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BIG DATA INNOVATION SEP 2015 | #18
16| THE PEOPLE YOU NEED ON YOUR TEAM Building a data team is difficult, so we look at the seven types of people you should be looking to recruit.
08| BIG DATA IS VITAL TO MODERN DAY ENTERTAINMENT Streamed entertainment is not just helped by data, in the world today it may not even be possible without it. We investigate why. 20| BIG DATA COULD BE THE NEW KEY TO SUCCESS IN SPORT With sports teams using more and more data to assist in pre and post performance work, is Big Data now the catalyst to success?
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EDITOR’S LETTER
W
elcome to Issue 18 of Big Data    Innovation.
The last few months have seen the importance of data, its security and the potential consequences that data breaches can have, brought into the media spotlight. With this added media interest has also come the potential for more financial and brand reputational damage at a time when more customer data is being collected and stored. This means that the necessity to protect data adequately has never been more important, but from the number of data hacks that we have seen in the past few years, it is becoming increasingly clear that not enough is being done in this regards to this effect. Data safety is a key element to organizations from small startups through to the largest and oldest governments in the world. David Cameron, the UK Prime Minister, actually cites cyber security as a key point to justify the increase in defence spending at a time of austerity. To touch on the vital role of security, Laura Denham takes us through the importance of data protection in pharma companies and the implications that this can have on not only the companies involved, but also on life and death decisions made by patients.
As mentioned, a rise in instances of hacking has also seen an increased interest from the media, which begs the question about whether or not this is a help or hinderance. It is true that a hack can only be as bad as how far the data is spread. The fewer people who access the data, the less damage can be done. Therefore, it could be argued that through an increase in media coverage, they are spreading the idea that this data is available and making the leaks worse. On the other hand, through making an example of companies who do not adequately protect the data they hold, they are making others make sure theirs is well protected. After the intense scrutiny of companies like eBay, Target and Ashley Madison after their hacks, other organizations took a look at their security, making it more complex for hackers to target other large companies. So perhaps, despite the clear correlation between reporting the hack and spreading the data held in it, the media holds one of the most important positions in terms of forcing companies to upgrade their systems. At least that is what we hope. As always, if you are interested in contributing or have any feedback on the magazine, please contact me at ghill@theiegroup.com
managing editor
George Hill editor Simon Barton
art director Oliver Godwin-Brown
contributors Bruno Polach Gabrielle Morse Laura Denham Ian Thomas Rick Delgado Olivia Timson
contents 05| GO BIG ON DATA MARKETERS Data has a key role to play in marketing and marketing teams need to make sure they are maximizing the opportunities given to them. 11| THE REFUGEE CRISIS AND THE EU, WILL BIG DATA/ANALYTICS BRING AN ANSWER? As the issues facing both those coming from war torn countries and the countries trying to house them increases, could data play a role to help? 14| DATA IN PHARMA NEEDS BETTER PROTECTION With pharma companies being increasingly targeted by cyber criminals, we need to secure pharma data or the consequences could be dire. 24| WHY DO YOU NEED A DATA DRIVEN CULTURE? Adopting a data driven culture is seen as a simple fashion by many, but the truth is that it can significantly benefit companies today. WRITE FOR US Do you want to contribute to our next issue? Contact ghill@ theiegroup.com for details
08| BIG DATA IS VITAL TO MODERN DAY ENTERTAINMENT Streamed entertainment is not just helped by data, in the world today it may not even be possible without it. We investigate why.
16 | THE SEVEN PEOPLE YOU NEED IN YOUR DATA TEAM Building a data team is difficult. In this article we look at the seven types of people you should be looking to recruit.
20| BIG DATA COULD BE THE NEW KEY TO SUCCESS IN SPORT With sports teams using more and more data to assist in pre and post performance work, is Big Data now the catalyst to success?
Tracking a customer’s journey is easier than ever, but defining that next communication has never been more complicated.
GO BIG Go Big On Data ON DATA Marketers MARKETERS GABRIELLE MORSE, BIG DATA WRITER
GABRIELLE MORSE, BIG DATA WRITER
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A customer’s initial interaction with a particular brand can shape their perceptions of it for a lifetime. The book ‘The One to One Future’ - written by eminent authors Don Peppers and Martha Rogers - has been the inspiration for much of the research around personalized marketing, but having been written in 1997, considerable progress has been made since its release.
