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MeasureCamp Paris 2019

MeasureCamp Paris 2019

A few weeks ago, I was lucky enough to attend the sixth annual MeasureCamp Paris. It was Harnham's second time sponsoring the event and is always an amazing opportunity for us to get face to face with Digital Analysts not only from France, but all over Europe. Once again, the place was filled with industry-leading minds, all of whom were passionate about sharing their knowledge and the day-to-day challenges they face. As you'd expect, it was a pleasure to have the chance to see some familiar faces and get to talk with a variety of professionals about the market and share our thoughts. Their insights were unbelievably valuable. Fortunately, we were able to give them a sneak peek of our new 2019 Salary Guide and discuss our findings alongside some general market trends.   One particularly interesting point of discussion surrounded how double the amount of professionals are using the Google stack of tools compared the to the Adobe equivalent. Many commented on what they see as a lack of training and investment within their company in other Analytics tools (e.g Adobe) or CRO tools (e.g. AB Tasty, Optimizely, even Kameleoon) compared to UK and Nordic enterprises. Outside of this, there were a number of discussions on how the Digital Analytics space is evolving, especially on the impact that diversified Web Analytics roles are having on the industry, and how secure professionals feel in their positions as a result of this. With many commenting that a lack of career progression is making them feel unsure of their current role, it may explain why our Salary Survey found that 80% of the Data & Analytics professional are open to potentially leaving for the right opportunity.  This year's MeasureCamp Paris was both bigger and better. It's clear that Digital Analysts remain incredibly thirsty for new ideas and ways to upskill and so it's no surprise, particularly from what we saw on the day, that the market and talent pool are stronger than ever.  If you'd like to discuss any of the trends I've mentioned above, or are looking for a new opportunity, do not hesitate to get in touch with me here. 

The Harnham 2019 Data & Analytics Salary Guide Is Here

The Harnham 2019 Data & Analytics Salary Guide Is Here

We are thrilled to announce the launch of our 2019 UK, US and European Salary Guides. With over 3,000 respondents globally, this year’s guides are our largest and most insightful yet.  Looking at your responses, it is overwhelmingly clear that the Data & Analytics industry is continuing to thrive. This has led to an incredibly active market with 77% of respondents in the UK and Europe, and 72% in the US, willing to leave their role for the right opportunity.  Salary expectations remain high, although we’re seeing that candidates often expect 2-10% more than they actually achieve when moving between roles.  Globally, we’ve also seen a change in the reasons people give for leaving a position, with a lack of career progression overtaking an uncompetitive salary as the main reason for seeking a change.   There also remains plenty of room for industry improvement when looking at gender parity; the UK market is only 25% female and this falls to 23% in the US and 21% across the rest of Europe.  In addition to our findings, the guides also include insights into a variety of markets and recommendations for both those hiring, and those seeking a new role.  You can download your copies of the UK, US and European guides here.

HOW BRANDS USE DATA TO CREATE SUCCESSFUL CAMPAIGNS

HOW BRANDS USE DATA TO CREATE SUCCESSFUL CAMPAIGNS

Make no mistake: making minor adjustments to an ad or campaign that’s meant to appeal to the masses just won’t cut it. Customers crave creativity. They want to be understood. Which is why people respond best to brands that do their homework, doing their research into what appeals to different groups. How should businesses appeal to their chosen segments, then, considering how diverse people are? Data, of course. Why Data? For one thing, it drives results and creates improved outcomes. Data also helps to prove the value of marketing, providing a bargaining chip for future budget discussions. And, most rewarding of all, brands get valuable insights into their target market. Which, in turn, leads to more well-targeted, profitable campaigns.  And if you think Data doesn’t belong in the world of creative campaigns, think again. As OpenJaw Technologies Chief Marketing Officer Colin Lewis argues: “Creativity is not just compatible with being Data-driven – Data can drive better creative.” Psychological profiling Strategic communications consultancy, Verbalisation, researches and analyses language to form valuable insights. Using its Rapid Audience Insights Diagnostic system, the company’s team of psychologists and researchers work out how an audience thinks. They also learn the actual words an audience uses, which they then use as the basis of a marketing strategy.  Based on their unique research and insights, Verbalisation has created several successful campaigns for high-profile brands. These include the #NotAnotherBrother campaign for counter-terrorism organisation Quilliam, which looked at the motivations of jihadists.  The campaign is now used by the UN and schools across the UK, as well as the US Department of Defense. It is the most viewed counter-extremism campaign of all time, with more than half a billion global media impressions.  Location, location, location Out-of-home (OOH) advertising. Yes, it goes way back, but it’s actually the only traditional advertising channel posting rapid growth. In fact, thanks to mobile-location Data, brands can target audiences quicker and with a greater chance of success than ever before.  Great news for JCDecaux (JCD), a leading OOH company with ads reaching 410 million people in over 4,000 cities. JCD now works with location Data to define and segment audiences. Doing so helps it decide where to place media, improve campaigns and measure resulting store footfall and purchases.  Knowledge, so they say, is power. Particularly when that involves knowing the whereabouts of the most coveted customers. Newly teamed up with identity resolution company, Neustar, JCD’s insights look stronger than ever. JCD can now understand which of its locations rank higher for any brand’s most desired audiences. All thanks to location Data and real-time behaviour analysis.  Personalised employee training Data doesn’t just boost the results of B2C brands; it can also be a vital shot in the arm for internal security training campaigns. Training provider, CybeReady, for instance, uses a Data science-driven approach to deliver cyber awareness training with a difference: its anti-phishing platform helps security teams quickly roll out and tailor campaigns to individual employees. In big companies, getting employees up to speed is especially challenging. With many locations, languages and time zones to contend with, Information Security teams have their work cut out.  CybeReady eliminates these challenges by delivering 12 personalised, 60-second simulations to each employee. In their first language, every year. What’s more, the training provider uses machine learning to analyse performance on a daily basis. This enables it to provide the most appropriate simulations to each individual. The result? IT teams save 160 hours each month and employee resilience increases five-fold. There’s no limit to what Data can do. If you’re a fan, we may have a role for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

