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 , spoke with Tudor about his work with self-service data and organizational transformation. What follows is an edited and condensed version of their conversation.
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.
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.
But as companies push ahead with more data than ever and are utilizing things like data lakes, a waterfall approach becomes less effective. 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. The truth of the demands for many organizations is that they will never be able to hire enough data engineers or data scientists to meet these growing business needs on their own.
Self-service data allows companies to meet these demands by focusing less on who is carrying out the work, in favor of the business outcomes.
Tudor: Companies will vary in their approach and certain metrics will be more important depending on the stakeholder. At GE Aviation, we focus on three self-service data metrics that shed light on financial benefits, how much value users are getting from the program, and innovation viability for the business.
First, there are financial KPIs. We can determine where there are impacts on the balance sheet income statement or how much revenue we can track from a return perspective.
The second important metric is utilization. We look at the number of unique users and track what percentage of them are staying active in a given period. For example, we might find that the self-service program has achieved 2,000 unique users across the ecosystem in the last three months.
A third area involves creating an innovation pipeline from tracking engagement and usage. For example, we look at every data artifact that has over 50 unique users in a month — every BI report, every analytic. Based off that, we can inform leadership about what is proving most important to users and may be worth further business investment.
