neon colors, big hair and spandex were all the rage. And by November, the Berlin Wall began to topple. Just five years earlier, Steve Jobs had unveiled the original Macintosh, and by 1989 it was clear to SAS co-founder John Sall that he could exploit its groundbreaking point-and-click GUI to give more researchers a way to explore and analyze data graphically without the need to code. JMP was born. With its creation came many new, groundbreaking discoveries in all industries and all areas of life and work. Over the next several months, we will be featuring a few of the many veteran superusers who have accomplished amazing things over the last 30 years to make the world a better place.    Pictured here are the employees that helped to ship out JMP Version 1.0  to 7 customer sites in October 1989. Version 1 shipped on two 3.5” floppy disks, included a 456-page printed manual, and required 1MB RAM to run (though 2MB was recommended). ... View more

“When building a predictive model, we find the JMP Pro interfaces to be very intuitive, allowing us to work closely with other JMP Pro users to build the model together.” -- Amy Clayman, Data-Driven Decisions Circle, VSE Beyond Spreadsheets is a blog series that highlights how JMP customers are augmenting their tools and processes for exploratory data analysis to make breakthrough discoveries. We talked with JMP users to learn more about how and why their organizations bring Excel data into JMP to create graphics and explore what-if scenarios. Our fifth and final interview in the series is with Amy Clayman, Consultant at VSE and owner of Financial Fluency LLC. She has more than 20 years of experience in corporate finance and public accounting and has been featured in CFO magazine. She is also the president of the Jewish Community Relation Council of Southern New Jersey. Fast Facts JMP user since 2014 Favorite JMP features: Graph Builder, Profiler and modeling capabilities. Proudest professional moment: We built a model in JMP Pro that accurately predicts customer behavior more than 85 percent of the time. We have integrated this algorithm into our data warehouse to share with other teams in our organization. How long have you been a JMP user? We selected JMP Pro a year ago. We had been exploring various predictive analysis software solutions for well over a year before selecting JMP Pro. We tested and used an Excel data mining add-in and various cloud-based solutions before we made our final decision. Tell us a little bit about the function of your department and how it contributes to your organization’s mission. Our department is referred to as the “Data-Driven Decisions Circle.” We are responsible for helping VSE use financial, operational and external data to guide the organization’s decision-making process. The group is dedicated to helping the company improve its profitability and ROI. For example, we are focused on the following initiatives: Understanding and enhancing the entire customer experience so that we can improve interactions and increase spending levels. Determining which media sources attract the most valuable customers and generate the best ROI. Discovering which programs, campaigns or system enhancements help improve VSE’s ROI. What do you like most about the type of work you do?  I started at VSE over 10 years ago to initially support its accounting and reporting needs; later, I became involved in its data analytics project. I love working with the people! VSE has an amazingly talented and compassionate staff. They challenge you to do your best work, and they like to have fun. Whether it’s people from the marketing, creative services, service delivery, technology or finance teams, every member of the organization wants to better understand what is happening and how we can improve the customer experience in a fiscally responsible manner. We often get inundated with questions that usually start with “Why did this happen?”, “What is the impact to the customer?”, and “How will this affect future revenues?” Sometimes I feel like the pathologist in a Law & Order episode. The company expects our group, in an unbiased manner, to dissect the event or problem and provide an explanation that will better enable the company to create a path to success. What is a professional accomplishment of which you are most proud? We built a model in JMP Pro to predict the annual spending category of a new member based on their behavior in their first seven days on the service. The model accurately predicts the correct category more than 85 percent of the time. We have integrated this algorithm into our data warehouse so these predictions are easily accessible to the media team. This model allows us to help our highly skilled media team quickly understand the revenue opportunities of the member’s media choices. Historically, the team may have chosen to wait several weeks or months before pausing or extending a program. Now we can provide them with additional intelligence about the likely outcome to complement their decision-making process. The magic in this process is centered on identifying and building the relevant data so that the algorithm tells the business user what is likely to happen with a high degree of confidence. Why do you like most about using JMP Pro? My group is responsible for educating and communicating to the key business users. We need to do this in a concise, thorough and organized fashion. The data visualization tools are an excellent starting point and allow us to communicate the trends quickly. Using features such as Graph Builder and Profiler allows us to tell the story – fast. As our team better understands the data using the visualization tools, we then look to identify patterns or relationships. In JMP, we use the modeling features to help predict potential outcomes and identify which attributes have the strongest correlation to the predicted outcome. Communicating these patterns and relationships helps our key business users create an improved customer experience. VSE is a highly collaborative environment. When building a predictive model, we find the JMP Pro interfaces to be very intuitive, allowing us to work closely with other JMP users to build the model together. Have you used spreadsheet programs in the past to conduct your statistical analysis? If so, can you describe the pros and cons? Yes, we have used several other programs. From my perspective, the pros of Microsoft Excel: Most finance professionals are comfortable with Excel, so the environment is familiar. It has some data visualization features that can be easily manipulated. It is inexpensive. And the cons of Microsoft Excel: Data visualization is very limited when compared to JMP Pro. Selection of data modeling techniques is limited. Ability to compare model results is limited. Ability to clean and prep the data for modeling is limited. There are some latency issues. We have limited access to trained experts who exclusively support this product. JMP allows us to more effectively understand, present and predict potential outcomes. The most important advantage to selecting JMP Pro over the other spreadsheet tools is the access to JMP’s exceptional technical staff. Our technical resource representative guides us on how to best use the software and is constantly educating us on the best approaches to get the most out of the tool. It is truly the combination of JMP Pro and the people at JMP that has helped us advance our mission to have data drive our decision-making process. We believe in our staff’s instincts, but we have an obligation to provide them with the most relevant information in the most intelligent fashion to help them lead our organization. What advice or best practices would you give to other companies that are currently relying on spreadsheet tools to conduct statistical analyses? Don’t be afraid of or overwhelmed by all of the functionality of JMP. We continue to migrate in stages. Your ability to grow as a professional in this field will be limited if you choose to only use a spreadsheet tool. The predictive analytics field is constantly evolving, and the tools and professionals you interact with will determine how effective you can be in this role. Do not sell yourself or your company short by using less sophisticated tools to address this need. Want to learn how to uncover information you might miss from using spreadsheets alone? Watch the new webcast case study, Going Beyond Spreadsheet Analytics With Visual Data Discovery, to see how a sports equipment and apparel manufacturer digs deep into the data to improve a supply chain process that was not working. ... View more

“I love being able to tell my students that if they learn JMP, it will be a skill they can put on their resume that will set them apart from other applicants.” -- Mary Ann Shifflet, Professor, Romain College of Business, University of Southern Indiana Beyond Spreadsheets is a blog series that highlights how JMP customers are augmenting their tools and processes for exploratory data analysis to make breakthrough discoveries. We are featuring Q&As with JMP users to learn more about how and why their organizations bring Excel data into JMP to create graphics and explore what-if scenarios. Our fourth interview in the series is with Mary Ann Shifflet, statistics professor and JMP advocate. Fast Facts JMP user since 2012 Favorite JMP features: Graph Builder, Distribution, Fit Model Proudest professional moment: Receiving the Dean’s Award for Teaching Excellence in 2012 for redesigning an Elementary Statistics course to incorporate JMP. What do you like most about the type of work you do?  There are two things that I really enjoy about teaching statistics to college students. One is seeing the lightbulb come on for someone who has been trying to understand a difficult concept. The other is a little more subtle; it’s knowing that I am teaching them one of the most important elements of their course work in the College of Business. All business decisions require the use of data, so laying that foundation is critical. If you look at the skills required for a career in business, many of them have to do with data analysis and problem solving – the skills taught in my course. Why do you use JMP in your teaching?  I use JMP in my teaching because I wanted students to be able to do real data analysis when they leave my class – or at the very least, be able to summarize and interpret data. I wanted to give them a tool that would allow them to develop a little bit of confidence in using data for business decisions. I had used other programs in my professional practice, but JMP was the one that we selected. Since incorporating JMP in the class, I am able to teach some relatively sophisticated regression modeling and diagnostics – sophisticated for a sophomore-level course, at least. This modeling would not be possible if we were doing the calculations by hand with scientific calculators or even if we were using Excel. In what ways have you used Excel for teaching statistical analysis? How is using JMP different? We, of course, use it [Excel] for data input and storage, but I have not used it much for analysis. I made the transition from using TI-84 calculators to JMP for analysis. I find JMP much easier to use than Excel and easier to incorporate in the class. There were a number of things to consider as we made this transition, not the least of which was availability. Any student who has a computer probably has Excel on it. Students didn’t have JMP available, which is why we felt it was essential to give them free access to it. We were able to do that through a campus-wide academic license. I find JMP makes it very easy for me to teach students the steps to use in acquiring the necessary output. The ease of use, along with the support features available with JMP, make it ideal for my purposes. How have students reacted to using JMP?  The initial feedback from students in the first experimental section was very positive, leading us to transition all of my sections to the JMP model more quickly than we had originally planned. As time has passed, I have received mostly positive feedback. I have had students come back after leaving the class and tell me they were able to use JMP for projects in other classes and even in some team competitions. Several students were able to obtain very good internships due to their experience with JMP in the classroom. Is there anything else you would like to mention?  Since JMP is such an easy tool to use, I have the opportunity to focus much more on the business decision aspects of data analysis. As a result, students leave the class truly able to use data to support their business decisions. I hear many people say that students need to be able to use Excel for data analysis since that may be their only data analysis option in many companies. While that may be true, I love being able to tell my students that if they learn JMP, it will be a skill they can put on their resume that will set them apart from other applicants. Knowing a software program like JMP communicates something different – and in my view, better – than simply knowing how to use Excel to analyze data. Ready to go beyond spreadsheets? Visit www.jmp.com/beyond and learn how to augment your existing processes for exploratory data analysis to uncover information you might miss from using spreadsheets alone. ... View more

