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.

Categories
138 Posts found

Creating Your Own ‘Unicorn’ Employee

The elusive unicorn. Once a mythical beast, it is now a term for a new mythical creature; the unicorn employee. As Data fields transition from generalist to more specialised niches within the industry, businesses are realising the need for more agile and adaptable employees. However, there is a bit of a twist on the business end. It’s a mix of a diversified skillset and expertise. Though one seems at odds with the other, it doesn’t have to be. On the flip side, employees want development, citing it as the number one reason for leaving a business in our 2019 Salary Guide. So, we thought we’d take a look and see where the disconnect was. After all, if employees want development and businesses want diversification, couldn’t the two come together for the benefit of all?  In a word, yes. Investment in employees through reskilling and upskilling are two important and useful ways to create your ideal employee and placement. There’s no better return on investment. Let’s take a look at how finding your unicorn employee can add value to your business. Think Outside The Job Description While the job description offers a good guideline of what’s expected in your day-to-day role, it can’t predict the changes, trends, and other issues within your industry. And in the digital tech world, change is the name of the game. Businesses need someone who can react quickly and effectively. Employees need to have skills either to manage the job themselves or know when to reach out to team members for help. This can’t happen in a siloed situation.  A few traits of the unicorn employee include flexibility, a curious nature, excited about learning new things, and the openness to offer suggestions within and across teams.  In a recent LinkedIn article, Hootsuite CEO Ryan Holmes captured it best. “Like actual unicorns, they’re hard to find, but once hired, offer up enormous benefits in the workplace. To name a few, they shatter expectations, raise the bar for everyone and are simply a joy to be around. Unicorn employees can literally take your business to the next level.” If you’re looking to hire a unicorn employee, look beyond their resume. While it’s a tool and will offer skills the employee has now, it can’t show you whether or not he or she will go the extra mile. And like the mythical beasts, these unicorn employees may come from surprising places, be open to new possibilities and think beyond the job description.  How to become a Unicorn Employee Be Coachable. Have a growth mind-set, a desire to learn, and find every opportunity to upskill. This is lifelong learning at its finest.Be a hard worker, but know when to rest and recharge.Raise the bar as a teammate and encourage others to do the same. Raise your Emotional Intelligence. Demonstrate empathy, be aware of your emotions and your teammates’, and help motivate others. How to Attract and Retain Top Talent As you plan your next level of hire in search of the not-so-elusive ‘unicorn’ employee, consider these few steps to get you on the right track. Understand job roles. The job beyond what’s on paper. See what it’s like on the other side and make assessments from the point of an employee. What’s their day-to-day routine, touchpoints, and technology turn-ons to help them do their job?Employee experience and technology are forever entwined. What tech skills do your potential employees have and what would they like to learn? Does their curiosity spark yours? Broaden your range. Encourage employee participation in technology decisions and include people from a wide range of levels and departments. Let them help with planning, selection, and design. After all, who better than those who use it and know it inside and out?To upskill is to create lifelong learning opportunities through classes and beyond. Other ways to upskill include exploring new mind-sets, developing diverse relationships, and redefining how people work. These few suggestions scratch the surface. Though, Gartner does offer these strategies in competing for talent. The demand for top talent and the scarcity of it hasn’t diminished, but with a few tweaks to your planning strategy, you can lay the groundwork for attracting great employees. If you’re an employee hoping to broaden your range, open to new technologies as well as reaching out in a department not your own, and a team motivator. You might be a ‘unicorn’ employee. Somewhere between the two, we hope you’ll meet and in next year’s guide, development and diversification won’t be quite as at odds with each other. Companies need to remember candidates are stakeholders in the hiring process. Candidates need to remember sometimes it comes down to education. Educate the company how you can best serve them. That question they ask – why do you want to work for this company? This is where you ‘wow’ them and educate them to your why. Be flexible in your hiring and consider flipping from top-down to manager-centric in a bottom-up approach. After all, who is better positioned to offer insights into how jobs are changing and the skills required for it. If you’re looking for a unicorn, or to make your mark on a company, check out our current vacancies or get in touch with one of our experts consultants to learn more. 

