Harnham Blog & News

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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Harnham's Brush with Fame

Harnham have partnered with The Charter School North Dulwich as corporate sponsors of their ‘Secret Charter’ event. The event sees the south London state school selling over 500 postcard-sized original pieces of art to raise funds for their Art, Drama and Music departments. Conceived by local parent Laura Stephens, the original concept was to auction art from both pupils and contributing parents.  Whilst designs from 30 of the school's best art students remain, the scope of contributors has rapidly expanded and now includes the work of local artists alongside celebrated greats including Tracey Emin, Sir Anthony Gormley, Julian Opie, and Gary Hume.  In addition to famous artists, several well-known names have contributed their own designs including James Corden, David Mitchell, Miranda Hart, Jo Brand, Jeremy Corbyn, and Hugh Grant.  The event itself, sponsored by Harnham and others, will be hosted by James Nesbitt, and will take place at Dulwich Picture Gallery on the 15th October 2018.  You can find out how to purchase a postcard and more information about the event here. 

Breaking Code: How Programmers and AI are Shaping the Internet of Tomorrow

Data. It’s what we do. But, before the data is read and analysed, before the engineers lay the foundation of infrastructure, it is the programmers who create the code – the building blocks upon which our tomorrow is built. And once a year, we celebrate the wizards behind the curtain.  In a nod to 8-bit systems, on the 256th day of the year, we celebrate Programmers’ Day. Innovators from around the world gather to share knowledge with leading experts from a variety of disciplines, such as privacy and trust, artificial intelligence, and discovery and identification. Together they will discuss the internet of tomorrow.  The Next Generation of Internet At the Next Generation Internet (NGI), users are empowered to make choices in the control and use of their data. Each field from artificial intelligent agents to distributed ledger technologies support highly secure, transparent, and resilient internet infrastructures. A variety of businesses are able to decide how best to evaluate their data through the use of social models, high accessibility, and language transparency. Seamless interaction of an individual’s environment regardless of age or physical condition will drive the next generation of the internet. But, like all things which progress, practically at the speed of light, there is an element of ‘buyer beware’, or in this case, from ‘coder to user beware’. Caveat Emptor or rather, Caveat Coder The understanding, creation, and use of algorithms has revolutionised technology in ways we couldn’t possibly have imagined a few decades ago. Digital and Quantitative Analysts aim to, with enough data, be able to predict some action or outcome. However, as algorithms learn, there can be severe consequences of unpredictable code.  We create technology to improve our quality of life and to make our tasks more efficient. Through our efforts, we’ve made great strides in medicine, transportation, the sciences, and communication. But, what happens when the algorithms on which the technology is run surpasses the human at the helm? What happens when it builds upon itself faster than we can teach it? Or predict the infinite variable outcomes? Predictive analytics can become useless, or worse dangerous.  Balance is Key Electro-mechanical systems we could test and verify before implementation are a thing of the past, and the role of Machine Learning takes front and centre. Unfortunately, without the ability to test algorithms exhaustively, we must walk a tightrope of test and hope. Faith in systems is a fine balance of Machine Learning and the idea that it is possible to update or rewrite a host of programs, essentially ‘teaching’ the machine how to correct itself. But, who is ultimately responsible? These, and other questions, may balance out in the long run, but until then, basic laws regarding intention or negligence will need to be rethought. Searching for a solution  In every evolution there are growing pains. But, there are also solutions. In the world of tech, it’s important to put the health of society first and profit second, a fine balancing act in itself. Though solutions remain elusive, there are precautions technology companies can employ. One such precaution is to make tech companies responsible for the actions of their products, whether it is lines of rogue code or keeping a close eye on avoiding the tangled mass of ‘spaghetti’ code which can endanger us or our environment. Want to weigh in on the debate and learn how you can help shape the internet of tomorrow? If you’re interested in Big Data and Analytics, we may have a role for you. Check out our current vacancies. To learn more, contact our UK team at +44 20 8408 6070 or email us at info@harnham.com.

