Open Source Analytics

This is a 2003 article titled Top Ten Mistakes Made by Entrepreneurs by Constance Bagley, an Associate Professor in Entrepreneurial Management Unit at Harvard Business School and a staff writer for HBS Working Knowledge. In this article, Constance discusses the most frequent flops made by entrepreneurs, everything from hiring the wrong lawyer to puffing up the business plan.
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You can trust the guys at Juice to put together great lists of resources. Their recent Essential Excel Skills blog post and the Excel Core Knowledge Wiki are excellent examples showcasing fundamental skills no analyst can do without.

Top Resources for Analysts: Excel, Data Analysis, and Business Intelligence is another list you shouldn’t miss.

If you work with data for a living, the following sites are worth a visit (or a subscription) to learn from some of the best, most passionate practitioners.

I recently got an email from James Gardner about Tom Davenport’s Free Webinar being organized today by Aquent in association with the American Marketing Association. Tom Davenport is a consultant and prolific writer (and speaker) on topics related to technology, analytics and data driven strategies.

Tom’s HBR article titled “Competing on Analytics” is based on his profiling of early adopters of Analytics that compete today based on data driven strategies. The research is also expected to be published in a book format in spring 2007.
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Most Data Warehousing projects fail. As many as 70-80% as per some claims. Still, no one talks about them.

Data Mining, Analytics and BI roll-outs are unlike any other project your organization may have undertaken. Political and non-technical issues can derail the fragile project which is anyway struggling to handle ambiguous and constantly changing requirements.
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A common technologist blindspot is that techies often do not understand what business/users want and how they are going to use it.

Technologists think, “Build it, and they will come.” But they’re building plenty of cool stuff, and consumers aren’t coming.

Techies are often so taken and smitten by their own technology that they fail to understand why and how it may not sell. Just because a technolgy is logically coherent and technically brilliant doesn’t mean it would sell. And sale is where the first ‘potential for value’ gets created, whether or not a technologist likes to admit it.

Most people will not switch to something new unless the perceived benefits far outweigh the perceived pain in switching. Pip Coburn, the author of “The Change Function: Why Some Technologies Take Off and Others Crash and Burn“, calls this the Change Function, and understanding this gives an insight into what’s likely to sell and what’s not.

Click here to read an article by Pip Coburn on the Change Function that appeared in May issue of Fast Company.

Those of you who have been using MySQL for sometime now would know that the MySQL 5.0 Online Reference Manual is not just a manual but also a repository of user comments exploring and solving common and/or deeper problems. If you are stuck with a particular problem that you find unable to frame a SQL for, the comments on the manual pages would usually have a solution.
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KETL is an open source ETL tool by Kinetic Networks that is gaining mindshare of late. It is currently downloadable as part of Bizgres BI project, but can be setup for other databases with a little tweaking.

KETL is different from Kettle, another open source ETL tool. You can read more about the similar names here at Nicholas Goodman’s blog. While Kettle is GUI oriented, KETL is scripted and probably more robust.

Read the KETL training doc to know more about its architecture and usage.

A simple point that most BI vendors and consultants seem to miss is that for BI to deliver upon its promise, it first has to be adopted by the end users. BI has to become simple and usable for the broad based adoption that is needed in today’s hypercompetitive world. It is the hidden Cost of Complexity that turns off end users and results in the fact that most BI projects fail.
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We’ve long held that Analytics and BI cannot (and should not) be viewed as yet another technology/tool for the traditional business. It IS NOT yet another IT project.

The true value of BI is in viewing (and nurturing) it as the cognitive base and a response model for an organization in dialog with the ‘external reality’. In that sense BI is a truly disruptive phenomenon, even though it is the logical next step for the all-round ‘digitization’ that has been taking place for the last few years.

Yet, most managers appear to be struggling to fit Analytics into their traditional repertoire instead of looking at it afresh and leveraging it like any disruptive technology should.

An interesting HBS Working Knowledge article about Disruptive Innovation that may give you some ideas: Six Keys to Building New Markets by Unleashing Disruptive Innovation : HBS Working Knowledge

Here’s an interesting article that applies Systems Theory concepts to BI and views it in the context of the organizational environment it operates in. If organizations are viewed as cognitive systems in dialog with their environment, then BI is a ‘technical artifact that encodes a description of the business environment (i.e., the data model).’ (more…)