One of our favorite topics to write about here at the APEX of Innovation is how companies can harness data and analytics to achieve and maintain competitive advantage. But the world of data and analytics is constantly changing and, as such, it follows that enterprise strategies must also evolve in the pursuit of this goal.

With that in mind, below are three critical steps to level up your data science and make it more strategic and widely accessible in your organization.

1. Focus on Strategic Problems

It can be tempting to go after the low-hanging fruit and focus data science efforts in areas where you have the most data. But is that really the most strategic approach? A recent HBR article outlined two hypothetical options for analysis: the first option uses insights that deepen the user experience using engagement data from a company’s apps, and the second involves using data to inform a bid for licensing rights that crop up every few years. 

The authors argue that the second option is more strategic and worthy of a data science project, even though more information would be readily available to support the first one. When considering which problems to tackle with your data scientists, make a conscious effort to analyze the strategic value of your data first rather than the amount of data that exists to support the analysis.

2. Democratize Data Science

Small teams of knowledge workers and managers can solve many problems and arrive at numerous data-driven decisions using relatively small amounts of data. Restricting data science solely to data scientists and similar roles significantly curtails the potential success of your data science initiatives. Particularly as advances in AI and automation continue and augmented or converged analytics becomes more widely available, democratizing data access throughout the organization will be critical.

3. Reassign Data Scientists

Another consideration is ensuring that the organization’s data scientists are deployed in a way that maximizes their impact. For example, the most experienced scientists might be best served to work on strategic-level projects, while others could assist business analysts or knowledge workers in addressing issues as they arise or training employees in analytics and data science principles. As with anything, there is no one-size-fits-all approach. But for data science to truly be a competitive differentiator, organizations must ensure that it is strategic, pervasive, and, perhaps most importantly, rooted in a culture of data literacy.

For more on how to do this effectively, take a look at this HBR article.