Download our 2018 UK & EU Salary Guides

We are thrilled to announce the release of the 2018 editions of our market-leading Salary Guides for the UK, US and Europe. Having spoken to thousands of Data & Analytics professionals across the globe, we gained invaluable insights into key industry salaries and trends across a wide variety of specialisms and sectors.  Our surveys are created for analysts, by analysts, and offer a detailed, on-the-ground look at what’s concerning talent in the industry. As with the last few years, 2018 has shown us that the data industry continues to grow and shows no sign of slowing, with demand for analysts still easily outstripping supply. The guides include salary and trend analysis across five key specialisms: Data & Technology, Data Science, Digital Analytics, Marketing & Insight, and Risk Analytics. You can download the UK & EU guides here. 

Our Top Five Tips For Telling Stories With Data

As the Data & Analytics marketplace continues to grow, what is it that makes a candidate stand out? More and more, employers are on the lookout for people with both hard and soft skills; those who cannot only interpret data, but possess the ability to translate and relay that data to key stakeholders.  To convey data in a cohesive, informative, and memorable way, we need to think beyond making something aesthetically pleasing. People connect with stories, be they fictional, personal, historical or otherwise. By utilising universal storytelling techniques, we can share data in a way that people intuitively connect with.  Here are our Top Five Tips for telling stories with data: Start With The Structure  Structures are the essential foundations that sit under any good story. Without a solid structure, the story we are telling can become confusing, distracting and unfocused. When presenting data, it is essential that we work to a clear structure to ensure that we can be understood.  All stories feature three things; a beginning, a middle, and an end. A story told through data is no different: The Beginning: What is the question that has been asked? What are we trying to learn from this information? The Middle: The Data itself. What the numbers say. The End: What insights can we gain from the data, what is the data really telling us? By sticking to this structure, we can ensure that each bit of information gathered is explained with the relevant context required to convey the most information possible.  When looking at several pieces of data, it makes sense to think of these as chapters. They may tell their own smaller story, but in the wider context of an overall narrative, they need to be in the correct order to make sense and not leave anyone confused.  Speak To Your Audience When presenting data, it is crucial to remember who your audience is. Whey they’re a novice, expert, or the chairman of your company, each individual has their own vested interested in what you are showing them. As a Data and Analytics professional, your job is to serve as curator, creating a story that feels tailored to each unique person.  In order to help understand how your audience might be best served by your story, it’s helpful to ask yourself the following questions: What information are the most interesting in? What information do they need to know the most? What is their daily routine?  Is this their big meeting of the day, or one of several back-to-back? What actions will they take off the back of your insights? By asking these questions, you should be able to curate your data in a way that is meaningful for your audience.  Find Your Characters The majority of data is based upon an initial human interaction. From a video viewed, to a product purchased, it’s easy to forget that at the end of the line is a real human being. By bringing this to the forefront of your insights you create a compelling new way to connect with your audience. Consider what this data actually meant when it was first gathered; who was that person and what does this information say about them? If you are able to create ‘personas’ or ‘characters’ from this data, you can present something tangible that people can connect and, potentially, even empathise with.  Even if you use existing data to reference a personal experience, you’re adding a sense of palpability that gives your insights depth.  Painting The Right Picture  As Data Visualisers will tell you, the most elaborate visual is not always the most appropriate way to convey your insights. The key is to always consider what tells the story best. A heat map may be perfect for telling a story of geographical differences but is likely to make no sense when conveying a customer journey.  The beauty of utilising different visual techniques is that they allow you to create an emotional impact with data, fully emphasising the meaning of your insights. David McCandless showcases how data can be visualised in various dynamic ways that create the most amount of meaning possible.  Start Big, Get Smaller Data presentations have the difficult challenge of needing to be both accessible and detailed. By ensuring that you have the big picture covered with enough context, you can ensure that everyone gets the headline takeaway.  Following this, you can highlight further insights that reveal more information for those who need to do a deeper dive. Much like in a good story, whilst you may understand the overall narrative the first time round, looking closer and revisiting certain parts should reveal more insights and nuances.  If you have the skills to turn Data & Analytics insights into compelling stories then we may have a role for you. Register with us or search the hundreds of jobs available on our site. 