When planning their next step, companies should holistically analyze specific customer journeys. As time and technology have moved on, company outputs have advanced at an unprecedented rate, yet according to Brian Solis - Principal Analyst at Altimeter Group - customer experience is now more important than the product. Technology now works to reinforce the customer’s desired experience, with social media, in particular, allowing companies to interact with consumers on an individual basis. Big Data analytics has an important role to play in this. The concept of ‘Generation C’ complicates the rigid demographical barriers - like race and age - which used to dominate marketing reports, but provides more detailed findings by instead concentrating on their online
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GO BIG ON DATA MARKETERS
behaviour and how they are digitally connected. Tracking a customer’s journey is easier than ever, but defining that next communication has never been more complicated. When planning their next step, companies should holistically analyze specific customer journeys. Each data point should be collated and then used to provide a detailed map of a customer’s entire history with an organization. With Gartner predicting that 9 in 10 companies will compete on customer experience alone in as early as 2016, marketers will increasingly rely on data to produce targeted campaigns and personalized offers. Unless a retailer has a real stock issue, company-wide reductions - which attempt to lure customers in to buy something which they probably wouldn’t have - are becoming less effective. Revenues increase when targeted adverts reward consumers for their continued loyalty. Although this seems like an extension of Maslow’s ‘Pavlovian Conditioning’, giving consumers a ‘treat’ every once in a while strengthens the bond between them and the company, this essentially guarantee their future service. Although data is rightfully seen as a key tool for organizations, it is only reliable when managed efficiently. Due to this, marketers - who want their data to be statistically valid - must use efficient data management systems.
As described by Alec Gardner: ‘The resulting insights are more likely to be statistically valid because the results are not based on the solicited input of a few people, but rather the actual behaviors and history of many people. By using these insights to develop the most targeted, personalized campaigns possible, organizations can sharpen their competitive edge and gain additional market share.’ A data driven approach to marketing is a must. Forbes contributor, Daniel Newman, states: ‘For marketers every tech trend hinges on Big Data’. Yet it’s not just identifying wider tec trends where marketers remain reliant on Big Data for, each individual story can be told through data, and that makes it essential for customer experience. Even though marketers must go big with data, they must understand that control is essential and that effective data management is needed.
Although data is rightfully seen as a key tool for organizations, it is only reliable when managed efficiently.
GO BIG ON DATA MARKETERS
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Big Data Is Vital To Modern Day Entertainment GEORGE HILL, MANAGING EDITOR
the more I consume, the more difficult it becomes to find anything new.
BIG DATA IS VITAL TO MODERN DAY ENTERTAINMENT
These are not just nice to have aspects of online entertainment platforms today though, it is an absolute necessity.
Today we are not just seeing companies utilizing Big Data to make the most of their business opportunities or to cut out waste. It has now become a key protagonist in the success of consumer entertainment platforms and technologies. A prime example is in the major battleground between Apple Music and Spotify; playlisting. To combat Apple’s apparent advantage in this area - its thousands of curated lists - Spotify created their ‘Discover Weekly’ playlists to help users find the kind of music that they are likely to appreciate. To do this, Spotify analyzes previous listening habits to create a playlist of 30 songs every week that the specific user will probably like. Apple, however, have taken the approach of curating lists based on explicitly selected content, but given the breadth of their user base, this is likely to become more data driven in the future. These are not just nice to have aspects of online entertainment platforms today though, they are an absolute necessity. One of the biggest changes in the way that companies provide entertainment content is that rather than specializing in a small number of items, they now provide a huge number of titles for their consumers. Netflix, for instance, has over 36,000 unique titles and this number is constantly growing. The ability to find the perfect film or TV show
