SAS 9.4 will offer  a straightforward migration path, using the same approaches proven in the migration to 9.2 and 9.3.

Vincent covered the main highlights but I'm going to concentrate on the new SAS foundation features that will benefit SAS analysts and programmers:

• In the area of analytics, we are seeing some of the new algorithms pioneered in our High Performance Analytics products implemented in an SMP (single server multiple cpu) environment in the “standard” editions of SAS/Stat and other analytic products.
• New languages such as DS2 to allow code to be submitted from Base SAS sessions to run in-database to perform advanced data manipulation without moving the data out of the database taking advantage of parallelisation.  Furthermore extending implementations of existing languages such as the ANSI 1999 compliant FEDSQL.
• Support for the latest operating system versions and other third party product versions, such as Microsoft Office 2013.
• New trigonometric functions such as COT, CSC and SEC.
• One of the areas that will be really exciting for many people will be the long awaited production availability of  ODS Layout and ODS Report Writing Interface.  For old timers who remember the DATA _NULL_ report writing with PUT statements, these new features allow unlimited flexibility in creating highly customized PDF and HTML documents.  Read this SGF paper for more information.
• ODS Graphics is now in its third generation and has many new features that give you more flexibility and control.   This combines with the LAYOUT to create publication ready content.
• The ODS EPUB destination  creates output optimised for eReaders and tablets.
• In SAS/Access a new pipeline implementation can improve performance especially when data is being streamed from a database into a complex SAS data step.
• One useful little goodie is the ZIP Filename engine; this allows reading and writing of files inside a zipped archive.

So when 9.4 is released, investigate the “What’s New in 9.4” documentation on the SAS Support site to find out details of these and many other new features.  I’m sure you will find some that will give you real value – and for each person they may be different.  I'll be very interested to hear from you as to what you found valuable so we can share them!

Stay tuned to this blog where I will discuss new features for SAS Administrators in a later post.

Want to find out more about the exciting announcements, you can attend SAS Forum Sydney on August 8th.

 Click here to register

First up is Paul Kent, SAS' Vice President of Big Data.  Paul discusses how SAS is parallelising our algorithms and math, fusing this together with advances in in-memory hardware to take advantage of what's happening in the community around Hadoop and cloud.

Keith Collins, SAS Chief Technology Officer introduces his thoughts around his new tagline "your cloud, their cloud or my cloud".  Keith discusses how SAS 9.4 will deliver cloud friendly infrastructure to all.

Tapan Patel, SAS Global Product Marketing Manager provides a rundown on how SAS is innovating to work with and within Hadoop; "After all big data does not equal Hadoop".

Wilson Raj, SAS Global Director Customer Intelligence discusses how the new edition of SAS Customer Intelligence can help bridge marketing effectiveness (customer impact and deep customer insights) and marketing efficiency (time-to-market, operational cost and  ROI outcomes) for your marketing department.

Subscribe to our blog to read Bill Gibson’s in depth breakdown of the key announcements from Global Forum 2013 and why its important to you.

BINGO!  If you missed the action or want to hear more about the exciting announcements, you can attend your local forums in the following countries.

 Click here to check them out

The title of my presentation will be The Emergent CMO: Combining Art & Science in Modern Marketing, and I thought I'd share a sneak preview of what I'll be talking about.

In a world where everything is connected, hybrids — people who are able to bridge multiple disciplines — wield tremendous power. They see the interrelationships between things that used to be isolated in silos, and they're able to creatively combine them in innovative new ways.

For instance, I've long championed the hybrid role of marketing technologists — technical professionals who apply their talents in the pursuit of marketing. These aren't just IT people working indirectly on behalf of marketing. They're technical professionals who actually identify themselves as marketers. They're genuinely passionate about marketing. And they're enthusiastically seeking ways to leverage their technical knowledge in the pursuit of breakthrough marketing ideas.

Of course, not everyone in marketing needs to be a marketing technologist.