HOW DEEP LEARNING IS TREATING HEALTH-BASED ISSUES

Hospitals are a complicated system of many moving parts both human and machine. In recent years, the role of humans driving the process, entering information, gathering individual records, or arranging medical and billing follow ups, has shifted. Paper records have become electronic health records and AI is helping streamline bulky processes. AI bots and programs free up time when it comes to arranging follow up medication or helping to make diagnoses and, in some cases, can assist physicians or surgeons making remote calls and decisions. As Machine Learning and AI enter healthcare, the application of Deep Learning, using data rather than task-based algorithms, is coming into its own. At this year’s KDD event, both Healthcare and Deep Learning were hot topics, with a day of programming dedicated to each. The Three Ingredients Driving AI Advances: Supply of digital data which can now be created. Development of algorithms to make artificial neural networks. Graphics Processing Unit (GPU) chip architecture pioneered by NVIDIA. GPUs are used by anyone working in Deep Learning and can be used in any number of ways, such as videos, graphics, and audio recordings to name a few. This type of usage has huge impact on Healthcare’s image, clinical data interpretation, and management.  For example, Radiology requires consultants to look at medical imagery to determine whether or not there are abnormalities. With the inclusion of Deep Learning, this process could be done in minutes or seconds rather than hours. This is especially important as a diagnosis made is based on findings in the radiological images. However, Radiology, is not the only instance where health management can utilise Deep Learning and AI. From helping to identify ideal treatments for patients, to helping administrators utilise their resources more effectively and efficiently, there is huge potential for implementation.  Predictive Analytics in Deep Learning Healthcare can be hard to predict. But, with the application of Machine Learning, there are some things we can focus on, starting by asking ourselves the following: Is it scalable? This may differ based on different hospital systems and how much data wrangling is involved. But, the more straightforward the answer, the better. Is it accurate?  Using Deep Learning data for electronic health records can greatly improve accuracy and avoid the distraction of false alarms. Predictive modelling can help Healthcare professionals answer the questions above more accurately, including determining which patient will have a particular outcome versus which patient will not. Though this model does not diagnose the patient, it does use the information from data gathered to identify the conditions in which the patient was being treated and predict outcomes. Like a human might pick up nonverbal signals, AI picks up signals based on the data it receives to and helps inform physician’s decisions. The Patient Journey  Whether it’s the customer journey or the patient journey, there is a path that needs to be followed. As Deep Learning helps fuel the use of AI in Healthcare, our patient journey becomes less stressful and more streamlined.  Below are a few ways Deep Learning is helping to facilitate a more efficient health management system: At Home: You go to a doctor because you don’t know what’s wrong. But, how do you know which doctor you should make an appointment with? AI can help. From your home PC, a few clicks and few questions can direct you to the correct provider for your needs. In the Waiting Room: To avoid long wait times, you can check in via an app, have an AI bot ask a number of questions for you to answer to help better prepare the physician for your visit with the goal of a quicker diagnosis. With the Doctor: Referrals are great. But, having to explain your health issue or record, can be daunting. In addition, the doctor to whom you’re referred may have to call your traditional physician and discuss, or he or she may have papers to read cutting into their time with you. Instead, AI standardises how the doctor reads the notes and can lay it out the way the doctor prefers, increasing your time with them and streamlining their process. Patient Follow Up: An AI bot based on Deep Learning algorithms can become part of a provider’s team, checking in, asking a few questions, and sending a friendly reminder email, text, or phone call to remind patients to take continue their course of treatment.  The introduction of Deep Learning into Data & Analytics has made an impact across many industries, but especially Healthcare. not the least of which has been healthcare. From speech recognition to Natural Language Processing, the effects have been informative and transformational. If you’re interested in Deep Learning, predictive analytics, or AI we may have a role for you. We specialise in Junior and Senior roles.  To learn more, check out our vacancies. You can also call us at +44 20 8408 6070 or email us at info@harnham.com.