“I want my students focusing on statistical methods, not on software.” -- Bruce McCullough, Professor, LeBow College of Business, Drexel University Beyond Spreadsheets is a blog series that highlights how JMP customers are augmenting their tools and processes for exploratory data analysis to make breakthrough discoveries. We talk with JMP users to understand how and why their organizations bring Excel data into JMP. Our third interview in this series is with Professor Bruce McCullough, who teaches statistics at Drexel University. Fast Facts JMP user since 2011 Favorite JMP features: Its superior visualization capabilities Proudest professional moment: Leading the American Economic Review to adopt a mandatory data-code archive for its articles In which courses do you and your students use JMP? I teach statistics to undergrads, MBAs, master’s degree students in business analytics, and PhD students. I use a variety of statistical packages in my own work. I use JMP when I teach undergrads and MBAs because it’s powerful and easy to learn. I have been using JMP in my teaching for about four years. What do you like most about the type of work you do? Every day is different, and if a day is boring, it’s my fault. I have written on the accuracy of statistical software, the replicability of published research and other topics. Lately, I have refocused my efforts toward data mining and predictive analytics. What is a professional accomplishment of which you are most proud? I co-authored the article that led the American Economic Review to adopt a mandatory data-code archive for the articles it publishes. Prior to this, research in economics journals was largely not replicable. After the American Economic Review took its action, all the other top economics journals followed suit. Why do you use JMP in your teaching? I want my students focusing on statistical methods, not on software. I need software that is easy to use, accurate, and available for PCs and Macs. Only a very few packages meet these requirements. I gave up on Minitab when Macs got popular because it was available only for PCs. I tried R with a GUI for a couple years, but the students just had too many problems with it. So I tried JMP, and found that it met all my needs. One principal advantage of JMP over the other packages that I considered is its superior visualization capabilities, which are either nonexistent or harder to use in the other packages. How have students reacted to using JMP? Students find JMP easy and intuitive. Of course, it was designed from the ground up for the sole purpose of doing statistics. All the statistical functionality of Excel is merely a kludge grafted onto a spreadsheet. Remember, spreadsheets are for accountants to balance numbers, not for doing statistics. Is there anything else you would like to mention? Many years ago I was on a panel with Jon Cryer, a statistician from the University of Iowa, and he showed a picture of a man holding a handsaw standing next to a giant redwood tree, captioned thusly: “Get the right tool for the right job! Friends don't let friends use Excel for statistics!” Ready to go beyond spreadsheets? Visit http://www.jmp.com/beyond and learn how to gather and prepare data for analysis from multiple sources. ... View more

“The software you use not only shapes what you learn from your data; it shapes the questions you ask!” -- Tom Treynor, Director, Zymergen The Beyond Spreadsheets blog series shows how JMP customers are augmenting their tools and processes for exploratory data analysis to make breakthrough discoveries. The series features Q&As with JMP users to learn more about how and why their organizations bring Excel data into JMP to create graphics and explore what-if scenarios. This week's Q&A is with Tom Treynor @ttreynor, Director at Zymergen and an experienced scientist, chemist and quality engineer. Fast Facts JMP user since 2011 Favorite JMP feature: JMP Profiler Proudest professional moment: "It was pretty cool the first time we reduced the unexplained variation in a fermentation process from over 10% to under 2%, yet it gets cooler every time we do it!" Tell us a little bit about the function of your department and how it contributes to your organization’s mission. Zymergen is harnessing the power of biology to make chemical products that are good for business, people and the environment. Every quarter my department, Test Operations, is making hundreds of thousands of measurements and distilling them into a handful of critical decisions about which engineered microbes should deliver the best process economics at industrial fermentation scales. However, the high throughput and low volumetric scales with which we operate at Zymergen pose significant false positive and false negative risks, respectively, so we also distill our data into critical decisions about how to improve the processes we use to improve the microbes. What do you like most about the type of work you do? Every week at work, I learn new things about biology, chemistry and physics, because every week my team is deriving new mechanistic insights through testing and refining our statistical models. Even now, when there are hundreds of things already tracked in our databases, we are still innovating better ways to measure them and discovering new things worth measuring. What do you like most about using JMP? The folks who built JMP recognize that any problem worth solving has both multiple factors and multiple competing responses (e.g., cost, quality, speed). Although JMP has many capabilities that derive from this insight (for example, the ability to decorate columns with information such as Specification Limits), they all come together so nicely in JMP’s Profiler platform. Another of JMP’s greatest capabilities is the way its user interface has been so well designed for training scientists and engineers to become applied statisticians themselves. What is a professional accomplishment of which you are most proud? It was pretty cool the first time we reduced the unexplained variation in a fermentation process from over 10% to under 2%, yet it gets cooler every time we do it! Although it is theoretically possible to achieve the same increase in decision quality by buying and operating more than 25 times as many bioreactors, I think it is more satisfying to increase the productivity of my team by an order of magnitude than to grow it by that amount. I can't wait to tackle some of the most challenging fermentation processes in my industry! How is using JMP different from using spreadsheets to conduct statistical analysis? The greatest power of spreadsheet programs is also their greatest weakness: They let you put anything anywhere. As my colleagues make the transition to JMP, they eventually realize the flexibility they have lost is not a bug, but a feature of their new statistical software. For example, no longer do they need to copy and paste their equations to other cells in their spreadsheet, since a JMP formula is applied automatically to every row in its column. Although it takes some practice to write formulae that explicitly incorporate all the conditional statements they had previously implied by selectively aggregating or copying and pasting in a spreadsheet, developing this skill makes it so much easier for them to read the many, many stories that each measurement has to tell. What advice or best practices would you give to other companies that are currently relying on spreadsheet tools to conduct statistical analyses? The software you use not only shapes what you learn from your data; it shapes the questions you ask! For example, we all learned how to make histograms in grade school, and yet so few of our colleagues have ever made a histogram in a spreadsheet. Although it can be done, and it doesn’t even take long once you know how to do it, making histograms in a spreadsheet is simply not as easy as making a scatterplot. As a result, our colleagues almost always default to making scatterplots without even considering other kinds of analysis. In fact, I bet few of you reading this even thought to make a histogram in your workplace until you started using JMP – and now you make histograms all the time. Why? Because with JMP you can make countless histograms, visualize outliers, test for normality and fit each distribution in under 10 seconds. JMP makes it so easy to ask the right questions and to perform the right analyses once you know how to use it. Ready to go beyond spreadsheets, too? Visit www.jmp.com/beyond to gain key insights on visualization, including how to determine what graphs should be used in certain situations for “what-if” analysis. ... View more

