Enterprise content management (ECM) platforms that have historically been employed to manage files are, thanks to the rise of AI, about to evolve into central repositories for keeping track of relationships between a much wider range of types of data.

Fresh off raising an additional $80 million in funding, M-Files is in the process of extending the metadata it created to track data stored in its ECM repository using machine learning algorithms and other forms of AI. That approach will enable the company to advance the development of a next-generation intelligent information management platform that is delivered via a software-as-a-service (SaaS) platform, said M-Files CEO Antti Nivala in an interview with VentureBeat.

The funding led by Bregal Milestone, a venture capital firm based in London, will be used to hire additional software engineers as well as expand the presence of M-Files in North America. Originally from Finland, Nivala recently moved to Austin, Texas to lead that effort. Current investors that participated in this latest round of funding include Partech, Tesi, and Draper Esprit.

M-Files is currently racing to expand its AI capabilities as direct rivals such as OpenText, Hyland, Alfresco, IBM, Oracle, and others make similar AI investments. The goal is to move ECM platforms beyond simply managing files by applying AI to metadata that describes the relationship between multiple types of content.

That capability, however, will not be limited to the data stored in the ECM platform. Instead, M-Files will be able to apply AI to metadata independently of the platform on which data might be stored, said Nivala.

As such, Nivala said M-Files now views document repositories such as Box and Dropbox as adjacent sources of metadata against which to apply algorithms. That AI capability will then make it simpler for organizations to manage data within the context of digital business processes that span multiple platforms, Nivala noted.

“What really matters to a business is the management layer,” he said.

In effect, providers of ECM platforms are now in an AI arms race to deliver information management platforms that go well beyond a single type of data. As IT continues to evolve, the types of data that organizations are employing have expanded well beyond simple files. Providers of ECM platforms need to be able to manage all the structured and unstructured data an organization interacts with to stay relevant, regardless of whether that data resides in a file, an object-based storage system in the cloud, or a relational database.

Unfortunately for many enterprises, the way many of them have historically managed data has become a major obstacle on the road to digital business transformation. Not only does much of that reside in isolated silos, it’s often conflicting. IT organizations are now spending a massive amount of time and energy trying to normalize data within a data lake to address that consistency issue. M-Files, however, sees an opportunity to apply AI and metadata in combination with each other to accelerate that process using tools that, for example, automate data classification, Nivala said.

Less clear at the moment is to what degree data and storage management processes might ultimately converge. Data has historically been managed independently of the platforms, such as storage arrays that are employed to house data. However, as machine learning and deep learning continue to evolve, the day when data and storage management might finally converge may not be as far off as it once seemed.

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