With the implementation of the EU’s GDPR in 2018, many businesses were forced to focus on quickly growing their data governance program to ensure compliance with this sweeping data privacy regulation. Businesses across the globe focused on data management tasks across systems, processes and departments to address the demands for data controls, categorization, approvals, and creation of processes and policies and processes required to handle data subject requests. However, this immediate enterprise-wide focus on data governance didn’t automatically translate to a better understanding of data or new, and deeper, analytical insights for business users.
Today, businesses use insights from data to boost productivity, increase revenue and gain valuable insights about customers that may otherwise go unnoticed. Enterprise data governance play a critical role in gaining these insights, from encouraging utilization among users to ensuring quality data assets. The role of data governance has expanded far beyond the narrow scope of regulatory compliance, and can have a transformative impact on organizations’ overall data strategy. But successful data governance begins with ensuring business users have a complete understanding of their data.
Data understanding goes far beyond clearing regulatory hurdles for data usage. Business users must understand what data is available, where it’s located, where it came from, what it means, and who to go to with questions or issues. Only then will they be able to effectively leverage that data for business insights and business decisions. That requires organizations to develop a framework of data governance program from the ground up, one that can not only ensure regulatory compliance, but also empower business users to leverage real-time data and analytics to identify key insights and make better business decisions.
Launching Enterprise Data Governance: The Beginning Stages
As with any enterprise-wide initiative, approval of a data governance program starts at the top. The best way to begin is to gain leadership buy-in and establish the right pricing and financial support. Next, the business must select the right team of data professionals to spearhead the initiative and evangelize data governance across the organization. In addition to crafting a strategy and plotting the steps to execute, this core data governance team must choose the tools to derive maximum value out of their data governance initiative.
Data management and analytics technologies have evolved considerably in just a few short years, so it’s important to take these advancements into consideration. For example, businesses no longer need to piece together disparate tools for various tasks like data quality. Instead, businesses can leverage an all-inclusive solution suite that delivers various capabilities for a complete 360-degree view of an organization’s data landscape. It should include all the details of enterprise data assets, from responsibilities, lineage and usage, to its associated definitions, synonyms, and business attributes. With a complete, fully integrated solution, business users are empowered to easily define, track and manage all aspects of their data throughout the data supply chain for maximum returns.
Once the business is set with modern technologies, it’s time to define their data governance methodology.
Constructing a Data Governance Methodology
Every organization is unique, which means that each data governance program should be tailored to meet the needs of the specific business. A successful data governance methodology needs to be a scalable, flexible and repeatable methodology that outlines the responsibilities and benefits for both the IT and business teams and encourages participation from everyone. Data governance is a powerful means to help build a data-driven culture, because seeing the value that data governance brings to everyday businesses processes and procedures drives business users to increasingly rely on data and collaborate to find data-driven solutions.
The methodology selected depends on the company’s chosen strategy and business priorities. Questions to answer may include: Is the business struggling with data quality issues? Does the same term have a different meaning based on the person or group? Are data lineage errors affecting the transparency of data assets?
Identifying the most pressing challenges and most impactful issues will determine the data governance priorities. And whatever the critical priorities are, it’s important to take a value-based approach to build support and foster collaboration across the enterprise to ensure the data governance approach is sustainable.
Data governance is a set of ongoing, repetitive exercises that must be continuously maintained and updated. An incremental approach may be the best tactic, taking small steps with a phased implementation to learn what worked and what didn’t, and refine the approach as you go. In addition, leveraging a modern solution suite will help simplify collaboration and get employees excited and engaged in the new activities and capabilities.
Data governance can make or break how successful a company is with their data assets. Identify how data governance can benefit the business, move it to the top of the priority list, and motivate team members to collaborate across the organization.
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