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for any occasion should theoretically be easy, but the reality is that with the individual tastes of people, it is not as simple as searching, as the database is so vast. This has made the suggestion engines on Netflix one of the most important elements to not only making the site useful, but fully useable. The difficulty of trying to find a specific film for a specific time and mood is then made considerably easier and the vast database of films becomes much more navigable. It is not only true of video content, where you will be entertained for several hours through films and series. We have seen in terms of music streaming that this needs to be done within the space of around 3 minutes, meaning that the next song being chosen needs to have been found after searching through your listening history. The longer your history, the better your recommendations, which is why Apple Music - less than 3 months old - is still lagging behind when it comes to data driven suggestions. This is something that they are likely to significantly improve in the future. All of this entertainment being shown to us based on our previous consumption has also meant that we watch and listen to more of it. Where I used to have a cassette with one album on, I would have needed to listen to that
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album through, then change the cassette to another album, which I would have previously bought. The same is true with any number of physical media types such as CDs, DVDs, Blurays and even records. Today by paying the same amount as one film or one album, I have access to millions of songs and tens of thousands of movies and TV shows. It means that I get bored far easier and the more I consume, the more difficult it becomes to find anything new. Through predictive algorithms it is possible to identify new and previously unheard/unwatched content, allowing further content to be consumed. Without this use of data within these entertainment platforms, it would be almost impossible to use them effectively and the way that most people use them today would be vastly different. In fact, when you look at the recent redesign of the Netflix site, you can see the importance of these suggestion algorithms - before I get to any kind of broad category I have 54 suggestions spread
across 9 rows of films, similarly, Spotify prominently place the discover weekly playlist for their users, making it almost impossible to miss. We have now reached a point where the size of the libraries has become almost meaningless as they are all as vast as each other. The key battlegrounds have now become how the content is filtered and shown, something that data has a pivotal role in.
Netflix for instance has over 36,000 unique titles
Refugee Crisis And The EU: Will Big Data/Analytics Provide Some Answers? BRUNO POLACH, PROGRAMME MANAGER, EBCG Numbers do not lie. Refugees from African countries and the Middle East are entering Europe in vast amounts as we speak, with the EU facing a humanitarian crisis of ‘biblical proportions‘. Pictures of full trains, boats and people in refugee camps in Europe are on all the news channels, and there are a lot of controversies around this topic discussed everywhere – including the upper echelons of the EU, voices of leaders like Merkel, Hollande and others calling for ‘fair’ distribution of migrants among EU member states. Some countries are approving of the refugees influx, others express clear disagreement and call for the breaking up of smugglers networks and addressing this issue thoroughly.
‘Europe cannot just get emotional,’(Matteo Renzi, Italian PM) “Today it’s everyone’s problem and we appeal for people to be rational. The emergency can only be managed with a strategic vision,’(Matteo Renzi, Italian PM) ‘German thoroughness is great, but what we need now is German flexibility,’(Angela Merkel, German Chancellor) I was wondering if the realm of Analytics/Big Data might be of systemic assistance here – what I am getting at is the move from assumptions/emotions/guesswork to clear vision/fact based decisions and rational thinking. It is obvious that the situation needs
to be dealt with effectively. Series of EU talks and summits are up and coming – but the question still remains: how do the leaders want to come up with a solution (preferably in a united manner)? Currently we know approximately how many migrants are coming from which countries (Syria, Eritrea, Afghanistan, etc.) – but can we look at the historical track record so far and predict more accurately how many more refugees we can expect over coming weeks and into which points of entry? Can military, police and humanitarian efforts be more coordinated and pre-emptive based on this? We need a thorough analysis of all the motivational factors of refugees to flee their homeland, broken down
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into specific countries, regions, demography, culture, conflicts and religious differences – beyond just two broad categories of war/ economically determined migration. How about worsening weather conditions in Europe in autumn and its potential impact? Can we analyze these in more detail and make some further predictions? There are advanced technologies like analytical geospatial maps available – is the EU effectively utilizing these to track the smugglers base camps, activities and next movements? Certainly there is a need for anti-trafficking cooperation and development policies for Africa, Middle East and the Balkans orchestrated together with EU.
