Unify Your Data: The Role of the Data Steward

When it comes to gathering the right data and finding the relationships that make that data more meaningful, there’s one role that knows how to do it best - the data steward. That’s why they are often referred to as data detectives.

The Trustee of an Organization’s Data

Data stewardship is an important role for an organization. The data steward is a trustee of an organization’s data. They don’t own the data, but with so many internal and external data sources available for use, the data steward’s responsibility is to understand what is available and how it connects to provide real value.

This deep understanding helps enforce information governance policies, eliminate data silos and reduce redundancies across data sources.

Building information processes around all this enterprise data is challenging. Organizations are dealing with more and more data every day and it’s coming in faster than ever before. But even more challenging for data stewards is the variety of the data.

From unstructured data such as documents, video, and social media to structured data like transactions, and semi-structured data such as email, the data steward is challenged with finding connections and correlating relationships between all these different data sets.

The process becomes one of trial and error using tactics such as talking to subject matter experts, sifting through mounds of documentation, and running sample queries.

Often, instead of waiting for IT to bring them the right datasets, data stewards will work with the data they already know and have access to. This may seem okay, but it’s a big problem. How does the data steward know that they are working with the right data if they don’t have access to all of it?

The Answer Lies in the Semantic Metadata Catalog

Remember that semantic metadata catalog that IT needs to create? This is where it shows its usefulness. Data stewards can shop for the datasets they need in an Amazon-like shopping cart experience and visual data models are automatically generated exposing the correlations between structured and unstructured data sets.

This machine-learning approach to automatically generating metadata and data models provides insights into which data sets should be analyzed and how they should be used. The data steward has the ability to change the model, editing and removing links and defining new links based on their understanding of the data.

This final data model can then be quickly provisioned for BI tools like Tableau, Qlik and Tibco Spotfire and used for data-driven decision making.

The entire process is straightforward and fast. It ensures the business has the data they need when they need it, and not a minute later.

This self-service data preparation model is how it should be. And it’s a model that works not only for the data steward but also for the citizen or business analyst. But that’s a story for another day.

Can’t wait to learn more? Download the eBook Unify Data across Silos to find out more about the challenges and opportunities related to data discovery and the teams that need it to happen.