By Sharad Varshney, the CEO of OvalEdge
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 ground-breaking. Here are three trends that will be worth noting next year.
Democratic data access: 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 may data and analytics tools accessible.
Low code/no code cloud applications democratize many aspects of IT infrastructure, and data analysis is a crucial component. The key to achieving the democratization of data is self-service.
Actionable steps include consolidating your data with a governance suite that provides governed access to anyone in your organization. From here, you can incorporate various other data analytics tools that don’t require input from IT or Data teams.
Self-service obliterates the data access hierarchy, making it easy for users to find and collaborate on data products. Ultimately, when users have greater access to data, they become literate, leading to increased data-driven innovation.
Data mesh: One of the most significant data technologies for 2023 will likely be data mesh architecture. Organizations are moving away from centralized data lakes that require dedicated data teams.
As well as 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-serve data platforms, and federated computational governance. These principles constitute architecture that organizations can apply business-wide to avoid back-ups and create shared ownership.
There are two steps any organization can take to begin the transition to 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 multiple 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.
Automated analytics: Automation is becoming an increasingly important factor in business processes, including data analytics. Automated analytics are highly mailable and can be applied to various data processes such as discovery and lineage building.
Ultimately, automation means little to no human intervention is required to run these processes, and in 2023, the technology will mature to the point that more companies will trust the process. Automated analytics has many benefits, including reduced costs, faster processing, and more significant opportunities to redirect data analytics professionals to revenue-generating activities.
Before you introduce automation into your analytics processes, you will need to audit your existing systems to derive where automation will provide the most business value. As with any data technology, the big bang approach is unwise. Instead, make the transition to automation targeted.
Sharad Varshney is a technologist turned entrepreneur. He has 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.