The real world, whether it be the physical world, for example machines, or the natural world, for example human and animal behaviour, is very complex with many factors, some unknown, determining their behaviour and responses to interventions. Even if every contributory factor to a phenomenon is known, it is unrealistic to expect that the unique …
I talked in my last blog post about the confusion that often emerges around how much data is enough to effectively deploy predictive analytics. I argued that sample selection is much more important than sample size when it comes to ensuring accurate results. As an example I talked about two political polls from the 1936 …
Predictive analytics – what can you do with your results? Read More »
When I’m talking to prospective clients something I hear a lot is ‘but we don’t really have enough data to do any data mining’. It’s a common misconception that you need vast terabytes of data to be able to do anything meaningful in terms of analytics. In fact there are a number of similar misconceptions …
Predictive analytics – how much data do you really need? Read More »
The Anna Karenina principle describes an endeavour in which a deficiency in any one of a number of factors dooms it to failure. Consequently, a successful endeavour (subject to this principle) is one where every possible deficiency has been avoided. The name of the principle derives from Leo Tolstoy's book Anna Karenina, which begins “Happy …
Deployment of analytics – a great example of the Anna Karenina principle? Read More »
I’ve worked in predictive analytics for many years and have seen that a key factor for increasing the prospects of a project being successful is using a structured approach based around a data mining methodology such as CRISP-DM (a quick declaration of interest here – I was one of the team who originally developed the …
How the CRISP-DM method can help your data mining project succeed Read More »
I speak to a lot of people in organisations just starting out on their analytics journey, organisations that have started to recognise that they could make better decisions if they could find the hidden patterns and nuggets of information in their data. Data talks and you can tell very quickly if it has something interesting …
The first step in predictive analytics – understanding your data Read More »
