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  • December 12, 2014

    Notes and links, December 12, 2014

    1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.

    For starters:

    I’ve also heard a couple of ideas about how predictive modeling can support BI. One is via my client Omer Trajman, whose startup ScalingData is still semi-stealthy, but says they’re “working at the intersection of big data and IT operations”. The idea goes something like this:

    Makes sense to me. (Edit: ScalingData subsequently launched, under the name Rocana.)

    * The word “cluster” could have been used here in a couple of different ways, so I decided to avoid it altogether.

    Finally, I’m hearing a variety of “smart ETL/data preparation” and “we recommend what columns you should join” stories. I don’t know how much machine learning there’s been in those to date, but it’s usually at least on the roadmap to make the systems (yet) smarter in the future. The end benefit is usually to facilitate BI.

    2. Discussion of graph DBMS can get confusing. For example: Read more

    October 10, 2014

    Notes on predictive modeling, October 10, 2014

    As planned, I’m getting more active in predictive modeling. Anyhow …

    1. I still believe most of what I said in a July, 2013 predictive modeling catch-all post. However, I haven’t heard as much subsequently about Ayasdi as I had expected to.

    2. The most controversial part of that post was probably the claim:

    I think the predictive modeling state of the art has become:

    • Cluster in some way.
    • Model separately on each cluster.

    In particular:

    3. Nutonian is now a client. I just had my first meeting with them this week. To a first approximation, they’re somewhat like KXEN (sophisticated math, non-linear models, ease of modeling, quasi-automagic feature selection), but with differences that start: Read more

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