Authored by: James Scherer
If your organization is on the road to digital transformation, there could be a Grand Canyon-size training gap up ahead just waiting to derail your strategy. It’s called data governance, and it’s an aspect of the digital environment essential to becoming data-driven. Data governance gives organizations knowledge about their data, and the insights to optimize its value. In this blog, we’ll give you the quick run-down on what data governance is, why it’s essential, who should be tasked with it, and how to get it successfully underway. If you’ve ever said: “Where is the data I need?”, “I don’t know what the data is in this column and I’m not alone?” or “Why does it take so long to onboard someone to the data team?”, this blog is for you.

  1. Data governance is not a project. It’s a process that’s part of any organization’s maturation with data enablement that leads to all employees speaking the same language about the data. Organizations often focus on data governance when they’re migrating data from legacy systems to an enterprise data warehouse. When done correctly, it gives employees the ability to contribute to and find knowledge of key data elements found throughout the enterprise while reducing unnecessary rework and building trust in the data.
  2. Every company has some form of data governance. Most early stages include notes about reports, tables, databases and sources. Data governance aims to expose and connect the siloed knowledge about the data – known as metadata – held by departments or small groups within the organization. It also allows organizations to separate the wheat from the chaff in terms of data that can enable business innovation and advantage.
  3. There’s a wide range of digital tools for data governance. Some companies begin their data governance work in Excel or SharePoint, and if you have a small volume of data or just a few people using it, those can suffice. There are also some really interesting platforms like Collibra and Alation that are purpose-built to enhance and accelerate data governance. For large organizations, platforms like these have critical features for reaching mature levels of data governance.
  4. Data governance has huge ROI in terms of business benefits. By standardizing the knowledge about data and the way employees refer to it, companies gain:
  • Reduced risk of inaccuracy in data use and reporting
  • More efficient data use
  • Enhanced ability to meet security and compliance requirements
  • Faster onboarding of new hires with data roles in both IT and Operations
  • Faster IT ticket resolution and troubleshooting
Effective data governance approaches generally fall into two categories. With bottom-up data governance, the organization identifies a team of employees who use the data and understand the value of data governance. Ideally, the team will include the analysts writing the reports and a sponsor who keeps the process moving. You need people that care about accuracy maintaining the content while allowing others the ability to comment, question and validate.
The team will use an iterative process that starts with prioritizing and defining the data elements. As they move along, they may add:
  • the data’s lineage (where is it in the source system)
  • listing of the front-end apps that use the data element
  • business rules that apply to the data element
  • transformation logic (a record of changes from the source system to the data warehouse)
  • the steward or subject matter expert (SME) for each key data element – the person who knows the most about that data or is responsible to address issues involving the data.
  • notes on data quality
The business rules are where data governance helps with compliance. In healthcare companies for instance, certain data elements can be designated as “identifiable” in the data governance system, which cues any employee to use that data according to compliance requirements.
With top-down data governance, an organization hires or designates a Chief Data Officer to lead the initiative.
For some organizations, the first step is to identify SMEs for certain data, then asking them to share documentation about the data they use and see how informative it is. A more technical approach is to connect to the source system(s) or your data warehouse(s) with a metadata management tool, and after exporting all the columns/tables descriptions, evaluating gaps between that and the SMEs’ understanding of the data.
Data governance should be a core element for any business, because the more mature your knowledge is about your data, the faster you can use and grow from it. Wherever your organization is on the road to becoming data-driven, TESCHGlobal has data engineers, analysts and SMEs experienced with data governance who can help. We can make it part of your next DW/ML/AI/MDM project to ensure your data is truly an asset to your organization.