“Spreadsheets are familiar tools, which are relatively simple to use. However, the downside is that they result in fragmented thinking.” -- Ken Franklin, Performance Measurement Program Manager, Highway Division of Oregon Department of Transportation (ODOT) JMP customers use an array of tools and processes for exploratory data analysis to make breakthrough discoveries. In the coming weeks in a series of blog posts we are calling Beyond Spreadsheets, you can read interviews with JMP users to learn more about how and why their organizations bring Excel data into JMP to create visualizations and explore what-if scenarios. Our first interview is with Ken Franklin, JMP advocate and provider of key support services to the Highway Division of ODOT including performance measurement, process improvement, leadership team facilitation, skill development, communications/publications, event planning, data systems maintenance, budget preparation and cash flow management. Fast Facts JMP user since 2014 Favorite JMP features: Graph Builder, Distribution platform, Control Chart platforms and interactive HTML files Proudest professional accomplishment: "I discovered that a key agency performance measure (KPM), in place for at least eight years, had been incorrectly calculated right from the beginning, rendering it wholly invalid. JMP was instrumental in analyzing various data sets and displaying critical data visualizations that made the measure’s problem obvious." Tell us a bit about the function of your department and how it contributes to your organization’s mission? I’m in the Strategic Business Services Branch of the Highway Division of the Oregon Department of Transportation (ODOT). We are a relatively new branch that combined some disparate units together, so a shared identity is still a work in progress. We provide support functions to the “mission pipes” of the Highway Division, which are transportation project planning, development, construction and maintenance. The support functions provided include performance measurement and reporting, process improvement, leadership team facilitation, skill development, communications/publications, event planning, data systems maintenance and development, budget preparation and reporting, and cash flow management. My role is performance measurement and reporting for the Highway Division. I inherited a very large quarterly business report (QBR) consisting of far too many static PowerPoint slides and too much information without clear purpose. These reports have been very labor-intensive to produce with too little resulting value when reviewed by executive management. JMP is helping me transition the QBR from an assortment of PDFs, Word documents, Excel spreadsheets, and separate and static graphics to a more useful quarterly report based more on data visualization and statistical analyses. The QBR is a challenging work-in-progress that is just one component of a performance measurement program that needs improvement from A to Z. What do you like most about the type of work you do?  What I like most is the opportunity to learn many different subjects and apply what I learn in a generally supportive environment. I’m in a unit that’s very much about continual improvement, overseen by a manager who is very generous in providing the necessary training and tools I need to increase my level of mastery in the many dimensions required. Of course, there’s always too much to learn – performance measurement process, strategic thinking, project portfolio management, data visualization, statistical analysis, JMP (!), ODOT processes, etc. – while still getting my operational work done. What is a professional accomplishment of which you are most proud? Most recently, I discovered that a key agency performance measure (KPM), in place for at least eight years, was incorrectly calculated right from the beginning, rendering it wholly invalid. However, none of the managers noticed because “analysis” stayed at an overly aggregated quarterly/yearly level. Only by “going under the hood” did we discover the error. An additional problem we discovered is that the data table for the measure (as well as all the other measures) is too narrow, limited to only process result data, with little to no upstream process data to provide causal insights on the resulting process performance. JMP was instrumental in analyzing various data sets and showing critical data visualizations that made the measure’s problem obvious. I also used JMP to show how our regions (five of them across the state) were manipulating an on-time delivery measure (common but unstated knowledge). Using Graph Builder, I developed a trellis display scatterplot that our state engineer saved to his smart phone. He uses it to demonstrate why the measure and forecasting process needs improvement. What do you like most about using JMP? The ease of dragging and dropping variables into Graph Builder and changing the representations on the fly. I find trellis displays quite useful and have never seen one via Excel. I agree with Stephen Few’s observation that most business process/quantitative problems can be addressed via good data visualizations, without needing to employ the more technical statistical analyses available in JMP. However, that statistical power is available when needed. The Distribution platform is great for dropping in a set of variables and looking for any obvious correlations. The Quality and Process platform, especially the control chart options, is proving quite useful in introducing statistical control process concepts to managers where measurement of business processes and systems have for too long been limited to point-to-point comparisons, ignoring the inherent variation in measurement data. The interactive HTML file capability is very useful for emailing others who don’t have a JMP license, allowing me to introduce them to more interactive data exploration. I’m also looking forward to using Query Builder in JMP 12 to automate as much of my performance reporting as possible. How is using JMP different from using spreadsheets? Spreadsheets, via Excel or Access, are the dominant tool for managing processes and generating reports and graphics at ODOT. Pivot tables and, to a limited degree, Power Pivot are also used (mostly by financial analysts). Spreadsheets are familiar tools and relatively simple to use. The downside is that they result in fragmented thinking. I see that phenomenon here and in other agencies that use balanced scorecard measurement approaches, with goals, processes, measures, etc., spread out across conference room walls in a big spreadsheet. Data analyses are typically separate graphs or, worse, tables of numbers. The big downside of this approach is that it fails to explore and reveal the interrelationships among variables; key patterns and insights are locked away in their respective cages. JMP and other data visualization tools can provide the capacity to explore data interrelationships with a degree of ease not available with the older spreadsheet tools. However, there are downsides. For most staff and managers, the learning curve for new tools is steep, so it is a big obstacle to carve out the time to learn something new. Another obstacle I’ve seen is the cost of purchasing licenses in strapped budget times, which comes down to staff making a sales pitch to management unfamiliar with the newer tools. Making the case for a high enough ROI to justify a purchase can be an impediment for those who are just learning the tool. For newbies, me included, the ease of analyzing data and displaying graphical results can jump (no pun intended!) ahead of one’s knowledge of when and how to best use a particular tool and how to best communicate results, which can lead to reports that contain more statistical analysis than their audiences can currently absorb and understand. Of course, no matter the tool used, “garbage in, garbage out” always rules. But that’s another problem. What advice or best practices would you give to other companies that are currently relying on spreadsheet tools to conduct statistical analyses?  Explore the alternatives! Better data visualization and analysis with far less effort and cost is available. (Yes, a software license costs money, but we aren’t accounting for all the wasted costs of paying analysts to do a lot clerical-level data-entry work instead of real data analysis.) Ready to go beyond spreadsheets, too? Visit www.jmp.com/beyond to learn how to build compelling reports and dynamic dashboards that you can easily share with others. ... View more

Launched more than 20 years ago, the Aerospace Systems Design Laboratory (ASDL) at the Georgia Institute of Technology was created to fill the gap between education and industry, with the goal of fully preparing graduates to meet the needs of employers from day one. Is the mission succeeding? I recently caught up with ASDL Research Engineer John Salmon to talk about the philosophy of ASDL and what’s new, including the lab’s use of MATLAB integration in JMP 11. Fast facts on ASDL: More than 250 graduate researchers and research engineers in 2013 Annual funding: ~$15 million Funding since inception: ~$150 million What do you like most about the vision of ASDL? The thing I value most is the commitment we’ve made to preparing engineers for working after graduation. We bring in industry partners like Boeing and Lockheed Martin and have the opportunity to work with them in kind of an advisor capacity. They bring their problems to us, and our students are able to work on them, so they have a feel for what will take place when they move out into their career path. ASDL provides an environment that allows people to experiment, fail, try again, innovate and ultimately succeed. Students are allowed to explore new ideas, go through all the literature and then investigate and experiment with these ideas that are very new. This prepares them for real-life engineering where creativity is needed, as opposed to  “engineering inside a bubble” that academic institutions sometimes seem to provide. Director Dimitri Mavris has said that “students come in as propulsion or fluid specialists and leave as more valuable systems integration specialists.” Can you talk about the training you do across disciplines? Our position is that there needs to be someone thinking about these problems presented from our industry partners at a very high level across all disciplines:  finance, performance, structures, aerodynamics, etc. Students are taught to be experts in a variety of different fields instead of a “super expert” in one field. For example, only a small portion of rocket scientists focus on just the rocket. We must look at the other things related to the rocket and trajectory, such as radars, intercommunications and the complexities of all the systems working together. This type of facilitation and training provides students with unique opportunities to learn the value of collaboration. What are some of your favorite features in JMP, and what are you most excited to use in JMP 11? One of my favorite new features in JMP 11 is the MATLAB interface. We often use MATLAB for model simulation since some models we receive from industry partners do not convert to JMP Scripting Language. In these cases, we use JMP for visualization, design of experiments and analysis. The real benefit of this new interface will be the ability to perform real-time analysis in presentations, so we don’t have to flip back and forth between two different types of software. There has been a movement in the past several years to develop interactive and dynamic dashboards for design review meetings. During the course of our sponsor meetings, additional information is revealed that we haven’t thought about, so parameters and assumptions need to be adjusted. Using the new interface, we can change the model during meetings, and it will reflect real time in the analysis. Other things I am excited about are new ways to introduce multithreading and perform analyses more quickly; enhanced visualization through Graph Builder and generalized regression; and script editor improvements to cut down on debugging time. Thanks to John Salmon for taking time to answer our questions. For more about the new JMP interface to MATLAB, JMP is offering a complimentary info kit that includes a white paper co-authored by Dr. Salmon.   ... View more

