Modern Data Governance | Best Practices Report - Q1 2021

Published: 30 July 2021

To support new directions in data-driven practices and platforms.

Data Governance

 Data governance (DG) must keep pace with increasingly popular data access and usage practices. For example, modern self-service is more than just a challenge for technology and data standards; it is a potential compliance infraction. A similar balance of data standards and business compliance is seen with broad data exploration and the far-reaching data scans of many analytics models and algorithms. As new data sources, data sets, SaaS applications, and cloud data platforms are added to today’s complex and hybrid data architectures, DG must extend its policies to cover these architectures, plus most of the enterprise. Adaptations to these changes, along with process and practice improvements, are leading to modern data governance, which is the next level beyond today’s DG.

Modernizing DG must address all of its many components, including business-driven compliance, technology-driven data standards, and people-and-process practices. Users should expect to modernize DG now and continuously improve it moving forward. Likewise, users should expect DG to grow and scale into a broad scope covering enterprise data sets, applications, cloud platforms, and use cases. The result will be holistic data governance, where most or all of the enterprise is governed with consistent standards for all governance policies and rules.

Over a third of respondents (38%) have created and enforced DG policies by serving on a DG board. Roughly a quarter (26%) are users who, in their work, have complied with DG policies. Despite modernization and other advances, few users consider their DG program highly successful (8%) or moderately successful (42%). The current pandemic and its recession have had little impact on DG (48%) or no impact (45%).

Most survey respondents (84%) feel that DG is extremely important. The vast majority (94%)consider modernizing DG an opportunity because it further ensures compliance, plus provides internal standards for improving data and its management. Although most DG programs are in good condition, two-thirds of respondents (66%) say their data governance program needs modernization.

The leading offenders are rogue data sets (72%), self-service data practices (56%), analytics sand boxes (33%), and data lakes (28%). Modern DG faces challenges, such as maintaining quality data and metadata (53%), convincing employees to adhere to governance policies (46%),keeping the data governance bureaucracy lean and agile (45%) and creating governance policies that are clear and usable (43%).

Respondents think that aspects of data governance can be successfully automated via software tools (72%). Tool functions that are valuable for modern DG include those for data cataloguing (71%), data lineage (68%), metadata (67%), master data (64%), and data quality (62%).

Most data governors have full-time jobs outside governance (56%) and also serve part-time on the DG committee. Most DG boards have one chairperson (44%), but the trend is toward two or more (38%). TDWI recommends two chairpersons, one each for business compliance and data standards. This way, each broad area is guided expertly, but the two can align to maximize the business impact of modern data governance.

This report canvasses current and future data governance strategies and best practices to help organizations understand the new requirements for data governance, plus how successful organizations are modernizing existing governance programs and toolsets. The intent is to assist with modernization planning so that data governance achieves maximum business impact.

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