This could perhaps be a great oppor- and migrations motivation factors – tunity for a concerted effort of Big only then can create comprehensive Blue (IBM), SAP, Big Four (Deloitte, list of measures which in turn can be EY, KPMG, PwC) and other Analytics/ used to agree on quota between EU Big Data purveyors to come up with members and also, perhaps, having comprehensive capabilities. They some tangible means to hold refucould create holistic reports and lists gee home countries accountable as of recommendations based on exist- well? ing internal and external data sets for EU leaders who then could start making fact-based decisions to deal with this crisis. Evolution - Mediterranean Sea Sea Arrivals
Once they have more solid refugee forecasts for coming weeks and months together with solid reasoning
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IS BETTER TOGETHER WITH Did you know? Ignite™ provides high performance, shared in-memory storage for Spark™ jobs Ignite also accelerates SQL execution dramatically for Spark jobs Like Spark, Ignite is a Top Level Project of the Apache Software Foundation Source: GridGain Systems
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DATA IN PHARMA NEEDS BETTER PROTECTION LAURA DENHAM, ORGANIZER, BIG DATA & ANALYTICS FOR PHARMA
The world has recently seen some of the largest and highest profile hacks ever.
12 percent of pharma companies have been attacked between seven and nine times
From the infidelity website Ashley Madison through to Target, organizations are seeing that they are being targeted by nefarious hackers in an attempt to either blackmail them or simply steal customer information. The pharma industry is no exception, and according to a recent survey, may be being hit in a
more sustained way than many other industries. A poll by Crown Records Management of 407 senior executives found that 12 percent of pharma companies have been attacked between seven and nine times, while 8 percent had been targeted between 13 and 15 times. These are not insignificant numbers and although 407 may not be a huge test group, it is demonstrative of the issues that pharma companies are facing with the storage of their data.
DATA IN PHARMA NEEDS BETTER PROTECTION
It is not simply a case of people stealing new ideas or user data, like on other websites either. Pharma companies need to be some of the most secretive due to the information they hold on things like patient care and test results from drugs. In order to achieve their ultimate goals of creating drugs that can be used to help fight certain diseases, they need to hold sometimes sensitive information. Should this get into the wrong hands, the implications can be profound, with legal cases and fines just the start.
UK customs recently seized £16m ($24.5m) worth of counterfeit drugs Given the speed of communication today, the reputation of a company can quickly be destroyed and this is never more important than in medicines, where people are using a company’s product to either protect or cure themselves. If this company has its data hacked, then the chances are that their reputation will be tarnished to the extent that customers may well avoid using them altogether.
were the second most seized commodity in the US. If you were to buy a fake phone you may have it break in your hands and waste a few hundred dollars, if you were to use fake medicine, the consequences could be much worse.
15 Pharma companies need to be some of the most secretive due to the information they hold
Through hacked data, it would be possible to create drugs that look like the real thing and may even attempt to try and recreate their effects. However, slight variations in active ingredients or the amounts in each dose can have terrible effects. It is a problem that is currently being taken very seriously. For instance UK customs recently seized £16m ($24.5m) worth of counterfeit drugs that were trying to be sold in Britain. Unless more is done to protect pharma companies’ data, this problem may increase even further. If the ingredients and processes behind the creation of these drugs are more easily available, we are likely to see the number of people attempting to bypass the law increase. So whereas a hack of an infidelity company could mean divorces, a hack on a pharma company can easily see a loss of life.
Another possibility with any hacked information is that the core aspects of a product could be stolen and mimicked. We have seen this extensively in the electronics market, where in 2013 and 2014 fake electronics
BIG DATA INNOVATION
THE SEVEN PEOPLE YOU NEED ON YOUR BIG DATA TEAM Ian Thomas, Principal Group Program Manager, Microsoft
Congratulations! You just got the call – you’ve been asked to start a data team to extract valuable customer insights from your product usage, improve your company’s marketing effectiveness, or make your boss look all “datasavvy” (hopefully not just the last one of these). And even better, you’ve been given carte blanche to go hire the best people! But now the panic sets in – who do you hire? Here’s a handy guide to the seven people you absolutely have to have on your data team. Once you have these seven in place, you can decide whether to style yourself more on John Sturges or Akira Kurosawa. Before we start, what kind of data team are we talking about here? The one I have in mind is a team that takes raw data from various sources (product telem-
etry, website data, campaign data, external data) and turns it into valuable insights that can be shared broadly across the organization. This team needs to understand both the technologies used to manage data, and the meaning of the data – a pretty challenging remit, and one that needs a pretty well-balanced team to execute.