However, I have come to believe that everyone in marketing should strive to become a broader kind of hybrid: a marketer scientist. Like a warrior poet or a philosopher king, but for modern marketing.

There's been a lot of talk lately about the art and science of marketing. Some believe that the explosion of data and technology in our field is pushing out the more creative, intuitive, and human aspects of marketing. This is sometimes framed as "science" replacing "art." I put those terms in quotes because their interpretation varies depending on who you're talking to and in what context. Generally, people seem to equate left-brain thinking with science and right-brain thinking with art.

But of course, that's highly oversimplified. As it turns out, real science is an incredibly creative discipline. Einstein was a great scientist because of his imagination, far more than his math. Vice versa, great art can be highly analytical and systematic in its execution. Beethoven's 9th Symphony is a work of creative beauty, but also one of great precision.

The art and science of marketing is not a trade-off between two opposing forces. Instead, modern marketing is a synthesis of these two worldviews. Marketing must embrace more science, but that doesn't mean it should abandon its art. We can be both creative and analytical. The key is to appreciate the strengths of these two worldviews and apply them in concert together.

That is the ideal of a hybrid marketer scientist.

To illustrate this, let's consider eight characteristics of a marketer scientist — four that emphasize the "marketer" half and four that emphasize the "scientist" half:

The marketer half of this hybrid combines:

• Storyteller
• Brand champion
• Experience designer
• Change agent

Marketers have always been storytellers and brand champions, and those responsibilities are more important than ever. Great storytelling must break through the Internet's content cacophony, where now everyone is a publisher with an industrial-strength printing press. And in the connected world of social media, every touchpoint with every customer can directly contribute to — or detract from — brand equity in a significant way.
Digital has blurred the line between customer communications and customer experiences. Stories are now best told through experiences, and experiences have become the backbone of the brand. Therefore, marketers must take the lead with experience design. Chief Customer Officer. Chief Experience Officer. These are marketing roles.

Because these changes in marketing are so significant — and they're happening so rapidly — ultimately a marketer must become a change agent, helping the organization as a whole embrace new opportunities and new approaches.

This is especially true with the scientist half of this hybrid, which combines:

• Data analyst
• Experimentalist
• Technologist
• Systems thinker

Marketers must be fluent with using data to make better decisions and to deliver better customers experiences. In particular, we must be able to harness data the way scientists do: providing inspiration for new ideas and — through controlled experiments — proving which of our theories are correct. The modern marketer must be an experimentalist, comfortable with continually trying new ideas on a small scale, with low risk, and then ramping up the demonstrated winners.

Not everyone needs to be a heavy-duty, code-wielding marketing technologist, that's true. But in a marketing environment where technology powers just about everything, every marketer should at least be willing to embrace technology as a means to an end. Scientists look to technology as better tools to push the frontier of what science is capable of achieving.

Finally, marketers must integrate these different pieces into a unified worldview as systems thinkers. Any one channel, tactic, approach, etc., cannot be viewed as an isolated component, but rather as part of a greater whole. That's certainly how customers experience our brands. This will require changes to the way in which we organize marketing — adopting new management methodologies such as agile marketing.

Admittedly, this is a lot under one hat.

Rest assured that not every marketer needs to be an expert in each of these eight characteristics. But every marketer should have a little piece of each of these integrated into their thinking. One marketer might be an expert storyteller, but only modestly proficient as using data to help tell those stories and measure which ones resonate best. Another marketer might be an expert technologist, but mostly embrace the role of brand champion in the context of making sure that their technical implementations fulfill brand promises.

Ultimately, being a marketer scientists is less about a specific set of skills and more of philosophy for modern marketing. Marketer scientists balance the art and science of marketing together in harmony.

• Organisations can no longer ignore the fact that numbers don’t lie.  Analytics is helping to identify trends and patterns to guide and implement more accurate and impactful decisions.
• Leaders of the future will be numbers driven, be it sports, government or business.  Data is a key asset that needs to mined for value.
• Once analytics is embraced to enhance one part of the business the competition will quickly catch up.  You must look to innovate and explore how analytics is used across the breadth of your business.