The Evolution Of The Data Engineer

The Evolution Of The Data Engineer

Every Data Science department worth its salt has at least one engineer on the team. Considered the “master builders,” Data Engineers design, implement and manage Data infrastructure. They lay down digital foundations and monitor performance. At least, that’s what they used to do.  Over the last few years, the role has shifted. Data Engineers have gone from mainly designing and building infrastructure, to a much more supportive and collaborative function.  Today, a key part of the engineer role is to help their Data Analyst and Data Scientist colleagues process and analyse data. In doing so, they are contributing to improved team productivity and, ultimately, the company’s bottom line. THE IMPACT OF THE CLOUD In the past, a Data Engineer would often move data to and from databases. They’d load it in a Data Warehouse, and create Data structures. Engineers would also be on hand to optimise Data while businesses upgraded or installed new servers.  And then along came the Cloud.  The rapid dominance of cloud computing meant that optimisation was no longer needed. And as the cloud made it easy for companies to scale up and down, there was less need for someone to manage the data infrastructure.   The collective adoption of the cloud has had a big impact on the function of Data Engineers. Because, provided a company has the funds, there is no longer the same demand for physical storage. Freed from endless scaling requests, engineers have more time to program and develop. They also spend more time curating data for better analytics.  AUTOMATING THE BORING BITS  Less than a decade ago, if start-ups wanted to run a sophisticated analytics program, they’d automatically hire a couple of Data Engineers. Without them, Data Analysts and Data Scientists wouldn’t have any Data. The engineers would extract it from operational systems, before giving analysts and business users access. They might also do some work to make the Data simpler to interpret.  In 2019, none of this extraction and transformation work is necessary. Companies can now buy off-the-shelf technology that does exactly what a Data Engineer used to do. As Tristan Handy, Founder and President of Fishtown Analytics, puts it: “Software is increasingly automating the boring parts of Data Engineering.”  STILL SOUGHT-AFTER  With automation hot on the Data Engineer’s tail, it can be tempting to ask whether they are still needed at all.  The answer is: yes, absolutely. When recruiting engineers, Data Strategist Michael Kaminsky says he looks for people “who are excited to partner with analysts and Data Scientists.” He wants a Data Engineer who knows when to pipe up with, “What you’re doing seems really inefficient, and I want to build something better.” Despite the rise in off-the-shelf solutions, engineers still play a key role in the Data Science team. The difference is simply that their priorities and tasks have shifted.  Today, innovation is the watchword. The best engineers are hugely collaborative, helping their teams go further, faster. It’s an exciting time to be a Data Engineer. If you’re interested in this field, we may have a job for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