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The Handyman
The Handyman can take a couple of battered, three-year-old servers, a copy of MySQL, a bunch of Excel sheets and a roll of duct tape and whip up a basic BI system in a couple of weeks. His work isn’t always the prettiest, and you should expect to replace it as you build out more production-ready systems, but the Handyman is an invaluable help as you explore datasets and look
THE SEVEN PEOPLE YOU NEED ON YOUR BIG DATA TEAM
to deliver value quickly (the key to successful data projects). Just make sure you don’t accidentally end up with a thousand people accessing the database he’s hosting under his desk every month for your month-end financial reporting (ahem). Really good handymen are pretty hard to find, but you may find them lurking in the corporate IT department (look for the person everybody else mentions when you make random requests for stuff), or in unlikely-seeming places like Finance. He’ll be the person with the really messy cubicle with half a dozen servers stuffed under his desk. The talents of the Handyman will only take you so far, however. If you want to run a quick and dirty analysis of the relationship between website usage, marketing campaign exposure, and product activations over the last couple of months, he’s your guy. But for the big stuff you’ll need the Open Source Guru.
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The Open Source Guru
I was tempted to call this person “The Hadoop Guru”. Or “The Storm Guru”, or “The Cassandra Guru”, or “The Spark Guru”, or… well, you get the idea. As you build out infrastructure to manage the large-scale datasets you’re going to need to deliver your insights, you need someone to help you navigate the bewildering array of technologies that has sprung up
in this space, and integrate them. Open Source Gurus share many characteristics in common with that most beloved urban stereotype, the Hipster. They profess to be free of corrupting commercial influence and pride themselves on plowing their own furrow, but in fact they are subject to the whims of fashion just as much as anyone else. Exhibit A: The enormous fuss over the world-changing effects of Hadoop, followed by the enormous fuss over the world-changing effects of Spark. Exhibit B: Beards (on the men, anyway). So be wary of Gurus who ascribe magical properties to a particular technology one day (“Impala’s, like, totally amazing”), only to drop it like ombre hair the next (“Impala? Don’t even talk to me about Impala. Sooooo embarrassing.”) Tell your Guru that they’ll need to live with their recommendations for at least two years. That’s the blink of an eye in traditional IT project timescales, but a lifetime in Internet/Open Source time, so it will focus her mind on whether they really think a technology has legs (vs. just wanting to play around with it to burnish their resumé).
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The Data Modeler
While your Open Source Guru can identify the right technologies for you to use to manage your data, and hopefully manage a group of developers to build out
17 the systems you need, deciding what to put in those shiny distributed databases is another matter. This is where the Data Modeler comes in. The Data Modeler can take an understanding of the dynamics of a particular business, product, or process (such as marketing execution) and turn that into a set of data structures that can be used effectively to reflect and understand those dynamics. Data modeling is one of the core skills of a Data Architect, which is a more identifiable job description (searching for “Data Architect” on LinkedIn generates about 20,000 results; “Data Modeler” only generates around 10,000). And indeed your Data Modeler may have other Data Architecture skills, such as database design or systems development (they may even be a bit of an Open Source Guru). But if you do hire a Data Architect, make sure you don’t get one with just those more technical skills, because you need datasets which are genuinely useful and descriptive more than you need datasets which are beautifully designed and have subsecond query response times (ideally, of course, you’d have both). In my experience, the data modeling skills are the rarer skills; so when you’re interviewing candidates, be sure to give them a couple of realworld tests to see how they would actually structure the data that you’re working with.
