Decoding Key Trends from Gartner's Hype Cycle for Data Management–2021 Report

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Decoding Key Trends from Gartner’s Hype Cycle for Data Management–2021 Report

February 21, 2022

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Data management has become critical for businesses to derive insights that can help them with informed decision-making. But the speed of innovation in the field is so high that the technology may have evolved further by the time a business understands a trend and implements it. Gartner provides insights into the Hype Cycle in the field to help data and analytics plan investments in data management technologies based on emerging and maturing trends. The two factors determining if a technology mentioned in the Hype Cycle depends on its feature richness and mainstream adoption.

Mature Data Management Solutions

Some of the technologies in the Hype Cycle are mature and provide minimum technical risks while optimizing business value. These include:

● Database management systems (DBMS): Some of the mature DBMS technologies include wide-column DBMS, time-series DBMS, and multimodel DBMS, with associated functions including SQL interfaces to object stores, in-memory data grids, and in-DBMS analytics. These are expected to become fully mature in the next two years. Some that will take up to five or more years are private cloud dbPaaS, graph DBMS, ledger DBMS, and distributed transactional databases.

● Data Warehouse Architectures: Logical data warehouse (LDW) is a definition given by Gartner to data warehouse architecture solutions that accommodates many architectural variations while using a logical layer to unify several data warehouse environments. While these are mature, data warehouse and analytics architectures components such as data lake, data hub strategy, and lakehouse are at various stages of maturing.

● Data Integration: Data integration and related areas are also seeing a lot of innovation, with areas such as data virtualization, tools for data preparation and data integration, iPaaS for data integration, and event stream processing, maturing fast. Metadata innovation, a new area that encompasses augmented data cataloging and metadata management solutions, is going to achieve maturity in five or more years. This is being driven by a need for metadata-driven data fabric

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Growing Segments

Some of the areas in data management that are fast becoming popular include:

● D&A Governance Platforms: An integrated platform, it leverages automated data curation services, providing support for decision management to all relevant participants. An integrated platform has become essential to provide consistent data and analytics governance, increasing the chances for the success of digital business initiatives.

● Edge Data Management: Valuable data with a shorter useful life span are often generated and used outside the data center and cloud environments. To capture it closer to the location and the time of origin, it requires a solution like edge data management. Businesses can leverage it to optimize resource utilization, improve decision-making with real-time data, and deliver value.

Intercloud Data Management: Unifying data stored in different clouds is essential to break siloes. Intercloud distributed data will enable providing a cohesive strategy for managing data stored in multiple cloud providers. Built on the foundation of multicloud capabilities, it will enable accessing and using data across clouds operationally.

Active Metadata Management: Determining data’s relevance and validity is essential for drawing meaningful insights today. Active metadata management enables this by using machine learning, data profiling, and graph analytics to enable identification of flawed data capture, cross-platform orchestration of data tools, inappropriate usage, cross-industry validation and verification processes, and logical fallacies. It can also help with analytic and data biases while facilitating auditing, transparency, and DataOps.

● Lakehouse: Getting value from data lake initiatives continues to remain a challenge for data and analytics leaders. Data lake used in conjunction with a data warehouse makes the data and analytics landscape more complex. A lakehouse unifies the two architectures for greater efficiency by combining the data lake’s semantic flexibility with the data warehouse’s production optimization and delivery capabilities. This supports the entire data lifecycle from ingesting the raw data to its refinement and final delivery of optimized data for consumption.

● DataOps: DataOps smoothens the consumption and use of data across the organization by improving communication between data managers and consumers. While enabling data flow integration across the organization, it facilitates the automation of data pipeline and lowers the cost of operations. In addition, better monitoring and observability increase data use and reuse and ensures transparency and reliability.

● Augmented Data Quality: Digital business initiatives require high-quality data to enable digital transformation at scale. With augmented data quality, businesses can improve insight discovery and accuracy, enabling informed decision-making. It can help to automate process workflows to reduce manual intervention and increase efficiency and productivity. ● Cloud Data Ecosystems: Today, cloud services are available as a packaged platform experience to overcome the requirement for proper integration to ensure the proper functioning of all the components as a cohesive unit. Cloud data ecosystems support the entire range of data workloads through a cohesive data management environment encompassing exploratory data science to production data warehousing. Some of the other benefits include unified access management, a common management framework for governance and metadata, and integrated augmented data management capabilities.

● Cloud Data Ecosystems: Today, cloud services are available as a packaged platform experience to overcome the requirement for proper integration to ensure the proper functioning of all the components as a cohesive unit. Cloud data ecosystems support the entire range of data workloads through a cohesive data management environment encompassing exploratory data science to production data warehousing. Some of the other benefits include unified access management, a common management framework for governance and metadata, and integrated augmented data management capabilities.

You might be interested in: Why Data Fabric is the key to next-gen Data Management?

Gartner identifies many more such trends across data management that can help leaders decide on the best mix of solutions for their business needs.

Indium Software, a data engineering and analytics services company, helps businesses make data-driven decisions to drive business outcomes. Indium leverages its expertise and experience in commercial and open-source tools to leverage the latest data management trends to create best-fit solutions. We can help to decode the key trends from the Gartner Hype Cycle and build and deploy the right solutions for your organization. Our expertise spans data fabric, data virtualization, data analytics, cloud, edge computing, and more.


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