In celebration of the International Year of Statistics, we reached out to a few British statisticians to ask them to share a few thoughts on statistics as a discipline, statisticians and applied statistics. The United Kingdom has a rich statistical history, and we are pleased to share some of these most interesting blog interviews. Our third interview is with Denise Lievesley, Head of School, School of Social Science and Public Policy, King's College London. Lievesley, one of the United Kingdom's leading social statisticians, is dedicated to campaigning for the evidence-based development of sound public policies in the UK and beyond. Her distinguished career includes posts as founding Chief Executive of the English Health and Social Care Information Centre; Director of Statistics at UNESCO, where she established its new Institute for Statistics; Director of the UK Data Archive; and professor of research methods in the mathematics department at the University of Essex. She was also the President of the Royal Statistical Society (1999-2001) and of the International Statistical Institute (2007-09). She will be a panelist at Discovery Summit 2013 to discuss the International Year of Statistics. Question:  How did you become interested in statistics? Denise: I studied statistics as an undergraduate because I was very good at, and a bit nerdy about, maths. At the time, I didn’t know what statistics was, but I was trying to find a subject that would use a lot of maths and could be applied with a particular relevance to society -- although I wasn’t sure I wanted to be a teacher. My father was in the Armed Services, so I had an opportunity to travel around quite a bit. Because I didn’t stay at any one school for very long, I was able to choose A levels for myself, so I did all the maths I could. In addition, I picked other subjects I enjoyed, like English (because I love reading) and art (because I love drawing and painting). I know that’s a peculiar combination of interests. But it turned out to be perfect. Maths is obviously useful to statistics, but English helps with communication. And, of course statistics is about patterns, providing a connection to art. When I was interviewed for a university place, I was told that normally candidates would have studied maths, physics and chemistry, and that accepting me into the statistics curriculum was a bit of a risk! Question: Can you comment on the relationship between mathematics and statistics? Denise: Mathematics is of course fundamental to statistics, but there are differences in the philosophies of the two fields. Statistics is the science of uncertainty, and mathematicians aren’t often comfortable with uncertainty. For applied statisticians, it’s not necessary to build the competence to develop theory, but nevertheless one does need to have a broad understanding of the underpinning mathematical theory in order to ensure sound science. Of course the diversity of statistical applications means that there are many branches of the subject. I consider myself to be an applied social statistician, and I love the fact that I can connect with researchers working in so many different fields. Question: Do you have any recommendations on bridging the communication gap between statisticians and other stakeholders? Denise: Well, unfortunately many statisticians don’t communicate as well as they should. Take statistical output: Even a simple table of numbers can be interpreted so that someone who is perhaps number-shy or extremely busy can take a clear message from the table and can draw conclusions about what actions might be needed. Most importantly, statisticians should provide the story of the data. But unfortunately, we often start out talking about data limitations, even focusing on why the data that’s been collected can’t be used instead of seeking ways to maximize its value. And often, too much detail is presented, meaning that users cannot extract the key messages from data. Thus, we have lots of work to do to improve our communication skills. We also need better training for our students and for junior official statisticians as they start working in government. They need to be able to write briefing papers for senior public servants and ministers who decide policy. Some briefings are filled with extraneous detail, or use impenetrable language, or concentrate only on rather esoteric caveats. This reminds me of a T-shirt from the ASA: “Being a statistician means never having to say you are certain.” Question: You are well-respected as one of the UK’s greatest social scientists. You’re also the first woman to serve as President of the International Statistical Institute. What would say to encourage more women to choose a career in statistics, traditionally a male-oriented field? Denise: I am not sure that it is a predominantly male field anymore. I was asked to speak at the  Young Statisticians Conference to talk about my career, and I was very pleased to see the large proportion of women present. More and more women are studying statistics, though there are still some gender divisions by application and field. So young women are more likely to choose careers in medical and social statistics, rather than the more theoretical and mathematical statistics paths. There remains a problem in some countries whereby girls are less likely to take mathematics and thus are less likely to have exposure to statistics. We don’t understand why girls shy away from mathematics, because there’s absolutely no evidence that they are weaker in the subject. When girls are taught separately from boys – as is often done in Catholic countries – girls are more likely to take mathematics and science. If girls are taught with boys, they seem to define these subjects as the male terrain! I would say to young women: “Be confident.” As it now stands, they seem to feel they have to excel in all areas – socially, as homemakers, as wives and mothers, in their careers. In addition to this, they worry about their appearance, and so they push themselves really hard. It really concerns me. I would say to them that it’s OK to take a shortcut. You don’t have to be fantastic at every area of your life, and you don’t have to do everything so brilliantly. Secondly, I would say that statistics is a fantastic career. You can specialize, or you can do what I’ve done – every few years I changed direction in a new field of application. The analytical skills you build up are really critical to a wide range of work activities. You can use the skills you have as a statistician every day in terms of analyzing financial data, examining KPIs or working out how to do performance reviews. Question: Can you comment on the strong connection between the statistical concepts you cover and how you make them relevant for real-world problems? Denise: At UNESCO, we had responsibility for collecting global data on educational policy and were responsible for reporting on a number of the millennium development goals. In this role, we  called countries to account. This fed into a "democratic" process enabling the public to view the progress of their society. There are many challenges in achieving comparability spanning over both space and time. You have to work at a country level, yet many countries have no functioning statistical system. The job is not just a practical one relating to data collection, but methodological issues around estimation, imputation, data augmentation and synthetic estimation also have to be addressed. For example, how do you measure annual illiteracy rates in every country of the world? It’s just not possible to supply good data annually – it would be far too time-consuming and expensive. If, however, you’ve got a 10-year measure (say from a survey), how do you link it with other educational system data to see if you’re on target for annual change from 2004 to 2015? We also sought out to measure the number of children in primary school in each country. So, what is the definition of children in primary school? What is the definition of "in school"? Indeed what is the definition of "school"? If half a child’s time is spent taking animals to the water hole or they’re working all day in manufacturing with just two hours of education in the evening, how does one account for that? So the challenges of the job ranged from raising statistical capacity (through advocacy for statistics) and addressing the practicalities of data collection, through to the diplomacy needed to challenge governments who sometimes lie with their data. The data could be wrong because they don’t have the resources to collect it, they haven’t prioritized statistical work, or it’s uncomfortable for them to say their children aren’t in school. You have to understand enough about diplomacy with the UN system and how you can appropriately challenge and how you can fight for the truth. And, development is not monotonically increasing. Countries have major setbacks – i.e., major wars and environmental disasters. Who defines progress, and how do we make judgments about what is acceptable? Sixty million children don’t have access to primary education –  this figure has plateaued in part because in Africa the number of children has increased. The birth rate is very high, and fortunately, we’ve managed to impact early childhood death, so more are living. It may not look like you’ve made progress, but maybe you have. You have to look at the whole picture in context. If you focus on only one element, then you can introduce (dis)incentives inadvertently. There are, of course, already incentives to show a particular picture. For example, perhaps some funding agencies will not continue to provide support to a country unless a certain level of progress has been demonstrated.  In such a case, the country will try to ensure the data show progress. We think purity of statistics is most important thing in the world, but guess what? There are many other factors to consider. Question: What was your favorite thing about being president of the Royal Statistical Society? Denise: RSS is really great institution, and it was a privilege to lead it. You get to see the diversity of statistics and get more involved with the whole of statistics profession. I made many right-angle turns in my career, but it’s always been within public sector. So, interacting with statisticians in university and industry was an exciting aspect. The UK is a much smaller country than the US, and this means the RSS is very different from the ASA. The RSS is consulted by the Government (along with sister organizations such as the two Mathematics societies and the Social Science Academy) on many public policy issues. So the RSS has a huge amount of influence, and I really enjoyed that. Question: Who are your favorite statisticians? Denise: I really think highly of a lot statisticians. David Cox has to be up there. We’re not in the same field, and we never worked together, but I applaud him for loads of reasons. There was a weekend event where we were both invited to listen to PhD students present on their research work. However weak a presentation was, he always managed to pull out the positives. He engaged with others, energized and encouraged – absolutely fantastic. He would be high on my list. Statisticians who have engaged with others and support younger statisticians – that really matters to me. I also am in awe of Peter Green, who I worked with when I was secretary of the RSS. I clearly remember sitting in a pub with him once while he explained Gibbs sampling to me. It’s always nice when somebody cares enough to help you understand these things. Question: What is your biggest professional success of which you are most proud? Denise: At UNESCO, I was brought in to dissolve a failing division of stats – failing not because of the staff, but because it reported up through the same part of the organization as janitorial, catering and other services. Of course, statistics is a service, but in the same way medical care is a service – a professional service. So I formed a new division, set the work programme and standards, established a governing board, recruited the staff, held an international competition for the country to host us and got it functioning really well. When I left after seven years, some of the ambassadors called the organization “the jewel in the crown of UNESCO.” I don’t take credit – well, partial credit! – but an awful lot of credit goes to 70 bright, committed staff who formed the new organization. Also, when I was 23 or 24, I developed a system in the UK for using post codes instead of the electoral register, which had many deficiencies. I am so pleased that post codes are still used by market research agencies and government research agencies today. Question: Do you have any additional words of wisdom that you’d like to share? Denise: I have a lot of success in my life, but I’ve had messes as well. When I was employed at the National Health Service, I whistle-blew and lost my job. That was a real setback in my career. My email was cut, and my colleagues were told not to contact me. I spent several months not being able to leave my house as I was so demoralised, but then rejoined the UN and went to work in Addis Ababa. At the time, it was a pretty good thing to do because you cannot go there and feel sorry for yourself. It was a really hard place to work, but you can’t think you’re the most important person in the world when you are surrounded with people who have nothing. Still it was very painful, and I was devastated. My words of wisdom would be that when you see this happening to someone else in the profession don’t abandon them or think that you can become damaged or contaminated by association with them. Also don’t give so much to your work that if some disaster happens you don’t have anything else in your life. You do come out stronger in a sense that such experiences make you realize that it is vital to have a work-life balance. ... View more

