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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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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. 

Kian Dixon

25. April 2019

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.

Femi Akintoye

17. April 2019

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. 

Wesley Taupin

11. April 2019

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.  

Marcel Mathis

04. April 2019

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