The Power Of Programmatic: How It Keeps On Converting

The Power Of Programmatic: How It Keeps On Converting

Applying to anything from digital out-of-home to mobile, social media to TV, Programmatic tech continues to develop at a furious pace. And as it gets more sophisticated so, too, does its power to fuel growth across multiple industries.  So it goes without saying that Programmatic is set to remain a valuable part of the Ad Tech toolkit. As it evolves, brands can measure and enhance their creative campaigns with ever-greater accuracy to improve conversion rates and engagement. Here are some of the latest ways automated ads have been helping brands increase their influence over customers. SHORTENING THE RETAIL SALES FUNNEL  The former might of traditional brand and advertising agency models is fading. Instead, we’re seeing the old sales funnel being redefined into a more direct buying journey. Omnichannel shopping is now the norm, and screens with their accompanying ads wield plenty of power in influencing how we shop.  Thanks to growing numbers of mobile purchases coupled with Programmatic technology, brands use Data to improve the customer experience, reduce acquisition costs and push more products into online shopping baskets.  And as more retailers prioritise selling stuff online, they gain more control over customer data. Which in turn feeds their automated ads and speeds up the buying journey further. BOOSTING BRANDED CONTENT REACH  No worldwide media corporation would last long without using technology to make the best use of its resources. So it makes sense that the BBC uses Programmatic ads to create greater access to its branded content. Using data, the BBC can see what particular audience segments are into, from trending topics to the devices they’re choosing to devour news and entertainment. From there, the broadcaster twins its own data with wider industry stats to form insights that help to shape its content strategy. Automated media buying also reduces the labour that traditionally accompanied ad campaigns. The key advantage being that it frees up staff to concentrate on more creative tasks, according to Luke Fox, the BBC’s Head of Programmatic for the Asia Pacific. As a direct result of the automated ads, the BBC’s media placement has become more focused and effective, with branded content “getting to the right people at the right time.” An advertiser’s dream come true, essentially.  It is minimal effort, too. Using Programmatic tech gives organisations better access to consumers all over the world, across a wide array of media such as podcasts.  CUSTOMISED MESSAGING We all know that personalisation is a tried-and-tested marketing strategy. So it’s no surprise that programmatic ads adapt to whoever they’re targeting. Ads adapt to multiple audience variants, from age, gender, income and location right down to the device we’re using. Through constant feedback, marketers can adjust their campaigns in real time, changing their message according to where customers are, what they’re doing and how they’re responding to the ad. In theory, as more brands move their media-buying in-house, the Programmatic process becomes easier to control and adapt. Zendesk’s director of digital Aurélien Dubot certainly thinks so. After the company moved its advertising in-house, Dubot says the decision has enabled them to make instant tweaks: “We don’t wait a week or three days to adjust things, we adjust it straight away.” Whether brands choose in-house or an agency for their media buying, one thing’s for certain: programmatic is a complex system that continues to bear fruit, provided brands set clear goals for what they’re trying to achieve. Ultimately, the results will only be as good as the data, along with the marketing team’s ability to analyse it. The Programmatic industry is growing. If you’re interested in Data & Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

Harnham's 2019 Salary Guide: The Launch Event

Harnham's 2019 Salary Guide: The Launch Event

The 2019 Harnham Salary Guides are nearly here. Last night saw a hundred of Data & Analytics' top professionals gather to get their hands on an advanced copy and hear from some of the best in the industry.  With talks from Tom Spencer (Aviva), Mark Ainsworth (Schroders), and Anna Decoudu (118 118 Money), attendees were treated to insights into some of the world's best Data teams.  A huge thank you to everyone who came along, we hope you found the evening as enlightening as we did.  Our UK, US and European Salary Guides will all launch online mid-June. To be one of the first to get your hands on a copy, sign up to our mailing list here. 

The Advantages And Disadvantages Of Computer Vision

The Advantages And Disadvantages Of Computer Vision

“Don’t judge a book by its cover”. We use this adage to remind ourselves to go deeper and to look beyond the superficial exterior. Except, sometimes, we can’t, or won’t. Sometimes, our perceptions are pre-programmed. Think family, peer pressure, and social influences. But what about computers? What do they see? In a digital landscape that demands privacy but needs information, what are the advantages and disadvantages of Computer Vision? The Good: Digital Superpowers  Let’s be clear, Computer Vision is not the same as image recognition, though they are often used interchangeably. Computer Vision is more than looking at pictures, it is closer to a superpower. It can see in the dark, through walls, and over long distances and, in a matter of moments, rifle through massive volumes of information and report back its findings. So, what does this mean? First and foremost, it means Computer Vision can support us in our daily activities and business. It may not seem like it at first glance, but much of what the computer sees is to our advantage. Let’s take a deeper look into the ways we use Computer Vision today. Big Data: From backup cameras on cars to traffic patterns, weather reports to shopping behaviours and everything in between. Everything we do, professional to personal, is being watched, recorded, and used for warning, learning, saving, spending, and social. Geo-Location: Want to know how to get from Point A to Point B? This is where Geo-location comes in. In order to navigate, the satellite must first pinpoint where we are and along the way, it can point out restaurants, shops, and services to ease us on our way.Medical Imaging: X-rays, ultrasounds, catheterisations, MRIs, CAT Scans, even LASIK are already in use. Add telemedicine and the possibilities are endless. The application of these functions will allow faster and more accurate diagnoses and help save lives.Sensors: Motion sensors that only turns a light on when a heat signature is nearby are already saving your home or business money on your electric bill. Now, during a shop visit when you are eyeing an intriguing product, your phone may buzz with a coupon for that very item. Computer Vision sensors are now tracking shopper movements to help optimize your shopping experience.Thermal Imaging: Heat signatures already help humans detect heat or gas and avoid dangerous areas, but soon this function will be integrated into every smart phone. Thermal imaging is no longer used just to catch dangerous environments, it’s used in sport. From determining drug use to statistics and strategy, this is yet another example . The Bad: Privacy Will Forever Change  Google is 20 years old this year. Facebook is 15. Between these two media tech giants, technological advances have ratcheted steadily toward the Catch-22 of both helping our daily lives, whilst exposing our data to our employers, governments, and advertisers. Computer Vision will allow them to see you and what you’re doing in photos and may make decisions based on something you did in your school or university days. We’re already pre-wired to make snap judgements and judge books by their cover, but what will these advancements do to our daily lives? Privacy will change forever.  We document our lives daily with little regard to the privacy settings on our favourite social media apps. GDPR has been a good start, but it’s deigned to protect businesses and create trust from consumers, rather than truly offer privacy. So far, the impact on our privacy has been limited as it still takes such a long time to sift through the amount of data available. However, the time is coming soon, where we’ll need to perhaps think of a privacy regulation businesses, employers, and governments must follow to protect the general population. Fahrenheit 451, 1984, and Animal Farm were once cautionary tales of a far-off future. But Big Brother is already watching and has been for quite some time. Police monitor YouTube videos. Mayors cite tweets to justify their actions. And we, thumb through our phones tagging friends and family without discretion.  Like every new technological advancement there are advantages and disadvantages. As Computer Vision becomes increasingly prevalent, we’ll all need to be aware of the kind of data we supply from to text to image. We can’t go back to the way things were, but we can learn about ourselves through the computer’s lens. And when it comes to computers and their capabilities, don’t judge a book its cover. If you’re interested in Data & Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants for more information. 