Data & Analytics in Munich

Three Reasons Why Munich Is The Place To Be For Data Analysts

As one of the world’s largest economies, Germany continues to attract tech talent from all over the world, and has even overtaken the UK in terms of intra-Europe tech immigration in recent years. Whilst Berlin may be the first place that comes to mind when thinking of places to live as a Data Analyst in Germany, with its numerous start-ups and international culture, there are several reasons why you should also consider the southern gem of Munich. Here are three of the best: A First-Class Quality Of Life While the first thing that comes to mind when thinking of Munich is often the world famous Oktoberfest and the beer induced crowds packed into small beer tents paying the equivalent of a year´s salary for a pint, this is not the only thing Munich has to offer. During the other 349 days of the year when Munich is not packed with Lederhosen-wearing crowds from all over the world, it is a tranquil, green place to live.  Munich is home to a number of large parks, including the beautiful Englischer Garten, Museums and a number of non-beer related cultural events throughout the year. It’s also the third largest city in Germany and, as such, has all the benefits that big city life has to offer. However, nature is never far away, with a beautiful mountain landscape just on the horizon, including the tallest mountain in Germany, the Zugspitze, which sits only 90 km away. On top of this, the transportation system in Munich is one of the best in the country; clean, efficient and so simple to use, it actually makes commuting bearable.  Expansive Opportunities  Most major European cities have seen a boom in the tech market in recent years and Munich is no exception. Not only home to some of the biggest global and German players such as Amazon, MunichRE, Man, Allianz and Linde, the city is also seeing an increasing amount of investment in tech start-ups.  This has led to tech talent, particularly Data & Analytics talent, being highly sought after by a number of the country’s biggest and best employers. And healthy competition means even healthier salaries. Even though Munich doesn’t have the lowest cost of living around, the average pay for Data Analysts is higher than in most other German cities, meaning you’ll get to make the most of your time away from the office.  A Thriving International Culture With 25-38% of Munich´s residents originating from other nations, more and more companies, big and small, are open to welcoming English speakers into their teams. While the culture in Munich still makes it easy to immerse oneself into the German language and culture, the city is also very welcoming to its international inhabitants.  Of course not everyone can speak English, but it is surprising how many people do. This makes getting around as a non-German speaker that much easier, especially considering that the Bavarian version of German can sometimes feel like a completely different language to what is spoken by the rest of the country.  Like every country, different cities attract different personalities and find the right place for you is crucial before making a move. But, with its high quality of life, great job prospects and international culture, Munich certainly has a lot to offer for any Data Analyst looking to move to or within Germany.  If you’re considering making a move to Munich, take a look at our latest opportunities, or get in touch and we can discuss what could work best for you. 

Our New Berlin Office

We've launched two new offices

I'm incredibly pleased to announce that this week we have launched two new offices.  The first, in central Berlin, will solidify our on-the-ground presence in the German capital and allow us to continue to develop our client base in this rapidly-growing market. Run by Senior Manager Peter Schroeter, under the guidance of our Director of Europe, Alex Hutchings, we're really excited to see the new space become a hub for Berlin's Data & Analytics talent.  Secondly, we've also opened a second Wimbledon office. Despite only moving in to our current home 18 months ago, our rapid growth has led to us opening an additional Executive Office to house our Operations team as we bring in more and more expert consultants. Fortunately, it's just over the road, so there's no need to grab a bus between meetings.  This continues to be a great time for Harnham and watch this space for more growth news in the not too distant future. 

Machine Learning: How AI Learns

Machine Learning: How AI Learns

Amazon has begun curating summer reading lists. How? Patterns. Facebook shows you ads for items you may have been searching for online. How? It learns from your browsing habits. Ever wondered how Facebook knows you were just looking at that pair of shoes or that particular guitar. The Data you feed it, feeds its brain. In other words, this is how Artificial Intelligence learns. Machine Learning. Whilst it can be disconcerting to know that a machine understands our buying habits, that’s not the only thing it’s used for. It’s also a pivotal tool in such areas as Bionformatics, Biostatistics, Computational Biology, Robotics, and more.  What is Machine Learning? Ultimately, it’s a method of Data Analysis which helps to automate model building and is part of Artificial Intelligence. In other words, it helps to solve Computational Biology problems by learning from Data to identify patterns and make decisions with little human intervention. This helps scientific researchers learn about many aspects of biology. However, running a Machine Learning project can be difficult for beginners, who may experience issues when trying to navigate the information without making mistakes or second guessing themselves. This is one of the reasons a Computational Biologist might want to upskill with a course or two in Machine Learning for a more robust understanding of the information being learned and applied.  The Good News and the Bad With each shift of industrial revolution, there has been one system which has made an indelible mark on our daily lives and the Fourth Industrial Revolution is no different. Just like we can no longer imagine factories without assembly lines, we can also no longer imagine not having Siri, Google Maps, or online recommendations. But, as exciting and as important as these things are, Machine Learning has become so crucial to our daily lives, so complex, it takes a technology expert to master it leaving it nearly inaccessible to those who could benefit from it. Why is Machine Learning Important? By building models to peel back the layers and discover connections, organisations can more easily and more quickly make improved decisions with little to no human intervention. Computational processing is both more affordable and more powerful. It’s possible to quickly scale and produce models which can analyse bigger and more complex data and there’s also a chance to identify opportunities and to help avoid any unknowns such as risk. Machine Learning is used in every industry from Retail to Financial Services to Healthcare. Here are just a few ways it has already transformed our world. Retail – Retailers are able to learn from their customers buying habits, predictive buying habits, how to personalise a shopping experience, price optimisation, and customer insights.Financial services – Machine Learning helps to prevent fraud and identify Data insights.Healthcare – Wearable devices allow for real-time data to assess a patient’s health. Medical professionals can also more quickly find red flags which can help improve diagnoses and treatment.Oil and gas – It cannot only help find where oil might be, but also predict refinery sensory failure, and streamline distribution.Transportation – Help to make routes more efficient and predict problems that could affect the bottom line. While humans can create at least one or two models a week; Machine Learning can create thousands.  Ultimately, the goal of Machine Learning is to understand the structure of Data. As it learns to determine what Data is needed for its structure, it can be easily automated and sift through Data until a pattern is found. This is how machines learn. If you’re looking to take your next step in the field of Machine Learning, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step.