Ensuring our customers are at the forefront of analytical innovation SAS released a series of groundbreaking announcements.  SAS’ Chief Executive Officer, Jim Goodnight and Chief Marketing Officer, Jim Davis took to main stage, assisted by customers JP Morgan Chase and OfficeMax, to demonstrate some of our newer offerings followed by discussion of vision. There were three themes driving our vision; analytics for everyone, everywhere and in everything.  I have broken down the themes into the key capabilities they bring to market.

Analytics for everyone

• SAS Visual Analytics next release will include  powerful new scenario analysis capabilities, adding prescriptive analytics to existing descriptive and predictive analytics, helping organisations rapidly and easily move to more advanced uses of analytics. SAS Visual Analytics will also support Windows 64-bit.
• At no additional charge for the high-performance procedures, customers currently licensing products like SAS/STAT®, SAS Analytics Pro and SAS® Enterprise Miner™ will receive this new functionality when they upgrade their respective analytic products on the newly released platform for SAS Business Analytics 9.4.  Making it easier for existing customers to access the power of high-performance analytics while leveraging current hardware and software investments

Analytics everywhere

• The June shipment of SAS 9.4 software is cloud ready allowing your IT team to meet requirements around security, authentication, scale and resiliency for private or public SAS cloud deployments.
• Availability of SAS High Performance analytics procedures in your current environment allowing you to take advantage of the capabilities of High Performance Analytics everywhere.
• SAS Web Editor, a Web-based tool for writing and running SAS code will require no local SAS software installation. Users simply connect to a website to access SAS code, files, projects and libraries, anytime and anywhere. SAS Web Editor supports multiple browsers including Safari, letting Mac users take full advantage of powerful SAS programming software capabilities. SAS Web Editor is available on iPads® that support iOS 6 or greater. Support for other tablets will follow.
• SAS Visual Analytics continues to enhance mobile business intelligence with increased collaboration with annotation markups and sharing, integration with mobile device management. Having on-the-go access to current, relevant information means faster decision cycles as critical information is always available anytime, anywhere – on iPads or Android tablets.

Analytics in everything

• The newly redesigned SAS® Customer Intelligence suite makes advanced analytics more accessible to all marketers to address today's challenges surrounding the effective management of customer relationships across channels, creation of meaningful customer experiences and making optimal decisions based on big data. Inside one application, marketers can plan, create and execute campaigns; optimise scenarios; and engage with customers across all channels. No longer will marketers have to manage work in disparate applications and interfaces.
• SAS is increasing the value analytics delivers by extending focus from “data at rest” to “data in motion”. Fast-moving organisations are embracing real-time data streaming into their enterprises by performing relational, procedural and pattern-matching analysis of structured and unstructured data as it is received to provide immediate action and insight.  Event Stream Processing and Enterprise Decision Management filters out the noise analysing high-volumes of data and events in motion to drastically cut time to action.
• SAS 9.4 provides access to numerous new features in our data management products, as well as enhanced Hadoop integration. Our goal is to decrease the time to value for SAS users while reducing the total cost of ownership for IT.

Subscribe to our blog to read Bill Gibson’s in depth breakdown of the key announcements from Global Forum 2013.

If you missed the action you can attend your local forums in the following countries.  Click here to check them out.

Recently on this blog we’ve been discussing data visualisation and how organisations, irrespective of size and industry, have data that can deliver insights. We’ve covered the gamut of topics starting with the business case that you can present, through to case studies from leading companies in South Asia, and on to more technical ‘how to’ posts. Finally we finished up with some invaluable tips on how to get started on your visualisation journey.

I thought it would be helpful to recap the coverage in one place – or if you want to see them all online, here’s the collection.

Ease your growing pains with data visualisation
An overview of the excitement around visualisation and what it will mean to you.

