Data is the life blood of organizations and businesses and is likely its most valuable asset after people. Because of its importance and role in daily operations and overall success, it needs to be managed just as any other business asset is managed. This management is prescribed and managed by data governance programs. Data Governance is the management of all of the data an organization has to ensure that high quality data exists throughout the complete data lifecycle. ( . A robust data governance program will exist to ensure data are available, usable, consistent, have integrity, and are secure.
The Four Pillars of Data Governance
People
People are important in the management of data. The data steward is an individual tasked with ensuring ensures the governance processes are followed, enforced, and ensures necessary governance improvements are made so that data delivers the most value possible for as long as it can in the data life cycle. This person may be part of a team of data stewards which may also include database administrators, business analysts, and business subject experts.
Processes
These are the key to the success of a data governance program. Any work with data: acquiring, collecting, munging, storing, modeling, analyzing, all need to be in line with the key metrics and goals of your organization. The accuracy accessibility, consistency, and completeness of the data are important, but these must be within the context of your business and should be refined and honed with an emphasis on the goals of your business.
Data quality is key to the data processes to ensure accuracy and completeness. Version control, data scrubbing, established workflows, and project management systems are all integral to data quality.
Technology
Data storage design and architecture is of utmost importance but no matter how large you start; your storage containers will eventually begin to fill up. Metadata management and master data management are crucial in gaining insights to data flow and will help anticipate needs and constraints. Sound metadata management practices will also improve transparency and security across your organization’s systems.
It is also important that the tools are in place to analyze and derive information and value from your data. Actionable insights are the reason the data was collected to begin with and its purpose is to add value to your organization.
Big data has driven the development of cloud architecture, and ushered in unstructured, or NoSQL, data that is different from traditional relational databases that were originally the focus of master data management. To address the complex relationships from these new data structures, graph data stores are being used more frequently in master data management.
Best practices
Best practices for data governance are important in order to establish a quality, robust program. However, the practices change and evolve rapidly as organizations flatten and big data continues to gain prominence. No matter how things change and evolve, it will always be critical to identify key stakeholders, employ meaningful metrics, communicate frequently across teams and the organization, and to strive to make data governance a practice and not a one-off project.
Final Thoughts
Data governance is a cornerstone to the success of your business or organization. The practice has gained prominence with the introduction of regulation such as Sorbanes-Oxley and is necessary to ensure information assets are properly and efficiently managed to ensure your organization and your customers are receiving the best value and results.
I hope this post was helpful in providing some insight to the basic concepts of data governance. If you found it helpful, or would like to learn more, please comment below or find me on Twitter.
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