Having completed co-chairing a successful Analytics 2013 conference in London last month, Bart Baesens, professor and author, offers his insights to give JMP Blog readers a sample of some of the topics he will address in this month’s installment of Analytically Speaking: Conversations with Thought Leaders webcasts. Question: You teach both university students and professionals. What analytical concepts do you think are the most important for analysts to have a solid understanding of? Bart: First and foremost, I think an analyst should be aware of the need to have high-quality data before any analytical project can be started. Next, he/she should have a solid understanding of both predictive (e.g., regression, classification) as well as descriptive (e.g., clustering, association/sequence rules) analytics. The analyst should know the techniques underlying, how to appropriately use and evaluate them, and also understand how the resulting quantitative models can be successfully embedded into a business environment (e.g., marketing, finance, fraud, risk). Finally, the development of a model monitoring and backtesting framework should also be properly taken into account. Question: You've been an advocate for applying survival analysis methods to customer events of interest. Do you see adoption of these methods increasing across industries? Bart: In fact, I do. Many firms nowadays use classification models to predict: e.g., loan default, churn or fraud. These models have typically been built using either logistic regression or decision trees. The obvious next step is to extend these models to not only predict if an event (e.g., default, churn, fraud) will take place, but also when it will take place. Survival analysis provides us with an excellent tool suite to do this since it allows us to predict the timing of events. Question: How are you and your fellow staffers incorporating more visualization in your analysis and/or in the presentation of results? Bart: We extensively use visualization during the preprocessing step of an analytical project since it allows us to have a basic feeling about potentially interesting patterns present in the underlying data. We also use visualization more and more in postprocessing as well since analytical models should not only be high-performing but also understandable. Appropriate visualization facilities are key to represent analytical models in a user-friendly way. Question: What new projects are you working on? Bart: We are working on various projects at the moment. From a technical perspective, we are currently working on evaluating analytical models in a profit-driven way, rather than a statistical way based on lift charts, ROC analysis, etc. We are also developing new social network techniques to do both churn and fraud detection. From an application perspective, we continue our work on credit risk and marketing analytics, but also explore new application areas such as business process analytics, whereby starting from an event log of an information system the underlying process model is being discovered. Join us for the webcast July 17 Bart Baesens is a consultant and an assistant professor in the Research Center for Management Informatics at University of Leuven. During this webcast premiere, he will be discussing some of his extensive research on predictive analytics, data mining and customer relationship management — including the latest on survival analysis, scaling analytic efforts and more. What else should you know about Bart? My favorite facts are that has a full-size UK phone booth in his garden in Belgium and that his favorite American food is key lime pie. We hope you’ll join us on July 17 for Bart's webcast. ... View more

In celebration of the International Year of Statistics, we reached out to a few British statisticians to ask them to share a few thoughts on statistics as a discipline, statisticians and applied statistics. The United Kingdom has a rich statistical history, and we are pleased to share some of these most interesting blog interviews. Our second interview in this series is with David Hand, Senior Research Investigator and Emeritus Professor of Mathematics, Imperial College and Chief Scientific Advisor, Winton Capital Management. He is a Fellow of the British Academy, has written more than 300 scientific papers and 25 books, and is well-known for his research in classification, data mining and consumer credit scoring. He has also served twice as president of the Royal Statistical Society. Question: What was your reaction when you first heard about celebrating the first-ever International Year of Statistics in 2013? David: I thought it an excellent idea. It will promote and facilitate activities which will raise awareness of the importance and relevance of statistics to just about all aspects of life. Question: The general perception about statistics seems to be changing to a much more favorable one than even a few years ago. What are your thoughts as to why that is? David: I entirely agree that this is the case. It is something many of us have been working towards for a long time, and I would like to think it is because those efforts are coming to fruition. It seems more likely, however, that it is a consequence of other forces – notably computer power and large data sets. Certainly data mining and big data have suddenly become very topical, attracting a great deal of attention from a variety of sources. It is also helped when industries which are seen as sexy, such as those relating to the World Wide Web and bioinformatics, are recognized as being built on statistical ideas. Question: The UK has been the home country of many famous statisticians — you among them. Who are your favorite statisticians and why? David: Certainly David Cox is the most impressive, having had a vast impact right across the range of theoretical and applied statistics. Question: What are you favorite contributions to statistics in the last several decades? David: I suppose two contributions, entirely different in nature, which have most delighted me, have been the bootstrap and the development of a deep and powerful theory for latent variable models. Question: What areas of statistics do you think have much to offer yet are underutilized? David: The key driver behind the development of new statistical ideas has been practical applications: new problems coming along which required novel solutions. Typically, once solutions have been developed, they rapidly find application in other domains. A couple of examples are experimental design, originally developed in agriculture but now applied everywhere, and factor analysis, developed in psychology, but now applied in geostatistics, finance and elsewhere. So, I don’t think the question is really the right one. Rather than looking at the areas of statistics and asking which might be more used, one should look at potential application areas and ask which of those might make more use of statistical ideas, and then perhaps what tools might be of use. That question is also very much in tune with the point you made earlier, that the general perception of statistics seems to be changing to a more favorable one. In fact, I think the use of statistics is growing everywhere, but one area which I think would benefit from more rapid deployment of the ideas is public policy. Question: Many people still view statistics as a branch of mathematics. Do you think the International Year of Statistics will make people more aware of statistics as its own distinct discipline (even though it can be applied to any discipline)? David: I think this is another battle we are winning. Not so long ago, many statisticians held to this view, but now I think hardly any do. It will take longer before this understanding spreads more widely. After all, many people still confuse mathematics with arithmetic. But to answer your question, yes. I do think the International Year of Statistics will help. Question: We applaud the Royal Statistical Society’s effort and long-term view with the GetStats campaign. Two years into it, how do you think it’s going? David: I think it’s going well. The trick is to keep up the momentum. These things often begin with a big splash and then fade from public awareness. The International Year of Statistics will help. Question: What other thoughts would you like to share? David: Just the general point that statistics is the science of extracting information from data. It is thus through statistics that we understand the world, make better decisions and improve the human condition. Thanks to David for taking the time to meet up with us to share his insights (and provide us with this great photo opp at The Roof Gardens on Kensington High Street in London). Look for more chats with UK statisticians coming in the JMP Blog. ... View more

Which of the following statements about our next featured guest for Analytically Speaking is NOT true? First, let me help you in revealing that we are very fortunate to be joined on April 18 by Dick De Veaux, Professor of Mathematics and Statistics, Williams College (and expert in applied statistics). Professor De Veaux: Has been noted as the official statistician for the Grateful Dead. Was injured at a statistical conference with Stu Hunter  after jumping off the stage. Obtained French citizenship and has studied painting under Jean Dupuy. Which are the truths, and which is the lie? Well, I will not reveal the lie here but hope you’ll register to tune in on Thursday at 1 p.m. EST to find out the answer.  During this interview, he'll share best practices on: Identifying appropriate problems for predictive analytics. Exploring and preparing data for analysis. Using a variety of techniques to build accurate predictive models. Evaluating the quality of data mining models. You’ll also have an opportunity to ask him your questions during our live Q&A session at the conclusion of the webcast. ... View more