1. Providing information in real-time and on-the-go
2. See smarter insights on large sets of data
3. Delivering insights with pure speed

Make better business decisions with Visual Analytics
In this post, Greg Wood explains how visualisation aids in the process of discovery and should form a key part of the decision making process and the overall analytics lifecycle.

Reducing decision bias with Visualisation
Using SAS® Visual Analytics, we see how novel insights can be found amongst the generalisations, and indeed overgeneralisations. Using a single data point, Minh Lam explores a retail dataset.

Overcoming the Top Four Challenges to More Effective Decision Making With Data Visualisation
The top four reasons to accelerate pace and increase accuracy of decision-making are:

1. Inability to quickly identify trends and patterns.
2. Lack of business self-serviceability.
3. The need to make informed decisions when and where it counts.
4. Data must be reliable.

BIG DATA: Communication is key!
Going beyond the big data hype, Des Viranna talks about how to ensure you are focusing effort on big data analytics activities that relate to your business objectives, and considers these from a perspective foles/skills, organizational structure, management processes and technology.

Look Before You Leap with Data Visualisation
In order to be a more effective decision maker we need to have the ability to spot patterns, identify opportunities for further analysis and convey results quickly. To achieve this in tabular reports alone is difficult as people tend to more naturally interpret a visual. Data visualisation is key in order to make better business decisions

Case study: Transport and logistics empowers managers to drive competitive advantage
The transportation and logistics industry is faced with the complex challenge of driving profits with different dynamics going against them, like new low-cost players coming into the market, rising fuel costs and consumers pushing for faster deadlines. These pressures drove one particular Australian trucking transport company to investigate a better way to empower their managers to maintain a competitive advantage and profitable business in a price sensitive industry. Data Visualisation was key.

The journey to building a useful model with data visualisation
As we undertake our journey through the Analytic Lifecycle towards building a model and beyond, being time efficient and accurate is key. Using the freely available SAS Visual Analytics Full Demo Michelle Homes looks at customer satisfaction and sales representative ratings with the toy company data.

Data Visualisation + Advanced Analytics = faster smarter decisions
Typically the benefits of leveraging analytics have been reserved for companies employing rocket scientists – abstract and nerdy.  However recent innovations in data visualisation, hardware, analytics and mobility are providing this same insight to anyone, anywhere and anytime.

Monkeys, visualisation and decision making
Don’t rely on chance with your decision making, when it comes to making better business decisions, we all need to be able to see what’s happening in your organisations, share what you see, and understand what will happen.

How to get started with data visualisation
If you’ve beein thinking about big data and how to get started with data visualisation, it’s time to ask yourself, do you know the value of your data? And if you don’t, how do you figure out what the value is? Excellent insights into beginning your journey.

Where are you in your data visualisation journey? Do tell us in the comments below.

To receive updates automatically, use the Subscribe feature on the right side of the window.

Start your own journey - try it for yourself for free right here.

It's well established that there's a huge amount of money in professional sport today, there's also a huge amount of data. In my first post,  I discussed the ability to link decisions on player purchases and the associated monetary outlay with the use of data.

In this follow up,  there is a focus on how data and analysis can contribute to the ability to pick that elusive winner.

 Predicting Winners

This is a big business, particularly in the US and around American Football (NFL) and baseball, although NFL is not like baseball, in which prediction is easier in terms of who will win and lose. Baseball is a game of large sample sizes and is really an individual's game of batter against pitcher, even if it is a team sport of nine players on each side. Whereas NFL is a team sport where the line-backer's actions affect the corner-backs and the right tackle makes or misses a block for the running back. If all these parts don’t click then it all falls apart.

The film Moneyball showed the power of statistical analysis within sport, (specifically baseball) through research and analysis, though this is a tip of a very lucrative iceberg. Indeed, times have changed from the days of sheep-skin donned scouts standing on a touchline, the use of data to make decisions in sport is crucial to success.

