Blog
Don’t overlook the value of quick wins in your analytics project
I was talking to a potential client recently who was frustrated by the lack of ‘real analysis’ happening in his organisation. He had a team of analysts working for him, most of whom were using Excel when he really wanted them to be using Python. Although we don’t generally recommend Excel for advanced analytics – see my earlier blog on this topic – it’s still a useful tool that can generate real insight and is heavily in use in many organisations. Quick wins are important This idea that ‘real analytics’ only happens if you’re using what are, for many people, […]
A first look at SPSS Modeler v18.2
In this video Jarlath Quinn takes a first look at SPSS Modeler v18.2 and demonstrates some of the new functionality that’s included within this release. IBM® SPSS® Modeler adds the following features in this release. New look and feel. A new modern interface theme is available via Tools > User Options > Display. For instructions on switching to the new theme. New data views. You can now right-click a data node and select View Data to examine and refine your data in new ways with advanced data visualizations. IBM Data Warehouse. Database modeling with IBM Netezza Analytics now supports IBM Data Warehouse. Gaussian Mixture node. A new Gaussian Mixture node is available on […]
Expert insights: Major Lester, founder of SPSS UK talks about fifty years of SPSS
This year SPSS is 50 years old. Development started in 1965 by a team of political scientists, frustrated at how much time they had to spent on manual data cleaning before they could start their analysis. The first release was in 1968 and by the end of the 1960s SPSS was in use in over 60 universities. SPSS expanded into the UK in 1985 and Major Lester was its first employee. We spoke to him about the history of SPSS, why it’s been so successful and how he sees its future. Lorna: Could you start off just by talking to […]
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 […]
Expert insight – Paul Jackson, Head of Advanced Analytics, Bonamy Finch
Can you tell us a bit about yourself, your background and how you came to be where you are career-wise? I started my career in 2001 at Research International’s Marketing Science Centre, following a degree in Sociology and Social Policy. Back then I was working mainly on analysis of market research surveys. Over time I built up my expertise in branding and segmentation and then joined Bonamy Finch, providers of advanced analytics services to global clients around the world, in 2007. Back then we were mainly serving clients and agencies with statistical analysis and support on surveys, mostly Segmentation, but […]
Expert insight – John Gill, Head of Insight and Analytics, Betfred
Can you start off by telling us a bit about your background, and how you came to be working in the analytics field? I came to analytics via a somewhat circuitous route. My first degree was in psychology, then I did a postgraduate qualification in newspaper journalism. Obviously psychology is all about the science of behaviour which is very relevant to the field I’m now in – analytics and gaming. Newspaper journalism has also been very useful as it’s given me an ability to communicate facts in a way that’s compelling, and that people can understand. Professionally, I started out […]
Expert insight – Nick Di Paulo, Lead Customer Researcher, Hyde Housing Group
Can you start off just by talking a little bit about your background and how you came to be working in analytics in the first place? Yes, I did research methods at university and was experienced at using spreadsheets and things like that, and that’s where I picked up SPSS, and then I got into survey design and have been working with it for seven or eight years now. In terms of job roles, I’ve moved around quite a bit but in similar kinds of areas with a focus on not for profit, and I’m now in a Customer Insight […]
5 key trends affecting technical training provision in predictive analytics
I have been in the business of delivering software applications and solutions that have advanced and predictive analytics at their core, in one form or another, for last 25 years. In a recent conversation with colleagues we were discussing how the wider market has changed and, more specifically, how this has affected the way that people who analyse data, create predictive models and help their organisations use them go about developing their skills and keeping them up to date. It feels to us that the dramatic developments in the availability and use of technology have had an equivalent impact on […]
Expert insight – Karsten Shaw, Director of Analytics, Populus
Could you start off just by telling us a bit about your background and how you came to be working in the area that you’re working in with analytics? I studied statistics with economics undergraduate level, then I went on to do a Masters in statistics at the LSE part-time whilst I was working at GFK. I worked on a joint project between the LSE and GFK looking at using statistical models to forecast demand for consumer goods. I’ve stayed in the industry since then. I’ve worked across a number of large organisations, and I’m now at Populus. What’s your […]
Expert insight – Matt Harrop, Head of Yield Management and Analytics, Ladbrokes Coral Ltd
What’s your background? How did you come to be working in analytics? I came from an accounting background and then moved into retail where I worked across a variety of teams in finance, marketing analytics and strategy for various different retailers. I then left that to join a consultancy working with loyalty card data. I really got into analytics in that role, and from there I moved across into the gambling industry which of course is very rich in terms of customer data and analytics. I’m now Head of Yield Management and Analytics, leading a team of eight people doing […]
