Trends Data Leaders Should Anticipate

An article in The Data Administration Newsletter, posted June 15, 2022, says that what the pandemic has done, for many, is highlight a need to future-proof their data environment from future disruptions. With more data flowing into businesses and a greater need to automate processes and maximize impact, these are the data trends that will define 2022.

The Need for External Data will Drive Increased Adoption of Data Catalogs: There are a few major data catalog providers, and their platforms are increasingly touted as a necessary component of a modern tech stack. Data-driven organizations (or those that aspire to be) are looking for solutions that let them discover, manage metadata, and supervise access from a single control panel. What’s unclear, however, is how organizations are managing the flow of external data into their centralized catalog environment.

Data Monetization Finds Its Footing: The data monetization market is set for rapid growth in 2022. According to a recent business intelligence report released by Data Bridge Market Research, the growth, size, and CAGR of data monetization is set to grow at a rate of 21.95% from 2022 to 2029.

Analytic Success Needs Multi-Cloud Support: More than ever, data is coming from everywhere and is distributed across environments. As Sudhir Hasbe recently wrote in Forbes, “Over 90% of large organizations already deploy multi-cloud architectures, and their data is distributed across several cloud providers.”

The Rise of Solutions That Support Intelligent Data Procurement: Being a data procurement officer must be difficult right now. The need for new data has never been more widely acknowledged, but budgets are tight and regulatory compliance is increasingly difficult to navigate. Being a data procurement specialist means working closely with your data team to understand their goals and analyzing what datasets they can use to achieve these goals.

“Data Fabric” Matures: Data Fabric is, according to Gartner, a “design concept that serves as an integrated layer of data and connecting processes.” It runs continuous analytics over metadata assets to support the deployment of integrated data across all environments, making it ready for machine reading and AI. The problem with data fabric to date is, like the pre-pandemic AI craze, that it’s a holistic design that comprises an entire ecosystem. In reality, there’s no current solution in the market that analyzes all your data assets, inferences connections between them, and discovers unique, business-relevant relationships. Data fabric is a goal, not a platform.

For much more, read Lewis Wynne-Jones’s article at Trends Data Leaders Should Anticipate –