This past Thursday, August 1, saw the release of the Gartner Magic Quadrant for Data Integration Tools for 2019. Information Builders is a Visionary, moving slightly up and to the right again this year, based on the strengths of the Omni-Gen™ platform and the Omni tools that come with it.
It’s a detailed take on a complex market. Consider the differences between some of the technologies listed:
- Data virtualization, where a tool can submit a query at one point and receive responses from any number of data sources that house the information needed
- Data replication, where data is moved as is from a source to a target, such as a data lake, without changing its content, structure, or form
- Bulk/batch (Gartner seems careful not to call this ETL, so I presume they intend this to cover ELT as well), where data of all types is taken from the source in bulk, transformed in some way, and then made available to consuming users and apps
- Data services orchestration, where data is made available to other applications through service calls and the data integration process takes advantage of application services in its environment
These are just four of seven: The others are data synchronization, message-oriented data movement, and stream data integration. In addition, the Gartner analysts tip their hats to data preparation requirements. What’s complicated here is that all of these are quite different and have different use cases as individual technologies – but they can also be combined within a single scenario.
For example, in our Omni-Gen projects we often find it most efficient to perform data replication to capture a complete copy of an application’s data, which is then processed in bulk/batch to determine what has changed as part of an ELT process, after which the changed data is standardized, cleansed, and mapped to master data using data services orchestration and other techniques (built into the platform, of course), and then finally made available through data services, data virtualization, and/or bulk/batch movement of data to a repository for consumption.
As a result, using the Gartner Magic Quadrant for Data Integration Tools requires a bit more thought than just looking at the Leaders' quadrant to create your short list. That’s rarely a good idea, in fact, and Gartner is the first to say so; even with simpler topics, there are things like service, vertical expertise, industry partnership, niche requirements, and other issues to consider before narrowing the field. However, with the Data Integration Magic Quadrant, I think it’s even more important than usual.
As you read the Magic Quadrant, it’s worth considering a few issues:
- What data consumption scenarios are you supporting? If you’re standardizing data for downstream analytical, data science, or operational needs, you’ll probably want to integrate many different data sources and create a unified view of the data subjects within them. This was traditionally done with ETL tools, though I suggest that more modern approaches to data integration, such as capturing and manipulating all of your data at the subject level as we do in Omni-Gen, will help
- What level of governance will you need? Some technologies, such as data replication, have no place for governance. Others, such as data services, could have governance built in if the environment is designed with that in mind. Some technologies, such as our own Omni-Gen platform, place special emphasis on data governance through the use of data quality, master data management (MDM), and related capabilities
- Who is expected to use the technology directly, and in what scenarios? Data prep tools are for individual business users, but aren’t generally used for creating standard data sets that will be used for a broad audience; data replication tools are often used by administrators; MDM platforms are used by a combination of IT-oriented data managers and business-oriented data stewards. When considering the enterprise, you might expect to see these combined: data replication feeding an MDM platform that handles the big picture, with business users using data prep tools to combine this governed data with external, personal, or other ungoverned data
We’re pleased with our position on the Gartner Magic Quadrant for Data Integration Tools and the way these issues are addressed in our entry. Gartner cites our relevance for diverse applicable cases, specifically noting the alignment with data integration support for data management and analytics requirements, as well as the synergy with data management infrastructure that combines with data quality and MDM. (I believe that the tight integration of data quality, master data management, and transactional data all in a single platform makes a big difference in that regard.) In addition, they mentioned the strengths of our customer relationships – we pride ourselves on partnering with our customers, both before buying and after implementation.
Download the Gartner Magic Quadrant for Data Integration Tools with our compliments.
Gartner, Magic Quadrant for Data Integration Tools, 1 August 2019, Ehtisham Zaidi, Eric Thoo, Nick Heudecker
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