A Q&A With Dyson’s Data Governance CDO

A Q&A With Dyson’s Data Governance CDO

Mridul Mathur is a skilled Senior Program Director with more than 15 years of experience working in businesses from Deutschebak to Dyson. He has a proven track record of successfully delivering large and complex cross-functional programs and building high performing teams from scratch. In last five years the main focus of his work has been in the area of Data Management to address the issues and challenges organisations have faced in the wake of various new regulations. Data Management and Data Governance are hot topics at the moment. Do you feel that attitudes have changed towards the fields since the beginning of your career? It’s been a very big shift. Going back to my involvement at Deutsche Bank around 2007, we were managing Data purely because we needed to create a Credit Risk position so that we could explain to the Bank of England and other regulators what we were doing. We didn’t really look beyond that.  But now, if you look at the industry, we want to use Data to not only calculate our Risk position but to derive value out of that Data.  It's something that can give a company a competitive advantage  one of those things that can significantly change a business. I personally feel that the turning point, not just for Deutsche Bank but for everybody was the market crash that happened in 2008.  A lot of the company did not have Data Management skills, or the ability to bring the Data together to understand exposures. Those who had exposure against Lehman, for example, could not recover any of the money they lost. That was the big turning point for all of them, when they actually lost hundreds of millions of dollars’ worth of revenue and loans overnight. They didn’t have the right Data, in the right place, and it cost them. What major issues do you see successful Data Governance facing over the next 12 months? I think we're still going through a phase of understanding and internalizing the issue. By that I mean that we understand that our Data is important and how it can help us not only manage Risk but create value. But, when it comes to actually applying it, we are hamstrung by two things:  One is that we haven't quite grasped the ways in which we can internalise that Data. We understand the value but the actual application is not really out there currently. Secondly, I think that in some places, we have too much activity. I've been in places where there have been competing Data agendas and competing Data Governance ideas. When people are not taking their organisational view and just looking to get ahead, it’s hard to achieve any real success.  If you were advising a company about to commence on a large Data Management transformation project, what advice would you give them? This links to the previous point really, and it’s a bigger issue in large companies. You need to have a business approach to Data Governance, as well as the IP capabilities to deal with a project of that scale. And what you find sometimes is that multiple groups get together and they each have a different view of what good looks like. They end up not communicating throughout the organisation and properly aligning everybody’s roles and responsibilities. These different agendas then end up causing issues because everyone has a different idea of what they want.  We need to be able to plan across the organization to get the right agenda and get the right properties in place. Then you can start the work, as opposed to each team just working where they think the biggest problem lies first.  What would you say are the biggest threats to a successful Data Management program? Obviously the above is one, but it leads to another which is really the lack of Senior Management sponsorship. If you don’t get the right level of sponsorship, then you don’t get the mandate to do what you need. This can cause huge delays and is definitely one of the biggest threats to your program being a success.  In finance, you worked within a highly regulated industry. How have your approaches changed now that you’re in a highly innovative, tech-driven environment? The approach is different. We do have challenges that others don't, but over and above, because we innovate and create things, there is an abundance of new information. Information protection and intellectual property protection is therefore at the top of the agenda. That drives the need for effective Data Governance and it really has to be at the forefront of the approach.  Data breaches have caused widespread reputational damage to companies such as Facebook and Yahoo. Have you found that companies now view Data protection as central to their commercial performance? Absolutely. People realize that they not only need Data to do their business, but they also need to protect that Data. These breaches have resulted in a greater importance being given to this function and every year I see it moving closer to the center of the organisation. There are very few large organisations left that haven’t recognized Data Protection as one of their formal functions. A lot of companies are now looking to build out their Data Protection teams from the ground up, starting with lower levels of analysts, but also management as well. It’s becoming a much greater priority and these big breaches are one of the driving factors.  What do you feel will be the most effective technical advancement within Data Management in 2019? I think, from a technological perspective, we still have some way to go with digital rights management. There’s now one or two solutions that are supposed to be at Enterprise level, but they’re not enough and they’re still not joining the digital rights management side of things with the Big Data Loss Prevention side.  So companies are having to rely on seeing this together with a combination of plugin software and various tools and technology. It’s sticking around the edges of the edges of a fix, but it’s not actually doing the job. I'd like to see these technologies develop because I think we're crying for some help in this area.  What is the biggest risk to their Data that businesses should be aware of? Not knowing where to get hold of Data. It is just mind boggling to me, that there has not been a single company that I have been a part of where we started a program and we knew where to get all our Data from. Obviously we knew where most of it was,  but we didn’t know where else it was and that what we were looking at was a comprehensive set of maps. It just continues to be the same at every business I have worked at.   What role does data governance have to play in protecting a business’ intellectual property? It plays a huge role. Firstly, a company needs to be very clear on their Data policies. This means regularly training teams on the importance of this, much like you would with health and safety. By clearly defining and educating people on the dos and don’ts of data handling you can better protect your intellectual property. I think getting the policy framework right and implementing it using digital rights management is crucial and good Data Governance relies on this.  When hiring for your teams, which traits or skills do you look for in candidates? There are two key parts; one is technical and the other non-technical. In my mind, it’s less about the technical because, ultimately, I just want someone who knows how to use ‘technology x’. They need to be able to make use of Data from a database, or be able to spot Data in an unstructured environment. But, for me, the most important skill is more of a characteristic: tenacity. I use the word tenacity because you have to put yourself out there. You have to ask people questions and you have to educate them. You can’t assume that people just understand Data you’re presenting them and you have to become their friends and learn to speak their language. It also really brings in the skill of being able to work with teams and across teams. Being a team player would absolutely be top of my list. Mridul spoke to Femi Akintoye, a Recruitment Consultant in our Data & Technology function. Take a look at our latest roles or get in touch with Femi.

