Data Governance Consulting

Data governance is an organization’s management of its data availability, usability, consistency, and data integrity and data security. Data governance defines who can take what action, upon what data, in what situations, using what methods and includes the processes, roles, policies, standards, and metrics for ensuring effective data management throughout the lifecycle of the data and for its use by the entire organization. Effective data governance empowers users to develop business insights from high-quality, secure, and trustworthy data.

Data governance often conjures up the idea of some central authority instituting a “culture of no,” but in reality it can be a powerful engine to scale the use and distribution of trusted data pipelines throughout the company. At Data Engineers we understand that data has always been an after thought and can help you along your journey so that its becomes a natural part of your workflow.

Data governance strategies should be able to meet the complex needs of modern companies. Data governance strategies should also enable organizations to develop and deliver trusted data to the right users in the right format and right time to harness business value. At the same time, with data governance in place, companies can confidently ensure data privacy, proactively complying with regulations, and allow easy collaboration with data professionals in every function.

Security and governance tools ensure sensitive data maintained by your organization is protected from inappropriate access and tampering and helps you achieve and maintain regulatory compliance. These tools support a wide range of operations, including risk assessment, intrusion detection/monitoring/notification, data masking, data cataloging, and more.

Data Governance Best Practices

Start Small

Take advantage of existing opportunities to improve data management rather than tacking everything in one go. Successful data governance include change management within the organization. Data Engineers can start the journey by simply documenting the new models they develop using tools such as dbt.

Identify your data domains and define controls

You have data coming in from multiple source systems which contain terminology based on the source system. When you are transforming the data you are renaming fields so they are in your business terminology to enable consistency. These schemas would be exposed out as data domains which would act as your access points into your data. Mapping out a detailed plan helps define automated workflow processes, approval thresholds, issues resolutions and more.

Measure Progress

Maintaining data governance best practices is an ongoing process. Including plans to regularly define, report, and measure against your data management goals. Metrics will naturally vary depending on your data size, scope, and sources, as well as how your data is disseminated both inside the organization — and potentially outside. A few metrics to consider include rate of adoption, number of data issues and events, and the program's overall cost, from data rectification to issue resolution. 

Create a recurring, repeatable process

Developing a data governance strategy is not a one-and-done project. As your data volumes grow, new data streams and access points will emerge. Devise a policy for periodic reviews of your data governance structure. 

Our Approach

At Data Engineers, we have embedded Data Governance into our DataOps Framework so that it becomes a first class citizen for a data engineer rather than an after thought. Data Catalog's, Data Masking, Data Exposures, Data Constraints are some of the data governance which can be solved by the solution. However to make Data Governance work within organizations, organization needs to adopt and allow it become part of its culture instead.