Big Data in practice

(This is a translation of an earlier post in my Norwegian blog. This translation was done by Ragnvald Sannes using Google Translate with a few amendments. This technology malarky is getting better and better, isn’t it?).
ml_mapI have just finished teaching four days of data analytics – proper programming and data collection. We (Chandler, Alessandra and the undersigned) have managed to trick over 30 executives and middle managers in Norway to attend a programming and statistics course (more or less, this is actually what analytics basically is), while sort of wondering how we did that. The students are motivated and hard-working and have many and smart questions – in a course taught in English. It is almost enough to make me stop complaining about the state of the world and education and other things.
Anyway – what are these students going to do with this course? We are working on real projects, in the sense that we require people to come up with a problem they will find out in their own job – preferably something that is actually important and where deep data analysis can make a difference. This has worked for almost all the groups: They work on real issues in real organizations – and that is incredibly fun for the teacher. Here is a list of the projects, so judge by yourself. (I do not identify any students here, but believe me – these people face these issues every day.) Well worth spending time on:
  • What is the correct price for newly built homes? A group is working to figure out how to price homes that are not built yet, for a large residential building company.
  • What is the tax effect of the sharing economy? This group (where one student works for the Tax Administration) tries to figure out how to identify people who cheat on the tax as Uber drivers – while making suggestions on how tax rules can be adapted to make it easy to follow the law.
  • What characterizes successful consulting proposals? A major consulting firm wants to use data from their CRM system (which documents the bidding process) to understand what kind of projects they will win or lose.
  • How to recognize money laundering transactions? A bank wants to find out if any of their customers are doing money laundering through online gaming companies.
  • How to offer benefits to customers with automated analysis? A company that supplies stock trading terminals wants to use data analysis to create a competitive edge.
  • How to segment Norwegian shareholders? A company that offers online trading of shares wants to identify segments of its customers to pinpoint and improve its marketing strategy.
  • How to lower costs and reduce the risk of production stoppages in a process business? A hydropower company wants to better understand when and why your power stations need repairs or maintenance.
  • How to identify customers who are in the process of terminating? A TV company wants to understand what characterizes “churn” – how can they identify customers who are about to leave them?
  • Why are some wines more popular than others? A group will work with search data from a wine site to find out what makes some wines more sought after than others.
  • Which customers will buy a new product? A group is working on data from a large bank that wants to offer its existing customers more services.
  • How to increase the recycling rate for waste in Oslo? REN – Oslo’s municipal trash service – wants to find out if you can organize routes and routines differently to better utilize trash trucks and recycling plants.
  • How to avoid being sold out for promotional items? One of Norway’s largest grocery chains wishes to improve their ordering routines so that customers do not get to the store and find out that there is no more left of the offer they wanted.
  • How to model fraud risk in maritime insurance? An insurance company wants to build a model to understand how to find customers attempting to fraud companies or authorities.
  • Which customers are about to leave us? A large transport company wants to find out which customers are about to go to a competitor so that they can take action before it happens.
  • What characterize students who drop out? BI enters 3500 new students each year, but some of them end after the first year. How can we find evidence that a student is about to drop out?
Common to all the projects – and so it’s with all the student projects I have advised since I started in this industry – is that you start with a big question and reduce it to something that can actually be answered. Then you look for data and find that you need to reduce it even more. Then you get problems that the data is either not found, unreliable or inadequate – and one has to figure out what to do with it. Finally, after about 90% of the time and money budget is gone, one can begin to think about analysis. And then there is a risk that you find nothing…
And that is an important lesson of this course: The goal is that the student should be able to know about actual data analysis to ask the right questions and have a realistic expectation of what kind of answer you actually can get.
There is a great demand for this course – so we have set up an additional course this fall. See you there!
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