How To Attract Data Scientists To Your Business (And How Not To)

How To Attract Data Scientists To Your Business (And How Not To)

Whilst the role of Data Scientist is still considered one of the most desirable around, many businesses are finding that a shortage of strong, experienced talent is preventing them from growing their teams sufficiently. With a huge demand for such a small talent base, enterprises have begun to ask what they can do to ensure that they can secure the skillsets they need.  If you’re looking at hiring a Data Scientist, there are a few key Do’s and Don’ts that you need to bear in mind: THE DO’S Create A Clear Career Path In most companies, a career path is defined. Usually you grow from junior to senior to manager etc. However, Data Scientists often like to become experts rather than moving up the traditional career ladder into people management roles. And, once a Data Scientists becomes an expert, they want to remain an expert. To do this, they need to keep up with the latest tools and data systems and continually improve. That’s why it’s important that you put in place a clear career path that suits the Data Scientists. In addition to the possibility of leading teams on projects, businesses should provide opportunities for financial progression that reflect growing skillsets in addition to increased responsibilities.  Let Them Be Inventive One of the biggest turn-offs for Data Scientists is lack of opportunities to try new techniques and technologies. Data Scientists can get bored easily if their tasks are not challenging enough. They want to work on a company’s most important and challenging functions and feel as though they are making an impact. If they are asked to spend their time on performing the same tasks all the time, they often feel under-utilised. Providing forward-looking projects, with innovative technologies, gives Data Scientists the opportunity to reinvent the way the company benefits from their Data. Provide Opportunities To Discover  As part of their attitude of constant improvement, Data Scientists often feel that attending conferences or meet-ups helps them become better at their role. Not only are these a chance for them to meet with their peers and exchange their Data Science knowledge, they can also discover new algorithms and methodologies that could be of benefit to your business. Businesses that allow the time and budget for their team to attend these are seen as much more attractive prospects for potential employees in a competitive market.  Give them the freedom they need Data Scientists are efficient workers who can both collaborate and work independently. Because of this, they expect their employers to trust that they will get the job done without feeling micro-managed. By offering flexible working, be it flexi-hours or working from home options, enterprises can make themselves a much more appealing place to work.  THE DON’TS Hire The Wrong Skillset As many companies begin to introduce Data teams into their business, they can often attempt to hire for the wrong job. Generally, this will be because they automatically jump to wanting to hire a Data Scientist, but actually need a different role placed first. For example; a company may be looking to hire a Machine Learning specialist, but their data pipeline hasn't even been built yet. There are many talented candidates out there who want to work with the latest technology and solve problems in complex ways. But the reality is that a lot of businesses aren’t ready for their capabilities yet. Before hiring, asses what skillsets you really need and be specific in your search.  Undervalue Their Capabilities  There are still a large number of organisations that do not value Data within their culture and Data professionals pick up on this incredibly quickly. If they feel that their work is under appreciated, and they know that there is high demand for what they do, they will not waste their time sticking around. Ask yourself how you see your Data team contributing to the company as a whole and make this clear within your organisation. Advanced Data Scientists don't want to work on dashboarding so make sure that their work will have an impact and explain how you see this happening during the interview process. Additionally, be aware of other financial implications that their hire may have. It’s likely that they’ll need a supporting Data Engineer to work with and, if they don’t have access to one, they have another reason to look elsewhere.  The Data Scientist market is a candidate-driven one and, as a result of this, businesses need to go the extra mile to ensure they get the best talent around. By offering a strong set of benefits, the opportunity to grow and progress, and an environment that values Data, enterprises can stand out amongst the crowd and attract the best Data Scientists on the market.  If you’re looking for support with your Data Science hiring process, get in touch with one of our expert consultants who will be able to advise you on the best way forward. 

