The fifth scenario or business use case for Customer Data Platform (CDP) technology is “Omnichannel & Offline Aggregation.” (The other four scenarios are described here: Scenario 1, Scenario 2, Scenario 3, Scenario 4.) Beyond Multi-Channel The ability to deliver content and services across multiple channels and devices is now taken…
The fourth scenario or business use case for Customer Data Platform (CDP) technology is Ecommerce Recommendations & Optimization. (The other three scenarios are described here: Scenario 1, Scenario 2, Scenario 3.) Similar But Different… In some respects, this scenario is similar to CDP Scenario 3: “Online Personalization & Experience Optimization.…
The third scenario or business use case for Customer Data Platform (CDP) technology is Online Personalization & Experience Optimization. (The first two scenarios are described here: Scenario 1, Scenario 2.) Personalization for Customer Experience (CX) If your enterprise seeks to optimize customer experience (CX), then segmentation and personalization will likely…
This is the second in a series of posts outlining key business use cases for Customer Data Platform (CDP) technology. As subscribers to RSG’s CDP vendor evaluations know, most vendors offer reporting and dashboard services as a backbone for analyzing unified customer profile information. Some then go a step further…
My topic was “Social Media for Competitive Advantage – A Study of Select Indian Organizations”. As part of this research, an attempt was made to study how companies use social media and a framework has been evolved that provides an approach for organizations interested in using social media for their own inter-and intra-organizational processes. This approach maps different sources of competitive advantage and provides an incremental and iterative roadmap for organizations to improve their readiness and usage.
I’ll make my thesis available for download as soon as I’m allowed to.
Thanks to several of you for your support and encouragement.
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:
Sending large files, using an email like interface. In fact, it’s rather simple file sharing interface mimics how users send an email.
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.
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.
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.
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.
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.
Personalization has always been a key aspect in almost all kinds of digital experiences. Some examples of commonly found personalisation use cases are: allowing users to customise their dashboards or user interfaces, showing content based on explicit user-defined criteria, showing content based on implicit criteria or even that based on user behaviour. All these required complex personalization systems, with processing and rules engines for creating and managing personalization rules. As a result, it has always been a non-trivial exercise to implement personalization in a resource effective way.
Artificial Intelligence (AI) and Machine Learning (ML) techniques have evolved and it is now much easier than ever to use these to implement personalization now. At a very simplistic level, personalization is about “predicting” what a user will like to see and then offering that to the user. You can make this prediction based on a complex hierarchy of rules or use historical data to make this prediction. The latter is exactly what machine learning based techniques can do for you.
Delivering Right Content to the Right People
Consider this common scenario: You want to show content that is relevant to the user. For example, let’s say you run an events site and want to show events that are relevant to the user. To do this, you could create multiple rules such as rules that match a user’s and event’s locations, or show events based on user interests and so on. This works great, with may be 5 rules. But consider a scenario where your users have 100s of profile and behavioural attributes and your events also have similar large number of attributes. So as you come up with more criteria, this rules based business becomes really messy and difficult to manage.
But with machine learning based techniques, you now have alternatives. Plus you no longer have to procure sophisticated personalisation systems. Instead, you can start writing very simple programs that can help you predict what kind of events a user would like to view depending on the events that other users with similar profiles viewed. You could use the same logic to display targeted news, movie recommendations or books. Some of these machine learning techniques are really simple and you can get started very easily.
Here’s another example for the same events web site. As an event organizer, you create a new event but are not sure what kind of pricing would work best. Again, if you think of this problem as a prediction problem, as in “predict price of new event given pricing of past events”, you could again use a simple prediction algorithm to recommend pricing based on pricing data for past events. Instead of events, you can use the same logic to price your new offerings or whatever. In addition, you can use this new data point as another input for your next prediction.
Start Small and Experiment
In addition to personalization, Digital Experience Management use cases can have several aspects for which you can start using machine learning. And 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.
Blockchain is best known for its use by the alternative and controversial currency market and most notably Bitcoin. But Blockchain is not Bitcoin nor is Blockchain an alternative/crypto currency. Rather Blockchain is an underlying distributed ledger technology (DLT) that can be used in many different contexts other than Bitcoin.
In fact, we are already seeing the experimental use of Blockchain in many non-currency related situations – for example, the management of healthcare records and the processing of shipping manifests. Specifically, in the context of Information Management, Blockchain can address a number of use cases. However, as with anything new in tech industry, Blockchain is also being used for information management use cases for which it is not a suitable platform.
This recently released report by analyst firm Deep Analysis (written by Alan Pelz-Sharpe and me) looks at the use of blockchain for information management. It explores the structure of this market and its future impact and growth potential.
The report’s ToC is as follows:
About this report
Introduction and a brief history
How does a blockchain actually work?
The key attributes of blockchain
Blockchains in action
Public versus Private Blockchains
The Market Structure
You can read more details, including exec summary as well as purchase the report here.