3 Ways To Level up Your Data Science

Organizations typically start their data science journey by hiring top talent and establishing centers of excellence to focus their data efforts. While this can work for some organizations, a new report in the Harvard Business Review cautioned against such an approach.

The article by Thomas C. Redman and Thomas H. Davenport offered suggestions around how companies can leverage data science to pull ahead of competitors. We outline three tips from it.

Focus on strategic problems

At various panel and round table discussions at CDOTrends, it is not uncommon to hear about the importance of identifying low-hanging fruits or focusing on areas where there is ample data. While this works well for digital transformation or cloud migration initiatives, Redman and Davenport do not consider this a wise use of a resource as scarce as data scientists.

Instead, they argue that organizations should get their data scientists to analyze strategic problems and make “big swing” decisions based on the insights that are revealed. This might take conscious effort, given the propensity to focus data science efforts on areas where the data is plentiful.

“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,” they wrote.

Democratize data science

Pointing to the many problems and data-driven decisions that small teams of knowledge workers and managers can solve using relatively small amounts of data, the authors advocated for the democratization of data science and the training of citizen data scientists.

As I noted last week, a data science education is not enough to produce data scientists that can land on their feet running. Assuming additional on-the-ground training is already available for data professionals, it should not be too much of a stretch to produce more rudimentary data science training programs that cater to other employees.

“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 [majority] of people and business opportunities,” they noted.

Aside from setting up data literacy programs, Redman and Davenport also suggested that companies look for basic data science skills in all their new hires – for all positions.

Reassign data scientists

Finally, businesses might want to reassign data scientists to maximize their impact. On this front, the organization’s center of excellence might potentially be tasked to assess whether limited and valuable data scientists are indeed distributed across the organization.

For example, the best and most experienced should be tasked to work on strategic-level projects, while others are assigned to either assist employees to address challenges or issues as they come up, or training employees in analytics and data science.

“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,” they explained.

To be clear, there is no one-size-fits-all strategy. However, it is time for businesses to stop treating data science as a tool that is useful only on occasion but to see it as a competitive advantage that will help them leapfrog into the future. Helmed by data literate leaders and employees, and the sky’s the limit.

“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,” Redman and Davenport summed up.

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