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## Computing The Really Optimal Tour Across The USA On The Cloud With Python
When Randy Olson's Computing the optimal road trip across the U.S. resulted in articles in the Washington Post , NY Daily News , Daily Mail , People Magazine , NY Times , NPR , and many other outlets, the mathematical optimization community got surprised, and almost shocked. It got surprised for a couple of reasons. First reason to be surprised, the road trip computed by Randy Olson was not optimal, i.e. there is a shorter tour. The first to publish the shorter tour was Bill Cook in... [More]
Tags: optimization cloud analytics python |

## Actionable Insights
It is good practice to eat your own food. I should be no exception. In my post on the role of data science I was blaming data scientists who left business users without any clue about how to use the insights they produce. I should do the same, and help businesses use the advice I gave in that post: Data science role is to enable data based decision making. What does it mean in practice for a business? It means that data scientists should not only provide interesting insights, but they also should care... [More]
Tags: decision big_data data_science analytics optimization |

## The Role Of Data Science
I am sure I'll get flamed for this post, given how hyped data science is. Let me first say that I do not pretend to define what data science is, others, probably more qualified than me, have done it well. For instance, I like this definition from Dawen Peng, as it speaks to an Operations Research person like me. I will rather focus on the role data science can have for business. What I see the most is data scientists analyzing data then publishing reports on insights they found in data. Just browse over... [More]
Tags: analytics data_science big_data |

## Step By Step Modeling Of PuzzlOr Electrifying Problem
PuzzlOr problems are nice because they are simplified versions of real world problems of interest. Last December problem is a simplified version of an interesting logistics problem. A variety of method can be used to attack them, see for instance this interesting post by Isaac Slavitt where he tried both a brute force search and simulated annealing. Not surprisingly, I will try CPLEX on it. Here is the statement of the problem A new city is being built which will include 20 distinct neighborhoods as shown by the house... [More]
Tags: opl programming optimization modeling analytics |

## Decision makers need decision support
How can you make an optimization application be accepted by decision makers? The answer I gave that in my last post was to provide interactive applications. It so happens that colleagues of mine already discussed that in an IBM book Optimization and Decision Support Design Guide I can't resist quoting some of it given how it captures what I tried to express in my previous post. Decision makers need decision support Decision makers will not use any analytics tool unless they trust it. Trust arises... [More]
Tags: decision optimization analytics |

## Interactive Optimization
In my last post I discussed how gamification could be used to overcome the resistance to automated decision making systems. The case discussed in my previous post was about a system that computes retail prices for hotel rooms. The point of gaming was to show that human intervention would degrade the business outcome. Prices set by humans lead to less revenue than prices set by the system. Interesting comments on that post made me realize that I have been a bit extreme in my will to make a point. While I stand by my... [More]
Tags: optimization decision analytics |

## We must show the pain before we can propose the cure
Part of my job is to inject optimization in IBM Anaytics solutions. During one of the discussions with solution teams we argued about a fairly general issue that can prevent prescriptive analytics adoption. I think it is worth sharing. Specifically, one colleague presented the following analytics classification. I said that we should rather use the one below (I discussed it in Prescriptive vs Predictive Analytics Explained .) where the question prescriptive analytics answers is " What should I do about it?... [More]
Tags: analytics optimization |

## Optimization Is Ready For Big Data: Part 4, Veracity
Big Data promise is to enable better decisions based on data. The idea seems appealing yet there is a caveat: is the data reliable enough to base decisions on it? Question is to what extent can we trust data? My experience shows that cleaning data can take up to 80% of an analytics project. This is well known, and is often called the veracity dimension of Big Data . Point is that most data in the Big Data era is uncertain, see for instance the figure below, taken from a post by John Poppelaars... [More]
Tags: optimization uncertainty big_data analytics |

## Optimization Is Ready For Big Data: Part 3, Variety
A colleague of mine once told me that Big Data should be called "All Data". Indeed, one of the key dimension of Big Data is to apply analytics techniques to all kind of data. Other dimensions include volume and velocity of data. Can optimization be applied to all sorts of data? I'd say yes despite the fact that optimization primarily deals with numerical data. Indeed, optimization has already been applied to a wide variety of data, much more than common knowledge may suggest. Let's see a few... [More]
Tags: big_data analytics optimization |

## Optimization Is Ready For Big Data: Part 2, Velocity
Proponents of Big Data boast about how it might help get personalized behavior from all the things and systems people interact with (web sites, mobile apps, customer support services, internet of things, etc) . These systems have to deal with data in motion such as web interaction, sensor feeds (eg body temperature), video, social media feeds, etc. Dealing with such data is the velocity dimension of Big Data . I have discussed how optimization could be applied to another big data dimension, namely large volume of data, in my... [More]
Tags: analytics prescriptive big_data optimization |