Once the model has been completed and validated it can be deployed and used by the business. This is often easier said than done. The business will only accept and use a model if understand it’s value and are convinced that it will help them improve their output. Cultivating a data-driven culture is something that has to be done as a part of the broader strategy of using analytics, and one of the more fundamental ways to help it along is to make sure that the business understands the analytics output. And that’s where Step 6 comes in.

6. Report the results in an understandable manner

A picture speaks a thousand words. Reporting results in terms of tables and tables of data and asking the business to look up tables, correlate with other tables, and so on may do nothing but confuse them and turn them off. It may be far more effective for the numbers to come to life and tell a story through the use of visualization. A number of analytics product suites include tools that are quite powerful in terms of presenting data visually. The right tools and facilities need to be selected so that the results are clearly and completely understood the first time around by people who are likely to be business leaders and business experts, but not data scientists. The lower the reliance on jargon the better.

There is a plethora of services, tools and technologies that are continuously being made available in the market even as the current ones mature. It’s easy to get lost in the maze, and so it helps if the leadership focus is not confused by what tools to use, but is maintained on how to move forward towards greater acceptance of analytics, and hence, success.