Why is your data catalog not enough for Data & Analytics governance?

Deploying a data catalog is a key enabler in the data transformation journey, but be careful to not overlook existing limits and shortcomings. A data catalog alone cannot be the cornerstone of your Data & Analytics governance and strategy. Here is why

Intro

Data catalog tools have become increasingly popular over time and grew in functionality to facilitate data users’ lives.

The purpose of data catalogs is to help organizations with data inventory and discovery — with the hope that this will also help them understand and govern these assets.

As such, a data catalog plays an important role in the data transformation but it is just one of many steps when building a data & analytics strategy and achieving its end-to-end governance.

Challenges data catalogs address

Before data catalogs existed, data management was done mostly manually, requiring complex and time-intensive efforts, leading to limited deployment and visibility. Members of data teams such as data engineers, data scientists, and data analysts were constantly struggling to identify data sources, understand associated policies, and manage to get access.

With the emergence of data platforms like data lakes, centralizing huge amounts of data, locating existing data and assessing their reusability became more and more complex. The lack of proper comprehensive inventory, documentation and policies tracking really became a major issue.

Here come data catalogs to the rescue, enabling organizations to answer key questions like:

• How do I quickly search for existing data?
• How can I track the source of data (a.k.a. data lineage)?
• How can I understand policies corresponding to a given data?
• How can I request access to data?
• How can I track how data is used to ensure security?

As such, the aim of data catalogs is to provide companies with a data inventory, automate metadata management, assist with managing policies and enable deploying data governance.

Typical limits of data catalogs

More than just risk and compliance, data & analytics governance should holistically address the entire data & analytics process, enabling safe, efficient, and reliable collaboration across all stakeholders: data, IT, business, finance, ...

Data catalogs frequently rely on a bottom-up approach and lack context around data in terms of business usage and in terms of contribution to business value or associated risks.

It is because they focus on the technical aspects of the data governance, that they are, in many cases, not really usable by non-technical users outside of data or IT teams. This directly leads to limiting data democratization capability and creating collaboration barriers.

Many data catalogs have a tight focus on data only and do not cover the full range of data-related assets to constitute the delivery value chain:

• Data pipelines (partially covered in lineage, rarely as assets)
• Data Science / ML / AI models
• Dashboards & Reports (partially covered in lineage, rarely as assets)
• APIs
• Etc.

And in the end, data catalogs most of the time fall short in answering the following questions:

• How can I get visibility on the actual business use and value of data across the existing data products?
• Where should I focus efforts in term of investments to deliver the most value or reduce the more risks?
• How does the data team create reusable data assets that will support multiple business cases or data products?
• How to manage the end-to-end lifecycle of data & analytics investments, in order to govern the full value chain. Or, how to coherently manage data governance and analytics governance and trusted AI governance?
• How do I communicate on the impact & value contribution of the data & analytics program?

How to enable your data strategy?

Drive faster and more accurate business insights, reduce redundant and inefficient work, increase reuse of data products, and build thriving data cultures. — Data world

In order to increase the business value of your data & analytics investments, you need to ensure that (1) they are actually used to generate insights (2) which contribute to generating value (3) and are controlled in terms of risk.

To achieve all the above, it is key to apply portfolio management principles, in order to get a holistic view of data assets and initiatives end-to-end.

Applying portfolio management to data & analytics consists of the following steps :

• Capturing the business strategy in terms of priorities and measurable success criteria (for example, based on existing OKRs),
• Tracking the expected vs realized outcomes from initiatives, by animating their full lifecycle
• Connecting initiatives to current and required data assets, along with associated criteria in terms of quality and policies

With this visibility, efforts can be properly prioritized and budgeted, risks can be properly assessed and managed, and data communities can be engaged to enforce governance workflows, drive collaboration and facilitate adoption.

And having engaged teams and communities is the way to ensure delivering well-designed and cost-effective Data & Analytics products that are Useful, Usable and Used (the 3Us)!

They are also the way to concretely animate and grow data literacy and data culture by engaging the organization within the data & analytics transformation.

Organizations are striving to leverage data & analytics to generate better insights and have an impact on their business strategy.

And in that context, having the right set of tools to support that ambition is a way to build a competitive advantage. While it’s essential to reference and document all the data a company amasses, data catalogs are missing a major piece of data strategy: value creation.

The solution is to complement the data catalog with a value management solution (like YOOI) to give yourself a competitive edge over your competitors: by building a stronger alignment, facilitating prioritization and decision making, encouraging collaboration and demonstrating tangible benefits from investments, you are able to really transform and are ready to accelerate further.