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Data scientists must learn to effectively communicate their findings

Richard Jones Archive

As information technology improves and companies become more adept at gathering and analyzing information, data-driven procedures are becoming the norm in the business world. Practically every company today is gathering data and using analytics to break it down.

The next step, however, is a little more complicated. Companies don't just need the ability to analyze information - they also need to communicate their findings. If an IT department can perform complex analytical tasks, that's step one, but after that, analysts must explain to other employees across various departments what it means.

According to Information Management, this requires a back-and-forth dialogue between technicians and other business leaders.

"Keep in mind that communication is mostly about listening," data quality expert Jim Harris cautioned. "Also, be prepared to face 'data denial' whenever data quality is discussed. This is a natural self-defense mechanism for the people responsible for business processes, technology and data, which is understandable because nobody likes to be blamed (or feel blamed) for causing or failing to fix data quality problems."

Verify business relevancy
Data analysts should communicate as clearly as possible about the relevance of their findings. Companies today are using data in a variety of areas - it can help them improve marketing, sales, finance, HR and many other corners of their operations. Business leaders should make sure they have a clear understanding of where exactly their findings can help.

Clarify data's uses
Harris cited the words of Donald Berwick, CEO of the Institute for Healthcare Improvement, who once gave a notable speech on the application of big data in healthcare. His message was that firms should be as specific as possible about how they plan to use their clusters of data. As he put it: "Some is not a number, and soon is not a time." In other words, a clearer picture of data's uses is essential.

Prioritize critical issues
Any good data scientist puts comprehensive thinking and careful planning into an analysis. Likewise, companies should exercise that same thoughtfulness when planning their applications of big data. They should identify clearly what issues are most important to address, and they shouldn't begin applying their data-driven findings without a complete blueprint mapped out in advance.

Thanks to all the innovations in data storage and analysis in recent years, companies have more potential now than ever with data. The next step is telling the world what they plan to do with it.