By Oskar Grondahl, Senior Director of Product Management at Qlik
Traditionally, data governance has focused on the management of data that’s already been transformed. However, in today’s data-driven enterprise, both data analysts and end users are leveraging modern BI tools to pull insights from raw or partially transformed data. This naturally creates friction between the data gatekeepers and the data consumers, which can stall out even the most progressive data-led organizations.
However, there is a path forward. That path lies in leveraging a model for governing data that can meet both the expectations of the business user, as well as the reasonable expectations of data governance and quality that the enterprise can support.
One of the main reasons for a more modern governance framework is to quickly deliver insights to end-users without compromising data quality. After all, the rise of self-serve analytics in large part was to solve the issue of slow data delivery.
There are a few key focus areas when implementing a data governance framework that matches the need for data at the speed of business.
1. Apply an End-to-End Perspective
Data governance must be implemented from end-to-end. This means the entire data landscape, from the data warehouse to the embedded analytics solution at the organization’s edge. Only with a complete view of data’s lifecycle and use can it be governed effectively. Doing so ensures data is being used by the right team members for the right purpose and role.
2. Include the Analytics Solution in the Governance Framework
Every mid to large sized organization usually has data warehouse governance strategies. However, they usually don’t account for what happens to the data after it leaves its nest. Modern leading-edge analytics platforms can combine and transform data from any source, raw data included, to drive unique insights. Where does the combined data go? Who is using it? Was it transformed yet again by another team for another purpose? How accurate is it?
As enterprise data democratization efforts expand, this situation repeats over and over again, creating the potential for misuse and ultimately distrust in data. This is why end-to-end data governance is crucial. Only by having an end-to-end perspective can organizations know with confidence where the data came from, and what happened to it before and after it was used for decisions, decisions that can have material impact on the business.
3. Leverage Automation
With increasing data volumes, there is no way to effectively apply governance at scale without automation. As data spreads into various systems like CRM and ERP, and is embedded into workflows like Slack, automating the access and use of various types of data is the only way to maintain governance integrity alongside data democratization strategies.
4. Think Simultaneously both Big and Small Picture
When designing governance frameworks and deploying solutions, remember to ensure that they support and reinforce the organization’s data strategy and goals. Do they ensure KPIs are standard across manufacturing, supply chain and operations? Enable senior executives with real-time data from across the business? Enable everyone to better leverage data in their day-to-day roles? Each scenario potentially requires different data governance policies and strategies. For example, the head of supply chain has different data access and analysis needs than someone working in the warehouse, but they both likely would benefit from more real-time access to data that improves their performance.
5. Test, Test, Test
Since analytics users are not used to considering governance or data quality, they must be educated. Encourage and train them on how to evaluate and raise questions about where their data came from, its quality, how it was transformed and how trustworthy it is before leveraging for decision making. Working with the IT and data teams to regularly test analytics data will ensure that governance is working.
Governance drives trust, which drives adoption. When more people in the organization know they can trust the data, they find all sorts of ways to apply it to decision making and partnering with other parts of the organization. Driving trust and collaboration centered around data puts the organization well on its way to becoming truly data driven.
Learn more about how Qlik’s data integration and cloud analytics solutions can help you turn insights into confident actions.