Last-Mile Governance is More than a Query Workbench

[jrineakter]

Enterprises adopt data catalogs for a variety of purposes. One of the most popular is data discovery. But in today’s governance-focused world, connecting data consumers with the assets and analysis needed to make informed business decisions requires more than a simple query workbench. Your data catalog must also be architected for last-mile governance.

What is last-mile governance?
Last-mile governance is a continuation of Agile Data Governance that enables organizations to curate well-informed datasets and then share them for greater collaboration within the enterprise. Data catalogs that deliver true last-mile governance offer both metadata management and data integration capabilities that align to five key concepts:


The first step to leveraging data assets for business decision making is cataloging all of your data sources – something that is no small feat given the proliferation of modern data and analytics ecosystems. The key here is extensibility.

In the context of a data catalog, extensibility relates to the platform’s ability to quickly and easily catalog new data sources without having to overhaul the underlying metadata models or greece whatsapp number data configuration, forcing a redeployment of infrastructure. Your data catalog should be able to absorb new information about your data and analytics ecosystem or represent new lines of business without costly re-engineering.

The most extensible data catalogs are cloud-native, feature a flexible metadata model, and offer open APIs to simplify data integration.

Understanding
In his book, Winning with Data, Tomasz Tunguz describes five main challenges companies must overcome to create data-driven cultures. Data obscurity and lack of understanding is one of them. Primarily a documentation problem, this can (at least partially) be addressed with the addition of a data dictionary and business glossary.

In a blog post for Stanford University, Stephanie Winningham defined a business glossary as “a central repository that contains key business terms whose names and definitions have been agreed upon by cross-functional subject matter experts.” It is designed for use by non-technical users.

A data dictionary on the other hand, “allows a group to describe data regarding the physical data structure, type, format, and length, as they exist within a data schema.” It’s primary purpose is for database admins and architects to document how and where the data is stored and how it must be referenced to consume it.