Business intelligence

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

July 31, 2013

“Disruption” in the software industry

I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify. :)

You probably know that the modern concept of disruption comes from Clayton Christensen, specifically in The Innovator’s Dilemma and its sequel, The Innovator’s Solution. The basic ideas are:

In response (this is the Innovator’s Solution part):

But not all cleverness is “disruption”.

Here are some of the examples that make me think of the whole subject. Read more

July 20, 2013

The refactoring of everything

I’ll start with three observations:

As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

April 25, 2013

Analytic application themes

I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.

1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.

Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.

Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:

2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:

Also arising fairly frequently are:

I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.

Read more

March 26, 2013

Platfora at the time of first GA

Well-resourced Silicon Valley start-ups typically announce their existence multiple times. Company formation, angel funding, Series A funding, Series B funding, company launch, product beta, and product general availability may not be 7 different “news events”, but they’re apt to be at least 3-4. Platfora, no exception to this rule, is hitting general availability today, and in connection with that I learned a bit more about what they are up to.

In simplest terms, Platfora offers exploratory business intelligence against Hadoop-based data. As per last weekend’s post about exploratory BI, a key requirement is speed; and so far as I can tell, any technological innovation Platfora offers relates to the need for speed. Specifically, I drilled into Platfora’s performance architecture on the query processing side (and associated data movement); Platfora also brags of rendering 100s of 1000s of “marks” quickly in HTML5 visualizations, but I haven’t a clue as to whether that’s much of an accomplishment in itself.

Platfora’s marketing suggests it obviates the need for a data warehouse at all; for most enterprises, of course, that is a great exaggeration. But another dubious aspect of Platfora marketing actually serves to understate the product’s merits — Platfora claims to have an “in-memory” product, when what’s really the case is that Platfora’s memory-centric technology uses both RAM and disk to manage larger data marts than could reasonably be fit into RAM alone. Expanding on what I wrote about Platfora when it de-stealthedRead more

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

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