Stop. What is Data Governance, Really?
At our recent onsite in Austin, the partners at Stacked Analytics engaged in a robust conversation about this topic. We work across all levels of analytics from strategy to execution, and we consistently observe uncertainty about what data governance actually is. The word governance in and of itself can conjure up images and feelings that are not so comfortable, even restrictive, but the fact is data governance is quite necessary and the foundation of all sustainable, successful data programs.
“Data governance refers to the overall management of the availability, usability, integrity and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures.”
In the Chief Data Officer’s Playbook by Caroline Carruthers and Peter Jackson, they devote an entire chapter to Data Governance. Why? They argue that data governance is the underpinning principle of any data capability and the introduction of GDPR in May 2018 brought the role and need for data governance into sharp focus. With COVID impacting workforces starting in March of 2020, the increased digitization only makes this argument stronger.
Effective data governance provides dual playbooks; one as a platform for developing important business outcomes (growth, efficiency, risk reduction, environmental impact) and two addressing oncoming regulatory pressures and technological changes impacting respective industries.
In the last couple of years, the data and analytics world has not just experienced the unprecedented emergence of regulations such as GDPR & CCPA resulting in the crumbling of the infamous cookie, but we are also experiencing and will continue to experience foundational technological changes along the way. Google has decided to really tick off the masses as they sunset Universal Analytics in 2023 driving toward a more session-based focus through GA4. Dbt has changed the playing field for creating more dynamic visibility into data transformations while the additional SaaS functions are delivering digitization capabilities (new data sources!) across virtually every business process out there.
Those companies that have effective data governance programs can both drive business performance and manage through the everchanging regulatory and technical landscape.
Where to Begin?
Start with the basics and grow.
Establish your own enterprise data governance framework which needs to fit into the wider organization, culture, and operating model. There is not a one size fits all approach as every enterprise is different in terms of structure and culture but also in a different place on the data maturity curve. Companies that are already data-centric will be more open to concepts around data governance whereas for organizations that are just beginning their data journey, the road could be more arduous.
The most important aspect to rolling out a data governance program is the need to bring people along. Since governance has people running to the hills or looking for workarounds, it is critical to convey the importance, applicability and benefits of data governance. Actively engage stakeholders and try to understand the best ways to help them to understand the importance and impact of a data governance program on their role and objectives within the enterprise.
RESOURCES
Source: The Chief Data Officer’s Playbook by Caroline Carruthers & Peter JacksonKey Components of Data Governance:
Policies - outline key ways to work with data and deliver insights
Processes - details of how the principles and policies will be applied and enforced
Organizational Design - assignment of ownership and responsibility for data
Data Architecture & Design - articulation of location, lineage, and relationships for key data
Technology - the tools required to manage data and provide standardized reporting
Principles of Data Governance:
Consistency of data without unnecessary duplication
Quality which is proactively assessed and standards set
Ownership and accountability are defined across the data life cycle and recorded data catalogs
Business alignment which ensures that data is regarded and treated as a key business asset
Access to relevant users, kept secure through access control without locking data down for no purpose
Provided trusted insight
The elements of a data maturity model can be measured and tracked over time as the data governance model takes root in the organization. This tracking will demonstrate progress and help to develop areas that need focus.
Data Maturity Model Elements:
Strategy
Leadership
Corporate governance
Framework
Policies
Information Risk
Architecture
Organization, Roles and Responsibilities
Metrics
Skills
Behavior
Tools
Data Governance is the underpinning principle of any data capability. Are you ready to start looking at your Data Governance capabilities?
Stacked Analytics’ Data Maturity Assessment can help you identify and start tracking your enterprise’s data maturity. Reach out if you would like to learn more.
