Navigating the Data Mesh Paradigm: Opportunities, Challenges, and the Path Forward

The data landscape has become more complex, as organizations recognize the need to leverage data and analytics for a competitive edge. Companies are collecting traditional structured data as well as text, machine-generated data, semistructured data, geospatial data, and more. Many organizations are transitioning to cloud platforms to harness the full potential of advanced analytics, including machine learning and natural language processing, and to accommodate intricate, diverse new analytical demands.

In this environment organizations face a number of challenges. In a recent TDWI survey, over 70% of respondents stated that they were struggling to get to the next stage of analytics maturity. There are many factors involved. One is that data is increasingly stored in multiple silos, making it hard to access all of the needed data for analytics. Silos also make it hard to get to a trusted version of the truth due to discrepancies between different systems that house the same data. Additionally, enterprises face a lack of alignment between parts of their businesses — usually between business and IT. A poor data or analytics strategy may impact analytics. Finally, the need for improved data literacy is a top priority among those we survey.

If organizations need timely access to diverse data to support new use cases made possible through advanced analytics, the question is whether or not they need a new approach to data, analytics, and people.

This is where data mesh comes in.

Read more in https://www.datavirtualizationblog.com. Originally published on August 24, 2023.