Now almost everything that happens on the pitch is collected across a vast number of sports and leagues. Now the scout and the analyst have become unexpected soul mates. The wealth of information collected from sports also needs to find a way to be presented to decision makers, visual approaches will be key in demonstrating this data to managers, scouts, players and the boardroom.

Back to prediction, the Moneyball phenomenon is big business in the US. A view of the websites of sports statistics and analysis operations called Football Outsiders, NFL Advanced Stats and Vegas will indicate how much data is collected and analysed with respect to NFL games. So how well do these organisations do in terms of using this data for prediction?

Fairly well would be the answer.

For example, in the postseason (the play-offs), Football Outsiders and NFL Advanced Stats, had excellent first rounds, Football Outsiders only missing the Bengals-Texans game and NFL Advanced Stats predicting them all. In the divisional round, both mis-predicted a game each. They both had the 49ers winning in the championship round, but had the Patriots winning too. Combine it all and Football Outsiders picked 6 out of 11, or were correct 55% of the time; NFL Advanced Stats was right seven out of 11, or 64%.

Vegas did better. They got all four games in the first round and all but one in the second round, but predicted one in three in the championship round and the Super Bowl, missing the Ravens' victories. That's a 73% accuracy rating overall in post-season.

Basically, one would expect the average system to do just a little better than get seven out of every 11 games correctly predicted – just like this NFL post-season. Sometimes they do slightly better, like 8-3 in a short game span, and slightly worse, like 6-5. I know from my own tipping that 6/10 (in the English Premier League) with no predictive modelling or statistical analysis is a pretty ‘good’ effort.

So sports and data go hand-in-hand it seems, it’s an area that is only going to get bigger given the amount of data collected. The key will be how best to analyse and visualise it in order to derive value from this rich array of information.

The first Monkey is covering his eyes : "See no Evil"
The second Monkey is covering his ears : " Hear no Evil"
The third Monkey is covering his mouth : "Speak no Evil"

While there are several meanings for this story including the idea of being of good mind, speech and action, the most popular is the reference to those who deal with impropriety by "turning a blind eye".

So how does that relate to visualisation and decision making?

See no evil. When it comes to making better business decisions we all really need to be able to see what's happening in our organisations. We need to pay attention to our current positions and be able to provide visibility across the business to issues that may be as simple as tracking your market share or internal fraud rates or as complex as forecasting sales and inventory for the next period. When it comes down to it we need to see, and visualisation techniques and technologies help, regardless of data size or complexity.

Hear no evil. In many situations I heard phrases like "but I didn’t know" or "I don't have that information". Organisations need to be able to share what they see, share and collaborate to ensure there is a solid base for all stakeholders to work from. Your visualisation, reporting and analytics should be visible, shareable and collaborative so no executive or decision maker can ever say "I didn't know!, nobody told me! and "I had not heard about that!"

Speak no evil. Strong visualisation coupled with advanced analytics and reporting will allow decision makers to not only see what is currently happening, they can also understand what is most likely to happen and what is the best outcome looking like for the organisation. As decision makers, we will lower the probability of error and drive higher quality forward looking decisions.

Don't rely on chance with your decision making.

Be wise - try visualisation for yourself for free right here.

Typically the benefits of leveraging analytics have been reserved for companies employing rocket scientists – abstract and nerdy.  However recent innovations in data visualisation, hardware, analytics and mobility are providing this same insight to anyone, anywhere and anytime.  With the introduction of SAS Visual Analytics, SAS is saying that there must be a shift in the mindset around analytics and precisely who it should be meant for. The key premise underlying this change is the belief that “Analytics is for Everyone”. Michelle Holmes provides two great examples on how to practically leverage these innovations from both the everyday manager and a data scientist perspective.

Leading organisations have adopted analytics in the board room and at all levels of the decision making process. By making analytics approachable, companies can drive its adoption across previously unreachable segments of users, hence instilling an analytics culture within the organisation. Analytics has been the backroom engine of some of the best performing global companies, providing insight to not just their key management and analysts, but as a corporate behavior that can positively impact all key business users in the way they gain insights and make better decisions. The results from analytics processes don’t always have to be meant for the few, and be boring and hard to consume.