How Netflix Got Big with Big Data

There’s little argument that Netflix have changed the game when it comes to how people consume entertainment. Whilst Amazon, Disney and Apple seek to replicate the success of Netflix’s model, they still lead the way with over 130 million subscribers worldwide and have just broken HBO’s 17-year streak as the most nominated ‘network’ at the Emmy’s with an astonishing 112 nominations.  Having begun life as a subscription-based DVD rental-service created in response to founder Reed Hasting’s frustration with late rental fines, Netflix were one of the first to offer video-streaming as an option for viewing films and TV. Now filled with scores of original programming, the secret to their success lies not just in creativity and innovation, but in Big Data.  Top Picks From Your Data When the former CEO of the now-defunct Blockbuster claimed: “Netflix doesn’t really have or do anything that we can’t or don’t already do ourselves”, he made a vital oversight. Whilst Netflix may have offered fewer films and TV shows at the time, they were already busy collecting, and utilising, customer data in a way that hadn’t been done before. This included: What do people search for? When do they watch a program? What device do they watch on? Do genre preferences vary with device? When do they stop watching? What shows are the likely to ‘binge’? Or even what are the horror films that people find too scary to watch until the end… Netflix used, and still uses, this information to create recommendations for each user, curating an individual experience based upon personal preferences. This technique has been incredibly successful with over 75% of viewer activity based upon these recommendations. And they continue to finesse how their collect their data, switching from a five-star rating system to a thumbs up/thumbs down model. Cameron Johnson, Netflix’s Director of Product Innovation had observed: “a difference between what [users] say, and what they do,”. For example, frequently-watched comedies were being awarded three stars, as opposed to occasionally-watched, but ‘more worthy’ documentaries being given five stars. By simplifying the system to a like/dislike set-up, Netflix can provide subscribers with recommendations “more aligned with what people actually play”.  Stream if you want to go faster Unlike traditional broadcast mediums, Netflix’s income doesn’t come from advertising, or a pay-per-view service, but subscribers. That means their main ambitions are to generate new subscribers and keep existing ones. If Netflix has data that tells them users who stream over a specific number of hours of programming are more likely to stay, they can place their focus on ensuring they watch at least that many hours. It’s highly likely that the introduction of the ‘skip-credits’ feature was a result of Netflix realising that this was the time when people were most likely to turn off, when the was an opportunity to encourage them to watch more.  Perhaps most interestingly of all, Netflix’s Big Data team are helping inform creativity. This ranges from supplying that data that helps personalise trailers for new content based on each subscriber’s preferences, to deciding which shows to commission. Netflix’s data told them that prison-based dramas, shows with strong-female ensembles, and programs with LGBT+ themes and characters were both popular, and shared a lot of audience overlap. With all this information at hand when they commissioned ‘Orange Is The New Black’ for a full series, Netflix could be sure that there was an audience for the show.  As more and more companies add their own streaming services, including Disney’s expected behemoth, this targeted original content is going to become more and more valuable for Netflix. Fortunately, they’re long-used to changing not just how people watch, but also what they watch.  Browse Our Collection If you’re looking to apply your understanding of Big Data to disrupt and revolutionise an industry, we may have a role for you. Take a look at our current selection of opportunities here. 

