Predictive Analytics and Prescriptive Analytics

Few days ago I read a post by Javier Lafuente Garcia clarifying the differences between predictive and prescriptive analytics. The author lays clear and concise definitions as well as points some applications of each type. I like the way he does that and I would like to share it.

Developing Big Data Analytics Strategy

The torrent of big data and analytics articles rarely touches on how to get to the point where all the nice charts and discoveries magically appear out of the analysts hands (keyboards actually). Getting an organization to the point of producing meaningful results requires a mountain of focused efforts. I have recently came across of an article that concisely and comprehensively covers the steps leading making (big) data analytics possible and meaningful.

Better Human Understanding, Not Big Data, Is the Future of Business

Recently I came across a nice article that articulates some of the issues with the data analytics in organizations and too much relying on it. In the height of the big data hype it is a sane reminder of the limitations of data as a business driver. No need retelling you the story - read it yourself here.

Markets' Natural Levels And Natural Drivers

Recent hype of all sorts of data analysis, big data and expanding application of complex math make more and more media and people refer to macroeconomic indicators when explaining what is going on. This is OK for overviews and inconsequential analysis to fill up air time and gap between drinks. When it comes to analysis that would have impact on business and government decision these factors have to be  accompanied with understanding of real drivers behind processes. Rarely I hear this type though. We should not forget that real-world processes are driven by real people and companies making decision based on their incentives, beliefs, legislation, etc. Maybe it is the scientist in me speaking but we should always search for the reasons  for one thing or another to happen. That leads to the idea of the "natural level" of each market or any other field of interest.

Faster Large Data Spredsheets: Alternative to the IF() Function

Spreadsheets with large data and large number of calculations are  part of the business landscape no matter we like it or not. These spreadsheets require greater focus on the design and the way calculations are performed. One of the culprits for poor performance in this type of spreadsheets is the overuse of the IF() function.This is probably the first of Excel function we learn and one we use the most, often neglecting its negative impact on performance. Recently I have done a project that required a calculation intensive spreadsheet model over relatively large data and good portion of these calculations were conditional. The effort for squeezing out the best performance led me to rethinking the way I handle the conditional formulas and I found an alternative of the IF() function that reaped great benefits. 

A New Book on Data Science: The Field Guide to Data Science

A great new book on data science is out. The authors are  the experts of Booz Allen Hamilton  - a name that does need an introduction.  The book covers in detail the definition and the basics of data science, provides insight on many topics and reveals some processes employed in the company. I like that it is very practical through the examples and guidelines for solving specific problems of many sorts a data scientist faces. It really is a field guide and helpful companion in contrast to some academical writing. And the best is that is free. Read more and download it here.

Friday Funny: Screen Size Matters

The life the modern person is a difficult one. From the suffer of choosing the where to eat to mind-numbing complexity of selecting the best AC temperature to the agony of the internet connection that loads Facebook for about 2 sec. I could add more and more but I would focus on just one. These days everyone is a big boss or phenomenal manager or colossal visionary at least according to job titles and Linkedin profiles (see more on that). It is not easy in the office environment either - everyone is sharply dressed and has an iPhone. It is very hard to navigate who is who, doing what and whom to show more respect. Here I offer my 5 cents in treating this terrible ailment.

Use Simulation to Make an Impact

The Data Warehouse Institute is a great  source for BI and data warehouse as well as analytics. The website has great articles, whitepapers, offers certification and education and organizes one of the best events in these areas. I have been following it for some time and I do recommend it for any professional.

Recently the website published a great article about the use of simulation in an organization - Use Simulation to Make an Impact. It covers the applications, recommends team structure and environment for proper and beneficial simulation. I like the clear structure and extensive coverage despite the small size. Enjoy!

The Almighty Linear Regression

If there is a stat for the most pervasive, used and abused analytical method I bet the linear regression would come at the top. No surprise in that - this method has same features as AK47 - simple, easy to use, reliable in most of its application, low entry level knowledge. It may not be the proper tool for some applications but still does a good job. Similarly to a weapon, it should be used with caution. Linear regression may not have the lethality of an AK47 but could cause significant damage if not used wisely.

Why Does US Have Such Stratospheric Healthcare Costs?

The debates related to Obama Care together with a project related to healthcare market I took part in revealed that healthcare costs in US are extremely high compared to other developed countries. Among the blizzard of who-said-what articles and media coverage on the topic perhaps many asked themselves the question of why are the costs so high on the first place. As soon I had a couple of hours I made a quick research and here is what I found.