How an Enterprise Data Catalog Fast Tracks Domain-Driven Data Governance

[jrineakter]

Putting low-rise jeans and boy bands aside for just a minute, this follow-up post focuses on how adopting an enterprise data catalog puts you on the fast track to establishing a domain-driven data governance methodology and enjoying the advantages that come with it.

Domain-driven design and domain experts
Until recently, larger organizations have gravitated toward a centralized, top-down approach to data governance, in an attempt to streamline operations and get things “under control.” But now that they’ve seen the error of perpetuating silos, slowing innovation to a crawl, and encouraging workaround shadow IT practices, they’re turning to data mesh and prompting a massive resurgence in the adoption of domain-driven design.

One of the major reasons for the return to domain-driven design is that no single team — or person — can be tasked with understanding all of an organization’s raw data. And cleaning it. And transforming it. And building data products from it. And so on.

For enterprise businesses generating incredible amounts of data, this approach — expecting a single centralized team to become expert in all of it — is completely unrealistic, leads to data australia whatsapp number data bottlenecks, and prevents the full extraction of data value. To combat these failures, just as your organization is divided into business units focused on their own functional areas — marketing, product, finance, etc. — you need to establish similar functional areas or business units, aka domains, for your data.

In data-governance speak, a domain is a high-level category created for the purpose of assigning accountability and responsibility for a functional area’s data. Often these domains are already known to you! They may be the way you already organize your business — by department, by function, by industry, by product, by process, etc.

Just as you wouldn’t expect one person at your business to be expert in every department, function, product, and process, you shouldn’t expect one person to be expert in all the data,either.

Instead, you should place ownership of a data domain in the hands of the people who work with that data most and understand it best, people with a strong foundation in the specific field and the systems where the data was generated, people who can ask questions that lead to valuable and actionable answers — your domain experts.