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Be the architect of your AI-driven future at "Blueprints for Generative AI."
Be the architect of your AI-driven future at "Blueprints for Generative AI."
Data governance is about understanding and managing the data within an organization. Traditionally, companies use data governance as a risk avoidance tactic or a way to make sure their data team is being compliant with strict policies around data governance.
People often confuse data governance with data management. This chart explains the difference.
The practice of data governance continues to evolve as organizations recognize the transformative power of data and metadata. Like we said before, the traditional top-down approach to data governance often focuses on risk and compliance. However, modern, data-driven organizations take a more holistic approach to data governance where everyone participates in the practice and focuses on learning and improving data over time. This is referred to as Agile Data Governance.
Before we get into agile, let’s first explore data governance using a top-down approach.
Like any culture change, the people in your company need to become your priority. Think about the team who will execute this change and implement these processes. Most companies encompass all of these roles on their data team:
Looking at the organization breakdown above, think about the structure of your data team from both a top-down and bottom-up perspective. There needs to be a strategic team who will be extracting the best practices for your team to use. At the same time, keep in mind that you also have data producers and consumers actively working on analytics projects.
Bridging the gap between your strategic team and data consumers is a critical step in creating an inclusive, data-driven organization. It eliminates the silos that were built through previous top-down efforts. After closing that gap, data consumers, data producers, and domain experts can easily collaborate to find the answers to critical business questions. Now that we have a clear understanding of our data team breakdown and how they need to be working with one another, start thinking about the processes that they need to carry out to drive more value with data.
Once again, we want to take the deeply proven best practices of Agile and Open software development and apply them to our Agile Data Governance process.
You can see how data producers quickly learn what’s working and what’s not about the data sources they’re curating so they can make improvements in real-time. Doing analytics with a living, evolving data asset focuses stakeholders and provides valuable insights at high frequency. That’s why Agile Data Governance practitioners see ROI in days instead of month.
By cataloging the work as it happens in your data catalog, and not only the “finished” analysis, teams continuously learn from each other and elevate their data literacy. That’s because people learn data skills and domain knowledge faster by doing the work and seeing their peers solve real problems.
Getting into a motion of constant iteration and knowledge capture will help your organization not only get faster answers to current issues, but ensure your data is reusable for future projects. To get here, make sure you have the right foundation and tools in place to set you up for success.
Only some tools are right for Agile Data Governance, in the same way the growth of Agile and Open-source software development demanded new tools. Agile software development meant throwing out heavyweight requirements docs and architecture diagrams that would take weeks to write.
While there are great tools that will help you implement agile on a data level, they will not completely support the processes that make Agile Data Governance valuable.
Is there a way to support this agility that companies are looking for?
Yes; the answer is DataOps.
DataOps is a management methodology that considers the people, processes, and tools involved in making your data supply chain more efficient. It encompasses an end-to-end approach for data management that produces a unified view of the entire data lifecycle.
Putting a collaborative data catalog at the center of your DataOps and Agile Data Governance processes is essential to getting a clear picture of your entire data supply chain.
“Within our data catalog, we can find and access data used by WPP agencies and teams so we can solve our clients’ problems. We also collaborate on data projects harnessing the virtualization capability of the platform whilst using the tools already familiar to us.”
Getting the processes down is one thing, but making sure you are constantly improving them is another. Data-driven cultures use what they have learned and apply that to their processes to make them more efficient. That’s what we are trying to achieve here with DataOps.
With new processes in place, becoming an expert on measuring your efforts will allow you and your team to fill gaps and find opportunities to drive innovation as you adopt these new methodologies.
Being able to measure the success you have in your data governance strategy is going to speed up your journey to becoming a data-driven organization.
If we are going to say that “data is the new oil”, we need to back that statement up by treating data as a product. Oil is constantly refined, but are you refining your data? Companies like Airbnb and Lyft are and that’s why they’re winning.
Airbnb drove active users from 30% of the company to more than 60% with the introduction of Data Portal and Data University.
They did this by measuring their data efforts and finding a gap in the usage of their data stack, solidifying how important measurement is to data-driven organizations.