How to get ahead in Risk Analytics

How To Get Ahead In Risk Analytics

In the world of Risk Management, top talent is always in high demand. Despite this, those who specialise in area know that progression can be difficult and, according to our 2019 Salary Survey, is the slowest in the industry. So, how can you differentiate yourself from the competition, and what steps can you take to make yourself the ideal candidate for that promotion or new job you’ve been hunting?  Whether you are looking to move somewhere new, or trying to climb the ladder in your current company, here are some ways that you can make sure you stand out.  Stay one step ahead in tech Traditionally, the Risk Analytics tech stack has comprised of SAS, SQL and VBA. SAS and SQL remain very much present, but we are also seeing a clear increase in the use of Open Source programming languages, such as R and Python. Unsurprisingly, a lot of Risk Modellers and Analysts are now spending their time in developing their skills in these languages. One might argue that if you know one language, there’s not too much work required to upskill in another when you take on a new role, but this isn’t necessarily true. By being proactive and evolving your skillset in your current position, your CV will have a much bigger impact when it lands on a Hiring Manager’s desk.  Over the past few years, we’ve also seen the arrival of Machine Learning and AI in the world of Risk. Whilst many businesses are still slow to embrace these technologies, do not be surprised to see them make a big impact over the next couple of years. Risk Analytics are catching up to the rest of the industry in regards to technology, and having the knowledge and skillsets required in these areas before they take off will only enhance you profile.  Business-driven Data  In the world of Risk Analytics, it is easy to think that if you have the right programming and analytical skills in the right tools, you’ve all got all you need. You might be off to a really good start, but there’s more to it than that. It about having the balance.  Yes, being data-driven and understanding complex model development is crucial to becoming a good performer in this industry but, what truly separates the good from the great, is business acumen. The ability to understand both what your analytics and models do, and how they impact the overall business is now at the top of most Hiring Manager’s lists.  A person with good quantitative skills will always see something that can be improved, but they also need to know when to stop and be happy with the result. The key to getting this right lies in their understanding of the business and the ability to answer questions like “If I sit and work on this for 8 more hours, will the real-world difference be worth that amount of time and resource?”. By viewing things through the prism of cost vs reward, and understanding that balance, you can demonstrate that your value to a business goes beyond your analytical skills.  React, adapt and attract In this world there are a few things we can take for a certainty; the sky is blue, it will rain on your day off, and there will always be new regulations for financial institutions. Because of the certainty of change, a key thing employers look for in candidates is the ability to react quickly and make changes as soon as they are needed. Fast growing companies such as Klarna, tink and iZettle may seem like fairy-tale success stories, but the real edge they have is their adaptability and agile culture. Whereas some traditional corporations and banks have lengthy and complicated processes required before they adapt to new regulations, these new companies embrace their agility and get things done.  The ability to be agile and adaptable is, therefore, something that a lot of businesses are starting to realise is key. Therefore, if you’re looking to get ahead, you should try to evolve these qualities in your working ways. If you are looking for something new, look to prove you are driven and do not fear change. If you can demonstrate that you are able to work with a business-oriented mindset and embrace change, you’ll stand out as a key player in your team.  Specialist vs Generalist  With the world of Risk Management offering a number of opportunities to become very specialised in very niche areas, it’s worth considering whether this approach is right for you. There are some definite pros, for example, if you are the best developer of PD models for non-retail, you will be highly sought after for roles in this area. Plus, high demand, and a shortage of skillsets means that you will be in a good position to seek a high salary and lots of benefits. However, this does mean that you are likely to only have the opportunity to work in this area for the foreseeable future and, for some, this can become repetitive and not provide enough of a challenge. Additionally, if you were ever were to apply to work in a new area because of this, you would likely find yourself overpriced and needing to take a step down in seniority.  The alternative, therefore, is to become more of a generalist, with a broader, but less advanced skillset. Think being able to play every instrument, but only knowing one song. There are definitely some clear benefits with this approach, not least the ability to work on a diverse set of projects, gain an excellent understanding of how Risk Management affects a business on every level, and be able to slot into a number of roles easily. You will also gain a better idea of which areas of Risk that you like, and which parts you dislike. Whilst many analysts begin as generalists before looking to specialise when they get promoted, they often find that their knowledge will not be as deep as their specialist counterparts. Therefore, it is likely they will have to take a step-down or make a sideways move before they can achieve that promotion.  There is no right or wrong when it comes to the specialist vs generalist argument. However, for those looking for faster progression early-on, a generalist approach may be better suited despite the fact that you may need to change approach before reaching the most senior levels.  Whilst demand will always be high for the best candidates, competition for promotions and senior roles in Risk Analytics remains fierce. Therefore, by proactively thinking about the ways that you work, how effective you are, your business focus, and what your ambitions are, you should be able to get the most out of your career.   If you’re looking to get ahead in Risk Analytics, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step. 

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

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

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

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

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

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. 

138 Posts found