To make analytics approachable, data visualisation is key. To convey the analysis results effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key aspects in a more intuitive way. SAS has managed to combine the best in advanced analytics and data visualisation, and coupled these with innovations around in-memory, advanced analytic server design and Hadoop. This combination of high-performance analytics and an easy-to-use exploration interface is important; big data brings a unique set of challenges for creating visualisations. If you are working with large data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. You may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. Here are a few examples of how companies and departments are leveraging the marriage of advanced analytics and data visualisation:

• SM Marketing Convergence Inc. (SM-MCI), an affiliate of SM Retail Group delivers customer insight with pure speed. The ability to analyse in seconds, billions of rows of shopping cart and loyalty data from multiple channels is key to making timely decisions on campaigns and products. Using data visualisation and advanced analytics SM-MCI now have a more in-depth understanding of customer buying patterns, behaviour and trends, and can use this insight to offer more relevant promotions, acquire new members and identify profitable up-sell opportunities.
• Australian Institute of Health Welfare (AIHW) and leading government agencies of today are evolving to focus on outcome-based-budgeting and evidence-based-policy making, improving the transparency and effectiveness of decision making.  AIHW have moved away from a traditional focus on historical reporting to supporting agencies with the ability to explore and understand the data helping them to apply the right analyses. Leveraging SAS Visual Analytics AIHW shares insights with other agencies ensuring they have the data they need, when they need it, and helping them draw insights from that data with visualisations.  Analysts now get the information they need in real-time, rather than make them wait 18 months for a report.  The marriage of advanced analytics and data visualisation are key to implementing and succeeding with these programs.
• Cosmos Bank provides information and insight in real-time to key executives on-the-go.  Executives and decision makers have easy and immediate access to explore dashboards and reports from their mobile devices in the board room and on the road, anytime and anywhere. Cosmos Bank executive management has access to up-to-the-minute risk intelligence and customer intelligence for decisions that keep the bank ahead of its competitors.
• Hong Kong Efficiency Unit (HKEU) obtains smarter insights from analysing data from 2.65 million calls, 98,000 e-mails and 300,000 complaints from citizens about infrastructure, health, defence and public services.  By applying advanced analytics, text analytics and data visualisation, the department can quickly identify key patterns and relationships in the data. HKEU make better decisions and has developed smarter strategies that improve delivery of services to the public. Click here to read more

If a picture is worth a thousand words, stop writing and check out some of the ways data visualisation can help all your employees make faster smarter decisions.

Learn how to tell your own story - try it for yourself for free right here.

So… you’ve been thinking about big data, data visualization, new data, dark data, volume, variety, velocity, value of data!!! Data, data, data.  It’s the new black.  It’s everywhere.  So where do you start?  From Evan Stubbs' recent post, we established that not everyone believes they have a big data problem, but everyone agrees that they have a dark data problem.  The obvious next question (to me anyway), is…

Do you know the value of your data?

To answer this question, let me ask you another question.  How many of you have constructed a business case and allocated a proportion of the value returned to the data store/source/mart/warehouse?  If you have, I am genuinely interested in learning about your initiative, how the business case was received and what the outcomes were.  Please write a comment below or send me an email!  For most of the rest of us, I’m sure it goes something like this… in a marketing example, all benefits are allocated to a mix of channels or product portfolio.  After all, these are the guys who sign up for delivering the benefits right?

How do we figure out what the value of our data is?

I think the answer is to start by hunting down the value of the decisions that you make that directly link to your strategy.  Can you make these decisions better? Could additional data make a difference if you had it? After all, the quality of the decision you make affects the impact it has on the value that decision delivers to the organization or to the customer.   How important is the decision?  Is it critical to a successful business outcome?   A good example from a recent client engagement is in relation to use of geo-data when studying churn.  Latitudinal and longitudinal data was captured and charted with churn score and used to visualise churn risk at an individual customer level by product.  What was the business outcome?  Sales reps could choose where to focus their time on the road! Internally, the visualisations were used to educate people on the size of the problem, the products affected (and price points) and how the issue was to be addressed.

