What is data modernization anyway? There are a lot of definitions out there. A quick google search returns results ranging from “migrating old data technology to modern tools and platforms” to “running data tools on the cloud” to “making data easier to use by putting it in a new data lakehouse”.
Generally, the consensus definition focuses on adopting new, modern data management tools. I have an issue with that. It’s insular. It’s technology-centric. And it doesn’t change how data is used, only where it’s stored, and to a limited extent how it’s managed.
Here’s my answer to the question ‘What is Data Modernization?’ Data modernization is a continual business strategy that (i) evaluates how data should be used to benefit business processes and (ii) evaluates new, modern technologies to improve the trustability and utilization of data. It attempts to change the way data is currently used by making it easy to find or ‘ubiquitous’ in the organization. The goal of data modernization is to boost data utilization, to make data readily available for every business process, analytic and AI developer and business user when and where they need it. A data modernization strategy has technology implications. Data storage, processing and management tools should be brought up to modern standards, to ensure the integration of data capabilities to better manage data to make it widely available.
Data management tools have been modernizing for 40+ years, from one computing paradigm to the next. Cloud is the latest. But simply taking our existing and fragmented tools – integration, quality, governance, cataloging, security, master data – and moving them to a “modern” cloud platform isn’t data modernization. We can’t progress if we keep building the same things from the ground up on new technologies. Data modernization has to be focused on boosting data utilization through deeper integration of data management technologies to improve the quality, trustability and context of data to make it more relevant to end users.
That’s why I’m excited about a few approaches in data management that are fresh and new. Data platforms and lakehouses. Data Mesh. Data Fabric. Data Sharing. All of them promote the interoperability of various data management tools to address data management use cases. And that’s a good thing – as they will improve the quality, trust, and findability of data to ultimately improve data utilization.
QSG’s monthly newsletter is filled with insights, best practices, and success stories from our customers’ experiences in utilizing modern technology to improve their business.