Expert insight – Emma Brooker, Customer Insight Manager, L&Q
What’s your background? How did you come to be working in analytics? I suppose accidentally really. I studied politics at university, then I happened upon a short-term contract for a market research company doing data entry and progressed from there. I found the work interesting and enjoyed the projects I worked on. I moved to L&Q six years ago and have progressed up to where I am now, running the customer insight team. Did you study statistics at university? I had done bits and pieces of analysis as part of my degree but it’s mostly been a case of learning […]
7 things you need to know about key driver analysis (KDA)
In most businesses it’s not enough to simply be measuring outcomes like customer satisfaction, sales, customer churn rates, subscription renewals, customer loyalty, cancellation rates and so on. To gain competitive advantage you also need to know what’s driving those outcomes. Which aspects of the service you provide most influence how likely someone is to renew their subscription at the end of the year? Which factors most drive recommendations? Key driver analysis (KDA) can help you to answer these kinds of questions. KDA enables you to look for relationships between aspects of customers’ attitudes, needs and behaviours that you’re interested in, […]
The benefits of survey research and how online platforms can make the research process more efficient and effective
Surveys remain one of the most popular forms of market research, and consequently we regularly work with customers on large survey-based market research projects. Most commonly this is customer satisfaction surveys, and these can be one of the most valuable forms of data that a company has. We also have clients who conduct broader market research, going beyond their own customer base in order to learn more about a particular market or segment in which they are interested. Of course surveys are not the only way you can find out what your customers think. Sometimes they’ll tell you what they […]
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 […]
How will the GDPR affect big data analytics?
With less than a year to go until it comes into effect, organisations are really starting to get to grips with what GDPR will mean in practice. We’ve talked to lots of customers who are concerned about the implications that GDPR might have for the way in which they collect and analyse customer data. Much of what constitutes ‘big data’ is personal data and the use of this kind of data definitely does have implications for data protection, privacy and individuals’ associated rights. And these rights are going to be strengthened by GDPR. So does this spell trouble for big […]
Thinking of hiring a data analyst? What skills should they have?
Many of our clients regularly hire new analysts and we’re often involved in discussions about what the core skills are that they should be looking for. Similarly, I often talk to people looking to build a career in analytics who want to know what skills they need to develop. The most skilled analysts are in high demand because they blend together a range of skills that are rarely found in a single person. Here are the things that I think are really key. Domain knowledge about your industry It’s not enough just to have the technical skills. As we have […]
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 […]
What do we mean when we talk about data modelling? An overview of different types of models
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 contribution of each factor to the phenomenon can be isolated and quantified. Thus, mathematical models are simplified representations of reality, but to be useful they must give realistic results and reveal meaningful insights. In his 1976 paper ‘Science and Statistics’ in the Journal 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 […]
Predictive operational analytics part 1
In this four part series Jarlath Quinn demonstrates Smart Vision’s new Repeatable Application Template, Predictive Operational Analytics. This Application Template combines market leading predictive analytical software, ready to use application templates and a fully supported professional services package to ensure rapid and effective implementation and deployment. Using IBM’s flagship data science tool, SPSS Modeler, Jarlath works through a case study example where a telecoms maintenance company applied the template in order to: 1) Apply sophisticated text mining to error messages and engineer logs 2) Develop a predictive model that identified whether or not a maintenance task would result is an […]
Predictive operational analytics part 2 – SPSS Modeler demo
In part two of our predictive operational analytics video series, Jarlath Quinn introduces the IBM SPSS Modeler Software before showing how to explore the example data from the case study and carry out text analytics of engineer logs to categorise the information and create key fields for further analysis. Watch part three of this series.
Predictive operational analytics part 3 – predictive part replacement
In part 3 of this predictive operational analytics guide Jarlath uses the results from the previous analysis stage to show how to build and assess a predictive model that identifies whether or not a part replacement will be required during a maintenance visit. We also see how to apply the model to new data so that predictions can be generated before the engineer is dispatched. Watch part four of this series.