Award-winning authors Brenda Ramírez and José G. Ramírez were our guests for the March installment of Analytically Speaking. If you missed their webcast, it’s now available on demand. During the interview, they discussed how traditional quality techniques – when updated with the latest advances in analytics and data visualization – help to gain new insights about processes, products and services. Due to the large volume of questions submitted by our viewing audience during the live webcast, we were unable to address all inquiries. But, the Ramírezes were kind enough to follow up on a few of them in this post-event blog interview. Question: Sometimes the EMP process doesn't provide the equivalent of a PT ratio for evaluating being able to discriminate conforming from nonconforming parts. What do you recommend for obtaining a comparable assessment of the classic AIAG based PT ratio using the EMP method? Answer: The EMP platform in JMP can generate the traditional gauge R&R results by selecting “EMP Gauge R&R Results.” The precision to tolerance (PT) ratio can then be easily computed using the formula 6*(Gauge R&R Std Dev)/(USL-LSL). In the EMP context, for discriminating between conforming and non-conforming parts, the probable error can be used to define manufacturing specifications that provide a given likelihood that the measured part is within the customer specifications. Question: Does an output that is unstable and unpredictable challenge us to evaluate if we have the right characteristics? Please cite an example showing the difference between a process parameter injection pressure versus a product characteristic part thickness. Answer: In our book, we describe the use of a process flowchart, which depicts the four dimensions of any process step: inputs, outputs, process knobs, and noise factors. Figure 2.1 illustrates these dimensions for a Lamination step. The process knobs (process parameters) may include speed, temperature and pressure, while the outputs (product characteristics) may include the bond strength, thickness and delamination resistance. The bond strength of a laminate, for example, is determined by the inputs and process knobs and can also be impacted by the noise factors. Design of experiments is used to characterize and optimize the process parameters, while manufacturing disciplines and adherence to quality systems are needed to run the process in a repeatable manner over time. Ideally, an effective strategy monitors and controls the process inputs, process knobs and noise factors, ensuring that the process outputs meet customer’s specifications. Question: Can your methodologies be used in a contact center environment? Answer: Reliability methods can be used, for example, to estimate the median resolution time of a particular customer issue. These methodologies can also be used to evaluate the impact of eliminating particular causes (waiting for parts, wrong information, weather issues, etc.) on the estimated median resolution time. Question: Can you comment on ways we might use JMP to analyze and optimize RF electronics data from antennae and microwave systems across multiple dimensions such as frequency spectrum, range of input power, across temperature range, etc.? Answer: This is an area of application that is more suited to functional data analysis since the responses of interest (VSWR, return loss, attenuation, etc.) are curves rather than points. However, in my experience, there are certain frequencies within the frequency spectrum that are key for certain applications. These key frequencies can be used to monitor the process using process behavior charts, and experiments can be designed to optimize responses at those particular frequencies. There are also ways of summarizing the information within a given curve. Question: In your experience, how do you handle an experiment done incorrectly by other parties, such as generating too many factors in the data that is difficult to narrow down by conventional cluster analysis or principal components? Answer: In an experiment with too many factors and a few runs, we may not be able to estimate all the main effects and important two-factor that we care about. For those effects that we can estimate, we need to look at the confounding structure to understand what we are estimating. For two-level designs, the JMP Screening platform (within the Analyze>Modeling) is good at picking out big effects, but it does not effectively test each effect. We can use this information, along with our engineering and scientific knowledge, to decide if they are practically significant. Question: You both have spent your entire careers working with engineers and scientists. What do you like most about working with them? Answer: Yes, we have been collaborating and learning from engineers and scientists for many years, working of some very interesting projects. These collaborations always challenge us to put the problem first and to use statistics as a catalyst to provide sensible solutions that have a business impact. Through this process, we have learned more about the subject matter, have found the right balance of statistical sophistication and practical value, and have honed our communication skills. As a result, we have found fun and interesting ways to teach statistical content in a relevant and useful way. Question: What are your favorite features in JMP? Answer: Some of the features we use the most are Summary within the Tables menu, the Graph Builder, the Control Chart Builder, the Custom Designer, and the Distribution platform, that we use to fit different distributions and to calculate Ppk with confidence intervals. ... View more

"In today's era of big data -- where the computer and network are everywhere and everything can be measured -- you need statistics to make that data useful." -- John Sall, Co-Founder and Executive Vice President of SAS Next Wednesday, the creator of JMP will offer more insights about statistics as he kicks off the 2013 Analytically Speaking webcast series. Tune in as he discusses: The importance of the International Year of Statistics. His favorite statistical contributions to society and the people who made them possible. The impact of statistics on our everyday lives. How SAS and JMP respond to major changes in the field of statistics. The importance of connecting with JMP users  and how that influences product development. Interesting and significant applications of JMP via a live demonstration. There will also be a question-and-answer session with Sall at the conclusion of the webcast. ... View more

"Curiosity and creativity are arguably inborn, but intuition for data comes from time spent exploring it," says Michael Berry, Business Intelligence Director at TripAdvisor. Berry and Gordon Linoff, who together co-founded the consultancy Data Miners Inc. and co-wrote the book Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, were our guests for this month’s webcast installment of Analytically Speaking. If you missed the webcast, it’s now available on demand. Due to the large volume of questions submitted by our viewing audience during the live webcast, we were unable to address all inquiries. But Berry and Linoff have followed up with answers to some of these questions about analytics. Question: Is there a specific process you follow for exploratory analysis when you get new data? For example, what graphs do you start with? Linoff: This is a very general question, and it depends very much on what the data looks like. Sometimes data is already processed into customer signatures, which are suitable for modeling. In this case, univariate statistics are the place to start. If there is a time component to the data, such as when a customer starts, then I would look at the distribution of values by time, typically using scatter charts or bar charts. Why do so many customers start in January? Or, why are non-payment stops higher in May? Raw data can be trickier. To have full confidence in the data, you need to understand the processing to be sure the data is consistent. Scatter plots of values and bar charts against time are key here. Berry: The first thing I look at is the distribution of each individual variable. I use histograms for that. Next, I am likely to use scatter plots to visualize relationships between variables I expect to be related. Question: How long would you recommend storing historical data? Linoff: Storage is cheap, so this becomes less of an issue over time. In some businesses, there is a clear need to store many years of data. In others, such as e-tailing, the need may be less obvious. However, you generally want at least 13 months of data to do year-over-year comparisons. You often want more. If, for instance, you are a top news site on the Web and you want to understand how election news drives reader behavior, you want to know what happened in 2008 and perhaps even 2004. Berry: There is no set answer, but I'd say "as long as it might be useful.” Some kinds of data should be saved "forever." This includes customer relationship details such as the acquisition channel and original product purchased. Question: What motivates you to write and teach about data mining? Linoff: LOL. That's what I do. I'm fundamentally a nerd at heart, and data mining is a great intersection of data, statistics and common sense. Berry:  I truly find the subject fascinating and I like to share my enthusiasm with the world. Question: What can I do to help shift my corporate culture from “gut” decision making to data-driven decision making? Linoff: You need buy-in at a high level. A Harvard Business Review issue dedicated to big data is a sign that executives care ─ or should care ─ about such issues. However, data-driven decision making can go against the current corporate culture. Sometimes you have to wait for obvious failures, and be ready to take advantage of the opportunities they present. For instance, in one case, we were able to move ahead with a survival forecasting system only when the previous forecasting system was so off that high-level executives noticed the discrepancy. Berry:  Cultural shifts are very difficult, but some successes will get noticed. Start with something where the benefits are easy to measure and easy to understand, such as increased conversion rate or increased retention. ... View more

With the International Year of Statistics quickly approaching, what better way to prepare ourselves than by gaining a little perspective from those who have helped to shape the world of statistics in the last century? This past summer, JMP's Senior Director of Analytic Strategy Anne Milley and I took a trip up to Yale University to conduct interviews with a couple of distinguished statistics celebrities: Stu Hunter and David Salsburg. During our time together, here are a few interesting facts we learned about Stu Hunter and David Salsburg: Stu Hunter helped John Tukey with his garden in order to spend more time with him to learn from him. David Salsburg was inspired to write his book The Lady Tasting Tea as a memorial to a friend so that his children would understand the impact he had made on the field of statistics. Stu Hunter was the founding editor of Technometrics journal, which debuted in February 1959. David Salsburg was the very first statistician hired by Pfizer Inc. During the interviews, Stu and David also shared some fascinating stories about their encounters with other statistics greats — Gertrude Cox, R.A. Fisher, H. Fairfield Smith and George Box. To hear these stories and more, tune in Wednesday when their webcast interview will premiere as part of the Analytically Speaking series. Other tidbits include: The evolution of the statistics field over the past 50 years — particularly automated design, automated variable selection, automated model building, “big data” and the surge in the use of Bayesian analysis. The role of women in statistics and how they have shaped the field. How to convey statistical findings to non-technical employees. The impact of fractional factorials. I hope you enjoy the webcast as much as we enjoyed producing it. ... View more

