Projin : 4 Ways to Democratize Data Science in Your Organization

Where do your data efforts lie? Many organizations leave data to a team of data scientists and focus their efforts where there is lots of data. While that approach may make sense on paper, using your data more strategically and more broadly across your organization — by using data to inform big swing decisions and by getting everyone involved – your company has a higher chance for a successful data science transformation.

To make data science more strategic and democratic in your company, take the following steps. First, focus on problems or opportunities with the highest level of strategic benefit. Second, develop “citizen data scientists” across the organization. Third, reprioritize data science efforts and reassign your data scientists. Finally, develop and communicate a broad vision of data science.

Many organizations have begun their data science journeys by starting “centers of excellence,” hiring the best data scientists they can and focusing their efforts where there is lots of data. In some respects, this makes good sense — after all, they don’t want to be late to the artificial intelligence or machine learning party. Plus, data scientists want to show off their latest tools.

But is this the best way to deploy this rare resource? For most companies, we think it unlikely. Rather, we advise companies to see data science both more strategically and broadly.

Consider strategic data science. While organizations have relatively few strategic problems, they are of special importance to the company. Even though there may be relatively little data to analyze for strategic problems and “big swing” decisions, companies should bring everything they can to such issues. Data science provides much more value than just big data algorithms — from more clearly formulating the problem, to analyzing what “small data” is available, to experimenting, to creating great graphics. The potential to come up with better insights using data science is enormous. Further, since senior managers must ultimately lead the data science transformation, engaging them in the data helps them more clearly see the benefits and better understand what they must contribute to the transformation.

But data science also must be democratized broadly. If data science is to be truly transformational, everyone must get in on the fun. Restricting data science to only the experts is a limiting proposition. Data science programs that focus on professional data scientists ignore the vast majority of people and business opportunities. For instance, organizations are loaded with problems and data-driven decisions that can be solved and made by small teams of knowledge workers, middle managers, and partners using small amounts of data in two to three months. These individuals, being at the front lines of the organization, already understand the business and don’t need to be taught it as data scientists do. And vendors of various types are now offering a variety of new tools that ease or automate many aspects of data science, including massaging data, creating algorithms, and creating code to deploy a model into production.

While the idea of an organization-wide data science transformation sounds overwhelming, there are ways you can get started. Based on our consulting, conversations with senior leaders, and research, we recommend the following interrelated steps to make data science more strategic and democratic in your company.

Focus on problems with the highest level of strategic benefit.

As previously noted, most organizations focus their data science efforts where they have the most data — even if they don’t mean to. Companies should consider a full range of other criteria, two of which are most important.

First, they must think of the long-term strategic importance of the problem or opportunity. Consider two options at a mid-sized media company: Option 1 involves looking for insights that deepen the user experience using data generated by engagement with its apps; Option 2 involves using data to inform a bid for certain licensing rights, something that comes up every couple of years. There is plenty of data in support of Option 1 — it is certainly important. But even as there is relatively little data in support of Option 2, it is strategic. Bidding too low and losing can do immediate and long-term harm; bidding too high takes away from profit.

Second, they also must consider the probability of project success. By “success” we mean delivering business benefits of equal or greater value than its proponents promised. It takes a lot to meet this standard, from developing a new insight or algorithm to convincing people to act on or use it to building it into the company’s processes and IT systems. Indeed, developing the insight or algorithm is often the easy step, and many such models are never deployed. Sponsors of potential data science projects should make a stone-cold sober evaluation of these factors. While there are no set answers, we think that evaluating projects in this way will lead them to do more small data projects and more-carefully chosen “moonshots.” DBS, Southeast Asia’s largest bank, has largely given up on moonshots after an early failure but is pursuing other small data projects aggressively throughout the bank. Moderna Therapeutics, the creator of a Covid-19 vaccine, has also eschewed moonshots in favor of less ambitious AI and digital projects.

Democratize data science in the organization.

We sometimes ask companies, “Which would you rather have: a newly-minted PhD data scientist or 20 people who can conduct basic analyses in their current jobs?” Almost all opt for the latter. It leads to our second recommendation — namely, develop “citizen data scientists.” There are plenty of good business intelligence tools and, increasingly, automated machine learning tools make it possible for good business analysts to perform quite sophisticated analyses. Royal Bank of Canada, for example has had great success in this regard.

Some companies, such as Eli Lilly and Travelers, take this advice even further. They provide data and analytical literacy programs for all their employees — and much of the content is tailored to the employee’s level and business function. They view it as an essential capability of their employees to understand different types of data, what can be done with them, and how analytics and AI can enable competitive advantage with data. Finally, of course, companies should look for basic data science skills in all their new hires, for all positions.

Re-prioritize data science efforts and reassign data scientists.

A company’s most senior and seasoned data scientists should be redeployed to strategic data science. One function of a center of excellence can be to assess whether scarce data science resources are working on the organization’s most important problems. Other data scientists may be tasked with helping other employees in the company resolve issues as they come up, assisting in the selection of analytical methods and graphics, reviewing projects to make sure results on sound footings, and training large numbers of people. We find the biggest barrier to taking these steps is perspectives that are too narrow. It simply doesn’t occur to most senior leaders that a data scientist might add value in a strategic context. Lower-level business managers may be reluctant to seek help. Finally, data scientists themselves are drawn to problems where there is lots of data.

Develop and communicate a broad vision of data science.

Think about five years from now: How will the company become, as the strategy consultant Ram Charan puts it, a “math house”? How will data and data science be employed throughout the organization? Is it:

  • Something you are still exploring
  • A tool that is useful from time to time
  • A source of competitive advantage
  • A fundamental capability deployed throughout the business
  • Something in between

There is no right answer — each industry sector and company are different. Still, we think too many companies have kicked the can down the road on this question for far too long. It is time to take it up in earnest.

Managers interested in sports may find that the Dallas Mavericks and Houston Rockets of the National Basketball Association may be role models here — employing data science in everything from player selection, to game-day tactics, to ticket pricing. Both teams not only employed data scientists earlier and in larger numbers than other NBA teams, but they also integrated them into key personnel and on-court decisions. In baseball, the Houston Astros, the Tampa Bay Rays, and now the LA Dodgers are analytically focused across the organization (though somewhat unethically in the case of the Astros).

It may seem obvious that companies should assign their best data scientists to strategic opportunities — even if there is relatively little data — but many don’t. Similarly, it seems highly reasonable to get everyone involved with data science, rather than letting scarce and highly paid data scientists do it all. Our long experience in working with organizations convinces us that, more than anything else, data science is about people and the more strategically and broadly you bring these people and data together, the better results you’ll see.

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