Friday, 29 April 2016

Is Analytics losing its competitive edge?

Since Tom Davenport wrote his ground-breaking HBR article on Competing on Analytics in 2006 a lot has changed in how we think about data and analytics and its impact on decision making. In the past 10 years the amount of data has gone sky high due to new technological developments like the Internet of Things. Also, data storage costs have plummeted so we no longer need to choose whether we would like to store the data or not.  Analytics technology has become readily available. Open source platforms like KNIME and R have lowered the adoption thresholds, providing access to state of art analytical methods to everyone. To monitor the impact of these developments on the way organisations use data and analytics MIT Sloan Management review sends out a survey on a regular basis. Recently they published their most recent findings in Beyond the Hype: The hard work behind analytics success. One of the key findings is that analytics seems to be losing its competitive edge.

Analytics has become table stakes

Comparing their survey results over several years MIT Sloan reports a decrease in the past 2 years in the number organisations that gained a competitive advantage in using analytics. An interesting finding, especially now when organisations seems to be set to leverage on the investments they have done in (big) data platforms, visualisation and analytics software. An obvious explanation for this decline is that more organisations are using analytics in their decision making, therefore it lowers the competitive advantage. In other words analytics has become table stakes. The use of analytics in decision making has become a required capability for some organisations to stay competitive. For example in the hospitality and airlines industry. All companies in those industries use analytics extensively to come up with the best offer for their customers. Without the extensive use of analytics they would not be able to compete. There are however more reasons for the reported decline in competitive advantage.

Step by step 

From the MIT Sloan report, several of the reported reasons for having difficulty in gaining a competitive edged with analytics are related to organisational focus and culture. The survey results show that this is due to lack of senior sponsorship. Also, senior management doesn’t use analytics in their strategic decision making. As a consequence there are only localised initiatives that have little impact. I see this happen in a lot of organisations. Many managers see value in using analytics in decision making but have difficulty convincing senior management in supporting them. There can be many reasons for that. It could be that senior management simply doesn’t not know what to expect from analytics and therefore avoid investing time and money in an activity with uncertain outcome. It could also be that the outcomes of analytics models are so counterintuitive senior management simple can’t believe the outcomes. There are several ways to change this and benefit more from analytics than just in local initiatives. Key is to take a step by step approach, starting with the current way of decision making and gradually introduce analytics to improve it. Simple steps with measurable impact. That way senior management can familiarise itself with what analytics can do and gain confidence in its outcomes. It can take some time, but each step will be an improvement and will grow the analytical competitiveness of the organisation.

Investing in People

One other main reason from the survey for having difficulty in gaining an edge with analytics is that organisations don’t know how to use the analytics insights. One important reason for this to happen is that analytics projects are not well embedded in a business context. Driven by the ambition to use data and analytics in decision making, organisations rush into doing analytics projects without taking enough time to assure the project addresses an important enough business issue, has clear objectives and scope and implementation plan. As a results insights from the analytics project are knocks on an open door or are too far of what the business needs or its unclear what to do with the outcomes.
Another reason I come across often is that analytics projects are started from the technology perspective: “We have bought analytics software, now what can we do with it?”. It should be the other way around. The required analytics software comes after understanding the business issue and the conditions under which it needs to be solved. Therefore analytics is more than buying software or hardware, people need to be trained to recognise business issues that can be solved from an analytics perspective and be able to choose the appropriate analytical methods and tools. The training will also result in a common understanding of the value of analytics for the organisation which in turn will help change the current way of decision making into one that incorporates the analytics insights.

So, has analytics become less competitive? The picture I get from the above reasons is that most organisations have difficulty changing into a new and more analytical way of working. Many organisations are just starting to use analytics, the MIT Sloan survey reports conforms this given the significant increase in first users (the Analytically Challenged Organisations). These organisations have high expectations on what they will get from analytics but will need to go through organisational changes and changes in the way decisions are made before the benefits of using analytics become visible. This will, following a Satir like change curve, at first cause a decrease in productivity causing in my opinion the lower expectation on the competitive gain these organisations expect to get from using analytics. But this will change over time, and end in a new and improved productivity level. As with any new capability or technology, you first need to learn how to walk, then run and then jump

Sunday, 3 April 2016

The most dangerous equation in the world

Each year Generali, one of the biggest insurers in the world, analyses the claims of its car insurance customers in the Netherlands. Results of that analysis can be found on their website. In their analysis, Generali relates the number of claims to where people live & drive, the age of the driver, the age of the car and the car brand. Some of these statistics provide insights that are to be expected. For example, you expect young drivers to have the highest claim rates, as their analysis confirms. Cars in less populated areas have the lowest claim rates, which seems plausible as well. There is however one finding that raised my eyebrows and that is that drivers of specific car brands have significant higher claim rates than others, suggesting that driving a car of a certain brand makes you either a better or a worse driver. This year Mazda drivers had the highest claim rates according to the Generali analysis, this was for the second year in a row. Drivers of a Citroen had the lowest claim rate, making them the safest drivers of 2015 according to Generali. So, is their truth in their finding and should you therefore avoid a Mazda driver or at least not buy a Mazda yourself? Generali’s statistics suggest you should, don’t they?

Putting it in perspective

Let’s take a closer look at the numbers. The claim rates themselves don’t tell much, but combining them with other data will. What will be interesting is to see how the distribution of car brands compares to the number of claims per brand. Unfortunately, but understandable, Generali only reports the relative difference in claims of a brand compared to the average claim rate. However, Generali claims that its findings apply to all drivers in the Netherlands, so it’s fair to assume that the distribution of car brands in their car insurance portfolio is similar to the overall distribution of car brands in the Netherlands. With data from the Netherlands Vehicle Authority (RDW) gathers, selecting only the brands reported by Generali, we find that 7,545,266 vehicles were registered in the Netherlands in 2015, with the following relative distribution over brands. Clearly Volkswagen, Opel and Peugeot are the biggest brands, while Skoda, Mitsubishi and Mazda are the smallest brands.

Does driving a Mazda make you the worst driver?

By plotting the population size per car brand against the relative claim performance per car brands an interesting pattern appears, the spread in claim performance is bigger when the population size decreases. So smaller car brands have a bigger spread in claim performance than bigger brands. This is the result of a not so well known statistical law, De Moivre’s Equation, which provides us with that standard deviation of the sampling distribution of the mean, σx=σ/ n. Howard Wainer named this equation the most dangerous equation in the world because too little people are aware of it and as a consequence made faulty decisions with serious impact. Look at the formula we see that the standard deviation of the mean is inversely proportional to the square root of the sample size. As a consequence car brands with a smaller number of cars in the Netherlands will have a larger variation in relative claim performance than bigger brands. To illustrate, a small brand with no claims will have the best claim performance in one year, while a small number of claims will make it the worst performing brand the next year. For the bigger brands this is not an issue. Note that the brand with the best claim performance last year was Skoda, this year Skoda was among the worst performers, De Moivre’s equation in action.

The most dangerous equation in the world

So, Generali’s claim that driving a Mazda makes you the worst driver in the road is much to strong. Whether you are a good or a bad driver depends on many things, but I doubt that it will be the brand of your car, and would require a much more detailed analysis. Hopefully Generali doesn’t take the brand of your car into account when calculating their premium levels. Chances are they either over or under price it when you choose to drive one of the rarer car brands. When you want to avoid that, better choose one of the larger car brands as it will be unlikely for them to end up being the worst performing category. Insurance claims are not the only subject affected by De Moivre’s equation. Wainer shows with some compelling examples what the consequences can be of being ignorant of the most dangerous equation of the world and why understanding variability is critical to avoid serious errors.