Business intelligence

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

March 24, 2013

Essential features of exploration/discovery BI

If I had my way, the business intelligence part of investigative analytics — i.e. , the class of business intelligence tools exemplified by QlikView and Tableau — would continue to be called “data exploration”. Exploration what’s actually going on, and it also carries connotations of the “fun” that users report having with the products. By way of contrast, I don’t know what “data discovery” means; the problem these tools solve is that the data has been insufficiently explored, not that it hasn’t been discovered at all. Still “data discovery” seems to be the term that’s winning.

Confusingly, the Teradata Aster library of functions is now called “Discovery” as well, although thankfully without the “data” modifier. Further marketing uses of the term “discovery” will surely follow.

Enough terminology. What sets exploration/discovery business intelligence tools apart? I think these products have two essential kinds of feature:

Read more

February 13, 2013

It’s hard to make data easy to analyze

It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.

Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:

*Complex event/stream processing terminology is always problematic.

My thoughts on all this start:  Read more

December 12, 2012

Some trends that will continue in 2013

I’m usually annoyed by lists of year-end predictions. Still, a reporter asked me for some, and I found one kind I was comfortable making.

Trends that I think will continue in 2013 include:

Growing attention to machine-generated data. Human-generated data grows at the rate business activity does, plus 0-25%. Machine-generated data grows at the rate of Moore’s Law, also plus 0-25%, which is a much higher total. In particular, the use of remote machine-generated data is becoming increasingly real.

Hadoop adoption. Everybody has the big bit bucket use case, largely because of machine-generated data. Even today’s technology is plenty good enough for that purpose, and hence justifies initial Hadoop adoption. Development of further Hadoop technology, which I post about frequently, is rapid. And so the Hadoop trend is very real.

Application SaaS. The on-premises application software industry has hopeless problems with product complexity and rigidity. Any suite new enough to cut the Gordian Knot is or will be SaaS (Software as a Service).

Newer BI interfaces. Advanced visualization — e.g. Tableau or QlikView — and mobile BI are both hot. So, more speculatively, are “social” BI (Business Intelligence) interfaces.

Price discounts. If you buy software at 50% of list price, you’re probably doing it wrong. Even 25% can be too high.

MySQL alternatives.  NoSQL and NewSQL products often are developed as MySQL alternatives. Oracle has actually done a good job on MySQL technology, but now its business practices are scaring companies away from MySQL commitments, and newer short-request SQL DBMS are ready for use.

Read more

December 9, 2012

Amazon Redshift and its implications

Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:

“We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.

The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.

So who should and will use Amazon Redshift? For starters, I’d say: Read more

November 13, 2012

The future of dashboards, if any

Business intelligence dashboards are frequently bashed. I slammed them back in 2006 and 2007. Mark Smith dropped the hammer last August. EIS, the most dashboard-like pre-1990s analytic technology, was also the most reviled. There are reasons for this disdain, but even so dashboards shouldn’t be dismissed entirely.

In essence, I’d say:

In particular: Read more

November 9, 2012

Analytic application subsystems

Imagine a website whose purpose is to encourage consumers to take actions — for example to click on an ad, click on the next page, or actually make a purchase. Best practices for such a site include:

Those predictive models themselves will keep changing, because:

In that situation, what would it mean to offer the website owner a predictive modeling “application”? Read more

November 5, 2012

Do you need an analytic RDBMS?

I can think of seven major reasons not to use an analytic RDBMS. One is good; but the other six seem pretty questionable, niche circumstances excepted, especially at this time.

The good reason to not have an analytic RDBMS is that most organizations can run perfectly well on some combination of:

Those enterprises, however, are generally not who I write for or about.

The six bad reasons to not have an analytic RDBMS all take the form “Can’t some other technology do the job better?”, namely:

Read more

November 5, 2012

Real-time confusion

I recently proposed a 2×2 matrix of BI use cases:

Let me now introduce another 2×2 matrix of analytic scenarios:

My point is that there are at least three different cool things people might think about when they want their analytics to be very fast:

There’s also one slightly boring one that however drives a lot of important applications: Read more

October 24, 2012

Introduction to Cirro

Stuart Frost, of DATAllegro fame, has started a small family of companies, and they’ve become my clients sort of as a group. The first one that I’m choosing to write about is Cirro, for which the basics are:

Data federation stories are often hard to understand because, until you drill down, they implausibly sound as if they do anything for everybody. That said, it’s reasonable to think of Cirro as a layer between Hadoop and your BI tool that:

In both cases, Cirro is calling on your data management software for help, RDBMS or Hadoop as the case may be.

More precisely, Cirro’s approach is: Read more

October 23, 2012

Introduction to Platfora

When I wrote last week that I have at least 5 clients claiming they’re uniquely positioned to support BI over Hadoop (most of whom partner with a 6th client, Tableau) the non-partnering exception I had in mind was Platfora, Ben Werther’s oh-so-stealthy startup that is finally de-stealthing today. Platfora combines:

The whole thing sounds like a perhaps more general and certainly non-SaaS version of what Metamarkets has been offering for a while.

The Platfora technical story starts: Read more

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