Key Takeaways
- The best data quality tools in the world don’t drive adoption. People-first data quality practices – including starting small with one to two use cases, building partnerships, and working through roadblocks – do.
- Celebrating and communicating small wins creates momentum that attracts adopters.
- Sustainable engagement requires ongoing evolution and responsiveness to changing business needs.
Most organizations approach data quality backwards. Leadership purchases an enterprise tool, mandates adoption across business units, and expects results. The outcome? Frustrated teams, resistance to change, and another underutilized technology investment collecting digital dust.
Jacqueline Paneque experienced this firsthand. As data governance leader at TE Connectivity – a global electronic components manufacturer – she inherited a data quality tool with a mandate that business teams adopt it.
Rather than force compliance, Paneque took a different path. She spoke at DATAVERSITY’s Data Governance & Information Quality (DGIQ) Conference, sharing how TE Connectivity transformed data quality chaos into sustainable adoption through six people-first practices. She showed that successful data quality management harmonizes people and processes, first, before implementing technical solutions.
Start Small with Targeted Use Cases
When Paneque assumed her data governance role, she encountered significant resistance. Business teams felt forced to adopt the data quality tool and demanded that it do almost everything.
Instead of getting overwhelmed, she learned her first lesson: “Start with one to two use cases that show value.” This different approach also enables engagement throughout the business.
She began by investigating current practices: “Individual business groups are likely already doing data quality work.” By understanding existing efforts, she could identify opportunities where the tool would enhance – rather than disrupt – ongoing initiatives.
This discovery process led her team to focus on a supply chain use case. She found that different teams interpreted plant workload data in diverse ways. These conversations also revealed existing data quality initiatives within certain business units that “could leverage what the data quality tool could do now” at scale.
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Build Strategic Partnerships
As functions within TE’s business units continued to have success with the tool, they became Paneque’s biggest advocates. Through this partnership, Paneque identified other existing initiatives across TE Connectivity that could benefit from the tool.
Paneque’s approach was straightforward. She identified motivated teams, demonstrated the tool’s capabilities, and accepted when it wasn’t a good fit. This method uncovered unexpected partnerships, including with the legal department – an unlikely candidate for data quality work.
Typically, legal teams focus on contracts and compliance, not data quality. However, Paneque saw an opening: “Legal had contracts with vendors or acquisitions. We took it as an opportunity to see what kinds of data governance and data quality requirements legal could use.”
The partnership worked because it aligned with the legal team’s existing priorities. Paneque’s advice: “Think outside the box and leverage the insights you get from leadership on their main focal points.” Strategic partnerships transform early adopters into advocates who drive momentum across the organization.
Celebrate and Communicate Small Wins
Paneque and her partners embraced the challenge of making a data quality tool work for them, achieving small wins. In this process, adopters moved away from a manual process, automated it through the application, and then evaluated its impacts on decision-making.
They saw small wins, where the data became more meaningful using the tool. They celebrated successes through podcasts and articles, a video, and a company newsletter. That way, other TE Connectivity teams could see the data quality work as advantageous to the business. Paneque said:
“Without having those small wins, data quality becomes an afterthought, until a problem surfaces. Communicating those issues and the impacts from the tool or data quality automation allowed us to prove the value to the business.”
This experience led TE Connectivity to see that the large adoption issue was not the data quality tool itself, but rather the challenge of harmonizing the people and processes on whether to stay with a tool or to move forward.
Prioritize Business Context Over Technology
Going for the newest gadget that says it will clean up all your data does not present the best pathway for correcting bad data. While some tools have great capabilities, “organizations and their teams need to know where they are going and have the processes in place to get there,” said Paneque.
She advised starting from the business problem and prioritizing its context to understand why business teams think the data is bad. She leveraged data literacy across teams to ensure there was a common language and governance support for data quality.
In TE Connectivity’s case, all core customer data fields were complete. However, business teams still reported poor data quality because a subset of customers lacked associated attributes that were critical for downstream workflows. Grasping those nuances and documenting them reduced the amount of rework TE Connectivity teams needed to do going forward.
Moreover, the conversations about data quality led Paneque to get an outside perspective on data quality during this phase. She saw a perfect opportunity to delete or archive unused data sets, based on older rules, saving storage space.
Work Through Roadblocks
Throughout Paneque’s presentation, she showed that data quality must take a journey through roadblocks before the trek becomes smoother.
She explained that as the data quality initiative got started, TE Connectivity faced many speed bumps in requiring the business to adopt a tool. But she and her governance team pressed on and learned from tackling each issue.
This practice meant taking different approaches to solve the challenges and evolving. In the process, TE Connectivity gained new leadership and a federated organizational structure, giving more control to each business sector and unit.
“We give each team some guidance on best practices, but let the group independently handle their data issues,” noted Paneque. With this scaffolding in place, the organization would consider the business input in response to these roadblocks. This pivoted decision-making from reacting to a sales tactic or a technical evaluation of the tool to the consensus of the consumers who used the data quality tool.
Embrace Change
Paneque acknowledged that TE Connectivity is just beginning its data quality journey, and it is ongoing. The company recently reevaluated the contract and the data quality tool. While a few teams adopted the tool, its clunky navigation and lack of connection failed to meet widespread adoption.
Paneque explained, “We wanted to do everything at once.” Instead, she adjusted the pace of decision-making with limited resources and business resistance to accepting a new tool.
She will use a proof of concept (POC) approach to validate data connections through the tool to the workflows and the overall user-friendliness of the interfaces. By adopting this more thoughtful tactic, she reached a smoother journey, a “holistic, simplified data quality landscape,” increasing adoption.
Conclusion
The data governance team at TE Connectivity realized that good data quality practices must evolve. Paneque noted:
“We may fit the business needs now, but they will change with time. In the future, as we integrate generative AI models and want AI governance, we will need to decide whether one exterior tool is sufficient.”
As new challenges like AI governance emerge, Paneque will keep pushing forward in keeping with the people-first lessons she has learned. TE Connectivity will handle changing data quality needs by understanding what it looks like for different business functions. She will start small, build partnerships, and work through roadblocks – all necessary to align teams across TE Connectivity.
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