As a global leader in technology research, Gartner supports enterprise organizations, non-profits, and government agencies by sharing information and in-depth analysis of emerging technological trends, tools, and products.
With the continued growth of big data over the past decade, Gartner has been especially invested in helping data and analytics (D&A) leaders make the right decisions for managing and generating value from data within their organizations.
With that in mind, our blog this week focuses on two recently released Gartner reports where ChaosSearch has been highlighted as a sample vendor in three growing categories:
The reports include:
- Gartner’s Hype Cycle for Data Management, 2022 report, published on June 30th, 2022, and
- Gartner’s Implementing Multicloud Data Integration report, published on June 13th, 2022.
Gartner Hype Cycle reports focus on a specific area of technological capabilities, reporting on the importance and business impact of technologies in that area. Hype cycle reports also assess the maturity of technologies by evaluating the current adoption level and estimating the number of years until mainstream adoption.
Gartner’s Hype Cycle for Data Management 2022 report includes more than 100 pages of valuable insight into 30+ data management technologies. Below, we highlight five insights from the report that you can use to inform the planning process for D&A initiatives within your organization.
We’ll also share our summary of Gartner’s Implementing Multicloud Data Integration report, which provides D&A leaders with valuable guidance for integrating data across multiple cloud environments.
READ: Managing Cloud Service Logs: Why It’s Difficult and How to Simplify It
5 Data Management Insights from Gartner’s 2022 Hype Cycle Report
1. Gartner Identifies Three Transformational New Technologies
Gartner releases an updated hype cycle report for Data Management each year, and this year’s report features three new technologies with transformative potential that did not appear in the 2021 edition: augmented FinOps, data marketplaces and exchanges, and data observability technologies.
Augmented FinOps technology applies artificial intelligence and machine learning practices, along with traditional DevOps concepts, to financial governance and cost optimization efforts in the cloud. This tech will allow organizations to automate the cost-optimization of cloud resources based on their stated business objectives.
Data observability technology provides mechanisms that allow enterprise IT and data teams to observe and manage the health of data pipelines and infrastructure with help from machine learning algorithms.
Data marketplaces and exchanges allow organizations to share data internally, removing silos that stifle collaboration and innovation, or monetize their data by selling it to third-parties via recurring subscriptions or a one-time transaction.
According to Gartner, all three of these technologies will mature in the next 5-10 years and have the potential to deliver transformational benefits.
2. Data Mesh Technology Will Become Obsolete
Despite being included in Gartner’s Data Management Hype Cycle for the first time, Data Mesh is essentially dead-on-arrival.
Along with two other data management technologies - Information Stewardship Applications, and iPaaS for Data Integration - Data Mesh technology is projected by Gartner to become obsolete before reaching the Plateau of Productivity, the final stage of the hype cycle where real-world benefits and the tools for realizing them are well established.
Why is this the case?
According to Gartner, data mesh solutions leverage business applications to capture data, then make that data available in a distributed model. A distributed approach to data management is often attempted after a centralized data management approach fails - usually due to poor delivery and implementation. But as new technologies and solutions mature to support a centralized approach to data access, distributed approaches like Data Mesh are expected to fall increasingly out of favor in enterprise IT.
Read: Data lake vs. data mesh: Which one is right for you?
3. Hype is Growing around Data Lakehouse Solutions
Identified as an “Innovation Trigger” in the 2021 version of Gartner’s Data Management Hype Cycle report, data lakehouses are the only technology to progress past this stage and approach the Peak of Inflated Expectations in 2022.
Gartner defines a data lakehouse as a “converged infrastructure environment that combines the semantic flexibility of a data lake with the production optimization and delivery of a data warehouse.”
Stated differently, a data lakehouse combines data lake storage philosophy (easy data ingest, schema-on-read, support for multiple data and query types, democratized access, and cost-effective storage) with data warehouse functionalities like data discovery, metadata management, data governance, SQL queries, batch and stream processing, and support for BI tools.
While connecting a data lake and a data warehouse to enable analytics can lead to high complexity and costly data movement, a singular data lakehouse solution combines the best features of both to increase efficiency and reduce the need for data movement and duplication.
Common Use Cases: SecOps & Threathunting | Cloud Operations | Embedded Analytics
4. Data Prep and Integration Tools Poised to Deliver High Benefits
As part of this Hype Cycle report, Gartner has published a special table known as a “Priority Matrix” for data management technologies. The table categorizes 37 technologies covered in the report based on two characteristics: the projected magnitude of benefits they can deliver, and the projected number of years until the technology reaches mainstream adoption.
Gartner’s Priority Matrix helps D&A professionals identify and adopt technologies that are reaching a late stage of maturity and expected to deliver substantial or even transformational benefits.
According to Gartner’s Priority Matrix, D&A professionals should consider investments in Augmented Data Management and in-DBMS Analytics, as well as Data Integration Tools and Data Preparation Tools. These technologies are anticipated to reach maturity in less than two years and will enable process improvements that help organizations increase their revenue or achieve measurable cost savings.
Watch: What is My Enterprise Data Maturity?
5. Event Stream Processing Technologies Show Transformative Potential
When Gartner evaluates the potential benefits of a Data Management technology, the highest benefit rating they award is “Transformational.” According to Gartner, a transformational technology will “enable new ways of doing business across industries that will result in major shifts in industry dynamics.”
So, what’s the next transformational technology coming down the data management pipeline? According to Gartner, it’s Event Stream Processing technology which gives organizations the ability to integrate or analyze data as it arrives, enabling situational awareness and near-real-time responses to new challenges and opportunities.
Event Stream Processing has been used to analyze data from financial markets, telecommunications, and IoT devices, as well as for supply chain and fleet management operations - but Gartner estimates that Event Stream Processing has been adopted by just 20-50% of its target audience.
According to the report,this technology will fully mature within 2-5 years and deliver transformational benefits for adopters. High growth is expected in the IoT and customer experience management sectors.
READ: Equifax Simplifies Cloud Operations Management Globally with Cloud Data Platform
Gartner Research Report Identifies Eight Categories of Data Integration Technology
Gartner reports that 75% of its clients now use more than one cloud service provider, creating a need for data integration tools that can bring together data from increasingly complex and distributed cloud environments. Gartner’s new Implementing Multicloud Data Integration report was designed to provide D&A leaders with guidance on cloud data integration by rigorously categorizing and analyzing the available data integration technologies in the marketplace.
As part of its analysis, Gartner defines four patterns of data integration (Data Ingestion, Data Consistency, Multistep Process Integration, and Composite Services) and three data integration styles (data-centric, event-centric, and application-centric), then maps them to specific multicloud requirements.
Finally, Gartner lists eight distinct categories of data integration tools, including a description and sample vendors for each one:
- Analytics Query Accelerators (AQA)
- Enterprise Service Bus [ESB]
- Data Integration
- Data Virtualization
- Full Life Cycle API Management
- Integration Platform as a Service (iPaaS)
- Intelligent Business Process Management Suites (iBPMS)
- Master Data Management (MDM)
This report can help D&A leaders determine which data integration technologies to implement based on their specific multicloud needs. It’s available on the Gartner website for clients only.
To learn more about Hype Cycles and Gartner’s industry-leading research methodologies, visit the Gartner website.
Looking to see ChaosSearch in action?
Additional Resources
Read the Case Study: Smarter Log Management in Fintech
Listen to the Podcast: The Data Management Triangle: Lake, Warehouse, Virtualization
Check out the Report: 2022 Data Delivery and Consumption Patterns Survey Report
