Predictive analytics

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What skills are needed to successfully run data science projects in the cloud?

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In my most recent blog post I wrote about the benefits and opportunities that cloud computing brings to data science projects and reciprocally, how data science projects provide a great way to demonstrate the value of cloud computing. In this post I want to discuss the cloud computing skills challenge and provide some advice on …

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An overview of the four main approaches to predictive analytics

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This infographic provides an overview of the four main families of approaches to predictive analytics. Prediction encompasses applications that aim to estimate or predict the values of a key target field. Segmentation refers to techniques such as cluster analysis which attempt to find the most ‘naturally occurring” groups within a dataset. Association modelling discovers groups …

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Using SPSS Modeler’s cache_compression setting to speed up your modelling

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There are a number of configuration settings associated with IBM SPSS Modeler Server that control its behaviour. The default settings aim to ensure that stream execution will complete successfully even if the host machine is being used by a number of other applications i.e. Modeler Server is trying to be a “good citizen”. However, if …

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Thinking of using spreadsheets for advanced analytics? Think again.

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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 …

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How to ensure advanced analytics gives you a concrete competitive advantage

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Most companies these days are aware of the potential value that advanced analytics could offer them. Lots of people are talking the talk of advanced analytics, hiring data scientists and investing in sophisticated analytics technologies. But these things on their own aren’t going to give you the competitive advantage that you’re hoping for. It’s not …

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How alternative interfaces can help you get more out of R

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Contemporary analytical platforms like SPSS and SAS represent the some of the earliest and yet longest-lived examples of proprietary software in the industry. When we think of the tectonic shifts the technology landscape has witnessed in last four decades, through the mainframe era, the rise of the PC, browser wars, the dotcom bubble, the smartphone …

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Why R can be hard to learn

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Many of the analysts we speak to are being pushed over to R, primarily because it’s open source and therefore a free alternative to commercial data analytics packages for which the costs can sometimes run into tens of thousands of pounds (or more). However, even experienced analysts often find that getting to grips with R …

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What’s the difference between business intelligence and predictive analytics?

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It’s not uncommon to talk to potential clients who consider themselves to already be very much data-driven in the way that they operate. However it’s very rare to find a potential client that truly is exploiting the full potential of the data that they hold. That’s because companies often confuse business intelligence with predictive analytics, …

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How repeatable application templates will maximise the effectiveness of your first predictive analytics project

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As we help our clients get up and running with the predictive analytics tools and skills they need, we see some trends emerging in terms of the kind of applications for which clients tend to use predictive analytics most commonly. These are what we call ‘repeatable application templates’. In my previous post I outlined the 4 …

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

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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 …

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

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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, …

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

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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 …

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10 reasons why your organisation is ready for prescriptive analytics

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Prescriptive analytics is a relatively recent development beyond the broader disciplines of predictive analytics and data science. In this blog post I want to explain a bit about what it is and discuss why I think many organisations are already on the road to using it, without even knowing it. Prescriptive analytics is the third …

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Using predictive analytics and the tax inspector’s nose to spot fraud

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Organisations hold information, lots of it. Often it’s all over the place and sometimes its not acknowledged as being useful but there is always lots of data from customer interaction and transaction information to production line or stocking information. The trick is learning how to use past data to do better in the future. Organisations …

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

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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 …

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Why not now? The barriers to adopting true predictive analytics.

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While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond briskly demolishes the commonly-held view that necessity is the mother of invention. In fact he argues that many …

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

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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 …

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Are the results of predictive analytics really that surprising?

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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 …

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6 ways to increase the value of your predictive analytics project

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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 …

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Getting started with advanced analytics – advice for analytics virgins

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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 …

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