Why A Good Work-Life Balance Is Better for Business

Contrary to American sitcoms, work life balance isn’t about sitting in coffee shops contemplating life and complaining about work. However, there are plenty of jobs where you can work from or in a coffee shop. The rise of virtual, remote, and contractual roles has contributed to the demand for work life balance. But, sometimes, in our tech-led world, where business can follow us anywhere, the balance becomes more about setting boundaries. It’s about putting down our mobile phones, closing our laptops, and dipping our toes into other waters.  Where Does Your Country Fit on the Work-Life Balance Scale? European countries have been leading the way with work-life balance for some time, with the Netherlands topping the list at number one. With the UK sitting at number 29 out of the 38 countries in the Organisation for Economic Co-operation and Development (OECD), what’s tipping the scales? 13% of British employees work 50 or more hours per week versus 0.5% of people in the Netherlands work those long hours. The average Brit is therefore only setting aside 14.9 hours for leisure and personal care (including eating and sleeping) a day versus those in the Netherlands who dedicate 15.9 hours. Countries in the Nordics work a maximum of 48-hours per week. However, the reality is significantly lower, with the Finnish working an average of 36.2 hours a week, the Swedes 35.9 hours, Norwegians at 34 hours, and the Danes just 32 hours.Denmark, Finland, Sweden, Norway, and Iceland have become renowned for fostering optimal work-life balance. But, though the Netherlands sits at the number one spot on the OECD, the Danes top the list as the happiest in the world. The Danish welfare model, characterised by quality of life and a good work-life balance offers: Flexible working conditions and social support networks, including maternity leave and childcare facilities. A high degree of flexibility at work – often including adaptable start times and the ability to work from home. Lunch breaks are often at a designated time each day, enabling colleagues to interact, eat together, and get away from their desks. There is a minimum 5 weeks’ paid holiday for all wage earners. The Danish welfare society is characterised by quality of life and a good work-life balance. Work-life balance for the Danes is a healthy balance of priorities. As important as career and ambition is, are is just as important to balance life outside work (pleasure, leisure, family, and health). This understanding of balance not only puts Denmark at the top of the international equality table, it also contributes to a generally high standard of living. Further research shows 33% of working American adults work over the weekend and on holidays. This, in turn, has led 66% to say they don’t feel they have a good work-life balance. One of the main drivers contributing to the need to always be “on” and available is 24/7 technology.  For example, if an employer emails, texts, or rings an employee at dinnertime, the employee often feels compelled to answer straightaway. While 57% of those surveyed feel technology has ruined the family dinner, 40% believe it is okay to answer an urgent call or email at the dinner table. So, it comes back to boundaries and not feeling guilty about ‘switching off’ for a few hours or a few days to ‘recharge’. What Companies are Doing to Improve Work-Life Balance  Nordic businesses remain at the top of the list for best work-life balance. Though much of it is dictated by strict Nordic Labour laws, companies outside the Nordics are beginning to take pages from their playbook.  At a business in Helsinki, Finland, employees are encouraged to go home on time at the end of their day. Often this falls around 5:00pm, though leaving earlier to say, go to a child’s sports activity, is always a guilt-free option.  Like many European businesses, employees also receive five weeks of paid vacation each year. Everyone gets stock options and teams are small with the ability to make autonomous decisions. The theory: this team is closest to the project, they know what is best for it. No management approval required, but only to help share in lessons learned. Many Nordic businesses have shortened hours and a focus on family. By putting family first, businesses report improved productivity and innovation, less absenteeism, and reductions in staff turnover. Other benefits can include: Ability to leave work 30-minutes early to pick up kids from school or take them to sports practice Ability to use sick days to take care of sick children Businesses regularly offer gym memberships, event discounts, leadership classes, and team-building exercises as well as opportunities for employees to take courses and further their education. At one business, in Sweden, for example, employees have access to a leisure centre and recreational activities such as fishing, tennis, and swimming. Though everyone has their own definition of what work-life balance means to them, it can be difficult to follow without government mandates, like in some European countries, or if you’re a small business. Our UK and Europe Salary Guide showed that, with over 98% of respondents working full time, at least some flexibility is now expected. We found that 53% of respondents work at home at least one day a week, and 56% have flexible working hours, highlighting that these ‘benefits’ are now becoming the norm.  Harnham Life As a business, we try to both reflect, and the lead the way with, developments that we see across the Data & Analytics industry. From ensuring our consultants leave on time two days per week to participate in pursuits outside work, to offering one fully-paid Charity Day per year, we place emphasis on creating an environment where our teams feel like they have a good work-life balance. By building a culture where a consultant can set up a book club or arrange a night out on the town, we have formed a business where employee welfare is prioritised.  Though everyone has their own definition of what work-life balance means to them, it can be difficult to follow without government mandates like in some European countries or if you’re a small business. The important thing is to do what’s right for you and sometimes turn off your phone, close your laptop, and meet up with some family or friends in that coffee shop.  Whether you’re looking for a permanent position with more benefits, or the freedom of a contract role, we’re here to help with your job search.  