Using Data & Analytics To Plan Your Perfect Ski Trip

Using Data & Analytics To Plan Your Perfect Ski Trip

The Ski season may be drawing to a close, but it’s never too early to start planning for next year. Born and raised in the mountains of Austria, I have been skiing all of my life. For me, it’s about freedom, enjoying the views and forgetting about everything else.  But, since I’ve stepped into the world of Data & Analytics, I started to asked myself “what can I learn from my work that I can apply to my skiing”? After having a look around, I began to discover ways in which Data could support my passion. I’ve pulled together some of the most interesting things I’ve discovered and created this handy guide to help you prepare for your next trip. Here’s how you can use data to create the perfect ski trip.  Follow the snow Anyone who has skied before knows about the uncertainty before a trip. Will there be enough snow? Will the weather be good? Which resort is the most suited to my ability? Fortunately, somebody has already pulled this information together for you. Two "web spiders" were built via Scrapy, a Python framework used for data extraction, the first of which extracted ski resort data. The second spider, on the other hand, extracted daily snowfall data for each resort (2009 - present). After collecting Data from more than 600 ski resorts and spitting it into 7 main regions, the spiders were able to form a conclusion. The framework then pulled out key metrics, including the difficulty of runs, meaning that skiers are now able to decide which resort is most suitable for their ability.  As for the weather, onthesnow.com has recorded snowfall data from all major resorts, every year since 2009. We all know that good snow makes any trip better, so the collected data here will help skiers ensure they are prepared for the right weather, or even plan their trip around where the snow will be best.  Optimise your skis Ski manufacturing is a refined and complicated process, with each ski requiring many different materials to be built. Unfortunately, this often results in the best skis running out quickly as supply outspeeds demand.  To help speed up and improve the process, companies are implementing technologies like IBM Cognos* that monitor entire supply chains so that no matter how much demand increases, they have the materials to meet it.   Additionally, since the majority of companies have become more data-driven, production time has been reduced by weeks. Predictions for future demand has also become 50% more accurate, resulting in a drop of 30% idle time on production lines. Skip the Queue Tired of queuing for the ski lift? There’s good news. As they begin to make the most of data, ski resorts are introducing RFID* (Radio Frequency Identification) systems. These involve visitors purchasing cards with RFID chips included, allowing them to skip queues at the lifts as there is no need to check for fake passes. The data can then be utilised for gamification platforms to turn a skier’s time on the slopes into an interactive experience.  The shift towards Big Data not only has advantages for the visitors, but the management are also benefiting. In the past, it has been difficult to analyse skier’s data. Now, with automated and proper data management, the numbers can be crunched seamlessly and marketing campaigns can be directed at how people actually choose to ski.   Carve a Better Technique Skiing isn’t always easy, especially if you haven’t grown up with it. Usually, ski instructors are the solution but, in the age of Data & Analytics, there are other solutions. Jamie Grant and co-founder Pruthvikar Reddy have created an app called Carv 2.0, which allows you to be your own teacher. It works by using a robust insert that fits between the shell of your ski boots and the liner. It then gathers data from 48 pressure sensitive pads, and nine motion sensors.  This data is fed to a connected match-box size tracker unit, sitting on the back of your boots, before being relayed via Bluetooth to the Carv App on your phone. Carv can then measure your speed, acceleration and ski orientation a staggering 300 times a second.  Thanks to a complex set of algorithms this data is then converted into an easy to follow graphic display on your phone’s screen as well as verbal feedback from Carvella. The accuracy of this real-time data could make it a better instructor than any individual person.  Data & Analytics are helping streamline every part of our lives. Whilst the above can’t guarantee a perfect ski trip, they can help us minimise risks and optimize our performance and experience.  If you’re able to use data to improve day-to-day living, we may have a role for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

