Academic life is very different from the business world. Help your data scientists understand what's expected of them.
I once watched an interview of a university data scientist who had been working on a project team that for seven years had been trying to solve a complex problem in genetics. The team concluded that it couldn't arrive at an answer with its research, so it decided to disband.
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In a university setting, it's acceptable to pursue theoretical research and analytics without necessarily arriving at an impactful outcome. It is also why it may be important to acculturate data scientists so they can adjust to the more stringent results orientations and real-world applications of businesses.
Harvard Business Review apparently thinks so, too. Thomas C. Redman, president of Data Quality Solutions, questioned whether data scientists understood the "why" behind their work in business.
"Data science, broadly defined, has been around for a long time," Redman said. "But the failure rates of big data projects in general and (artificial intelligence) projects in particular remain disturbingly high. And despite the hype (e.g., "data is the new oil"), companies have yet to cite the contributions of data science to their bottom lines.
Why are data scientists struggling to adapt to business culture?
Businesses themselves don't understand what the data science discipline is, the work backgrounds from where data scientists are coming, and what it's going to take to acculturate these highly trained data engineers to how a business operates and what it needs.
Many data scientists have lived their lives in environments funded by university grants that enabled them to pursue highly theoretical projects that are all about the quest for answers but not necessarily about finding definitive solutions for why customers seem to be suddenly favoring another brand, or why your manufactured products are suddenly experiencing more failures.
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Companies also struggle with integrating data scientists with their existing business and IT workforces. Often, existing business units and IT have little in common with data scientists, and there are no existing workflows that can help them learn how to optimally work together.
Another issue is that businesses aren't always sure what (and when) to expect analytics and results out of their big data projects. Successful use cases exist in most industries, but companies still don't have a good feel for knowing when a data science or analytics project is moving forward and when it is stagnating.
How to help data scientists adapt
Here are some ways companies can address these issues so they get improved data science performance.
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Orient data scientist mindsets to the business. Orienting data scientists to the goals, demands, and expectations of the business--which are likely to be more stringent than those in a university research setting--should be the first order of business. These differences should also be brought up in job interviews because it gives applicants the opportunity to decide if they want to work in a business setting or explain how familiar they are with company life.
Expect results, and articulate those expectations. Companies are going to expect results at earlier stages than university and research organizations do. Timelines and desired results expectations should be set for data science and analytics projects. If a project can't deliver, it should be reviewed and potentially terminated.
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Don't make data science an island. Data scientists stay in step with the business when they are continuously interacting with business staff and IT professionals who already understand the needs of the business. This encourages inter-departmental collaboration that contributes to positive business results.
Remember that data science isn't IT. Although businesses will set more stringent demands for data science than most data scientists are accustomed to, it's still important for the business to remember there is a difference. The data science discipline is an interactive exercise on data, analysis, and algorithms until the results reach 95% accuracy. Getting to this point isn't always predictable, and there will be both setbacks and failures. That is the nature of human and software reasoning.
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