Analysis of business intelligence pioneer Cognos. Also covered is Applix, vendor of the memory-centric MOLAP tool TM1, which was acquired by Cognos. Related subjects include:

January 14, 2016

BI and quasi-DBMS

I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:

Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.

*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.

My top historical examples include:

Read more

November 10, 2013

RDBMS and their bundle-mates

Relational DBMS used to be fairly straightforward product suites, which boiled down to:

Now, however, most RDBMS are sold as part of something bigger.

Read more

August 14, 2013

The two sides of BI

As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:

*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.

Key aspects of BI platform technology can include:

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April 7, 2012

Many kinds of memory-centric data management

I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

Getting more specific than that is hard, however, because:

Consider, for example, some of the in-memory data management ideas kicking around. Read more

February 21, 2012

The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments

This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:

The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be.  Read more

November 21, 2011

Some big-vendor execution questions, and why they matter

When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:

Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:

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January 3, 2011

The six useful things you can do with analytic technology

I seem to be in the mode of sharing some of my frameworks for thinking about analytic technology. Here’s another one.

Ultimately, there are six useful things you can do with analytic technology:

Technology vendors often cite similar taxonomies, claiming to have all the categories (as they conceive them) nicely represented, in slickly integrated fashion. They exaggerate. Most of these categories are in rapid flux, and the rest should be. Analytic technology still has a long way to go.

In more detail:  Read more

September 20, 2010

Some thoughts on the announcement that IBM is buying Netezza

As you’ve probably read, IBM and Netezza announced a deal today for IBM to buy Netezza. I didn’t sit in on the conference call, but I’ve seen the reporting. Naturally, I have some quick thoughts, which I’ve broken up into several sections below:

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July 28, 2009

Initial reactions to IBM acquiring SPSS

IBM is acquiring SPSS.  My initial thoughts (questions by Eric Lai of Computerworld) include:

1) good buy for IBM? why or why not?

Yes. The integration of predictive analytics with other analytic or operational technologies is still ahead of us, so there was a lot of value to be gained from SPSS beyond what it had standalone.  (That said, I haven’t actually looked at the numbers, so I have no comment on the price.)

By the way, SPSS coined the phrase “predictive analytics”, with the rest of the industry then coming around to use it. As with all successful marketing phrases, it’s somewhat misleading, in that it’s not wholly focused on prediction.

2) how does it position IBM vs. competitors?

IBM’s ownership immediately makes SPSS a stronger competitor to SAS. Any advantage to the rest of IBM depends on the integration roadmap and execution.

3) How does this particularly affect SAP and SAS and Oracle, IBM’s closest competitors by revenue according to IDC’s figures?

If one of Oracle or SAP had bought SPSS, it would have given them a competitive advantage against the other, in the integration of predictive analytics with packaged operational apps. That’s a missed opportunity for each.

One notable point is that SPSS is more SQL-oriented than SAS. Thus, SPSS has gotten performance benefits from Oracle’s in-database data mining technology that SAS apparently hasn’t.

IBM’s done a good job of keeping its acquired products working well with Oracle and other competitive DBMS in the past, and SPSS will surely be no exception.

Obviously, if IBM does a good job of Cognos/SPSS integration, that’s bad for competitors, starting with Oracle and SAP/Business Objects. So far business intelligence/predictive analytics integration has been pretty minor, because nobody’s figured out how to do it right, but some day that will change. Hmm — I feel another “Future of … ” post coming on.

4) Do you predict further M&A?

Always. 🙂

Related links

February 7, 2009

Analytics’ role in a frightening economy

I chatted yesterday with the general business side (as opposed to the trading operation) of a household-name brokerage firm, one that’s in no immediate financial peril. It seems their #1 analytic-technology priority right now is changing planning from an annual to a monthly cycle.* That’s a smart idea. While it’s especially important in their business, larger enterprises of all kinds should consider following suit. Read more

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