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THE SEVEN PEOPLE YOU NEED ON YOUR BIG DATA TEAM
The Deep Diver
Between the Handyman, the Open Source Guru, and the Data Modeler, you should have the skills on your team to build out some useful, scalable datasets and systems that you can start to interrogate for insights. But who to generate the insights? Enter the Deep Diver. Deep Divers (often known as Data Scientists) love to spend time wallowing in data to uncover interesting patterns and relationships. A good one has the technical skills to be able to pull data from source systems, the analytical skills to use something like R to manipulate and transform the data, and the statistical skills to ensure that his conclusions are statistically valid (i.e. he doesn’t mix up correlation with causation, or make pronouncements on tiny sample sizes). As your team becomes more sophisticated, you may also look to your Deep Diver to provide Machine Learning (ML) capabilities, to help you build out predictive models and optimization algorithms. If your Deep Diver is good at these aspects of his job, then he may not turn out to be terribly good at taking direction, or communicating his findings. For the first of these, you need to find someone that your Deep Diver respects (this could be you), and use them to nudge his work in the right direction without being overly directive (because one of the magical properties of a really good Deep
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Diver is that he may take his analysis in an unexpected but valuable direction that no one had thought of before). For the second problem – getting the Deep Diver’s insights out of his head – pair him with a Storyteller (see below).
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The Storyteller
The Storyteller’s yin is to the Deep Diver’s yang. Storytellers love explaining stuff to people. You could have built a great set of data systems, and be performing some really cutting-edge analysis, but without a Storyteller, you won’t be able to get these insights out to a broad audience. Finding a good Storyteller is pretty challenging. You do want someone who understands data quite well, so that she can grasp the complexities and limitations of the material she’s working with; but it’s a rare person indeed who can be really deep in data skills and also have good instincts around communications. The thing your Storyteller should prize above all else is clarity. It takes significant effort and talent to take a complex set of statistical conclusions and distil them into a simple message that people can take action on. Your Storyteller will need to balance the inherent uncertainty of the data with the ability to make concrete recommendations. Another good skill for a Storyteller
to have is data visualization. Some of the most light bulb-lighting moments I have seen with data have been where just the right visualization has been employed to bring the data to life. If your Storyteller can balance this skill (possibly even with some light visualization development capability, like using D3.js; at the very least, being a dab hand with Excel and PowerPoint or equivalent tools) with her narrative capabilities, you’ll have a really valuable player. There’s no one place you need to go to find Storytellers – they can be lurking in all sorts of fields. You might find that one of your developers is actually really good at putting together presentations, or one of your marketing people is really into data. You may also find that there are people in places like Finance or Market Research who can spin a good yarn about a set of numbers – poach them.
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The Snoop
These next two people – The Snoop and The Privacy Wonk – come as a pair. Let’s start with the Snoop. Many analysis projects are hampered by a lack of primary data – the product, or website, or marketing campaign isn’t instrumented, or you aren’t capturing certain information about your customers (such as age, or gender), or you don’t know what other products your customers are using, or what they think about them.
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THE SEVEN PEOPLE YOU NEED ON YOUR BIG DATA TEAM
The Snoop hates this. He cannot understand why every last piece of data about your customers, their interests, opinions and behaviors, is not available for analysis, and he will push relentlessly to get this data. He doesn’t care about the privacy implications of all this – that’s the Privacy Wonk’s job. If the Snoop sounds like an exhausting pain in the ass, then you’re right – this person is the one who has the team rolling their eyes as he outlines his latest plan to remotely activate people’s webcams so you can perform facial recognition and get a better Unique User metric. But he performs an invaluable service by constantly challenging the rest of the team (and other parts of the company that might supply data, such as product engineering) to be thinking about instrumentation and data collection, and getting better data to work with. The good news is that you may not have to hire a dedicated Snoop – you may already have one hanging around. For example, your manager may be the perfect Snoop (though you should probably not tell him or her that this is how you refer to them). Or one of your major stakeholders can act in this capacity; or perhaps one of your Deep Divers. The important thing is not to shut the Snoop down out of hand, because it takes relentless determination to get better quality data, and the Snoop can quarterback that effort. And so long as you have a good Privacy Wonk for him to work with, things shouldn’t get too out of hand.
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The Privacy Wonk
The Privacy Wonk is unlikely to be the most popular member of your team, either. It’s their job to constantly get on everyone’s nerves by identifying privacy issues related to the work you’re doing. You need the Privacy Wonk, of course, to keep you out of trouble – with the authorities, but also with your customers. There’s a large gap between what is technically legal (which itself varies by jurisdiction) and what users will find acceptable, so it pays to have someone whose job it is to figure out what the right balance between these two is. But while you may dread the idea of having such a buzz-killing person around, I’ve actually found that people tend to make more conservative decisions around data use when they don’t have access to high-quality advice about what they can do, because they’re afraid of accidentally breaking some law or other. So the Wonk turns out to be a pretty essential member of the team, and even regarded with some affection.
best compromise between maximizing your data-driven capabilities and respecting your users’ privacy.
