1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.
- The predictive experimentation I wrote about over Thanksgiving calls naturally for some BI/dashboarding to monitor how it’s going.
- If you think about Nutonian’s pitch, it can be approximated as “Root-cause analysis so easy a business analyst can do it.” That could be interesting to jump to after BI has turned up anomalies. And it should be pretty easy to whip up a UI for choosing a data set and objective function to model on, since those are both things that the BI tool would know how to get to anyway.
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:
- Suppose we have lots of logs about lots of things.* Machine learning can help:
- Notice what’s an anomaly.
- Group* together things that seem to be experiencing similar anomalies.
- That can inform a BI-plus interface for a human to figure out what is happening.
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
|Categories: Business intelligence, Greenplum, Hadoop, Hortonworks, Log analysis, Neo Technology and Neo4j, Nutonian, Predictive modeling and advanced analytics, RDF and graphs, WibiData||5 Comments|
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.
- It is always possible to instead go with a single model formally.
- A lot of people think accuracy, ease-of-use, or both are better served by a true single-model approach.
- Conversely, if you have a single model that’s pretty good, it’s natural to look at the subset of the data for which it works poorly and examine that first. Voila! You’ve just done a kind of clustering.
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