Reading Freakonomics

I spent this last weekend with a small but juicy book named Freakonomics. Freakonomics is about society and economy but it's also about knowledge extracted from data. It's a book clearly about data mining results but the authors make a clear efford to remove the focus from data mining, machine learning, statistics, etc. One can just find some timid references like "algorithm" and "data to be mined here". Instead, there is a strong focus on the making of interesting questions and getting the correct data to answer the questions.

Although my work is not directly related to analytics, I've always made some effort to include data analysis in my APO related work (perhaps because I used it a lot in college research). And that last conclusion goes along with my experience over the years. In a couple of situations I was able to get interesting results from data, but in most cases the techniques were not so important. After the right data for a good question reveals unexpected results, everything else is just easy.

Looking back in time, I think knowing more about analytics has helped me in a different way. Knowing the data and making good questions is an iterative process. Having some tools to do experiments is a good way to keep the loop running and the more time spent with the data, the higher is the probability of making the "golden" question.

Freakonomics was a pleasure to read.

Microsoft SCM

SAP, I2, Oracle and others fight hard to be the second most important tool in the supply chain management business. Yes, the second, because we all know that Excel is the software that runs most supply chains (sometimes alone, sometimes together with an ERP/APS).

So it was with interest that I checked this Microsoft initiative to build SCM toolsets based on Office software.

After reading the story I got two feelings. First, they are right. Second, it seems something from the past. If it was done 10 years ago it could make a difference, but now it seems to lack the magic ingredient of internet collaboration (the social buzz).

But again, it's interesting and I will try the toolkit one of these days. I can easily imagine the best of supply chain planning being done on an improved Excel based framework.

I cannot resist making a quote from the document:

"That said, an OBA is a great way to surface information out of a SOA, and having LOB applications exposed as services makes it easier to build support for cross-functional processes into an OBA."

When I read this stuff, I can't take my mind from this cartoon. Damm, bad cartoon, go away please. Let me be a grown-up.

The SOA thing as I would explain to my mother

Take SAP SCM tool as an example. To be able to do transportation planning the addresses of customers, vendors and plants need to be defined in geographical coordinates, so that it becomes possible to define and optimize the distance between places. This task of finding the latitude and longitude from the address data is called geocoding.

With SCM comes a plug-in software that one can install to do the geocoding. Of course this is not a very powerful tool, it comes with a limited database of addresses and just for some countries. This is the traditional architecture, the software has some plug-in that solves a particular need.

But with some little work one can plug the SCM to use the geocoding web service provided by Yahoo or Google. That way one can get very good geocoding without the burden of having to buy, install and update a large geocoding address database. This is the SOA architecture. The software uses a stand-alone webservice internal or external to the company.

OY19 - Customizing Cross-System Viewer

Only recently I was told about this transaction. With OY19 one can check customizing between systems (for example compare contents of transports or a list of tables to see data differences). It would have saved me time in the past, so maybe you can also find it useful.

PS. Humm, happy new year!