Our Top Five Tips For Your Data Scientist Interview

Our Top Five Tips For Your Data Scientist Interview

The role of Data Scientist is one of the most in-demand jobs in the tech world now. But, given that it is still a relatively new job, in a relatively new field, a lot of companies are still struggling to source enough quality candidates for their team. Despite the demand, tech companies are very specific about the candidates they’d like to hire. Passing a Data Science interview can be very tricky, especially considering that businesses are looking for the right technical knowledge, business sense, and culture fit.  With this in mind, here are five key tips for the Data Science interviewing process. By making sure you are prepared for the below, you’ll be able to ensure that you don’t sell yourself short. Have A Concise Overview Of Your Project Experience It’s imperative you prepare an overview of your successful Data Science projects. Hiring Managers aren’t interested in getting into every detail of your completed projects, but they do want to know that you have the right experience. Focus on key factors, highlighting the types of projects you’ve been working on and the successes you had as a result of those projects. Keep your achievements clear and concise. Show Your Communication Skills A good Data Scientist is more than just a good programmer. You need to be able to show that you can translate your findings into insights that can be understood by non-technical people in the business. During your interview, Hiring Managers may test your ability to step away from role-specific language. This is to asses whether you know how to engage with non-technical colleagues and parts of the business who may not understand the value of Data Science to the company. Bring Out Your USPs Companies will potentially be interviewing several candidates for a specific role, so it’s important that you are able to stand out. Consider what you have achieved that your fellow interviewees may not have. One good way to stand out is to have articles published on popular Data Science websites/blogs. From my experience, Hiring Managers see this as a big plus and it makes for a great talking point during the interview process. If you are looking to do this, you should always choose a unique topic and not something that is already explored a lot by others. On a similar note, you could highlight the Data Science projects you’ve achieved outside of work through platforms such as Kaggle.Know Your Computer Science Fundamentals Having a decent knowledge of Computer Science fundamentals, like algorithms is essential, especially if you are interviewing with tech companies. Whilst there are other elements to the role, you can expect questions related to programming, so for a Junior Data Scientist, I’d recommend practicing coding for a few days before your interview (if you are not doing this already in your day to day job). Have An Understanding Of How You’ll Fit In At The Company For some Tech companies, particularly start-ups, cultural fit is just as important, if not more so, than how good you are at coding. They’ll want to understand how you would react to different scenarios at work and whether or not you share the values of their company. For this reason, don’t be surprised to see a few team members join the interview as they look to see how you’ll fit in. Make sure you take a look at the company’s website, read their blogs and articles, and check their social media feeds in advance so that you have a good understanding of what the business is like. Remember, culture is a two-way fit so it’s about making sure the business is right for you, as much as it is right for them. The interviewing process can be tricky but, at Harnham, our expert consultants are here to support you through the entire recruitment process. We will always make sure you are prepared for your interview and will run you though the topics you can expect to come up. If you’re looking to take the next step in your Data & Analytics career, take a look at our latest opportunities or get in touch with one of our expert consultants to learn more. 