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Bonus member: The Cat Herder (you)
The one person we haven’t really covered is the person who needs to keep all of the other seven working effectively together: To stop the Open Source Guru from sneering at the Handyman’s handiwork; to ensure the Data Modeler and Deep Diver work together so that the right measures and dimensionality are exposed in the datasets you publish; and to referee the debates between the Snoop and the Privacy Wonk. This is you, of course – The Cat Herder. If you can assemble a team with at least one of the above people, plus probably a few developers for the Open Source Guru to boss about, you’ll be well on the way to unlocking a ton of value from the data in your organization.
Of course, if you do as I suggest, and make sure you have a Privacy Wonk and a Snoop on your team, then you are condemning both to an eternal feud in the style of the Corleones and Tattaglias (though hopefully without the bloodshed). But this is, as they euphemistically say, a “healthy tension” – with these two pulling against one another you will end up with the
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BIG DATA COULD BE THE NEW KEY TO SUCCESS IN SPORTS RICK DELGADO, TECHNOLOGY WRITER
If a team wants to taste long-lasting success, big data seems to be the way to go.
BIG DATA COULD BE THE NEW KEY TO SUCCESS IN SPORTS
When it comes to getting more wins and championships, sports teams will look for any edge they can find that will put them over the competition. Perhaps it’s signing a superstar to the team. Or maybe it’s drafting that promising up-and-comer. Or perhaps it’s investing more in scouting and training. No single answer can account for a team’s success, but one new area of technology is quickly proving to be a valuable strategy for every sport out there: Big Data. While mostly associated with the business world, Big Data has made some major inroads in sports. What started in baseball with the Moneyball phenomenon has quickly transformed into a movement featuring some of the latest technological advancements and brilliant analytical minds. If a team wants to taste long-lasting success, Big Data seems to be the way to go. Teams are quickly catching on to how successful Big Data can make them. A recent study from Temple University shows that an impressive 97% of Major League Baseball teams employ data analytics professionals. The National Basketball Association comes in second at 80% of teams, with the National Football League (56%) and the National
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Hockey League (23%) bringing up the rear. Teams that use Big Data analysis tools see it as a chance to study deeper layers of the game, gaining understanding that goes far beyond what normal statistics show. They’re making sure to use the latest technological equipment to do so. Motion tracking technology has been around for years, but only recently has it been deployed in regular sports venues. Soccer stadiums around the world have installed a system of eight cameras which helps teams track player movement and interactions. This leads to nearly 1.5 million data points every game, and considering how it’s used in 12,000 soccer games globally, that’s a lot of data to analyze. With the help of automated algorithms, teams and organizations can get a better sense of how their players are performing and where changes to strategy can come into play. Soccer isn’t the only sport utilizing motion tracking technology. Every arena in the NBA uses a similar system, helping data experts measure player efficiency and how effective they are on the defensive end. Based off the findings from Big Data
BIG DATA INNOVATION
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BIG DATA COULD BE THE NEW KEY TO SUCCESS IN SPORTS
analytics, the NBA game has changed considerably in just the past few years. Now there’s a much greater emphasis on taking 3-point shots and playing at a fast tempo. The teams that use these strategies the most also happen to be among the most successful in the league. This use of Big Data can also extend to college basketball as well. Duke is one of the few teams that uses tracking cameras, and they just won the national championship. In addition to motion tracking cameras, teams are also using wearable technology to collect data in order to get a better view of each player’s physical fitness. This can also be used to prevent sports injuries before they happen. By monitoring player stress levels and knowing exactly how much strain they are placing on their bodies, teams can urge more caution and limit playing time when necessary. In the NFL, sensors in equipment can measure how intense someone is playing and how hard they are hit. With analysis of historical data, teams can make sure players are checked for injuries after particularly vicious collisions.