“Once you can define a problem, it’s a matter of looking for the right techniques and right methodologies to apply to it. And, there are many, many, many methodologies out there. The question is really: Can you pick the right one? And oftentimes that depends on: Did you define the problem in the right way?”  -- Kaiser Fung, Analytically Speaking webcast, Sept. 10, 2012 How is data represented graphically in the news media, and what story does it really tell? Is data science different from statistics? Can the communication barriers that exist between statisticians and business analysts really be broken? What role does discovery play in predictive analytics? Recently, we had the privilege of hearing Kaiser Fung address these questions during the September installment of Analytically Speaking (now available for on-demand viewing in case you missed it). Fung, the Vice President of Business Intelligence and Analytics at Vimeo and author of Numbers Rule Your World (book and blog), also discussed five statistical principles they don’t teach you in statistics class and why his preference for line charts creates lots of controversial posts to his pioneering blog, Junk Charts. Because Kaiser was such a popular speaker, we were unable to respond live to all of the questions submitted by our webcast audience, so he kindly agreed to answer a few more in this post-event interview. Question: What are some of the most common worst practices you see in information visualization? Kaiser: The absolute worst practice is plotting for plotting’s sake. There are many charts out there where I can’t figure out what question the designer wanted to address. The next problem is using bad data. Another issue is favoring pretty things that distort our perception: bubble chart is an example. On Junk Charts, I use a trifecta checkup that tests each chart on those three criteria. Question: An analyst’s ultimate goal is to provide data support for strategic decision making. What are the key qualifications the analyst should have in order to contribute to business decision making? Kaiser: At the minimum, the analyst needs to be able to mentally process a lot of numbers, and extract the information out. It’s very easy to get bogged down by details. Big data magnifies this problem. I'd much rather see a high-quality analysis on a subset of data than a poor analysis of a mountain of data. Next, the analyst must keep the business goal in mind at all times. A simple analysis using boring techniques frequently produces a much larger impact than complicated methodologies that improve the status quo by some immaterial amount, even if it’s statistically significant. Lastly, there are many intangible qualities that one finds in successful analysts. Curiosity, perseverance, humility, knowing how and when to cut corners, time management, etc., are all useful. Question: In the Analytically Speaking webcast, you mentioned that you only show the final story to your audiences and that a lot of stuff is “dumped.” What's the typical amount of time spent on data quality check, actual analysis and writing up the final story documentation? I believe I am spending too much time on the first two steps. Do you have any tips to improve the story-telling techniques so all analyses are worth it? Kaiser: Here's my ideal scenario: You spend a month on the project, you go through hypothesis after hypothesis, you hit many dead ends, but finally you find an answer you can live with. You love this answer because it addressed the question simply, and you are able to throw out most of the other work because it turned out to be irrelevant or have much weaker effects than your final answer. So now you have a simple theory with solid data to back it up. It all fits onto one slide, and you found the one chart to bring it all together. I’d walk into that meeting with the one slide. The manager gets the story, and it solves his/her problem. Question: How would you define a story? Kaiser: By "story" I mean something written in English with only a few key numbers. Imagine you have 10 slides. Pull out the 10 headers and put them on a piece of paper. Do you have a story? Typically, it has a conclusion, then it has points that support the conclusion. It’s like writing an argument for a history class; the only difference is you have data as some of your raw materials. Question: Can you please share your views on the analyst and analytical tools? How can they best complement each other? Kaiser: I always think the analyst comes before the tools. If the analyst is clear about how to look at the data, he/she can find tools that would facilitate that analysis. That’s why on Junk Charts, I rarely talk about tools. If you really want me to talk about tools, then I’d say tools have to simplify the analyst’s life. Half-developed tools inevitably create extra work for the analyst. Question: Can you address how you handle messy data with lots of noise? And do you typically start your process by assessing the clean data or raw data, or something outside of the data collection? Kaiser: This question can be interpreted in two ways. The noise may be a feature of your data, or it is erroneously added to your data. I always spend time figuring out how the data is collected, ideally talking to the person who collected the data. Always ask yourself how big a deal the errors are before spending time correcting them. If your data has high variance, that’s when you need statistical methods. Question: What recommendations do you have for breaking down perceived barriers between statisticians and executives? Kaiser: My favorite tip is learn to speak their language. Don’t try to teach [executives] math. ... 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On Nov. 16, Chris Gotwalt will present a new webcast on Best Practices in Model Comparison, which will include a sneak peek of JMP 10 (due to be released this upcoming March). I recently caught up with Chris to find out how he was enjoying his new role at JMP, what new features we can look forward to in JMP 10 and what inspires him and his teams to create these new features we’ll soon be enjoying. In April, Chris was promoted to Director of Statistical Research and Development overseeing both statistical development and testing teams and has been with JMP for 10 years. His undergraduate degree is in mathematics, and he holds a PhD in statistics. He discovered his interest in the field during the last semester of college, after he realized that statistics combined many of his diverse interests, from math to computer science and philosophy of science. Chris’ main focus in his new role also happens to be what he’s enjoying most -- collecting customer feedback and working hard to ensure that a product that’s being released is completely customer-motivated.  “When we begin to create and look at revisions and enhancements, we actually visualize that there is someone on the other side anticipating and waiting to use what we are developing. I’m motivated by making things easier for our customers and by improving elements that will help successfully accomplish their tasks. Everything we do, we do because we have someone very particular in mind,” he says. Other primary goals of the statistical development team and testing teams are to shake out usability snags swiftly and to provide more and more reliable analytical capabilities with each release. What kinds of questions does R&D ask? Here’s a good starting template from Chris to provide the foundation for a new emergence of ideas: Ask yourself every week: What can we do better? What would make JMP easier to teach? Where do people get frustrated? What’s something our customers have to do over and over again that could be automated? What can we do to help our customers who are working to fulfill organizational or governmental mandates? What tools would make things easier for them? Members of Chris’ team seek out multiple outlets for gaining this feedback, internally from sales, SAS education and marketing peers, but also externally through conferences, WebEx preview sessions, alpha and beta presentations, meetings with leading university professors, statistical counsels, related professional organizations and users groups.  One of Chris’ feedback channels provided unexpected results, leading the direction for huge changes in JMP 10. Chris began reviewing tracks from technical support and found that control charts were the fourth most-used platform in JMP, as measured by number of technical support tracks generated. “I was very surprised to make that discovery. We hadn’t done anything substantial with that platform in six or seven years, but once we saw the tech support logs, I immediately met with Leo Wright in product marketing to examine what would be the next generation for control chart tools.” Also, some of the greatest perspectives come from the instructors in SAS education, which has for many years been a source of ideas for the development of JMP. Every class is 30 new people and 30 new perspectives on usability, analyses and questions related to industry-specific challenges. “Sometimes instructors may get a question for which they don’t have an answer. So, they write a script. The script gets popular, and then people add to it to use and share. It’s when this happens that we realize it should be native to JMP,” Chris explains. What is the result of all of this feedback? JMP 10 launches in March of 2012 and will include these new capabilities: Control Chart Builder Nonlinear Regression made simple Design evaluation platform New measurement systems analysis platform Reliability Forecasting Partial Least Squares Model Comparison platform One-Click Bootstrapping of any quantity in JMP Reliability Growth Analysis My final question to Chris was "What recommendations would you make to folks just getting started with analytics?" His answer: "Find out where your company’s data is, load it into JMP and start asking questions. Look for surprises. Where are the problem areas? What are people asking questions about? I frequently shift development priorities, based on seeing what our customers are trying to use our product for each day. Share your information with others and ask what does this mean? Most importantly, allow yourself to be surprised."  The learning loop is continuous, and the JMP statistical development team is wrapping up work on JMP 10, and will soon be adding new nonlinear models and data mining techniques that will be part of JMP 11. Chris concludes, “We’re excited because there’s a lot we can do to make our product more modern and usable.” ... View more

Whether you steer strategy from the C-suite or execute operations from your cubical, you can use analytics to transform your organization.  I have continued to gain greater appreciation for the value of analytics at the many conferences that JMP has participated in this fall, including Predictive Analytics World in New York last week. Next week’s Predictive Analytics Innovation Summit in Chicago will offer more wonderful opportunities to learn from analytics experts and engaged practitioners alike about fresh ways to improve contributions to your organization and improve day-to-day processes.  If you’re headed to Predictive Analytics Innovation Summit, don’t miss the JMP Analytics Exchange Panel, which will convene the morning of Friday, Nov. 4. Panelists will address some of the successes and challenges they’ve encountered in data-driven decision making. Discussion topics will include: Getting started on the analytic journey. Evaluating the importance of visual presentation in influencing organizational decisions. Getting your analyses more broadly adopted across the organization. Best practices for establishing an analytic center of excellence. Attracting and retaining analytic talent. Overcoming organizational reluctance to an analytic approach. Anne Milley, Senior Director of Analytics Strategy for JMP, will be the moderator, and panelists include: Vicki A. Barbur, PhD, Vice President of Research and Development, Cardinal Health. Bill Kolinger, Senior Vice President Market Information Manager, Bank of America. Dick De Veaux, PhD, Professor of Mathematics and Statistics, Williams College. Mike Cramer, Director of Operations Research, McDonald’s. ... View more

Well, Wall Street cares so much about making fast decisions that it is laying dedicated high-speed lines so that the data for program trades can be processed faster and orders executed more quickly. If Wall Street can do all this in milliseconds, won’t it make a difference to you to get your indicators sooner, in seconds instead of hours, in time for a fast response to make a difference? Speed was the theme of the day today, where I joined hundreds of others at Predictive Analytics World to talk about how to get things done more quickly -- and what a more perfect place to host this topic of discussion than in the heart of fast-paced midtown Manhattan?  John Sall, SAS co-founder and chief architect of JMP delivered a 20-minute keynote address on speed -- specifically how it’s possible to explore your data, even big data, more quickly. The question people commonly ask themselves: Is there too much data to use interactive graphics? The more things you are able to view, the more discoveries you can make and realize value faster. Rich graphics with more features and more dimensions greatly enable this process, and animation helps too. Fast visualization is key. Here’s what I learned from John’s visual data exploration (in a matter of 18 minutes to be exact). In viewing birth records from the CDC over the last 50 years using Graph Builder capabilities in JMP: Women 30 and older have heavier babies. Male babies tend to weigh about 100 grams more than female babies. Not many babies are born on weekends. Why? Scheduled births surround doctors’ busy schedules. Some babies have weighed as much as 22 tons. Oh wait, no, that was just missing values in the data table. Time it took to find out these few facts from 12.8 million births: 3 minutes. In viewing ASA data to better understand challenges with airline traffic, he grouped the data by month and years, using the heat map graphing capabilities to discover that: There we lots of delays across the US in December of 1999 and 2000. No tracking mechanism was in place in 1987 to track delays. It’s easy to create a scatterplot of delay by distance and use binning to see where the data is heaviest. Time it took to make these observations by sifting through 123 million cases documented: less than 2 minutes.  In viewing social survey data and labor force statistics using 5,416 variables, John discovered: What makes people happy (this may be a separate blog post altogether, but I can say that health condition and financial stability were among the top-ranked variables). If you have 12 people in your household, you are unlikely to be happy. Yes, this is truly what the data showed and did garner many laughs from the PAW audience. Total time to discover what makes people happy: 2.5 minutes. In closing, John indicated that in addition to all the speed JMP enables on the desktop, SAS has made great strides in high-performance analytics. Customers have been able to tackle problems that previously were intractable. Some improvements: Reducing analytics processing time from 11 hours to 10 seconds, in other cases 30 hours to 2 hours. Driving a 15 percent improvement in customer retention rates. Modeling 195 million customer records in real time. It made us happy that John joined us today to share these insights. I turned to tell him so, but he was already on his way out the door to catch the high-speed train at Penn Station to his next engagement for the day. ... View more

