Log analysis

Discussion of how data warehousing and analytic technologies are applied to logfile analysis. Related subjects include:

August 3, 2015

Data messes

A lot of what I hear and talk about boils down to “data is a mess”. Below is a very partial list of examples.

To a first approximation, one would expect operational data to be rather clean. After all, it drives and/or records business transactions. So if something goes awry, the result can be lost money, disappointed customers, or worse, and those are outcomes to be strenuously avoided. Up to a point, that’s indeed true, at least at businesses large enough to be properly automated. (Unlike, for example — :) — mine.)

Even so, operational data has some canonical problems. First, it could be inaccurate; somebody can just misspell or otherwise botch an entry. Further, there are multiple ways data can be unreachable, typically because it’s:

Inconsistency can take multiple forms, including:  Read more

May 26, 2015

IT-centric notes on the future of health care

It’s difficult to project the rate of IT change in health care, because:

Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:

These vastly greater amounts of data cited above will allow for greatly changed analytics.
Read more

May 13, 2015

Notes on analytic technology, May 13, 2015

1. There are multiple ways in which analytics is inherently modular. For example:

Also, analytics is inherently iterative.

If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.

2. In 2011, I wrote, in the context of agile predictive analytics, that

… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.

I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises.  Read more

March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

February 22, 2015

Data models

7-10 years ago, I repeatedly argued the viewpoints:

Since then, however:

So it’s probably best to revisit all that in a somewhat organized way.

Read more

December 31, 2014

Notes on machine-generated data, year-end 2014

Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.

1. There are many kinds of machine-generated data. Important categories include:

That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.

2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more

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.

* 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 26, 2014

Datameer at the time of Datameer 5.0

Datameer checked in, having recently announced general availability of Datameer 5.0. So far as I understood, Datameer is still clearly in the investigative analytics business, in that:

Key aspects include:

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

October 5, 2014

Streaming for Hadoop

The genesis of this post is that:

Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.

In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more

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