BIG DATA INNOVATION
Other sports are just scratching the surface of what Big Data can offer. Tennis can embed rackets with sensors to measure how well a player is hitting the ball, which can give them insight on how to improve. The same principle is used in golfing equipment to help golf players improve their swings. Formula 1 cars use sensors to collect a wealth of information to know precisely when would be best to refuel and replace tires. Olympic athletes are even using Big Data to measure the quality of sleep they’re getting. All in all, the very nature of sports is changing, all to help athletes reach new levels of excellence. The future of sports with Big Data is a bright one. As the technology improves and more experts enter the field, teams will only become more adept at interpreting and using data to become more successful. The level of competition will increase, and more excitement will result. In the end, everyone involved, from the teams to the players to the fans, will benefit.
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CHIEF DATA OFFICER SUMMIT December 1 & 2, 2015 | New York
ADVERT Vision, Strategy & Oversight for Data
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Speakers Include
BIG DATA INNOVATION
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DATA VISUALISATION SUMMIT NOVEMBER 11 & 12, 2015 | LONDON #DataVizLDN
Telling Your Story With Data
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Why do you
Need a Data-Driven
data-driven organizations experience a 27% year-on-year increase in revenue, compared to 7% for other organizations
Culture?
OLIVIA TIMSON, BIG DATA WRITER
A report released last year by the Aberdeen Group, ‘The Executive’s Guide to Effective Analytics,’ revealed that data-driven organizations experience a 27% year-on-year increase in revenue, compared to 7% for other organizations. Furthermore, 83% saw their process cycle times improve, whereas just 39% of organizations that weren’t classified as data driven bettered their’s, and 12% cut their operating expenses from the prior year, compared to 1% of other organizations. We are in an age in which all processes are driven by technology, and companies are consuming and generating data at accelerating rates and exponentially increasing quantities. If they are not leveraging this data for actionable insights, they are losing their competitive edge to a firm that is using it. In order to truly see the benefits though, it is not simply a case of buying a
bit of software that will churn out insights, or even hiring in people with the requisite skills to make sense of it, it takes a whole shift in company culture. It needs the whole organization - every team and every individual - to be taking it into consideration at all times, for everybody to be collecting it, looking for insights, and using it in their decision making processes. By focusing on fact-based insights, the number of arguments within teams and among different C-suite executives are decreased, and there is less of a reliance on ‘gut instinct’ - the fairly nebulous concept that traditionally drove both decisions and disagreements. A study by MIT Sloan Management Review and SAS ‘The Analytics Mandate’ concluded that an ‘analytics culture’ is the driving factor in achieving competitive advantage from data. David Kiron, executive editor for MIT Sloan
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WHY DO YOU NEED A DATA-DRIVEN CULTURE?
Management Review, noted of the study’s discoveries that: ’We found that in companies with a strong analytics culture, decisionmaking norms include the use of analytics, even if the results challenge views held by senior management. This differentiates those companies from others, where often management experience overrides insights from data.’ A data-driven culture empowers everybody in the organization, regardless of their experience, to bring their ideas to the table, so long as they are supported by the data. By encouraging everybody in the company to share their insights, the floor is opened to a far wider range of people, which drives innovation. This also improves staff morale, with employees considering themselves more valued as their ideas take on a higher importance. There are a number of other ways that data can be of benefit. It is useful in identifying waste and unnecessary processes, which means that costs can be cut appropriately. By making
fact-based decisions, risk is also hugely lessened and there is a far more knowledge around the likely outcome. You can also know and serve your customers better. Alignment between internal operating effectiveness and external customer experience is made possible, and information can be shared across an organization that helps to get everybody on the same page when it comes to what the customer wants. There are a number of challenges to implementing a data-driven culture. C-suite executives could perceive it as a threat to their power, for a start. Its development needs somebody in place who will really drive it forward, and make sure that everybody adapts their working practises, which they may have held for years. However, while it may be a challenge, it is certainly one worth overcoming to stay ahead of the competition.
making fact-based decisions, risk is also hugely lessened and there is a far more knowledge around the likely outcome
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an ‘analytics culture’ is the driving factor in achieving competitive advantage from data
big data analytics innovation summit beijing Speakers Include ie.
& Analytics Innovation
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