Is Your Company Suffering from the Data Science Drought? 4 Questions to Ask Yourself to Find Out

No one would argue that in-house data expertise is a competitive necessity in today’s B2B marketplace. However, the truth is that effectively harnessing data’s potential is a real challenge that many are still tackling. In part, that’s because the supply of data science skillsets falls short of its demand, leaving many companies scrambling to stay ahead of the curve.

In a recent report and corresponding infographic, researchers at University of California, Riverside point to what they call a “global data scientist drought.” The basic takeaway is this: the volume and speed at which data is generated today means that data management must be a top business priority for all companies. The problem? Global demand for data scientists is projected to exceed supply by more than 50% by the end of this year.

And the shortage is taking its toll on companies today: 43% report that their lack of appropriate analytical skills is a key challenge and, in response to the scarcity of relevant skills sets in the job market, are turning toward in-house skill-building. 63% of companies rely on both formal and on-the-job training of current employees to help keep up with data projects.

These companies are bending under the strain of new data demands and a lack of the appropriate skills needed to address them. But how do you know if you’re one of these companies struggling to manage data? How do you determine if you’re taking full advantage of the promises of big data? How can you tell if you’re not keeping up with emerging data demands? We’ve rounded up a set of questions that can help you come clean with yourself about potential big data shortcomings and reveal a roadmap to overcoming them.

#1 Do you still view data science as a technical, not a business, initiative?

Thomas Redman, president of Data Quality Solutions, says this is one of the worst mistakes companies make – hiring an army of talented scientists, giving them access to the data, and turning them loose to find out how to put it to work. Instead, companies should be thoughtful about their placement of data scientists, aligning talent with specific business functions and therefore specific goals. The takeaway here? Linking data science roles to bigger strategic goals is key to making the most of their skills sets and aligning data projects with company growth –  and if you’re not doing it, you’re probably not getting the most out of your data team.

#2 Do you have a well-articulated data strategy?

Let’s start by defining a data strategy: A comprehensive and actionable foundation for an organization’s ability to harness and leverage data. Your data strategy should define your data analytics goals and detail plans, tactics, and a roadmap for accomplishing them. But most important is firm-wide adoption to ensure that data goals are aligned throughout the company. A well-articulated data strategy provides a clear set of data management guidelines for everyone to follow, regardless of division or role. This, in turn, facilitates clear communication, transparency, better standardization across departments, and more tangible results for data projects. If most of your data management happens at the desk of a handful of individuals, there’s a strong likelihood you’re missing out on the value that a company-wide data strategy can create.

#3 How much of your data is siloed?

We all have a lot of data, more than ever before. But as The Knowlton Group points out, not only is the size of data increasing, it’s also become more disparate. If you find yourself with huge piles of data across separate divisions that have no way of “talking” to each other, then you might be failing to tap into your true data potential. Well-integrated data has richer value, which means deeper insights and bigger returns. Companies that effectively harness data science know how to create systems of integrated data sources by making integration a “priority during the vendor/software evaluation process, instead of an afterthought post-implementation.” If you have a sense that your different data sources could be doing more together, you may be underinvesting in the data science needed to create a more integrated system.

#4 Do you feel your efficiency lagging?

The drivers of inefficiency are myriad, and chances are your firm has a solid track record of tracing and understanding the company-specific bottlenecks that tend to slow you down. But the reality is, if data isn’t at the center of any business function, then that function is probably not reaching its efficiency potential, whether you know it or not.

Good data scientists are focused on delivering quality data and analytics that their business counterparts can understand, interpret, and make use of to improve performance. They should be encouraged to pull on threads – something that looks out of place in the data, deceptively small anomalies, a notion that performance isn’t quite right in one division or another. While most inklings won’t lead to a major discovery of inefficiency, encouraging the practice will empower your data scientists to notice and speak up when there is something fundamentally wrong, leading to major efficiency gains you didn’t even know you were looking for. The main takeaway from not just our experience, but also from most of the reports and articles we read is that the efficiency savings associated with investing in data science well outweigh the initial cost.

If you have an inkling that you could be doing a better job at putting your data to work, you’re not alone. And asking yourself these four questions is a good first step on a path to better data management.


Keep Reading:

Harvard Business Review: Are you Setting your Data Scientists up to Fail?

Infographic: The Data Science Drought

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