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TDWI Upside - Where Data Means Business

Data Democratization Tops List of Data-Centric Trends for 2023

Organizations are always looking to do more with their data. Here are three data-centric areas enterprises will likely focus on in 2023.

The data industry is continuing to grow at an unprecedented rate. The past year has seen some monumental developments in the data analytics and warehousing space, thanks in no small part to the increasing addition of AI technologies. With this foundation, 2023 promises to be even more groundbreaking.

For Further Reading:

Q&A: Data Mesh/Data Fabric Implementation Tips for Success 

Empowering Everyone to Make Decisions with Confidence

Data Automation: The Heart of Data Warehouse Modernization 

Trend #1: Data access democratization will grow

Perhaps the most significant shift that we'll see in 2023 is a push for more democratization in the data analytics space. Every data-driven company must realize that if they are to achieve the company-wide insights they strive for, they need to make data and analytics tools accessible to more users.

Low- and no-code cloud applications have already begun to democratize many aspects of IT infrastructure, of which data analysis is a crucial component. The key to achieving the continuing democratization of data is self-service. 

One way to promote self-service analytics is to consolidate your data with a governance suite that provides governed access to anyone in your organization. From there, you can incorporate other data analytics tools and processes that don't require input from IT or data teams such as data querying and report generation.. 

Trend #2: Data mesh popularity increases

As organizations move away from centralized data lakes that require dedicated data teams, they’re looking to the distributed nature of the data mesh as an alternative.

As well as creating countless bottlenecks, data lake infrastructure relies on data teams to understand the needs and requirements of every business user and department, which is incredibly difficult. A data mesh is a decentralized architecture that disseminates data asset ownership to the individual or team with the greatest knowledge. 

Four principles determine the data mesh concept: domain ownership, data as a product, self-service data platforms, and federated computational governance. These principles constitute architecture that organizations can apply across their business to avoid delays and create shared ownership.

There are two steps any organization can take to begin the transition to a data mesh. The first is to create domain-focused teams that own the various data assets and assign users to them. The second is to divide the data in your organization into domains that echo the domain team structure. At this point, you have the system to inform the tools you use to develop a data mesh architecture for your company.

Trend #3: Analytics becomes more automated

Automation is becoming an increasingly important factor in business processes, including data analytics. Automated analytics is highly malleable and can be applied to a variety of data processes such as discovery and lineage building. 

Ideally, automation means little to no human intervention is required to run these processes. In 2023, the technology will mature such that more companies will trust it. The many benefits of automated analytics -- including reduced costs, faster processing, and more significant opportunities to redirect data analytics professionals to revenue-generating activities -- will prove too attractive for organizations to resist. 

However, before you introduce automation into your analytics processes, you will need to audit your existing systems to determine where automation will provide the most business value. As with any data technology, the big bang approach is unwise. Instead, make a targeted transition to automation.

About the Author

Sharad Varshney is the co-founder and chief executive officer at OvalEdge, creators of a data catalog and data governance tool. He founded OvalEdge to blend his unique experience in big data technology and process management into creating a much-needed data management product. He has a nuclear engineering degree from IIT, the premier institute of technology in India. You can reach the author via email or LinkedIn.

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