Key Fraud Trends: How to Stay Safe in the Changing Fraudscape

Sharing and collecting data is part of our everyday lives. Whether our information is shared over social media, e-commerce sites, banks, or elsewhere, this can open up risks.  2017 saw the highest number of identity fraud cases ever, an increase in young people ‘money muling’ and higher bank account takeovers for over-60s. Whilst overall fraud incidences fell 6%, these cases highlight just some of the changing trends as fraud issues stem more from misuse than ever before. Dixons Carphone, Facebook and Ticketmaster are just three cases you may recognise from a string of high profile data breaches this year. Technological advances, more accessible and available data, coupled with an increased sophistication of fraud schemes, makes it more likely that data breaches and fraud attacks will become regular news items. But how is the fraud landscape changing and can technological advances be advantageous in detecting and reducing fraud? Identity fraud increasing for under 21s In June 2018, Dixons Carphone found an attack enabled unauthorised access to personal data from 1.2 million customers. It’s now been uncovered that the number is much higher, closer to ten times initial estimates. Whilst no financial information was directly accessed, personal data such as names, addresses and emails enable fraudsters to fake an identity. Younger fake identities are used more for product and asset purchases which typically require less stringent checks, such as mobile phone contracts and short-term loans.  In 2017, Cifas, a non-profit organisation working to reduce and prevent fraud and financial crime, reported the highest number of identity fraud cases ever. Under 21s are most at risk seeing a 30% increase as they engage more with online retail accounts. Whereas previously identity theft would manifest itself in fraudulent card and bank account activity, it’s now being used to make false insurance claims and asset conversion calling for stronger detection in these industries.  Young People Used as Money Mules This age group aren’t only being targeted for identity theft; there’s a 27% uplift in young people acting as money mules. ‘Money muling’ is a serious offence that carries a 14-year prison sentence in the UK. In most cases, younger people are recruited with the lure of large cash payments to facilitate movement of funds through their account, taking a cut as they go.  In a world where young lives are glamourised and luxurious goods are displayed over social media, this cut can be particularly appealing. Whether aware, believing the reward outweighs the risk, or unaware a money laundering crime is being committed, deeper fraud controls are needed across social media as much as bank accounts. This raises the question as to whether banks should be linking social media to customer details to stop money laundering early on? Increased bank account takeover for over 60s Cifas also reported an increase in account takeovers for over 60s for the same period. Seen by fraudsters as a less tech-savvy and therefore more susceptible demographic, over 60s are increasingly being targeted with online and social engineering scams. The same features which can make some over 60s a target for these scams, can also mean that account takeovers are not immediately noticed and reported, posing yet another difficulty for fraud monitoring and prevention. Vigilance and proactiveness is key. Here are three tips to get you started: Never give personal or security information to someone who contacts you out of the blue, either online, on the phone, or face to face. Always phone and check with the company first. If you make the call then you know you can trust the person on the other end. Check with your bank to see if they offer an elder fraud initiative such as a monitoring service that scans for suspicious activity and alerts customers and their families or educates seniors on types of scams and how to avoid them. When in doubt about something, delay and seek a second opinion. Check with your local library, government offices, or non-profit organisation for more top tips to stay safe from scams and social engineering.   Industry approach Traditionally, financial services organisations have been at the forefront of developing fraud controls; they are often the ones most impacted by the financial risk (the monetary cost of the attacks on their business) and regulatory risk (ensuring their business is adhering to regulations and controls). However, with modern day trends and the changing nature of fraud, all industries need to be focused on reputational risks and prevention. Single big events like Facebook and Dixon Carphone’s data breaches can have a far-reaching impact.  But, there is light at the end of the tunnel. Monzo, an online bank, which bills itself as the future of banking has stepped up the game when it comes to their customer’s security. Upon reports of fraudulent activity on customer cards, they took immediate action to correct the problem. Then they took things a step further, introducing digital analytics to help identify trends and patterns. As patterns emerged, Monzo then notified both the breached business and the authorities. Perhaps a cross-industry collaborative approach is needed as, after all, fraudsters are collaborating. By doing so, businesses will become more proactive, rather than reactive, and can put measures in place to stop potential fraud. If you’ve got a nose for numbers and want to help secure the reputation of businesses the world over, we may have a role for you.  To learn more, call our UK team at +44 020 8408 6070 or email us at ukinfo@harnham.com

Welcome to Harnham's New Home

After months of planning, building and fine tuning we are delighted to introduce you to our new website. We’ve worked tirelessly to produce an innovative new mobile-first site designed to give you the best access to our array of hundreds of jobs and industry-leading expertise.  Here you’ll find all the information you need about who we are, our specialist teams and how we can help.  So come on in, take a look around, and get in touch if you’d like us to help with your job search or hiring needs. 

