Three questions to ask when reading articles about artificial intelligence

You may have noticed by now that there seem to be a couple of recurring themes in the plethora of articles and news programmes about artificial intelligence (AI). These themes can be summed up as a) “The dangers of AI” and b) “The limitations of AI”. Articles addressing the dangers of AI tend to focus on issues such as the threat of widespread job losses to AI, the possibility of inherent bias (such as racism and sexism), the lack of transparency in decisions made by AI systems and, as a result, the inability to plead your case with AI (“Computer […]

Thinking of using spreadsheets for advanced analytics? Think again.

When we’re talking to potential clients about advanced analytics we often ask them what tools they’re currently using. More often than not they say they’re using spreadsheets. Spreadsheets are one of the most widely used tools for statistical analysis and of course, most businesses couldn’t run without them. However, when it comes to advanced analytics spreadsheets have some very significant limitations. Use them beyond their capabilities and the potential cost can be significant. As with anything, it’s important to use the right tool for the job. So, what are the things you need to consider when it comes to using […]

Fear and loathing in machine learning

Over the past two years I’ve noticed a steady stream of articles in the mainstream press and business journals centred on the themes of a) the dangers of machine learning 1 2 or b) the limitations of machine learning 3 4. Many of these articles refer to incidents where machine learning initiatives have echoed and exasperated our own biases, prejudices and (frankly racist) behaviours 5. Others have focused on their limitations with providing the sorts of ‘informed, idiosyncratic’ recommendations that humans find effortless. However, for those of us that work in the field of predictive analytics where many of the […]

Which data science tools should you learn?

I’ve blogged several times now about different aspects of data science. A conversation I’ve been having more and more frequently now is about what tools people should learn if they’re hoping to develop a career in data science. Obviously there are many different factors to be taken into account here. You’ll want to think about whether there’s a tool that’s the standard in your particular industry. You’ll also want to consider whether you want to specialize in a particular area of data science and build a reputation as an expert in a range of related tools, or whether you’d prefer […]

Data science is everywhere, so why no data scientists to be seen?

Data science is everywhere at the moment. Nearly as everywhere as big data, but not quite. Books out there are making the concepts behind statistics and predictive analytics more and more accessible not only to those in business making decisions everyday but also to the average man or woman on the street.  Try Super Crunchers by Ian Ayres, Moneyball (the book  or the film which has the advantage of featuring Brad Pitt and therefore making the business of statistics much sexier than it has been),Freakonomics or the newer Superfreakonomics or pretty much anything by Malcolm Gladwell. All of these books have […]

Understanding what drives your net promoter score – how data science can help

The concept of the net promoter score was introduced to the world in Frederick Reichheld’s seminal Harvard Business Review article The One Number You Need to Grow in 2003. Reichheld’s research led him to believe that there was a deep and intrinsic link between profitable growth and customer loyalty. Two years of research revealed that, for most industries, the single best indicator of customer loyalty could be measured by asking people how likely they would be to recommend a particular company to a friend or relative. Reichheld and his colleagues at Bain and Company partnered with Satmetrix to develop a recommendation scale running […]

Nine tips for effective data mining

In my career I’ve seen many examples of successful and unsuccessful data mining projects. I’m often asked how clients can maximise the chances of their project being successful and, based on the many projects I’ve been involved with over the years, I think there are 9 things that really help. When these factors are in place it always suggests to me that the project has a much higher likelihood of success. Think carefully about which projects you take on. To maximise your chances of success try and focus on those projects which are most clearly aligned with important business issues […]

A first look at IBM’s Watson Analytics

An analytical tool for the business user. It isn’t a new idea, more a nut that many of us have been trying to crack for years. Today, of course, this problem is more pressing than ever. There are more questions that can be answered with analytics, more data to analyse and fewer trained analysts to go round. And, as IBM rightly point out, analysts (and other data specialists) can be a bottleneck in the process. Where I’ve seen it work best in the past is generally when a software app, rather than a tool (like IBM/SPSS or SAS), has been designed […]

Predictive analytics – five ways your business can benefit

When talking to companies who have yet to invest in predictive analytics I am often asked if I am sure that they will benefit. Will it be worth the time and effort involved in setting up a predictive analytics project? What kinds of benefits can they realistically expect? Are they large enough to reap the benefits of predictive analytics? This last question is the easiest to answer. If you’re selling direct to consumers and you have a sufficient customers that you cannot realistically have a genuine personal relationship with each one of them then the chances are that predictive analytics […]

Do you have to be a data scientist to do predictive analytics?

What is a data scientist? The predictive analytics field seems to love nothing more than giving a new name to an established concept. In my last post I argued that the concept of ‘big data’ itself is nothing new. Over the last few years I’ve seen more and more job ads recruiting ‘data scientists’  and I find myself rather unjustifiably irritated by the rise of this new job title. I can’t quite put my finger on what it is that irritates me about it but I think it’s tied up with the creation of a new job title for something which isn’t really all that new, and a […]

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 […]

Predictive analytics: how small improvements can deliver big results

It never ceases to surprise me at the wide array of interesting and smart folks we have the privilege to meet through the course of our work at Smart Vision Europe. We are two months into 2014 and I have been checking back on my diary to make sure there are no loose ends. I’m encouraged by how busy we are and by the diverse range of businesses we’re talking to – so much so that I thought it was worth sharing. The purpose of sharing is not to showboat about how busy we are but to reinforce the point […]

Five myths about big data

Big data is everywhere at the moment. There’s a lot of talk about it, much of which presents big data as a problem to be solved rather than as an opportunity to be seized. There are many organisations that could be using big data for powerful predictive analytics but aren’t, because they’ve bought into one of the many myths about it that are in common circulation. Here are five of the big data myths that I come across most frequently in my work 1. Big data is new The terminology is new (and time will tell if it’s a term […]

Do you really need special software for predictive analytics?

When I’m talking to prospective clients, one of the questions I’m regularly asked is why they should invest in expensive predictive analytics software when they already have Excel. Excel has some useful features and for many organisations it can be a useful toe in the water of analytics but it’s really not built for the rigors of true predictive analytics. Organisations that stick to using Excel will never be able to truly reap the potential rewards that predictive analytics can offer. I’ve recently been working with a large B2B online publishing organisation and my experiences there illustrate this point perfectly. […]