all bits considered data to information to knowledge


Lots of little brothers… all watching you

Predictive analytics at its best... and worst.  Charles Duhigg's article How Companies Learn Your Secrets published in New York Times opens a big can of worms here. The truth is that we are getting better and better with predictive analysis aided by ever powerful computers and software, and better mathematical models... and we are getting closer to the point where our secrets do not even have to be stolen as they could be inferred from mountains of tiny clues we left behind as we are going after our daily lives.

The key to make this happen, the facilitator is unique identifiers we acquire with our credit cards, loyalty cards and other numbers that could be used to track your activities. It has its uses - such as prevent fraud, prepare for an eventual disaster and so on.. But there is more insidious side to the predictive nalytics - instead of Big Brother watching we have hundreds of small ones actively engaged into collecting and trading our personally identifiable information - something we are only too happy to give away for a few pennies in discounts on overpriced merchandise.  So goes our privacy - not with a bang but with a whimper



Mining for Software Product Lines

The ultimate re-use idea of product lines grows upon common patterns where attributes and processes of seemingly different products converge. Specifically, for the Software Product Lines, the commonalities - according to SEI authoritative text - could be found in

  • Requirements
  • Architecture
  • Components
  • Modeling and Analysis
  • Testing
  • Planning
  • Processes
  • People

Analyzing organization’s portfolio of “software intensive systems” might be helpful in uncovering hidden patterns which could then be coalesced into “Product Lines” (assuming that a business rationale exists for doing so).

For example, if formal requirements tools and repositories are used, they could be mined for patterns to discover potential candidates for product lines; some discoveries might not be as obvious as “common sense analysis”.


Who mines the miners?

Organizations like to keep their cards close to the chest.  For a long time BI/analytics was all in-house affair: tools, skills and - especially! - data. The shift towards distributed computing models such SaaS and PaaS change everything.

The data needed for analysis might not be owned by the company; it might live - virtually - anywhere: public domain, subscription service, social networks such as Facebook, geographical data from Google Maps or Microsoft Earth. This is the secret ingredient for the analysis, and just as every true secret it hides in plain sight.

SAP has announced that its flagship analytics BI - Business Objects 4.1 - will have even tighter integration with Google Maps API, going beyond location services…

One can’t help but wonder  what data Google gets to keep for its own analytic endeavors as it tracks each call to its services.  Could it be that the corporate secrets are leaking out through usage patterns?