Gartner’s Data Management Hype Cycle pinpoints 4 transformative technologies

Global data network. Image: Pixabay
Gartner believes the adoption of data management technology remains central to the move toward digital business. (Pixabay)

To move into the digital age, businesses must rely on data management, but technology has not only a life cycle, but a Hype Cycle, too, according to research firm Gartner. What’s more, less than a handful are expected to be transformative.

Gartner published its first Hype Cycle for Data Management last week to help chief information officers, chief data officers, and other data and analytics workers understand the maturity of the data management technologies, such as file analysis and data catalogs, they’re evaluating and using.

“Data management continues to be central to the move toward digital business,” Donald Feinberg, vice president and distinguished analyst at Gartner, said in a news release. “As requirements change within the architecture of the organization and place greater demands on underlying technology, the maturity and capability of many of the technologies highlighted in the Hype Cycle will advance rapidly. Recent years have seen many new additions to the Hype Cycle, including in-memory, cloud, data virtualization, advanced analytics, data as a service, machine learning, graph, non-relational and Hadoop."

Two technologies in the cycle to take note of are Hadoop and SQL. Both show the effect of cloud on data management. For instance, Hadoop distributions become obsolete before reaching the cycle’s Plateau of Productivity “because the complexity and questionable usefulness of the entire Hadoop stack is causing many organizations to reconsider its role in their information infrastructure,” the announcement states. Organizations are looking instead at cloud-based options with on-demand pricing and fit-for-purpose data processing options.

SQL interfaces to cloud object stores, on the other hand, sit at the Innovation Trigger stage and are expected to reach the Plateau of Productivity within two to five years, Feinberg said. “They enable organizations to interact with data stored in the cloud, using a familiar SQL syntax. Object stores are well suited to storing large volumes of multistructured data, typical of data lakes,” he added.

Thirty-five other technologies, including database encryption and data-as-a-service, are part of the cycle, but only four are transformative, the report finds. Event stream processing (ESP) and operational in-memory data management systems (IMDBMS) are expected to reach the plateau within two to five years, while blockchain and distributed ledgers will likely take five to 10.

Let’s take a closer look at each:

  • ESP is a main enabler of digital business, algorithmic business—or, X—and intelligent business operations, and it’s maturing quickly, the announcement states. “Stream analytics provided by ESP software improves the quality of decision-making by presenting information that could otherwise be overlooked.”
  • IMDBMS can speed data transactions by 100 to 1,000 times, but they have drawbacks. One, the infrastructure to support it is costly, and two, they need “persistence models that meet high levels of availability to meet transaction [service-level agreements],” the report states.
  • Blockchain, originally developed as the accounting method for Bitcoin, involves public distributed ledgers. They have high visibility, but organizations are cautious about the concept because of scalability, risk and governance concerns. In the short term, businesses will likely use these ledgers for operational efficiency gains, but Gartner predicts “a complete reformation of whole industries and commercial activity as the programmable economy develops and ledgers contribute to the monetization of new ecosystems.”

Distributed ledgers—those that are shared and synchronized across sites, countries or organizations—have unclear value propositions, Gartner found, but they’re gaining traction because they could overcome the problems associated with blockchain.