Data governance is a collection of processes and practices designed to enable an organization to formally manage its data resources. It is becoming an increasingly important method of assimilating the flood of information that businesses are required to cope with to remain competitive and operate efficiently. Implementing a viable data governance strategy involves going through multiple steps that are put in place with an organized approach. It’s not the kind of thing that you can set up on the fly.
Why Data Governance?
Enterprise data management (EDM) is an organizational concern that is growing in importance along with the amount of information that it attempts to address. Some specific factors that influence the adoption of a data governance initiative include:
- Increasing volumes of data from diverse sources introducing inconsistencies that hinder using the information for business decisions;
- The impact of regulatory standards that require organizations to understand their data resources and how they are being used;
- The need for a common language regarding enterprise data assets so they can be used efficiently throughout the organization.
While it may be a rocky and winding road to developing a successful data governance program, the results can be very beneficial. Some of the advantages of data governance are:
- Increased accuracy associated with regulatory compliance;
- Reduced data management costs;
- A greater value of organizational data resources;
- Standardization of data systems, policies, and procedures;
- Improved data quality monitoring and tracking;
- More transparency regarding data-centric activities.
Despite these substantial benefits, some organizations are lagging in their efforts to enact data governance. But even the most disinterested management teams may eventually see the light.
Realizing Your Organization has a Data Problem
Coming to the realization that you need data governance as part of your EDM efforts is the necessary first step in its implementation. In some respects, organizations that refuse to get on board the data governance train are denying they have a problem in the same way that individuals reject the idea that they may have substance abuse or money management issues. At some point, the denials give way to an understanding that things must change if they are ever to get better.
As organizations evolve, it can be easy for individual departments to build silos around their specific data resources and procedures that make it difficult to efficiently share them throughout the enterprise. Even though the information may contain the same granular elements, the different ways it is handled make inter-departmental use a challenge.
A simple example is when the sales, marketing, and shipping departments all use different formats and terminology for customer records. Sharing information between departments requires knowledge of multiple data formats and may make it complicated to use automated methods and perform analytical operations. Internal productivity would be increased by everyone using the same data formats and language.
Lowering productivity is bad enough, but misunderstandings caused by multiple data naming standards or definitions can thwart the ability of a business to interact with its customers. Potential sales can be missed due to mistakenly interpreting data coming from the marketing team. This kind of problem can be minimized or eliminated with consistent definitions that are a big part of data governance.
Initial Steps Toward Data Governance
Implementing an effective data governance program demands a high degree of planning and collaboration. Decisions made around the organization’s data need to be made with the concurrence of all stakeholders if the program is to stand a chance of being successful. A team needs to be assembled to address the various aspects of the program such as data ownership and stewardship.
The complex endeavor of developing data governance requires a strong foundation built on a shared language concerning enterprise data resources. IDERA’s ER/Studio Enterprise Team Edition is an excellent collaborative tool that helps your team build that foundation. It has many features that are instrumental in creating data governance in your enterprise.
ER/Studio Enterprise Team Edition can be used to discover and document your current data assets and build out an organization-wide model. Collaboration allows you to implement naming standards and a data dictionary for better consistency throughout the enterprise. Data models are easily shared in the tool’s repository and permissions can be set limiting access where necessary. ER/Studio is a great tool for implementing data governance so your organization can enjoy its wide array of benefits.