OpenText acquires Hightail (formerly, YouSendIt)

ECM vendor OpenText has acquired Hightail. Hightail, formerly called YouSendIt, offers a file-sharing and sync service.

OpenText now has at least four file-sharing and sync services. In addition to Hightail, they have OpenText Core and OpenText Tempo Box. Documentum acquisition also gave them EMC Leap, which has some overlaps with cloud-based file-sharing and collaboration services.

OpenText has a history of acquiring multiple overlapping products and services. So nothing new or surprising there.

Here’s a quick summary of how these products differ though.

Overlapping products but there are some differences

Hightail is offered as a public cloud-based SaaS service. It focusses on two major aspects:

  1. Sending large files, using an email like interface. In fact, it’s rather simple file sharing interface mimics how users send an email.
  2. Targets creative teams.

Both of these make it suitable for multi-media use cases. In fact, it has a separate product called “Creative Collaboration” that provides collaborative features for creative teams.

OpenText Core is also a public cloud-based SaaS service. It integrates with both Documentum and Content Suite (OpenText’s two ECM offerings), meaning you can access it from within Content Server’s or Documentum’s user interfaces. So if you want to share content stored in your say on-premise Content Server with external users, you can do it via OpenText Core.

You can of course use Core as a stand-alone file sharing service without using Content Server.

Finally, OpenText Tempo Box provides similar file sharing capabilities but is based on OpenText Content Suite platform. You can deploy it on-premise or in a cloud-hosted environment. You can use it with an existing Content Server repository too. You can also take advantage of all the sophisticated ECM features provided by the underlying Content Server. The key point to remember is that it is based-on and needs OpenText’s Content Suite. As a result, it is probably an overkill for relatively simpler file-sharing use cases.

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Figure: User interfaces of OpenText Core and Hightail. Source: OpenText and Hightail

If you are evaluating OpenText’s file-sharing, sync and collaboration offerings, you will find many overlapping products and services. However, not all file sharing services are same and there are differences in the use cases they target, functionality they offer as well as other aspects such as their deployment model and so on. Also remember that you have several other options as well for file-sharing and sync services. If you’d like help navigating the ECM, Document Management or Enterprise File-sharing marketplaces, please feel free to email me.

 

ECM and Machine Learning – What are Box, IBM, OpenText and other Vendors doing?

There are many use cases in Enterprise Content Management (ECM) for which Machine Learning can be deployed. In fact, i’d argue that you can apply machine learning in all the stages of content life cycle. You can apply:

  • Supervised learning e.g, to automatically classify images, archive documents, delete files no longer required (and not likely required in future), classify records and many more
  • Unsupervised learning e.g, to tag audio and videos, improve your business processes (e.g., approve a credit limit based on a machine learning algorithm instead of fixed rules), bundle related documents using clustering and so on

What are ECM vendors currently offering?

Not much i’d say. These are still early days.

To be fair, Artificial Intelligence and Machine Learning have been used for a long time in enterprise applications but their usage has really been for really complicated scenarios such as enterprise search (e.g., for for proximity, sounds etc) or sentiment analysis of social media content. But it has never been easy to use machine learning for relatively simpler use cases. Additionally, no vendor provided any SDKs or APIs using which you could use machine learning on your own for your specific use cases.

But things are gradually changing and vendors are upping their game.

In particular, the “infrastructure” ECM vendors – IBM, Oracle, OpenText and Microsoft — all have AI and ML offerings that integrate with their ECM systems to varying degrees.

OpenText Magellan is OpenText’s AI + ML engine based on open source technologies such as Apache Spark (for data processing), Spark ML (for machine learning), Jupyter and Hadoop. Magellan is integrated with other OpenText products (including Content, Experience Suites and others) and offers some pre-integrated solutions. Specifically for ECM, you apply machine learning algorithms to find related documents, classify them, do content analysis and analyse patterns. You can of course create your own machine learning programs using Python, R or Scala.

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Figure: Predictive analytics using OpenText Magellan. Source: OpenText

IBM’s Watson and Microsoft Azure Machine Learning get integrated with several other enterprise applications and also have connectors for their own repositories (FileNet P8 and Office365).

Amongst the specialised ECM vendors, Box is going to make its offerings generally available this year.

Box introduced Box Skills in October 2017. It’s still in beta but appears promising. You can apply machine learning to images, audios and videos stored in Box to extract additional metadata, create transcripts (for audio and video files), use facial recognition to identify people and so on. In addition, you will also be able to integrate with external providers (e.g., IBM’s Watson) to create your own machine learning use cases with content stored in Box.

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Figure: Automatic classification (tags) using image recognition in Box. Source: Box.com

Finally, there are some service providers such as Zaizi who provide machine learning solutions for specific products (Zaizi is an Alfresco partner).

Don’t wait for your vendors to start offering AI and ML

The rate at which content repositories are exploding, you will need to resort to automatic ways of classifying content and automating other aspects of content life cycle. It will soon be impossible to do all of that manually and Machine Learning provides a good alternative for those type of functionalities. If the ECM vendor provides AI/ML capabilities, that’s excellent because you not only need access to machine learning libraries but also need to integrate them with the underlying repository, security model and processes. An AI/ML engine that is pre-integrated will be hugely useful. But if your vendor doesn’t provide these capabilities yet, you still have alternatives. I’ve said this before and it applies to ECM as well:

There is no need to wait for your vendors to start offering additional AI/ML capabilities. Almost all programming languages provide APIs and libraries for all kinds of machine learning algorithms for clustering, classifications, predictions, regression, sentiment analysis and so on. The key point is that AI and ML have now evolved to a point where entry barriers are really low. You can start experimenting with simpler use cases and then graduate to more sophisticated use cases, once you are comfortable with basic ones.

If you would like more  information or advice, we’d be happy to help. Please feel free to fill the form below or email.

 

ECM Maturity Model

Or ECM3 was released yesterday.

We’ve worked on it for some time now and it has been an excellent experience for me, personally and professionally. There’s a companion site that hosts the model and where we’ll discuss it. As Tony Byrne says, it is a V1 and the idea is to let the community participate and make it more robust.  Feel free to comment and give us your feedback. If you’d like to participate and be a part of it, here’s the link.