Having the data is not enough.  People need to understand that data and its impact on the business and their KPIs.  Visualisation can help communicate the value proposition for you.

The timing of that decision also plays a major factor.  If the data is available for you to make a decision 24 hours after it is recorded in the business is this ok?  Should this data be available more frequently and can others benefit from having access to it?  In the churn analysis example, if the data is more than 24 hours old, it is likely that the sales rep may have left that area to work on leads for a different location, so in this instance, yes, 24 hours is ok, but only just.

Learn from the experience of others.  Given we are learning so much so quickly as the cost of processing and storage technologies decrease, one of the best ways to learn is to keep an eye on what others are doing.  Focusing on generic themes such as “better understanding of customer behaviour” also allows you to look across industry.  Great examples of these in two different industries are the Tesco Clubcard story and Capitol One.

Terry Leahy, Tesco’s CEO had this to say about following the customer. “The most reliable voice is the customer. If you’re prepared to listen to them, they’ll tell you about their lives, what they need, what is good about your business and what’s not ... how your business can be improved”.  In the same article he went on to talk about how using data to connect business goals to every day tasks is “the steering wheel” for the company, and that “data is priceless”.

Notice that both of these are not new ideas, but both are still relevant and worthy of being aspirational in my opinion!

Find a problem and then go looking for the right data.  Explore the data to try and identify the cause of the problem.  Discuss the insights with others involved in the business process and form as many hypotheses as are practical.  Then, experiment to try and disprove or prove them.  In running your tests, make sure that the experiments are designed to minimize the uncertainty in the process and quantify what can be controlled.  That way you can be certain of the results.

My view on where to start… Identify the decision that you can’t make accurately enough because you don’t have the right data at the right time and use new technology (and in particular visualisation) to communicate its value across your organization.

Start your own journey - try it for yourself for free right here.

Two of the obvious applications for collecting and analysing sports data are:

1. Evaluating the value of players.
2. Predicting the winner.

I will discuss each of these in turn; in this posting the evaluation of players, in a subsequent posting the use of analytics to predict outcomes.

Evaluating the value of a player

It sounds obvious, but it can help clubs make decisions on transfer fees and where there might be hidden value, or better likely returns on what are quite often huge outlays on transfer fees and salaries in modern sport.

A coach might not have heard of a player in the Dutch second tier of their football league – but he might have similar attributes to a player in the top division currently being viewed. Data will improve the due diligence process when spending considerable sums. After all, in many other industries such rigorous evaluation is required, why not in sport?

We talk about there being ‘big data’ out there, sport is no different - the vastness of the data, much of it not in the public domain, is a statistician’s delight. Players' movements can be tracked every tenth of a second, while everything that happens on the ball – around 2,000 events in a match – is collected. Counting a pass is not enough; an analyst will plot where it went, whether it was driven or chipped, played as a through-ball or to feet, which foot it was kicked with, and more.

Such detail gives the back-room staff a huge amount of information to explore and dissect. A technical scout might watch a game and note that a midfielder is more confident passing to his left than right. By pooling that player's data across a number of games – and controlling it for the state of each match and different formation and tactics – the scout can assess whether it's a blip or a trend.

Similarly, simply viewing a forward's goal tally might not provide the complete story of performance. Plotting actual goals against what was statistically expected – given the quality of chances and strength of the opposition – provides greater information and may unearth a hidden gem of a player.

There is ongoing suspicion about data, some of it, surely, is down to clubs not going public about what they learn – understandably so, given how valuable an edge it might be. Some remain sceptical that analytics can ever apply to a sport as fluid as football.

But if Moneyball is mentioned in a room full of fans it might help quell any likely scepticism as to the use of data in sport.

In a future posting I will discuss how predictive analytics is utilised in a sporting context.