February 22, 2013

Should you offer “complete” analytic applications?

WibiData is essentially on the trajectory:

The same, it turns out, is true of Causata.* Talking with them both the same day led me to write this post.

*Differences between the companies include:

I know WibiData (client since they had <10 employees) much better than Causata (one conversation ever).

The problem for those vendors and other analytic application aspirants is that it is very hard to offer a complete analytic application. In particular:

*There are various semantic issues as to whether the correct word is “personalization”, “customization”, etc. In this post, I’m ignoring them. 🙂

My proposed answer starts:

Maybe the “complete” app is, from the customer’s standpoint, at least a “good start”. Maybe you really can deliver an awesome application for a narrow area of functionality — and the customer adopts it with confidence, knowing that she can integrate the core technology into a broader suite if she wants to.

As I’m telling the story, the real differentiation is apt to be in the subsystem, not in the finished app. So for a sanity check, let’s consider when would that might be the case. Examples that come to mind include:

I don’t think any of those cases are sufficient to undermine my conclusions, namely:


5 Responses to “Should you offer “complete” analytic applications?”

  1. Brian Stone on March 4th, 2013 5:33 pm

    Curt, makes complete sense, we agree.

    At Causata, our first complete predictive analytic application was website personalization. We stop short of building or providing a CMS; we integrate our profile, segment and decision APIs with CQ5 and other custom CMS’.

    Our second complete predictive analytic application was email content targeting with API integrations to ExactTarget, CheetahMail, Conversen, and more.

    We are now working closely with our customers on predictive analytic applications for targeted advertising and CRM. Each application we provide tends to be industry-specific; that makes it easier to deploy and generate faster ROI.

    AS you correctly point out, the underpinning of our enterprise-class DMP is an analytic sub-system built on top of our event-driven data store (in HBase), our Identity Graph and Machine Learning algorithms. This is the core of our technical secret sauce.

    We don’t think we need to build a CMS, a Content and Ad Server, or a CRM system in order to deliver great value and a complete solution to data scientists, analysts and even marketers.

    Thank you again for raising awareness for this subject and related next-generation technology.

  2. Jon L on March 13th, 2013 5:34 pm

    “Each application we provide tends to be industry-specific; that makes it easier to deploy and generate faster ROI.”

    As analysis is democratized and business users need to make quicker decisions based on their data, having industry-specific applications makes it easier for them to leverage predictive analytic tools.

    At a recent NY Enterprise Technology Meetup event we had a demo from Visual Revenue which showed off its platform focused on the media industry. It offers the only real-time analytics solution that is designed specifically to enhance the hand of editors in data driven newsrooms.

    You can check out a video of their demo at http://youtu.be/bkCn_nJbey4

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