Data Democratization Is an Evolution and Not a Revolution — GE Aerospace VP - Digital Technology Lean and Data Transformation

(US & Canada) Maureen Butler, VP - Digital Technology Lean and Data Transformation at GE Aerospace, speaks with Channie Mize, General Manager at Slalom, in a video interview about democratizing data access within the organization, justifying investments in data, training people, and meeting the data requirements for running analytics.

Butler acknowledges that the challenge of managing data will always exist, but she emphasizes that democratizing data and providing accurate reporting from trusted sources can reduce the issues. She identifies a lack of trust in data as a core reason for these problems. By focusing on essential metrics—such as key performance indicators (KPIs)—and ensuring the quality and reliability of the data sources, the reliance on manual processes like Excel diminishes naturally. However, she notes that this shift is an evolutionary process, not a quick fix, requiring patience and careful management.

She also highlights the importance of gaining executive support by showcasing tangible wins. Executives and leaders are often pressed for time and need clear evidence that their investment in enterprise data management is paying off. These small victories build trust and pave the way for foundational improvements in the future. Butler shares a recent example from the credit organization, where efforts to clean up and standardize customer data have led to fewer issues with billing and customer onboarding. These incremental improvements, while subtle, signal progress to leadership and reinforce that the strategy is working effectively.

Further elaborating on the transition, Butler emphasizes the need for patience when implementing enterprise data management. At GE Aerospace, she explains, the process took about two and a half years, with the initial focus on educating the organization about core concepts like master data, critical data elements, and processes such as Create, Read, Update, and Deactivate (CRUD).

Butler notes that ensuring data quality requires stringent access control—it's not feasible to allow thousands of people to manage data while expecting consistency and standardization. After this education phase, early adopters within the organization began embracing the changes, though it took roughly 18 months before the investment started showing measurable results.

Speaking about the investments in talent, Butler points out that success in data management demands both hiring and training data stewards, as well as investing in the necessary tools and platforms, which takes time and resources. However, once this foundation is established, the organization begins to see tangible value, leading to increased momentum as more stakeholders recognize the benefits.

When asked how much data is required for running analytics, Butler highlights the importance of understanding the variety and depth of data required to run business processes effectively. In her view, organizations need to manage multiple data domains, such as customer, product, supplier, and finance data, to support end-to-end processes like order-to-cash. This means that data from several sources, including employee and part data, must work together to maintain accuracy and efficiency.

She also points out that simply cleaning up one domain, like customer data, won't solve everything because of the interdependencies across various data elements. Instead, Butler notes that businesses are now focusing on identifying the most critical data elements (CDEs) required to run processes or analytics. By honing in on these crucial elements and controlling access to who can create and update them, organizations can ensure data quality remains intact. The key, according to Butler, is not just cleaning the data but maintaining control over its accuracy to prevent degradation over time.

CDO Magazine appreciates Maureen Butler for sharing her insights with our global community.