Disruptive Dynasties: From Wimbledon to the World Cup

A Royal Wedding. World Cup 2018. Wimbledon. The last few months have seen a whirlwind of activity in the UK. A few years ago, who would have predicted a royal wedding to an American actress? Or the upset at Wimbledon in both the women’s and the men’s finals? And, of course, who could forget England’s unprecedented run or France’s leap to World Cup victory with their 4-2 win over Croatia. With such significant shocks at both the World Cup and Wimbledon signal, we have to ask ourselves; is this a turning of the tide?  Federer is still reaching for his 21st Grand Slam title. Serena Williams reached the Wimbledon finals a few months after having a baby and having suffered a pulled pectoral muscle. Both dynasties on the grass faced opponents breathing fire, hungry for the win. But whilst The Championships led to some unexpected results, it's the World Cup 2018 that really shook the boat.What Data and Predictive Analytics Taught Us We’ve all done it. Making predictions based on historical data, the always was, and the dynasties of a well-oiled machine, is our best way of guessing how our favourite competitions will work out. We think ‘if Team A has played this way, that way, or won year-on-year’ then surely, it will be that way again. But sometimes, as Steve Lohr points out:“Listening to the data is important … but so is experience and intuition.  After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?” Perhaps one of the reasons for this year’s lack of predictability has been that the best performances have come from unexpected sources. Ronaldo, Messi and Neymar Jr. all under performed in Russia, leaving room for Croatia’s golden generation to shine and France’s youthful side to make their mark. This explanation is supported by FiveThirtyEight’s World Cup Doppelganger tool, which offers a look at the statistical footprint of every player since 1966. From this, we can see that the breakout performances of 2018 were from teams that, with the exception of France, you may not have expected at the beginning of the tournament; Belgium, England, Mexico, and Switzerland:Kylian Mbappé, France, 19 Romelu Lukaku, Belgium, 25  Kieran Trippier, England, 27 Hirving Lozano, Mexico, 22 Xherdan Shaqiri, Switzerland, 26 Kylian Mbappe, at 19, is the youngest and the first teenager to score in a World Cup since Pele in 1958. With further breakout performances from players such as Russia’s Aleksandr Golovin makes it clear there’s room to grow, giving new life to recruitment trends. Even in football, diversity is key. The Best is Yet to Come Like this year’s Wimbledon upsets, the 2018 World Cup suggests that there are new dynasties in the making. Though France has just claimed their second ever World Cup trophy, this is only the beginning for their current squad. According to TransferMarkt.com, of France’s top 13 players, only two are older than 25 and, at 19, star player Kylian Mbappe is the first teenager to score at a World Cup since Pele in 1958. The future is looking bright for Les Bleus. Looking Beyond the Obvious Whilst we often use predictive analytics in sports, sometimes we need someone who can see beyond the obvious trends and analyse what unexpected events may occur. If you’re interested in analytics and ready to take the world by storm, we may have a role for you. We specialise in Junior and Senior roles. To lean more, check out our current vacancies, call our UK team on +44 20 8408 6070, or email us at ukinfo@harnham.com. 

How Digital Analytics Are Changing The High Street

As customers, we are now looking for more and more personalisation in our shopping experiences. We expect recommendations suited to our tastes and budgets, as well as a seamless customer journey. However, this has come at the cost of the more traditional shopping trip. The retail industry, long leading the way in utilising data and insights to provide unique, tailored online experiences, has left their own bricks and mortar high street stores at risk of redundancy. Now that AI is entering the Customer Journey, there is more necessity than ever for these outlets to evolve how they operate and apply the tools they have available to develop their stores,  advancing their back office processes and in-store experiences.  Having initially been applied to just the customer journey, digital analytics are now being used to help shape every touch point throughout the sales process. From the design of the store, to sales predictions, through to product conception and final purchase. Evolving The Experience As retail executives have begun to take a closer look at their own operations, it has become clear that they need to go beyond just having enough staff during their busy seasons. With many of us now using our phones to make online price comparisons whilst in-store, the entire experience needs to change. This has facilitated a move from predictive analytics to prescriptive analytics, with data analysis being used to optimise store operations, set pricing models, and dictate the future of the high street store. Minding The Store If you’ve ever been to a busy store with more customers than cashiers, you’ll understand one of the major challenges retail businesses face. Compared to the few clicks required for us to search for, purchase, and ship an eCommerce order, having to stand in a length queue seems like a lot of effort, even for us British. It’s here where in-store analytics shine. Store owners can manage operations by optimising the number of staff required based on historical data and various scenarios gleaned from the data. Above and beyond traffic numbers, retailers can ultilise other trends and data to go one step further; weather predictions, location intelligence, peak hours and product availability provide them with the opportunity to precision manage their operations and maximise profit margin.  Beyond Customer Data Against big online retailers, such as Amazon, one of the biggest challenges has been pricing. A survey from Vista found that 81% of the British public still see the high-street store as ‘vital to the shopping experience’ and so, to maintain this level of necessity against falling online prices, shops must continue to evolve. Some leading outlets are already using new technologies to enhance the in-store experience by introducing Augmented Reality (AR) into their stores. Both Topshop and Gap have installed AR mirrors into certain outlets. Looking into these would allow you to see how the clothes you are trying on may look in different colours and styles, whilst Specsavers have an in-store app that lets you asses the best shape and size glasses for your face shape. Whilst such schemes are still in their early stages, they could be the answer for ensuring that the high-street store remains an essential part of the shopping experience. A Guiding Hand Retail businesses are now looking for a guiding hand to support them in calculating gathered data, as well as to make recommendations for future innovation. If you're looking for a permanent or contract Data & Analytics position within retail, we may have a role for you. Check out our current vacancies here. Alternatively, you can call us at +44 20 8408 6070, or email us at ukinfo@harnham.com.

