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

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

April 24, 2012

Notes on the Hadoop and HBase markets

I visited my clients at Cloudera and Hortonworks last week, along with scads of other companies. A few of the takeaways were:

March 31, 2012

Our clients, and where they are located

From time to time, I disclose our vendor client lists. Another iteration is below, the first since a little over a year ago. To be clear:

For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. Read more

February 11, 2012

Applications of an analytic kind

The most straightforward approach to the applications business is:

However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:

I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.

Reasons that analytic applications are commonly less complete than the transactional kind include: Read more

February 6, 2012

WibiData, derived data, and analytic schema flexibility

My clients at Odiago, vendors of WibiData, have changed their company name simply to WibiData. Even better, they blogged with more detail as to how WibiData works, in what is essentially a follow-on to my original WibiData post last October. Among other virtues, WibiData turns out to be a poster child for my views on derived data and the corresponding schema evolution.

Interesting quotes include:

WibiData is designed to store … transactional data side-by-side with profile and other derived data attributes.

… the ability to add new ad-hoc columns to a table enables more flexible analysis: output data that is the result of one analytic pipeline is stored adjacent to its input data, meaning that you can easily use this as input to second- or third-order derived data as well.

schemas can vary over time; you can easily add a field to a record, or delete a field. … But even though you start collecting that new data, your existing analysis pipelines can treat records like they always did; programs that don’t yet know about the new cookie are still compatible with both the old records already collected, and the new records with the additional field. New programs fill in default values for old data recorded before a field was added, applying the new schema at read time.

schemas for every column are stored in a data dictionary that matches column names with their schemas, as well as human-readable descriptions of the data.

Interesting aspects of the post that don’t lend themselves as well to being excerpted include:

November 2, 2011

The cool aspects of Odiago WibiData

Christophe Bisciglia and Aaron Kimball have a new company.

WibiData is designed for management of, investigative analytics on, and operational analytics on consumer internet data, the main examples of which are web site traffic and personalization and their analogues for games and/or mobile devices. The core WibiData technology, built on HBase and Hadoop,* is a data management and analytic execution layer. That’s where the secret sauce resides. Also included are:

The whole thing is in beta, with about three (paying) beta customers.

*And Avro and so on.

The core ideas of WibiData include:

Read more

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