Empowering Data Governance at Vanderbilt University

Vanderbilt University, situated in Nashville, Tennessee is a prominent private research institution. Approximately a decade ago, the university's institutional research group took on the significant task of handling institutional-level reporting. This endeavor led to the establishment of a dedicated data team, which provided support to various academic departments in fulfilling their data-related requirements. However, in the course of their operations, they confronted challenges stemming from lack of clarity in business term definitions and data calculations.

To address these challenges, the university adopted an external tool to establish uniformity and documentation in both business and technical term definitions. Despite the tool's evolution and incorporation of new features, its adoption remained constrained, largely serving a solitary business team's needs, while overlooking the comprehensive data governance demands of the entire organization. Ultimately this led to the termination of the tool's license. The team transferred all data definitions to a spreadsheet, worsening the complexities of maintenance.

In recent years, Vanderbilt University has implemented a data governance program, and the data team has undergone organizational restructuring. Newly appointed to the team, the Chief Data Officer (CDO) recognized that certain teams weren't utilizing available data effectively. This emphasized the necessity for a more centralized approach to data governance. As a strategic response, the decision was made to initiate an internal data governance review of prevailing data practices, processes, and strategies. The goal was clear: identify opportunities for enhancement that would pave the way for a more streamlined and effective data ecosystem.

The internal data governance review brought to light several areas for potential enhancement:

• lack of a data classification model implemented,
• decentralized data inventory,
• lack of proper data terms, systems, and definitions documentation.
• There was pressing need for the team to gain a clear understanding of the range and location of data collected across the university as well as its classification.

To address the gaps identified in the internal review, the team decided to introduce a data catalog system. This system would act as a central hub for comprehensive data documentation and functioning as a gateway to navigate and manage the data landscape effectively.

In their pursuit of an apt data catalog solution, the team began an extensive market analysis, surveying a range of available products and identifying the major contenders in the field. What they encountered were predominantly high-cost systems demanding substantial resources and investments, far surpassing their allocated budget for the year. Fortunately, amidst these major players, Dataedo emerged as a viable option.

To evaluate how Dataedo would function within their complex operational landscape, the team opted for a 3-month Proof of Concept. This project proved to be successful, playing a pivotal role in showcasing the value and compatibility of Dataedo with their requirements.

To rationalize the allocation of resources for Dataedo, the team undertook a thorough cost analysis by comparing it against the expense of other prominent solutions dominating the market. This comparative evaluation revealed substantially higher costs associated with the alternatives. The combination of reasonable pricing, coupled with a seamless and swift implementation process, made Dataedo an easily justifiable choice.

The team's adoption of Dataedo took precedence as they started a fresh project involving a system full of data concerning alumni and donors—an arena entirely new to the team. Adding to the complexity, this dataset posed certain quality concerns.

The primary objective was to swiftly comprehend the nature of this data and its potential utility. Recognizing the potential, the team identified avenues where Dataedo could help. Leveraging the data profiling feature, they identified segments of quality data and areas where improvements were needed.

The documentation process was methodically divided between the data governance program and data stewards. The data governance team undertook the responsibility of tool administration and configuration, establishing a structured framework that guided the data stewards in documenting the essential elements. This approach ensured consistency throughout the entire spectrum of documentation, catering to all stakeholders uniformly. Furthermore, the data governance team extended crucial support and conducted requisite training sessions.

Responsibility is now being passed to the data stewards, using their specialized knowledge to closely document the data aspects. This collaboration between data governance and data stewards ensures a comprehensive and coherent documentation process.

The team recognized the substantial value offered by the data lineage feature, particularly in critical scenarios such as incident response. Data lineage within Dataedo facilitated not only the documentation of terms but also provided a clear trajectory of data origins. This enabled them to trace the data's path back to its source systems and comprehend the transformations it underwent during its journey.

Moreover, the team recognized Dataedo as an ideal solution for tackling data quality concerns and implementing industry best practices, with data lineage emerging as a pivotal component within this context.

Furthermore, Dataedo played a pivotal role in enhancing data literacy within the organization. It became a powerful tool for educating data consumers about the nuances of data terms and enlightening them about the sources of these terms. In this regard, the data lineage feature once again demonstrated its tremendous value.

Vanderbilt University's journey to implement Dataedo as part of its data governance strategy showcases the challenges and opportunities that educational institutions face when managing data in a diverse and complex environment. By focusing on one data set first and collaborating with data experts, the institution was able to lay a strong foundation for accurate data documentation, classification, and improved data governance practices. The ongoing efforts to educate stakeholders and promote a culture of data stewardship remain critical to their success in the evolving landscape of data management.

For other educational institutions seeking a data catalog solution, Vanderbilt University recommends the following approach:

1. Assess Data Governance Maturity: Understand your organization's current maturity levels in terms of data governance pillars such as data quality, data literacy, data security, and privacy.
2. Identify Gaps and Needs: Determine where a tool like Dataedo can make the most impact and help address specific data governance challenges. It can be related to data quality (data profiling, lineage), data literacy (educating data consumers), or data security.
3. Define Goals Before Tool Selection: Avoid rushing to adopt a new tool without a clear understanding of what you aim to achieve. Define your objectives and requirements first.