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.... [More]
Tags: decision analytics optimization 
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... [More]
Tags: decision optimization 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... [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 analytics big_data 
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... [More]
Tags: big_data optimization analytics 
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: big_data prescriptive optimization analytics 
Optimization Is Ready For Big Data: Part 1, Volume
I had the honor to give a tutorial at a Big Data and Optimization seminar, thanks to an invitation from John Poppelaars , One of the topics I discussed seemed to resonate well. Let me try to explain it here.
The first thing people think of when they hear about Big Data is large data volume. There are other dimensions than volume in Big Data, see Big Data For Dummies for instance, but let's focus on large data sets. Is current optimization technology ready for the huge data sets available now... [More]
Tags: optimization predictive prescriptive analytics big_data 
Trying Decision Optimization on Cloud beta: part 3, OPL
We have been using CPLEX on the cloud in a previous post . Let us now look at models written with our OPL modeling language. Using a high level modeling language such as OPL can be more productive . It also enables the use of constraint programming if need be.
We assume from now on that we have both an IBM ID and a valid registration for IBM Decision Optimization on Cloud. If not, then please refer to the onboarding steps described here .
Let us go to the... [More]

Trying Decision Optimization on Cloud beta: part 2, CPLEX
In our previous entry we made a demo tour of our Decision Optimization on Cloud service. Let us now start real work with CPLEX. The first step is to get an IBM ID. If you don't have one yet, then you can start here . The next step is to go to the registration page that is accessible in various ways from the demo page .
It lets you create an IBM ID if you don't have one. Assuming you have got one, we move to the next page by clicking on the Already have an IBM... [More]
Tags: cloud analytics oaas optimization saas 
Trying Decision Optimization on Cloud beta: part 1, Demo
We just announced the open beta for our forthcoming Decision Optimization on Cloud offering. This is a major step forward in making optimization more consumable by born in the web applications. The first drop of our service enable operations research (OR) practitioners to solve their problems online via a very simple interaction. We will expose service APIs in future drops, stay tuned.
Let us look at how the service can be used right now. It assumes you have one or several optimization problems to solve,... [More]

2015 Prediction: Prescriptive Analytics Will Make It
This is prediction season. I never played that game so far, but felt compelled to do so after reading quite a few predictions about what will happen in 2015 around Analytics and Big Data. I won't repeat what seems to be a consensus, and will refer to two specific lists that I found more interesting than others.
The first list is by Nathan Brixius, from Microsoft. Here are the top items, I'll let you read Nathan's blog to get the meat behind the titles.
Adoption of higher productivity analytics... [More]
Tags: big_data analytics prescriptive optimization 
2014 Top Posts
The 5 most read entries for this blog in 2014 are the following.
The Analytics Maturity Model
Solving the hardest Sudoku  part 1
How Were Rosetta/Philae Operations Scheduled?
Memory Locality
Data Science Is Hard: A Look At Sotchi Olympics
You can access the top entries of all time via this link .
I would like to thank again all the readers that made... [More]

Analytics For Vehicle Routing (and for Santa)
How could we have helped Santa deliver its gifts as fast as possible? An OR practitioner will probably model Santa's problem as a traveling salesman problem ( TSP ). Indeed Santa needs to compute the shortest route through all kids homes. The same OR practitioner may also want to model that deliveries must occur at night. Instead of a TSP, the problem becomes a vehicle routing with time windows (VRPTW) problem. Although common wisdom, these models may miss the point as we shall see below.
Before discussing what... [More]
Tags: analytic prescriptive predictive optimization 
Prescriptive vs Predictive Analytics Explained
Imagine for one second that you are Loïc Peyron , the recent winner of the transatlantic yatch race La Route Du Rhum . What would you use to reach your destination as fast as possible? Of course, you would work on getting the fastest possible yacht. You would also train to maneuver your ship the best possible way. But once the race is on and you're on the sea, what tools would you use to decide in which direction you should steer the ship?
Credit: Wikipedia
... [More]
Tags: analytic optimization predictive prescriptive 
How Were Rosetta/Philae Operations Scheduled?
Unless you live unplugged you certainly saw the astounding pictures of Comet 67P/Churyumov–Gerasimenko taken by the Philae lander. Besides producing nice images, Philae embarked scientifc instruments , each developed by a European laboratory, to accomplish scientific experiments when approaching, and after landing on the comet. Given that communication takes about 25 minutes between Earth and Philae once landed, it was very important to carefully plan every operations of the mission in advance. Indeed, there... [More]
Tags: analytics constraint_programming 