Top 5 Trends for Data Management in 2022

[jrineakter]

If the last two years are any indicator, maybe we’ll end up calling this the “decade of data,” with next gen data observability, data catalog, data integration platforms, cloud data warehouses, and more making big news and bringing in big funds.

We’ve seen the rise of dbt and the analytics engineer, a focus on data fluency and dataops, and AI finally trending from the fantastical to the practical. Where the late ‘10s focused a lot on modern business intelligence, data science, and a continued shift to the cloud, the ‘20s have been about managing and transforming the underlying data.

So what does 2022 hold in store? If you’ve been following our web show and podcast, Catalog & Cocktails, we’re talking to leaders in the data and analytics space. And between what they’ve shared and our own thoughts, here are the biggest trends:



1. Data, meet metadata
As the cloud, SaaS, and the modern data stack expand their dominance, the data landscape is getting more and more complex. There was a day where you could analyze your stored procedures india whatsapp number data and your Informatica or Microsoft ETL configurations to get a lot of visibility. But now, with Python scripts, Dagster pipelines, dbt, microservices, streaming, data lakes, data lake houses... visibility has become scattered and difficult to piece together.

Metadata platforms, led by catalogs and observability solutions, will continue to grow in popularity. Leaders will be marked by speed to implement, ease of use, and adoption by a broad set of data personas, intelligence, openness, and interoperability.



2. Knowledge graph gets hyped
Knowledge graph trends in data management

Machine learning has become synonymous with AI, powering intelligent use cases from image recognition, better classification, recommendation engines, automatic finance, smart cities, cars, homes, and more. But what machine learning has lacked is nuance, context, and explainability.

Why does Facebook have such a powerful news feed? Why do Apple and Amazon have such impressive voice recognition? Why does Google nail search results, and Netflix show recommendations? Because under the hood is a knowledge graph. And it infuses data, context, semantics, and relationships together in a queryable, analyzable web.

In the future, AI will have common sense and be built on the back of knowledge graphs. And virtually every company and service will incorporate one knowledge graph or many. But before we get too far ahead of ourselves, there will be a lot of hype. Which companies are actually built on a knowledge graph and able to tap into their context? Many fewer than will claim.

For data management, some machine learning features are impactful, but many simply skim the surface and are cosmetic in nature. A real knowledge graph means you'll be able to operationalize your data and analytics assets with meaningful automation.