An overview of the four main approaches to predictive analytics

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 of categories that are likely to co-occur such as items in a shopping basket. Forecasting refers to methods that extrapolate time trends such as sales of products so businesses can anticipate future demand. Each family is further detailed with colour coded examples of the popular […]

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

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 Modeler Server is the primary application on the host, then tweaking these settings can reduce the execution times of some streams significantly. One of these settings is cache_compression. The cache_compression setting is used to control whether data that gets spilled to disk is compressed before being written to […]

Choosing a predictive analytics project

At Smart Vision we’re in a pretty strong position to talk authoritatively about the reality of predictive analytics. That’s because we’re comprised of a team of veteran practitioners with decades of experience where we’ve all witnessed plenty of success stories but also one or two ‘data science’ train wrecks. Moreover, like anyone else, we’re exposed to the seemingly constant torrent of stories about the latest developments in machine learning, data science or AI. But we’re often struck by the fact that there seems to be such a focus on emphasising the power of analytics or on explaining how machine learning […]

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 […]

Analytics predictions for 2018

As it is the end of the year I’ve decided to devote this blog post to some thoughts on where we see the analytics market going in 2018. Unlocking the analytics potential of unstructured data Enterprises are collecting ever greater quantities of unstructured data. A recent survey by Forbes showed that the number of organisations with over 100 terabytes of unstructured data has doubled in the last two years. However the same survey showed that only 32% of organisations have successfully analysed unstructured data. We see this changing in 2018 as the power and sophistication of text analytics platforms develops […]

How to ensure advanced analytics gives you a concrete competitive advantage

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 possible to bolt advanced analytics as an adjunct to your marketing or strategy function and hope that the magic will happen just like that. We regularly talk to people who’ve invested a lot of money in advanced analytics software and are wondering why they’re not […]

Do I need SPSS Statistics or Modeler? How to choose the right product for your needs

We often talk to people who are unsure whether they need SPSS Statistics or whether SPSS Modeler might be more suited to their needs. In fact, it’s not always a clear cut choice as to which tool is more appropriate as it depends on the context in which the technology might be used. With that in mind I thought it might be helpful to develop a little infographic to lay out the sorts of things that you should be thinking about when choosing between SPSS Modeler and SPSS Statistics. We can think of the choice as a sort of continuum, […]

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 […]

How alternative interfaces can help you get more out of R

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 revolution to the age of the cloud and big data not to mention the number of once seemingly ubiquitous software tools that no longer dominate the marketplace, it’s incredible to think that the first versions of SPSS and SAS were developed as far back as […]

Why R can be hard to learn

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 can be a difficult business. Many people view R as being notoriously difficult to learn. There are a number of reasons why this is the case. Lack of consistency In some ways the open source nature of R is its biggest weakness as well as […]

Six questions to ask before you opt for open source software

It’s not uncommon for people to say to us that they don’t understand why they should pay for industry standard analytics products like SPSS or SAS when there are strong open source alternatives freely available such as R. Indeed the development of R has really transformed the analytics marketplace in many ways. It’s tempting to make a comparison between R and commercial alternatives such as SPSS and SAS on price grounds alone. When you look at it that way it might seem as though there’s no contest. SPSS and SAS can both involve a significant investment whereas R is free. […]

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

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, or think that once they’re using their data for business intelligence that they’re doing all they can to get value from it. Neither of these things is true. Predictive analytics is not the same thing as business intelligence, and if you’re just using your data […]

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

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 reasons  why we believe predictive analytics is a low risk, high return way for many companies to achieve competitive advantage. I have re-capped these below for reference: Implementing predictive analytics is less expensive, quicker and lower risk than almost any other kind of technology-enabled project You already have […]

The A-Z of analytics with IBM SPSS Modeler

A is for Automation  Why bother trying out loads of modelling techniques to see which one works best when Modeler can do that for you? Modeler can test many permutations of the same algorithm and multiple instances of different methods before selecting the best performers according to a pre-specified criteria. Oh and it will also automatically prepare your data so you can get the best results from your analysis. B is for Boosting and Bagging Boosting is a key technique in Modeler that can generate more accurate models. It works by building the same model multiple times but each time […]

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 […]

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 […]

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, […]

10 reasons why your organisation is ready for prescriptive analytics

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 of three phases of analytics. Phase one, descriptive analytics, is about mining historical data to understand the reasons behind past performance. Phase two, predictive analytics, uses rules and algorithms to determine the probability of an event occurring in the future. Phase three, prescriptive analytics, takes […]

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 […]

Is there a business case for the Chief Analytics Officer?

The past decade has seen huge growth in the practical use of data mining and analytics. Increasingly, analytics is being used not just to inform decision makers; it is being embedded into operational systems and processes. As analytics becomes increasingly business critical, I share a few thoughts on the question: is it time for the Chief Analytics Officer (CAO)? Contributions of a CAO Executive officer (CxO) roles are responsible for setting strategic objectives, making strategic decisions, leading employees to achieve objectives, directing day-to-day operations, executing change, and monitoring performance against the business strategy. A good starting point for defining the […]

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

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 are a lot bigger than they once were with more and more employees who have a shorter and shorter tenure. These changes have significant implications for how analytics is done. Where data volumes are small and employees are knowledgeable it’s often possible to get a […]

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 – 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 […]

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 […]

Why should charities invest in in-house predictive analytics?

Predictive analytics can really pay dividends for charities but I often find that such organisations are reluctant to invest in it or unsure of what value it can really bring. Smart Vision Europe has worked with quite a number of charities with the aim of helping them benefit from the power of predictive analytics. Over time, I’ve found that a number of common themes come up during the initial conversations with such organisations. The lovely people to whom we speak are working hard on behalf of their organisations and, regardless of the nature of the charity, tend to be wrestling […]

Assessing the value of your customers

Moving beyond RFM analysis to create truly data-driven customer segments RFM stands for recency, frequency, monetary value and is a method for calculating or assessing overall customer value.  Its most common use is in direct marketing or database marketing and it is particularly heavily used by retailers and charities. RFM analysis is predicated on the idea that your most valuable customers are those who spent most recently, who spend most often (frequency) and who spend the most (monetary value). Most organisations hold this kind of data about their customers which makes RFM analysis relatively accessible and easy to do. Typically […]

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. […]