Choose the Right Data Governance Tool for Your Enterprise


After the GDPR, data governance is everybody’s job. It’s not just the responsibility of database admins, corporate counsel, or Senior Management. Part of the change that data protection policies are intended to bring about is personal accountability and responsibilities for protecting your own and everyone else’s data in your workplace. So that means customer service representatives, clinicians, software engineers, truck drivers, are all liable for the careful stewardship of employee, patient, and customer data.

Why Data Governance Matter?

When developing systems, governance is largely about analyzing the data and requirements to determine the rules for data handling, security, syntax, and definitions. The foundational work for governance and data quality management needs to be done when developing systems to maximize data quality. To a large degree, the controls and functional parameters determine the level of quality that can be maintained over the life of the system. For example, whenever possible, structured lists should be used for data that will be used for analysis after the system is deployed so you don’t want those fields to be developed as free-form text fields because that would open the door for bad data to enter the data pool for analysis. In some cases, it is unavoidable because some information has to be collected as free-form data so when that is the case, you want controls in place that minimize the potential for bad data.

Data is becoming the core corporate asset that will determine the success of your business. You can only exploit your data assets and do a successful digital transformation if you are able to govern your data. This means that it is imperative to deploy a data governance framework that fits your organization and your future business objectives and business models. That framework must control the data standards needed for this journey and delegate the required roles and responsibilities within your organization and in relation to the business ecosystem where your company operates.

A well-managed data governance framework will underpin the business transformation toward operating on a digital platform at many levels within an organization:

  • Management: For top-management, this will ensure the oversight of corporate data assets, their value, and their impact on the changing business operations and market opportunities
  • Finance: For finance, this will safeguard consistent and accurate reporting
  • Sales: For sales and marketing this will enable trustworthy insight into customer preferences and behavior
  • Procurement: For procurement and supply chain management this will fortify cost reduction and operational efficiency initiatives based on exploiting data and business ecosystem collaboration
  • Production: For production, this will be essential in deploying automation
  • Legal: For legal and compliance this will be the only way to meet increasing regulation requirements


Data Governance Operating Model

The Data Governance Operating Model implements a data strategy (i.e., why govern data?) by establishing the foundation for all your data stewardship and data management activities.

It can be subdivided into three categories each addressing a key design question.

  1. The asset model, which deals with how an organization structures its data assets, ranging from the physical layer of systems and data structures through to the logical and business layers where everything comes together in terms of the relations between the various assets and how they are used by the Business. This covers the What and Where of data governance.
  2. The stewardship model, which allows an organization to understand existing ownership of data, identify gaps, assign and monitor roles and responsibilities for its data assets, start from individuals or teams and identify the data assets they work with/produce and simultaneously start from data assets and have a clear view of ownership. This covers the Who of data governance.
  3. The execution model, which deals with how organizations orchestrate the collaboration between their different parts, particularly with regards to how knowledge about data is gathered, how data is understood, and if/when it can be trusted. This last component is critical to governance and cannot exist without the previous two being in place. This covers the How, When, and Why of data governance.


Capabilities to Look For in Data Governance Tools

  • Data Classification – Different types of collected data would fall in varying levels of importance. Hence it is essential to classify and categorize that data as early in the chain as possible. Data classifying is a crucial first step towards establishing good data governance.
  • Data Lineage – Data lineage is about understanding how and where the data has originated and its processing logic and destination. It gives visibility and also helps in tracing errors back to the root cause in a typical BI process. The data lineage is vital to create trust in the data.
  • Data Storage and Security – You must then capture legal requirements, compliance requirements, and company policies on data privacy and security. Strong data governance must include data backups. You must understand the schedules and recovery processes. Governance will require a good understanding of the exact number of copies of data, how long they are meant to be kept, and who has access to them.
  • Data Ownership and Stewardship – Data ownership is not about holding the data but about providing it’s access to other business units so that they can also benefit from it. Data stewardship is about managing the data quality in terms of accessibility, accuracy, completeness, consistency, and updating.



When data issues occur, doing root cause analysis is again needed to assess the source of the problem and identify a logical solution. More than ever, data governance is vital for companies to remain responsive. It is also important to open up new and innovative fields of business, for example by big data analyses, which do not permit the persistence of backward thinking and overhauled structures.