Extending Data Science Skills Across the Organization
A couple of decades ago, only highly specialized professionals could create a well-designed website. Today, practically anybody can build a good looking site in an afternoon using no-code platforms like Wix or Wordpress.
We’re not quite there yet with data analytics, but we’re getting there quickly. It’s become commonplace for companies across industries to want to be “data-driven,” but the reality is that most organizations still struggle to attract and retain the data scientists who can help them create value from their information.
This talent shortage is one of at least six reasons why business leaders should consider building data science capacity throughout their organizations.
1. Building a Talent Pipeline
When it comes to data analytics talent, you can either compete for it, or you can create it. By training up people who are already on staff, companies ensure that they have ready access to data science expertise. Also, longtime employees who acquire analytics skills may be more likely to stick around than the data science experts who will continue to be bombarded by offers, even after they are hired.
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2. Subject Matter Expertise
Too often, the people within an organization who have access to data analytics tools aren’t particularly close to the problems that the data may help to solve. We can’t expect data scientists to be experts in finance, marketing, customer service, manufacturing, logistics and all of the other departments and functions that make a company run. Even if they have access to data from different lines of business, data scientists may lack the subject matter expertise to always put it to good use. One way to solve this is to simply reverse the equation: bringing one new skill (data science) to departments across the enterprise, rather than expecting data scientists to master every other aspect of the business.
3. Improved Collaboration
The past few years have shown us just how important collaboration is to productivity. Collaborative authoring tools like Google Docs, for instance, have completely upended traditional workflows (No more emailing different versions of a document around and losing track of which one is the latest.). And especially during the pandemic, video collaboration became central to many companies’ operating models. Collaborative no-code analytics tools aren’t yet widely available, but these solutions are currently being developed. Once they’re ready, they will help stakeholders across different business units work together in real-time to collaboratively solve problems.
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4. Faster Time-to-Insight
For most organizations, the time between someone having an idea about how to solve a problem with analytics, and actually being able to test that idea out, is far too long. The COVID-19 pandemic showed just how quickly conditions on the ground can shift, and businesses need to be able to quickly arrive at data-driven insights to adjust before they’re left behind. Data science teams are typically already overloaded with projects, making it difficult for them to respond rapidly to new developments. But by creating analytics capabilities throughout the organization, companies can tighten up their analytics timelines.
5. Diversity of Ideas
When more people in an organization have data analytics skills, they can try out a greater number of ideas, increasing the likelihood that they’ll land on valuable insights. Often, the answers lie in surprising places. One car manufacturer that I’ve worked with used data science to help business leaders figure out the source of manufacturing delays. A number of stakeholders in the company had assumed that the problem lay with the complexity of a certain vehicle’s power train. But in fact, analytics revealed that the delays were actually being caused by a seemingly minor change in an accessory package.
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6. Awareness of Potential Problems
Mark Twain is credited with saying that there are three kinds of lies: “lies, damned lies and statistics.” The idea here isn’t that data is never helpful, but rather that statistics are sometimes used to support faulty conclusions. (If you confuse correlation for causation, you might decide that carrying an umbrella causes it to rain.) Most people without analytics training aren’t familiar with common data science traps. Have you ever heard of Simpson’s paradox, for example? Have your employees? If not, it may be difficult for stakeholders across your organization to spot problems with how analytics are being used.
The idea of becoming trained in analytics may at first seem intimidating to line-of-business employees. But remember: The idea of getting trained in Microsoft Excel was once intimidating. Today, basic computing skills are simply part of the job for nearly all knowledge workers. In the coming years, we may see the same thing happen for data science.
About the Author
Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he co-leads the Data Systems Group and is part of the Systems Community of Research(CoR). He is also the Founding Co-Director of MIT Data System and AI Lab (DSAIL).