I would like to write my thesis on organizational issues around business intelligence. Instead of the typical BI technology slant, I want to examine best practices in organization design, talent development, and operational processes for BI and business analytics. I’m still working out the details but it could explore topics such as:

• Factors that would influence one organizational design over another — e.g. centralized vs. decentralized, utility model of buying analytic talent vs. embed capability in the business,
• Approaches for talent development — e.g. grow internally, hire from outside, essential skills, etc.,
• Operational management of business intelligence projects — what processes must be in place, who owns them, IT-ownership vs. business-ownership, agile vs. big bang project management,
• Enterprise cultural and change management issues related to becoming an analytic organization.

To help me settle on a topic, I’m searching for materials that discuss these ideas. If you have ideas on the topics or potential resources, please drop me a note or enter a comment. In the near term, I’m specifically looking for:

1. Current resources that explore these topics including books, webcasts, journal articles, white papers, etc.  Must be published in the past two years.
2. Names of thought leaders regarding the people/process side of business intelligence with design and implementation experience in larger enterprises.  I’ve seen most of the big consulting company and vendor materials.  I’d like to develop a list of boutique firms and independent thought leaders to get a different perspective on the topic.

Thanks much. Stay tuned. I hope to eventually publish this thesis.

Share this:

Next-generation BI has arrived, and three major factors are driving it: the spread of predictive analytics, more real-time performance monitoring, and much faster analysis, thanks to in-memory BI. A fourth factor, software as a service, promises to further alter the BI market by helping companies get these next-generation systems running more quickly.

Interesting article with a number of good points.  What’s your take?

Share this:

HBR:  Does randomness have a role to play in birding?

Sibley:

Even then these vagrant birds (ones not expected to be in a certain location) tend to show up in places that have a pattern of rare birds turning up.  So they’re probably not random occurrence but a pattern we haven’t worked out yet.

ThinkingAnalytically discussion starters:

• Can you think of a time that an apparent set of random data turned out to be an undiscovered pattern?  What was it?
• How do you avoid claiming a random situation too early?

Use the comments to contribute.

Purchase full the HBR article here.

Share this:

• Number of debates a candidate participated in (represented by slice)
• Political affiliation (represented by color
• Number of references a candidate made to other candidates (number of outgoing arrows)
• Number of references made to a particular candidate from other candidates (incoming arrows)
• Timing of such references within a particular debate (location of out going arrow)
• Timing of references over time (slices are organized by sequence of debates)

The technique is relatively simple but yet powerful. Though it may look like the output of my childhood spirograph, it is more like an inter-relationship digraph and generates interesting observations such as:

• Democrats apparently talk about each other much more than the republicans
• Democrat candidates that dropped earlier tended to be referenced fewer times (no surprise)
• Every candidate mentioned Clinton at least once

What do you see?

Thanks to A Beautiful WWW for bringing this to my attention!

Share this:

Leaders should get out ahead of this trend to ensure that information makes organizations more rather than less effective. Information is often power; broadening access and increasing transparency will inevitably influence organizational politics and power structures. Environments that celebrate making choices on a factual basis must beware of analysis paralysis.

The close by suggesting the following books:

• Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Boston: Harvard Business School Press, 2007. (see my entry on wikiBookNotes.org)
• John Riedl and Joseph Konstan with Eric Vrooman, Word of Mouse: The Marketing Power of Collaborative Filtering, New York: Warner Books, 2002.
• Stefan H. Thomke, Experimentation Matters: Unlocking the Potential of New Technologies for Innovation, Boston: Harvard Business School Press, 2003.
• David Weinberger, Everything Is Miscellaneous: The Power of the New Digital Disorder, New York: Times Books, 2007.

I’ve only read the first.  I’m interested in any reviews of the others.

Share this:

In today’s world, litigation typically involves the review of thousands, if not millions, of e-docs. Though shredders can permanently destroy a paper document, past e-mails and e-docs hide in all types of digital nooks and crannies. Unfortunately, many companies have not kept up with the proliferation of e-documents. But that’s no excuse when in the middle of litigation. If there’s a chance that a relevant e-doc exists, it typically must be found, reviewed and deemed relevant or not.

Sean McNee describes a recent case where a small legal team representing a small time inventor was able to use visual analytics to review and analyze the contents of 50 million pages of electronic documentation in just a few months. (Yes, 50 million pages.) He states,

“Visual analytics tools identify nouns and noun phrases in a series of messages, then visually cluster the documents together according to similarities in subject matter”

Apparently some Fortune 1000 companies have seen three-fold productivity increases using visual tools. According to the article, “U.S. corporations spend nearly $5 billion a year analyzing emails for litigation, regulatory requests and investigations.” A three-fold increase in productivity yields signficant benefits!

Read more at BI Review Online.

Share this:

Technorati Tags: business intelligence, acquisition, ibm, cognos, PeopleSoft, Siebel

Share this: