Four reasons why getting started with predictive analytics is simpler than you think

We spend a great deal of our time at Smart Vision helping our clients to establish the use of predictive analytics in their business. For many organisations, getting started with predictive analytics can feel like a real departure from more traditional and familiar areas of activity. That said, organisations in almost every industry sector are becoming increasingly aware that to maintain a competitive edge it’s necessary to have detailed customer, product and operational insight; and that data analysis and modelling of organisational data is a required capability and key source of competitive advantage. In this post I want to talk about […]

Data science projects – what skills do you need and where can you get them from?

Data science is on the rise. A couple of years back Harvard Business Review suggested that ‘data scientist’ is the sexiest job title of the twenty first century and the hype around data science shows no sign of abating. The term ‘data scientist’ itself was only coined in 2008 but since then the number of data science roles in organisations has grown exponentially, as the volume of data available for analysis also grows. But this presents a challenge for organisations – in such a new and fast-changing field how can they identify the skills they need, find appropriate people who have those skills, […]

Predictive analytics – what can you do with your results?

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 US presidential election. The Literary Digest used a large (2.4 million) but heavily biased sample and got the prediction badly wrong. George Gallup, by comparison, got to within 1% of the actual election result using a much smaller sample (only 50,000) but that was much […]

Predictive analytics – how much data do you really need?

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 about data mining and predictive analytics that I want to talk about in this blog post. Myth one: it’s only worth mining huge datasets It’s certainly true that many data mining projects do involve working with massive datasets and these tend to be the ones […]

Deployment of analytics – a great example of the Anna Karenina principle?

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 families are all alike; every unhappy family is unhappy in its own way.” In this blog post I want to focus on one particular phase of the predictive analytics process – deployment.  As the Anna Karenina principle suggests, there are an infinite number of ways […]

Are the results of predictive analytics really that surprising?

It’s common to hear claims about how businesses will be transformed through the use of predictive analytics techniques with surprising and shock results that they never would have imagined to be the case. It’s particularly common for product or service vendors to make these kinds of claims when in pursuit of a possible sale. After all, who could resist the prospect of a magic analytics bullet that promises to completely transform their organization? I completely agree that the business benefits of analytics can be significant, but I think it’s a myth that predictive analytics will always produce surprising results that […]

How the CRISP-DM method can help your data mining project succeed

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 CRISP-DM methodology). First published in 2001, CRISP-DM remains one of the most widely used data mining/predictive analytics methodologies. I believe its longevity in a rapidly changing area stems from a number of characteristics: It encourages data miners to focus on business goals, so as to […]

The first step in predictive analytics – understanding your data

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 to say. With all the current hype around big data the irony is that, in my experience, the most common worry in the early stages of investigation is that the organisation doesn't have anything to analyse. They are waiting for a new CRM system or […]

6 ways to increase the value of your predictive analytics project

In my last blog post I talked about how it’s now possible to automate large parts of your predictive analytics projects, removing the need to get stuck into the complex statistics yourself. In this post I’ll suggest some ways to maximise the chances of your predictive analytics projects being successful. 1.     Use a proven analytics methodology such as CRISP-DM  By leveraging the knowledge of expert data analysts, methodologies like CRISP-DM (cross industry standard process for data mining) provide a clear roadmap of the key tasks involved in successful predictive analytics initiatives. In short, methodologies like this make the whole process […]

Getting started with advanced analytics – advice for analytics virgins

What do you think of when you hear phrases like predictive analytics, data mining or machine learning? For many people these terms sound suspiciously like ‘statistics on steroids’ and unfortunately, even in the more data-centric and numerate industries, that isn’t likely to elicit the most enthusiastic of responses. To be fair that’s hardly surprising, as I’m prepared to bet that you won’t have encountered that many people with especially fond memories of undergraduate stats modules (unless of course you teach a undergraduate stats module in which case this might be news to you).  So let’s ask a slightly different question […]