November 19, 2007

Usability engineering is crucial

From a review of the rather powerful Fair Isaac Blaze Advisor, which will surely be far less successful than its functionality deserves:

But employing a usability expert when designing the tools and observing how users interact with them would go a long way toward improving their usefulness.

My mind utterly boggles each time I discover that a large software vendor still doesn’t seem to have realized this. Or maybe Fair Isaac did do usability engineering, but entrusted it to a blithering incompetent. That frankly would be more reassuring than them not having tried at all.

March 19, 2007

Three ways to market analytics-related technology

“Decision support”, “information centers”, “business intelligence”, “analytic technology”, and “information services” have been around, in one form or other, for 35+ years. For most of that time, there have been two fundamental ways to sell, market, and position them:

More recently – especially the past five years – there’s been a third way:

as early-generation implementations get replaced by newer ones.

At the 50,000 foot level, here’s some of what I see going on:

Related links

March 16, 2007

Have analytics vendors rediscovered ease-of-deployment?

Business intelligence (BI) used to be characterized by speed and cost-effectiveness — short sales cycles, low-cost departmental purchases and deployments, evasion of IT departments’ strangleholds of data, and so on and so forth. That focus has blurred, as BI vendors have increasingly focused on analytic applications or enterprise-wide standardization sales. But increasingly I’m seeing signs that the pendulum has swung at least partway back. For example:

It’s about time.

October 5, 2006

The problem with dashboards, and business intelligence segmented

It is becoming ever clearer that dashboards aren’t working out too well, any more than predecessor technologies like EIS (Executive Information Systems) did. The recurring problem with these technologies is that if they’re mind-numbingly simple, people don’t find them very useful; but if they’re not, people are overwhelmed and still don’t find them useful. This column by Sandra Gittlen does a good job of spelling the problem out.

I think there are lots of problems like that in BI, and what we need to do is step back and consider all the different kinds of BI that enterprises value and need. More precisely, let’s consider the major kinds of use of BI, because it seems that each calls for different kinds of technological support. Here’s one possible list:

Here’s what I mean by each category. Read more

October 4, 2006

KXEN and Verix try to disrupt the data mining market

Data mining is hugely important, but it does have issues with accessibility. The traditional model of data mining goes something like this:

  1. Data is assembled in a data warehouse from transactional information, with all the effort and expense that requires. Maybe more data is even deliberately gathered. Or maybe the data is in large part acquired, at moderate cost, from third-party providers like credit bureaus.
  2. The database experts fire up long-running, expensive data extraction processes to select data for analysis. Often, special data warehousing technology is used just for that purpose.
  3. The statistical experts pound away at the data in their dungeons, torturing it until it reveals its secrets.
  4. The results are made available to business operating units, both as reports and in the form of executable models.

Each in its own way, KXEN and Verix (the imminent new name of the company now called Business Events) want to change all that.
Read more

October 4, 2006

Data mining requires data

Data mining requires and justifies huge investments. The smallest part is the data mining software itself. A much bigger part is the investment in data warehouse technology, a subject about which I’ve been posting extensively recently on DBMS But there’s yet another part to the picture, namely investing in actually gathering data for analysis, that I’ve written about, most recently in a blog I posted elsewhere and am now copying below.
Read more

September 11, 2006

My actual column on data mining

In a couple of recent posts about data mining, I referenced a Computerworld column due to run September 11. Wonder of wonders, they got it posted on the very first day. Here’s a link.

September 8, 2006

Where does data mining succeed, and why?

As previously noted, I have a Computerworld column coming out next week on data mining. The heart of the column is an enumeration of markets where data mining applications were having genuine success. Before I sat down to actually write the column, my list went something like this:

Read more

September 2, 2006

Further information on data mining

My September Computerworld column (I’ll post a link, no sooner than September 11) is about data mining. As promised in that column, here are some links and guides to further work on the subject.

September 2, 2006

KDD 2006 conference on data mining and knowledge discovery

I went to the KDD 2006 (Knowledge Discovery in Databases) conference in Philadelphia last week. It was an interesting, if weird experience. The conference had been billed to me as the place where all the world’s great data mining/KDD experts gather. This turns out to have been old news; the conference has apparently fallen off some the past 2-3 years. What are left are an academic conference and a small trade show that seem to be only loosely coupled. Here’s what I experienced at each.
Read more

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