Hunted’s Ben & Danni On Cybersecurity

Hunted’s Ben & Danni On Cybersecurity

Ben Owen and Danni Brooke are the Co-Directors for the EMEA Practice at Fortalice Solutions, a leading global cyber security and intelligence operations company.  They travel globally to assist clients with their cyber security requirements, bespoke training needs, intelligence and investigations both online and physical and counter fraud training/consultation. They deliver and manage a portfolio of pro-active intelligence solutions to keep people, nations and businesses safe from threats and head up the EMEA operations.  Ben and Danni also feature on the hit Channel 4 show, Hunted and Celebrity Hunted which has been airing for over four years with another series set to be filmed this summer. I caught up with them recently to discuss the latest Fraud, tools and challenges for the Cybersecurity industry. Cybersecurity is an ever-changing landscape. What trends do you anticipate for the next 12 months and beyond? It is always difficult to pin down what the next real trend is going to be in the Cybersecurity space as adversaries are becoming ever more sophisticated.  What was once a very difficult process for skilled individuals is becoming more readily available to novices with advances in software, particularly those shared on the Dark Web. What is an inevitable threat trend in the next 12-months and beyond is the exponential rise in the Internet of Things (IoT).  With a world where everything is hooked up to the web, it is apparent that tech companies selling these devices are under immense pressure to get products to market. The need for speed could mean that some security principles and best practices may be overlooked.   As the UK encountered during the Mirai Botnet attack of 2016, a network of electronic devices acting in concert can cripple the internet or, worst case, become a weapon that could cause actual physical damage as well as cyber damage, power stations, hospital networks to name but a few.   How have Data & Analytics impacted the detection, and prevention, of cyber-crime? A company will have to protect themselves against an enormous amount of cyber threats every second.  A cyber-criminal will only need one successful attempt. Data & Analytics are proving successful in the fight against cyber-crime and their proactive and holistic approach is at keeping people and businesses safe.  Of course, it is Data that is being stolen, but very often Data can come to the rescue.  It helps in a number of ways, e.g. identifying anomalies in employee and contractor computer usage and patterns, detecting irregularities in networks, identifies irregularities in device behaviour (a huge advantage with the rise of the IoT). What one must remember, however, is the people behind the Data.  You can’t simply collect Data and assume you will be able to detect and respond with the right actions.  You need the people with the right analytical skills to sift through the Data, find the right signals and then react to the threat with an appropriate and timely response.   What tools and technologies do you think will become increasingly important in the fraud and cyber-crime landscape? Here at Fortalice we are investing a lot of time into coverage of the Dark Web.  We live in a rapidly changing digital landscape. Criminals, fraudsters, and others are now operating with more sophistication and anonymity. Where do they go to exchange fraudulent details and ideas about current victims? What medium do they use to discuss organisational targets or new ways of defrauding companies? The answer is the Dark Web.  Traditionally, companies fight fraud from the inside out. We want to change this landscape by accessing the entirety of the Dark Web, its pages, shady storefronts, and treasure troves of Data, and drawing on monitoring toolsets to give our clients a 360-degree resource for identifying adversarial communications and movements. It’s all about Internet coverage.  Wherever it is difficult to find – that’s where your threat will be.   A final point to this question is one of sharing tools and techniques.  A collaborative approach is always a good way of making sure the wider audience benefits.  We always work with our clients and offer other services and support outside of our remit to make sure they’re fully protected from a cyber and physical space.   What are the biggest security threats for businesses? Security is fundamentally broken because the design of many security solutions does not design for the human psyche.  Security solutions are bolted on, clunky, and hard to use but because security teams prioritise defending against easier cyber threats, they often don’t focus on the hardware side. The biggest risk to companies and individuals is always defined by the Data that is most important to you or to the business.  For individuals, this might be privacy or identity. For businesses, this could be customer Data, intellectual property, and the company’s money in the bank. The reality is that business executives can’t outspend the (cybersecurity) issue and they must be prepared. Cybersecurity no longer exists in a vacuum and it must be elevated to the conversations held in the boardroom and with senior leadership as well as entire divisions, departments, and organisations. For someone trying to get into security analytics, what skills do you think are key to being successful in the industry? The detail is in the name of the role.  A huge ability to interpret large amounts of technical Data is key to the role, as well as being able to assimilate what it means and how to action it.  Risk management is also key to this role.  Very often you will identify potential risks and you will have to triage those priorities on your own as co-workers won’t have the technical expertise to assist.  You will need to be able to communicate successfully to all levels of a workforce and last but by no means least – a good sense of humour!  When you think you have gotten to understand a new threat or vulnerability a new one will replace it within seconds.  Time to put the kettle on, smile, and get back to work with your analytical prowess.   Within fraud, it's well known that criminals are sharing their approaches, is this mirrored in cyber-security and if so, how is the industry combating this? Criminal collaboration is huge on the web.  First of all, there is no talent shortage for fraud rings or cybercriminals. There are no requirements for fancy university degrees or certifications and the crime ring pays for performance.  They don’t care what you look like, how you dress, or if you clock in during normal work hours. They care about getting the job done - hacking into and stealing information from others. Together they are sadly stronger and more effective.  On Dark Web forums, you will see fraudsters sharing and selling their ‘IP’ knowing that others will also contribute. That way they are all winners.  In the private world ideas equal money. That is of course not a bad thing for business, but it is bad for collaboration. Businesses generally don’t like to share ideas with one another because it has taken them lots of time and expense to get to their product or solution. As cliché as this comment sounds - we have to change this landscape for the greater good.  There are lots of smart government initiatives for national defences in cyber security and fighting high-end cyber-crime but seldom does this have a positive impact locally with smaller businesses.  There is a huge amount of information out there for individuals and advice, but we need to bridge the gap still between criminal collaboration and that of the good guys. If you could change one thing in the industry, what would it be? The mind set of security professionals that humans are the weakest link. We’re not! Humans are at risk because technology is by design, open.  I’d also change the mind set of those not in the Cyber Security industry.  All too often the severity of what is being reported is not taken seriously, nor are budgets set aside for cyber security issues.  That said, it is improving but there is a long way to go.  Ben and Danni spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.

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