What is Data Governance?
Experian’s new 2020 Global data management report states an almost incredible statistic—78% of organizations believe they are losing money due to poor governance of data assets even though 85% understand that data is one of their most valuable business assets. Companies are still struggling to answer basic questions about their own data, such as:
- Are we complying with our data usage licenses?
- Are we exposed to regulatory risks?
- Can we trust our data?
- Are we using data in a consistent manner?
- Are data consumers using certified data?
- Do we have stale data?
- Do we have redundant data?
This is happening because while we all know that data is a critical part of an organization’s DNA, but it’s rare to find anyone who will identify themselves as a data owner.
So why are companies having so much trouble managing their most critical data elements?
Formalized data governance is at the stage where innovators and early adopters are fast reaping the promised rewards offered by the program. Yet, many organizations are still misunderstanding the purpose behind true data governance. Successful data governance requires organizational alignment, an enterprise-wide framework, clearly defined business requirements, and detailed objectives to achieve data understanding and trust. However, many data governance programs fail to achieve their goals because data leaders can’t create a comprehensive data governance structure to deal with the most common data problems.
To foster a strong data governance program, companies need to establish a basic definition of data governance, build a diverse enterprise team, and identify requirements for success.
Defining data governance
Organizations might tweak the description of data governance based on their unique culture and the business drivers of their governance program, but the definition applies across the board. Data governance is an enterprise framework that aligns people, processes, and technology, helping them to understand data with the goal of transforming it into an enterprise asset. Data governance delivers visibility and understanding of the data. This strengthens accountability for data assets while enabling organizations to unlock analytical insights needed to improve business outcomes and fuel growth.
In addition, data governance is critical for regulatory compliance. Visibility into data allows to reduce risks presented by it. With data privacy laws like General Data Protection Regulation (GDPR) the and the California Consumer Privacy Act (CCPA), governance helps organizations track consent receipts, appropriate usage, data subject rights requests, and personal data location to ensure compliance with data privacy laws throughout different states and countries. It is also critical along lines of business—sales, marketing, customer service, finance, HR, and more.
Thus, aata governance requires a strong team to develop and mature the program. Organizations must attain buy-in from every single business unit, including IT, to ensure they all work in partnership with one another.
Read our Whitepaper
Are you looking for additional information about data governance? Read our whitepaper to learn more about data governance requirements and building an enterprise-wide framework.
Building a data governance team
The data governance team needs a leader to steer the program. Typically, a Chief Data Officer (CDO) or another senior executive will take on the main leadership role. The CDO oversees the team, communicating procedures and monitoring success. CDO’s ensure that data governance efforts stay on track, on budget, on time, and generate the expected ROI.
Under the CDO is a group of data managers from different departments – sales, IT, human resources, marketing, finance, etc. Together, the CDO and the data governance managers create and establish data governance processes, policies, and procedures.
As a group, the data governance team establishes common data definitions, builds data catalog institutes and monitors data quality creates governance metrics, meets compliance standards and assigns roles and responsibilities to data owners, users and stewards.
Typically, these teams are also responsible for ensuring data integrity remains intact as information flows across a business’s data supply chain. Team members handle the analysis of data sets, assemble easy to read reports for business users, and answer any outstanding data questions. Data users must then follow all established guidelines defined by the data governance team and report any data anomalies they find to the right data owner.
The data governance team is critical for success. Still, there are other important factors to ensure that a company’s data governance initiatives work.
Achieving data governance prosperity
To ensure data governance mitigates the risks of poor data management and enables a company to effectively utilize its data assets, organizations must understand the key elements of data governance success, including:
- Commitment and ongoing support from all senior leadership.
- A plan that clarifies responsibilities, accountability (persona-based metrics) and properly sets expectations in line with business goals.
- Clear, measurable metrics, starting with a basic inventory of terms governed and data quality statistics.
- An applied agile management methodology that demonstrates success through short-term goals, so all participants can track ongoing progress.
- Constant communication to ensure tasks are completed on time, goals and objectives are met, and stakeholders are informed of progress.
- Data quality with high integrity information improves data value, minimizes the compliance risks of bad data, and encourages data utilization.
By understanding the importance of data governance and taking the steps necessary to build a strong program, businesses can quickly turn data into insights and gain a competitive edge.
Read our whitepaper MDM to Data Governance to learn more about data governance requirements and building an enterprise-wide framework.