DAMA Houston hosted an in-person event titled “Data Management Journey: Lessons Learned” on October 30, 2024. Mark Harris shared insights about the role of data management in modern businesses. Through discussing his own journey developing new data management platforms, Mark shared a framework for thinking about key considerations for leaders who may find themselves at a similar place in their data journey. The presentation provided a framework for thinking about how to improve data integrity, efficiency, and governance.
Here’s a summary of the key points and takeaways from the event.
1. Streamlining Data Catalogs for Enhanced Communication
Data catalogs are essential for aligning different departments within a company on the same data points, and this consequently improves communication and decision-making. In one organization, the strategy adopted involved reducing the number of attributes in the data catalog from 600 to 150. This reduction simplified data handling and improved clarity across teams. The guiding principle behind this approach was that if an attribute is not utilized by multiple groups, it is unnecessary and should not be cataloged. This method not only underscores the importance of efficiency and relevance in data management but also ensures that the data catalog serves real operational needs rather than becoming a cumbersome repository of unused data. By focusing on essential data attributes, the company can better manage its resources and improve data quality, which in turn supports more accurate analytics and business intelligence.
2. Enhancing Data Integrity and Reducing Redundancy
Maintaining data integrity and ensuring efficient data capture are critical for any organization’s success. To address this, it’s essential to develop a robust protocol where data is entered no more than twice, minimizing redundancy which is a frequent issue that can lead to data discrepancies and operational inefficiencies. This organized approach simplifies processes and enhances the quality of data. By ensuring that all data entered into the system is accurate and reliable from the outset, organizations can avoid costly corrections and data cleansing efforts later on. This strategy is important to optimizing data lifecycle management, making it a foundational practice for companies aiming to leverage data as a strategic asset.
3. Aligning Data Management with Core Values
Effective data management is not just a technical necessity; it is also a strategic asset that should resonate with a company’s core values. Mark shared how at a previous employer, although the team was proficient in managing service-level agreements and operational turnaround, the lack of strategic executive support restricted the scope of their achievements. This experience highlighted the importance of leadership that understands and champions data initiatives. In contrast, at a subsequent company, Mark’s regular interactions with the CFO and direct engagements with data owners eventually cultivated a culture where data initiatives became linked to enhancing business value and operational agility. Creating a culture that aligns data management with larger organizational goals can transform the impact of data strategies across the business.
4. Data Governance Reimagined as Data Excellence
Understanding the cultural sensitivities around the term “governance” in entrepreneurial settings was key to the strategic renaming of Data Governance to Data Excellence. This rebranding was designed to weave data governance more naturally into daily business operations, shifting the emphasis from control to enhancing data quality and utility. Just as executive support is important, so too is the perception of data management within a company’s culture. This change reflected a broader move towards valuing data as a cornerstone of business excellence rather than a regulatory necessity.
5. From Business-Driven to Centralized Data Approaches
After realizing that the business-driven approach wasn’t working well, employees realized that they needed to pivot to a centralized approach. The business-driven approach to data management was characterized by decentralized decision-making where individual departments or business units managed their own data according to their specific priorities and needs. This method often led to inconsistencies in data handling and difficulty in data integration across the organization. As teams faced these challenges, it became clear that a more unified approach was necessary, which led to a strategic pivot to a centralized data management model. This new strategy involved developing a Proof of Concept (POC) for a central data asset team aimed at enhancing data connectivity and reliability. The shift to a centralized model not only addresses the operational challenges but also aligns with the evolving needs for robust, integrated data systems that can support dynamic business environments effectively.
Takeaway: The Importance of Reliable Data for Business Decisions
A key takeaway from the presentation was the role of creating a culture that supports data reliability. Many businesses suffer because they cannot trust their data to make informed decisions, often leading to redundant verifications and wasted resources. The presentation emphasized the need to establish a culture that prioritizes data reliability and accessibility. These insights can be useful for any professional looking to enhance their organization’s data management practices. As businesses continue to evolve in an increasingly data-driven world, understanding and implementing these principles will be crucial for maintaining competitive advantage and operational excellence.