What Can Data and Insights Offer Wimbledon Fans?

From the World Cup to Wimbledon, London is alive with a summer of sport. With one just down the road, and the other ‘coming home’, both big events share plenty of similarities; die-hard fans, world-class athletes and, of course, a nod to the numbers. Coaches, players, and pundits have spent years analysing every stat and offering their expertise, but now artificial intelligence is providing fans with brand new tailored experiences. At the start of the start of the FIFA 2018 World Cup, we wondered if data analytics could deliver world cup glory and, with a couple more weeks to go, we’ll learn soon enough. But a little closer to home, Wimbledon is using data and insights to curate their audience’s experience… AI Offers Tailored Fan Experiences For the past few years, the All England Lawn Tennis Club (AELTC) have been partnering with IBM to develop an AI experience that analyses emotions and creates instant highlights. IBM’s Watson has entered the game and will curate match highlights by recording the movements, emotions, and even noises of both players and the crowd. As Federer reaches for his 20th Grand Slam and Nadal looks to replicate his success at Roland Garros, emotions will run high, giving AI the emotional intelligence stats it seeks. This is where AI meets EI. Whilst Wimbledon will be filled with people on the grass and in the stands, the highlighted packages will be purely based on data, with zero human involvement. These are then sent to the AELTC, who will upload the clips to their website, apps, and social media accounts. What Will Watson Look For? <!--[if !supportLists]-->·         <!--[endif]-->In the crowd - any gesticulation – raised arms, fist pumping, yelling, and cheering <!--[if !supportLists]-->·         <!--[endif]-->In the players – tensions and emotions Delivered within 10 minutes, the highlights are reviewed by an editorial team who ultimately decide what gets published. From Chatbot To Storyteller Wimbledon.com, its Facebook page, and Messenger app offers offsite users the opportunity to interact with Fred, a social assistant. Named for Fred Perry, and powered by Watson, Fred operates through Facebook Messenger to allow access to tournament news and information. The original, first generation Fred, a primitive chatbot from just three years ago, could talk to people and answer questions about Wimbledon’s history, player data, or fixtures. These days, however, he can send you curated highlight packages and clips, using the vast amounts of collected data by Watson. An Optimised Digital Experience Beyond Fred, AI is working on “bringing stories to life” on the Club’s revamped website, mobile app and social platforms; the redesigned Wimbledon.com offers more adaptive capabilities whilst the app now includes an offline mode for ease of use. Their digital toolkit, however, goes far beyond fan experience. There is also a personalised website specific to the players, providing them with match schedules and offering insights into how they’re performing and what they can do better. According to Alexandra Willis, head of digital and content at the AELTC, “63 million people visited the site (via mobile) during the Championships last year alone, while 80% of users also access Wimbledon.com through a desktop.” Though the AELTC plans to continuously innovate, surprise and delight their fans, media director Mick Desmond believes its important to keep content “on brand.” By fusing quality data with advancing technologies, they can create a brand experience that is truly tailored for the individual, without losing what makes Wimbledon unique. If you’re interested in data and analytics, Harnham may have a role for you. Check out our current vacancies, call us at +44 20 8408 6070, or email us at ukinfo@harnham.com.

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