The New Leadership Mindset for Data & Analytics
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For companies that struggle with data transformations, underthinking organizational change is often a bigger problem than technology issues. A company can have powerful tools and meaningful data at its disposal, but without the proper education and processes to put that data in the hands of the right people and provide business context, extracting value can prove difficult.
In 2016, Jonathan Tudor founded a self-service data program at GE Aviation aimed exactly at this problem. By recognizing that success would depend on empowering users beyond the data engineering and analytics teams, he was able to encourage buy-in from across the organization, increase engagement, and create cross-functional partnerships.
Ally MacDonald, senior editor at MIT Sloan Management Review, spoke with Tudor about his work with self-service data and organizational transformation. What follows is an edited and condensed version of their conversation.
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MIT Sloan Management Review: What is a self-service data program?
Jonathan Tudor: The idea with self-service data is, rather than hiring endless numbers of highly competitive data talent, why not take your existing intellectual capital and people capital within the company and empower them to do their own data analytics work? In a self-service system, line-of-business professionals and analysts in the company can access and work with data and data visualization directly, and they are supported by, but not dependent on, IT and data professionals to carry out their work.
This kind of program allows companies to remove technical boundaries and empowers people to use their own subject matter expertise — after all, they know the problems they’re tackling best, and they know what data they need — to generate insights and execute their work.
When data is fundamental to how you run your organization, analytics are needed in all parts of the company very quickly and are key to business outcomes.
How has this new approach evolved to play a role in organizations?
Tudor: It’s helpful to look back in history. When we think of business intelligence [BI] and data warehousing, this has often been siloed within organizations. BI teams do all the work to gather the data, structure it (and hope it’s structured correctly), and then deliver those insights to the customer. That process is similar to what we see in a waterfall method in IT, which is very sequential and dependent on the people carrying out each task.