I, like most people, am not a fan of unsolicited email, but discovered one well worth my time on Sept. 11. On that particular morning, I had received a note in my inbox from Meetup founder, Scott Heiferman. In it, he describes an inspiring story of community building in New York City after 9/11. Prior to the attack on the Twin Towers, Heiferman, a resident of downtown Manhattan, confessed that the only time he thought about his neighbors was when he hoped they wouldn’t bother him. But of course, that all changed for him after 9/11 as it did for most New Yorkers. He became invigorated by the idea that the Internet could be used to get people off the Internet and grow local communities. His passion turned into an idea that worked. Thousands upon thousands of Meetup groups have formed since that time, and all of those people getting together are getting together because of one thing -- a common interest, passion or goal.  Meetup now has more than 10 million members. On Oct. 19, we hope you will join us to learn about the one passion we have in common -- predictive analytics. This year JMP is happy to be sponsoring the NYC Predictive Analytics Meetup that will be held in conjunction with the Predictive Analytics World conference. Sam Gardner of JMP will open the meeting with a presentation on the integration of JMP and R, followed by John W. Emerson, Associate Professor of Statistics at Yale University, who will discuss Bayesians, Frequentists, and Big Data: Musings on Statistics in the 21st Century. The event is complimentary and will be held at the Hilton New York. For more information and to register, visit the Predictive Analytics World site. ... View more

Gordon Linoff has been a self-starter in many ways. To name a few, he, along with Michael Berry, founded Data Miners Inc. in 1998. Gordon was also one of the first experts who SAS looked to in the 1990s to start the ever-popular SAS Business Knowledge Series. He has consulted for a wide range of companies including Bank of America, BT, The Limited, The New York Times, T-Mobile, The Teaching Company and Pfizer, and he has authored some of the most widely respected data mining books in the world. In a couple of weeks, Gordon will be hosting a special breakfast during our annual users conference, Discovery Summit 2011 in Denver. I caught up with Gordon recently to discuss what cool new projects he's been working on lately. How would you describe survival mining? Tell me about some of the interesting work that you’ve been doing in this area. Data mining is the process of analyzing large amounts of data to solve real-world problems. Survival data mining is specifically applied to time-to-event problems. The classic example is trying to understand when customers will leave. The question is not whether or not a customer will eventually stop. The question is how long will the customer remain as a customer. This simple example of using survival analysis for understanding customer retention has several direct uses. Customer profitability is related to two factors: how profitable is a customer per unit time times how long is a customer expected to stay? The second half of this equation is survival analysis. Another use is forecasting customer numbers in the future. Forecasts built from the bottom-up – using survival analysis – are more powerful than forecasts built from the top-down because you can more easily drill down to find the factors that influence the forecast. And, customer retention is only one application. Since I started using survival data mining about 10 years ago, I see time-to-event problems everywhere. The question is not whether a customer will reactivate, but how long until the reactivation. The question is not whether or not a customer will make a purchase, but how long until the next purchase. What retailers call purchase velocity can be measured and visualized using survival data mining. You’ve now authored many books, one of which is now in its third edition. Explain some of the new material that you cover in this edition that hasn’t been included in past. What are some of the key insights you want people to take away from this book? Data Mining Techniques for Marketing, Sales, and Customer Relationship Managementwas one of the first books to cover data mining. It is also the first data mining book to have a third edition. Although inspired by the earlier two editions, Michael Berry and I rewrote all the chapters, added several new ones and combined some of the earlier ones. The book is still divided into three parts. The first few chapters are an overview of data mining and the data mining process, illustrated with stories based on what we've learned over the years. The bulk of the book is the techniques chapters, with a separate chapter for each major technique. The rest of the book puts data mining into the context of the business, with discussions on data, data warehousing and more case studies. Perhaps the most interesting new chapter is the chapter on text mining, which includes a wonderful case study about successes that DirecTV has had using text mining. Another important new chapter covers principal components and singular value decomposition. This chapter was quite challenging to write, since our book emphasizes understanding the techniques over matrix algebra. Clustering is such an important family of techniques that we split the original chapter in two, adding much more discussion of techniques, such as expectation maximization clustering, hierarchical clustering and other clustering methods.  Longtime SAS users will recognize you as a crucial instructor for many years with the SAS Business Knowledge Series. What has been your favorite class to teach and why? All my courses are my favorite. Michael and I actually helped found the Business Knowledge Series more than 10 years ago. SAS has had a long history of providing excellent training for its software. In the late 1990s, they decide to leverage the investment in training by bringing in outside experts to talk about different subjects, and we were the first trainers to offer such a course. Your consulting works spans many industries (and many countries). What are you currently working on, and what has been most interesting to you about this project? I work on a wide variety of projects. One of my projects is working with a large, content-providing website to encourage customers to become paying subscribers. This is challenging because the volume of data being gathered is so large. I recently finished a small project outside the business area. FORGE is a program in New Jersey designed to help women parolees adjust to life outside prison – and to prevent recidivism. This allowed me to apply survival analysis to an interesting domain, where the purpose is to help people and prevent crime. Some of my work is more on the "data" side more than on the "mining" side. For the past year, I have been working with the Lehman Brothers estate, helping to make sense of the data that describes the derivative transactions involved in the bankruptcy proceedings. ... View more

On Sept. 16, more than 40 JMP users gathered at John Hopkins University in Laurel, Maryland, for the first Mid-Atlantic JMP Users Group. Attendees included the JMP users in the photo at left: Jose Ramirez, Jen Barb, Susan Dorsey, Manny Uy and Mel Alexander. Several dynamic speakers were featured from the commercial, government and academic sectors to provide a variety of perspectives, including Mel Alexander from the University of Maryland Medical School, Jose Ramirez of W.L. Gore & Associates and Manny Uy of John Hopkins University. Topics included “Avoiding Futile Drug Administration Using JMP Software”, “Evaluation of Chemical and Biological Detectors with JMP Custom Designs” and “Statistical Intervals: Confidence, Prediction and Enclosure.” John Sall, Executive VP and Co-Founder of SAS, was also on hand to deliver a keynote address on the new capabilities of JMP 8 scheduled for released in mid-November. Attendance reflected the same variety of disciplines, with around 50 percent of attendees from academia, 25 percent from commercial and 25 percent from the government. The users group plans another meeting on Dec. 10 at the University of Maryland's School of Nursing; that meeting will feature the use of JMP in the biomedical, health and life sciences, as well as a forum to discuss the beta version of JMP 8. Looking ahead to 2009, the group hopes to host a first-quarter meeting at the US National Institutes of Heath in Bethesda, Maryland, that will focus on JMP Genomics. In addition to Sall and Uy (second and third from left in the photo below), Hope Walker and Doug Robinson of JMP (first and far right) also attended the meeting. ... View more

New JMP users groups are sprouting up all over the US in 2008! In the last six months alone, three new groups have launched. I'm pleased to announce the addition of a fourth users group community. On Oct. 29, the Austin Users Group will hold its first meeting in the SAS Austin Office. The meeting will feature dynamic speakers John Sall, Executive VP and Co-Founder of SAS, along with Six Sigma expert Dr. Harrison “Skip” Weed of Dell. Starting a new JMP users group provides great networking opportunities with others who share common interests and have a similar professional background. Better yet, user group members have premier access to information and demonstrations on the latest JMP features and to the ears of our developers as they work on future developments. If you’re in the Austin area, we hope you’ll join this group to: • Learn about the latest JMP products, capabilities and services. • Improve your efficiency and productivity by learning new coding and analysis techniques. • Discover the new and innovative ways others are solving real-world problems. During the first meeting in Austin, Sall will provide a sneak preview of some of the amazing new features in JMP 8, including a new drag-and-drop graphing capability and a more powerful simulation capability. This event is complimentary, and dinner will be served. ... View more