How do organizations spread and use analytic knowledge?

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For analytic evolution to happen effectively, organizations need a methodology to communicate and reinforce analytic culture.There are many ways to achieve great analytic cultures, but all great analytic cultures share some common elements. The best organizations spread analytic knowledge throughout the business, value analytics in support of decision making and build infrastructure that allows many (or all) employees to use analytics as part of their jobs.

These days it’s hard not to read a newspaper, magazine or online article and not run into the many technology buzzwords thrown around as solutions to the nagging problems organizations face: data lakes, deep learning, machine learning, artificial intelligence (AI), Industry 4.0, just to name a few.

But a lack of technology is rarely the problem. Too often organizations throw layers of technology at problems and expect results, or worse, put technology in place and then look for a problem to solve. In a recent article, Oliver Schabenberger, Executive Vice President, COO and CTO of SAS, talks about the importance of evolving people, evolving process and looking for opportunity, rather than focusing purely on evolving technology.

And for that evolution to happen effectively, organizations need a methodology to communicate and reinforce analytic culture. They also need empowering tools to ensure that analytics are broadly used. Why? The more brains engaged in solving problems, the better. The more people using analytics and communicating results, the stronger an organization’s decisions and knowledge.

If your industry or organization has a high level of process and product innovation, you have a higher need for analytics in order to remain competitive in today’s marketplace.

Data itself is also changing. It's unstructured, in the form of images, sound, functions or even videos.Problems are changing and the amount of data has never been greater. The types of problems organizations are trying to solve are large and complex. Data is deep and may contain millions (or billions) of records. Some problems are wide (thousands of columns). The data itself is also changing. It’s unstructured (for example, free text from surveys), in the form of images, sound, functions or even videos.

Fortunately, while the challenges with data have never been greater, so have the opportunities to face these challenges with software. Computers have made discovery and analysis from many angles possible. And software can make complex analytic techniques once available only to a few statisticians in an organization now available to many applied practitioners; software allows more brains to solve problems.

But solving problems is not the end of software needs in an organization. The last key ingredient is effective communication. In order to communicate effectively with analytics, you need to show results in a live format. For example, perform “what-if” analyses on the fly. This builds trust in analytic results among those who make the decisions. With increasingly global workforces, statistical visualizations also can transcend language barriers. And the standardization of reports leads to discussion of the problem and not the data.

See how the newest member of the JMP family, JMP Live, helps with effective communication.