As organizations continue on their data maturity journey, data governance becomes an important consideration for organizations. However, in non-regulated industries, the value of Data Governance is less clear. Additionally Data Governance leadership is inexperienced or playing dual role along with Data Strategy or Analytics and are themselves unsure of how to proceed. In such cases, it is important to launch and run a lean data governance program that focuses on making data more agile. In this article we share our experience on how Data Governance leaders in non-regulated industries can unleash data agility and drive value to their business.
What is the role of Data Governance Leaders that do not come from legacy Data Governance backgrounds?
Data governance can vary from industry to industry. For instance, both manufacturing and healthcare have stringent data governance guidelines compared to consumer goods companies. But the former must maintain compliance with these strict rules due to existing laws whereas the latter may do so to leverage benefits like improved insights. This means that in some industries Data Governance is not solely for compliance but to leverage data to fuel business innovation.
However, in such organizations, it is common for Data Leaders to not have a Data Governance background. They themselves are looking to figure out what Data Governance means. They have arrived at the realization that the current approach of frameworks, and policy-driven, stewardship programs are slow, resource-intensive, and a challenge to get buy-in from executives and business units. They are looking for a place to start and actions to undertake.
What is the objective of data governance when it is not about compliance & reporting?
The PRIMARY GOALS of data governance are to drive business value, manage and protect data assets, ensure better decision making, reduce costs, build repeatable processes, and ensure smooth overall operation. It ensures that data can be leveraged to drive business innovation and efficiency. This means more business functions and processes should use increasingly available data to generate timely insights and make profitable decisions.
In other words :
Data Governance is about making DATA MEANINGFUL. That means data is Accessible, Usable, and Reliable.
Accessible: Means easy to find and has streamlined process to get access
Usable: Data is documented. It has description, context, business semantics and provenance
Reliable: Means that data is high quality, endorsed, and has social proof attached to it
How do you make data more agile?
In order to makIn order to make data agile, one should have a unified, centralized view of data across a complex data landscape. This means that one should generate and leverage rich metadata such as types, formats, source, documentation, current quality, where used, business context, and semantics. These insights should be leveraged to establish a process to detect and fix bad data.
Now that we’ve understood what should be done, Let’s understand what challenges leaders face to even get started?
Challenges with enabling Data Governance?
30% of Data Governance programs don’t launch or die on the vine”
“50% of Data Governance Programs result in 0 ROI”
With these statistics, It is evident that there are serious challenges in not only kickstarting good data governance programs but managing them as well.
And here they are. There are three main categories.
- That the data itself is dirty, it’s siloed. It doesn’t have the metadata that we would like. Raw Data implicitly doesn’t have good metadata. It requires curation to make it usable. The traditional way of doing that was by having large data stewardship programs to classify and curate data. However, as the data velocity increases, this becomes impractical. On top of that, data complexity is growing – Data across clouds and data that is structured or unstructured. This makes traditional data governance techniques impossible to implement. That’s one category that you’re dealing with.
- You may not have the tooling and skills today. So you might have been looking at these different approaches out there, and you’re trying to figure out what’s required.
- Where do I start? You know, there’s very pricey options. And then finally, you’re realizing the options are not great. You know, they’re all very heavy frameworks, policy driven. Positive and frameworks and stewardship programs.
They’re slow to implement or intensive resource intensive, they’re expensive, you know, trying to get such an enormous solution. bought in a boss, exec teams, it’s hard enough with one exec, let alone the whole executive team lines of businesses. And then you want to be able to encourage, integrate that into your downstream solutions. So it’s fraught with challenges in terms of implementation.
What is the better approach to Agile Data Governance?
- a Centralized Data Catalog that can scan your entire data stack including your analytics layer
- that can automate classification and assist with curation to drive efficiency and productivity
- that can be rapidly setup and deployed so it improves time to value
- that is easy to use, so you engage broader organization to consume and contribute to this knowledge
Let’s take a look at DvSum’s ML Data Catalog as an example:
- With powerful AI-enabled algorithms, DvSum automatically scans disparate data sources including data lakes, reporting layers, and semi-structured data to accurately catalog, classify, and curate data from your numerous siloed data sources and makes it available as a centralized, actionable Data Catalog.
- Automatic metadata harvesting and mapping
- Brings the data catalog to where business users need it, via APIs
- Deploy in weeks instead of months and years. With Out-of-the-box connectors to 150+ sources, you can scan your entire data stack without needing to build or buy connectors
- An intuitive, business-friendly interface requires little to no training for both Data Teams and users.
- With an intuitive knowledge interface, data and analytics teams can gain visibility into the entire data landscape They can easily find, evaluate, and confidently choose the best data for their needs.
- With a universal access plugin, business users can consume the Catalog inside their application & reports to gain literacy and trust in the data used to generate insights
- SaaS tool with TCO that averages 10X less than of other Data Governance solutions in the market
Traditional approach to Data Governance is ineffective in a modern data-driven non-regulatory enterprise are not sufficient in today’s complex and evolving data landscape. To drive success, you need lean, flexible data governance that is easy to launch, implement, manage.
Launch Lean Data Governance
Make Data Accessible, Usable, and Reliable.