Enterprise Data Management, Governance & Activation Strategies Across Heterogeneous Environments
The global enterprise data management market size reached $78.32 billion in 2020 and is expected to register a CAGR of 9.6% through the middle of the decade.
Issues with data quality, data sprawl, and address validation threaten to hamper the growth of the global enterprise data management market as organizations continue to support on-prem infrastructures while migrating critical workloads to cloud environments.
Organizations are seeing a huge opportunity to use data to establish a competitive advantage, but 70% of organizations stillnaren't able to realize tangible and measurable value from data.
The global enterprise data management market size reached $78.32 billion in 2020 and is expected to register a CAGR of 9.6% through the middle of the decade, according to the latest analysis by Emergen Research. Revenue growth of the global enterprise data management market will be driven by the increasing need to manage business data more effectively.
However, issues with data quality, data sprawl, and address validation threaten to hamper the growth of the global enterprise data management market as organizations continue to support on-prem infrastructures while migrating critical workloads to cloud environments.
“Organizations are seeing a huge opportunity to use data to establish a competitive advantage. The issue is that 70% of organizations aren't able to realize tangible and measurable value from data. So while organizations look for ways to differentiate themselves and connect better with their customers, they struggle to understand their data inventory and management systems across today’s complex hybrid environments,” says Bruno Aziza, Head of Data & Analytics, Google Cloud, in a podcast interview with BizTechReports.
According to Gartner, the lack of enterprise-wide standards for governance is a significant barrier to achieving data performance objectives. It prevents the transparency needed across infrastructure assets scattered across cloud and on-prem environments.
Bruno Aziza, Google Cloud
“The first step for being able to govern data is being able to discover it. So you need an answer to the question: What is your data catalog strategy in a world characterized by multiple cloud resources and where data is kind of everywhere? Once data is cataloged, a systematic strategy must be developed for establishing data management and security policies that determine who needs what access to perform their jobs,” says Aziza.
Converging Models of Data Management
Organizations have a number of options on how they can accomplish this objective. Data resources can be centralized for management by IT in a common repository for sophisticated data analytics. Conversely, virtualization strategies can be applied to implement a decentralized model that keeps data in its native environment.
“What we're seeing is that these two models are kind of converging. Business technology leaders are centralizing data, but they're decentralizing analytics. Recent developments in automation, data fabric, and data mesh strategies have evolved to the point where organizations can create enterprise-wide policies that enable intelligent enforcement governance that allow data specialist teams to manage data centrally while allowing business teams to help themselves to the data they need,” explains Aziza.
Consolidating data, he adds, enables enterprises to run machine learning and artificial intelligence applications at a much larger scale to help organizations move faster and harvest better insights across the organization.
The combination of centralizing data and decentralizing analytics seems to be a formula that works for many organizations. The trend, however, is creating the need for organizations to develop new skill sets and competencies that integrate current and emerging technologies. This is particularly true when it comes to data scientists and engineers. Companies can not seem to hire these talent categories fast enough to manage data at the speed and scale necessary to compete effectively in today’s business environment.
“It's not just a technical change that enterprises are making; they are also making a cultural change. The idea around modernization is not to simply take the old processes and then put them in the cloud. It is about applying the objectives of business transformation to modernize technology investments,” says Aziza.
There are great opportunities to leverage data management technologies that enable organizations to share insights at scale in a secure manner.
“The key is to learn how to leverage existing best practices of the organization and build them up to meet future state objectives. This requires a clear vision of the destination and establish meaningful metrics that will track progress toward the objective,” concludes Aziza.
For more information on BizTechReport podcast interviews, please contact Melissa Fisher at MFisher@BizTechReports.com.