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The InterSystems IRIS data platform enables IT organizations to manage multiple types of data via a single platform, said Scott Gnau, vice president of data platforms at InterSystems. The alliance with AtScale, a provider of a layer of sematic software for integrating analytics and business intelligence (BI) applications, allows InterSystems to also embed an adaptive analytics capability into the core data platform, Gnau added.
In addition, InterSystems has added SQL extensions for analytics applications to its core database to enable queries and self-service analytics against a virtual online analytics platform (OLAP) cube. That layer also boosts the performance of these applications on the IRIS data platform by as much as 30% with requiring IT teams to deploy a separate platform, Gnau said.
Integration to reduce clutter
InterSystems is making a case for an integrated data platform that in addition to supporting multiple types of data also includes data, integration, analytics, application development, and application programming interface (API) management capabilities in a single platform. In effect, InterSystems provides all the capabilities of a data fabric within a platform that is simpler to maintain than a set of disparate products that organizations would have to acquire, deploy, and maintain on their own, said Gnau.
“The fact that IT organizations are being asked to set up multiple data stores and then set up data pipelines is one reason so many data lakes are becoming data swamps,” added Gnau. “Everything to us is a pipeline.”
IT teams also have the option to deploy the IRIS platform in the cloud or an on-premises IT environment as they see fit, or consume it as a managed service, noted Gnau.
In addition to offering its core platform, InterSystems also makes available an edition that is pre-configured for health care organizations, which typically need to aggregate and manage multiple types of data. Most recently, the company revealed that the Ministry of Defense (MoD) in the UK is employing that platform to make it easier for medics to access health care data.
It’s not clear to what degree organizations are opting for multi-modal databases versus deploying separate data stores for each type of data that needs to be accessed. It’s simpler for IT teams to centralize the management of data using a multi-modal database. However, developers that tend to be more concerned about performance today exercise a lot of influence over the selection of databases. As a result, it’s not uncommon for IT teams to find themselves managing multiple backend data management and storage platforms.
Of course, as the total cost of managing data continues to rise, so too does the pressure to unify those backend platforms in a way that makes it simpler for a database administrator (DBA) to manage. Developers over time also frequently lose interest in data management tasks and platforms as they move on to their next application development project. There are also now chief data officers (CDOs) in many organizations that are exercising more control over how data is managed.
One way or another, more order is being brought to data management as enterprise IT organizations launch digital business transformation initiatives. In many cases, those projects are infused with AI capabilities that require machine learning algorithms to be trained on massive amounts of diverse data.
Data may be the new oil for these projects, but as it turns out, many organizations are now discovering to their chagrin they are not especially good at refining that data.
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