Intersystems Adds Data Manufacturing Capability to Multimodal Platform
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The InterSystems IRIS data platform enables IT organizations to manage multiple types of data through a single platform, said Scott Gnau, vice president of data platforms at InterSystems. The alliance with AtScale, a provider of a semiconductor software layer for the integration of analytics and business intelligence (BI) applications, allows InterSystems to also integrate adaptive analytics capability into the platform. basic data form, Gnau added.
In addition, InterSystems added SQL Extensions for Analysis Applications to its main database to enable self-service queries and analysis on an online virtual analysis platform (OLAP) cube. This layer also increases the performance of these applications on the IRIS data platform by up to 30%, forcing IT teams to deploy a separate platform, Gnau said.
Integration to reduce clutter
InterSystems advocates for an integrated data platform that, in addition to supporting multiple types of data, also includes capabilities for data management, integration, analytics, application development, and data management. application programming interface (API) in a single platform. Indeed, InterSystems provides all the capabilities of a data structure within a platform that is easier to maintain than a set of disparate products that organizations would need to acquire, deploy and maintain on their own, said Gnau. .
âThe fact that IT departments are asked to configure multiple data stores and then set up data pipelines is one of the reasons why so many data lakes turn into data swamps,â Gnau added. âEverything for us is a pipeline. “
IT teams also have the option of deploying the IRIS platform in the cloud or in an on-premises IT environment as they see fit, or consuming it as a managed service, Gnau noted.
In addition to offering its core platform, InterSystems also provides a preconfigured edition for healthcare organizations, which typically need to aggregate and manage multiple types of data. More recently, the company revealed that the UK’s Department of Defense (MoD) is using this platform to make healthcare data easier for doctors to access.
It is not clear to what extent organizations are opting for multimodal databases versus deploying separate data stores for each type of data they need to access. It’s easier for IT teams to centralize data management using a multimodal database. However, developers who tend to be more concerned with performance today have a big influence on database selection. Therefore, it’s not uncommon for IT teams to find themselves managing multiple backend data storage and management platforms.
Of course, as the total cost of managing data continues to rise, the pressure to unify these core platforms is also increasing so as to simplify the management of a database administrator (DBA). Over time, developers also frequently lose interest in data management tasks and platforms as they move on to their next application development project. There are also now Data Directors (CDOs) in many organizations who have more control over how data is managed.
One way or another, more order is put in place in data management as enterprise IT organizations launch initiatives for the digital transformation of the business. In many cases, these projects are endowed with AI capabilities that require training machine learning algorithms on massive amounts of diverse data.
Data may be the new oil for these projects, but it turns out that many organizations are now discovering to their dismay that they are not particularly good at refining this data.
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