Business intelligence

Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:

November 19, 2015

The questionably named Cloudera Navigator Optimizer

I only have mixed success at getting my clients to reach out to me for messaging advice when they’re introducing something new. Cloudera Navigator Optimizer, which is being announced along with Cloudera 5.5, is one of my failures in that respect; I heard about it for the first time Tuesday afternoon. I hate the name. I hate some of the slides I saw. But I do like one part of the messaging, namely the statement that this is about “refactoring” queries.

All messaging quibbles aside, I think the Cloudera Navigator Optimizer story is actually pretty interesting, and perhaps not just to users of SQL-on-Hadoop technologies such as Hive (which I guess I’d put in that category for simplicity) or Impala. As I understand Cloudera Navigator Optimizer:

Read more

October 26, 2015

Differentiation in business intelligence

Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:

That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:

And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.

Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more

September 28, 2015

Introduction to Cloudera Kudu

This is part of a three-post series on Kudu, a new data storage system from Cloudera.

Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.

*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes. :)

For starters:

Read more

September 17, 2015

Rocana’s world

For starters:

Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:

Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:

Read more

September 14, 2015

DataStax and Cassandra update

MongoDB isn’t the only company I reached out to recently for an update. Another is DataStax. I chatted mainly with Patrick McFadin, somebody with whom I’ve had strong consulting relationships at a user and vendor both. But Rachel Pedreschi contributed the marvelous phrase “twinkling dashboard”.

It seems fair to say that in most cases:

Those generalities, in my opinion, make good technical sense. Even so, there are some edge cases or counterexamples, such as:

*And so a gas company is doing lightweight analysis on boiler temperatures, which it regards as hot data. :)

While most of the specifics are different, I’d say similar things about MongoDB, Cassandra, or any other NoSQL DBMS that comes to mind: Read more

September 10, 2015

MongoDB update

One pleasure in talking with my clients at MongoDB is that few things are NDA. So let’s start with some numbers:

Also >530 staff, and I think that number is a little out of date.

MongoDB lacks many capabilities RDBMS users take for granted. MongoDB 3.2, which I gather is slated for early November, narrows that gap, but only by a little. Features include:

There’s also a closed-source database introspection tool coming, currently codenamed MongoDB Scout.  Read more

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

July 7, 2015

Zoomdata and the Vs

Let’s start with some terminology biases:

So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.

It turns out that:

*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.

Core aspects of Zoomdata’s technical strategy include:  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

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