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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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Fighting Crime with Data: An Ethical Dilemma

Can you be guilty of a crime you’ve yet to commit? That’s the premise of Steven Spielberg’s 2002 sci-fi thriller ‘Minority Report’. But could it actually be closer to reality than you think.   As technology has advanced, law enforcement has had to adapt. With criminals utilising increasingly sophisticated methods to achieve their goals, our police forces have had to continuously evolve their approach in order to keep up.   New digital advances have refined crime-solving techniques to the point where they can even predict the likelihood of a specific crime occurring. But with our personal data at stake, where do we draw the line between privacy and public safety?  Caught on Camera   The digital transformation has led to many breakthroughs over the past few decades, originating with fingerprint analysis, through to the advanced Machine Learning models now used to tackle Fraud and analyse Credit Risk.   With an estimated one camera per every 14 individuals in the UK, CCTV coverage is particularly dense. And, with the introduction of AI technologies, their use in solving crimes is likely to increase even further.   IC Realtime’s Ella uses Computer Vision to analyse what is happening within a video. With the ability to recognise thousands of natural language queries, Ella can let users search footage for exactly what they’re after; from specific vehicles, to clothes of a certain colour. With only the quality of CCTV cameras holding it back, we’re likely to see technology like this become mainstream in the near future.   Some more widespread technologies, however, are already playing their part in solving crimes. Detectives are currently seeking audio recordings from an Amazon Echo thought to be active during an alleged murder. However, as with previous requests for encrypted phone data, debate continues around what duty tech companies have to their customer’s privacy.  Hotspots and Hunches Whilst Big Data has been used to help solve crime for a while, we’ve only seen it begin to play a preventive role over the past few years. By using Predictive Analytics tools such as HunchLab to counter crime, law enforcement services can:  Direct resources to crime hotspots where they are most needed.  Produce statistical evidence that can be shared with local and national-level politicians to help inform and shape policy.   Make informed requests for additional funding where necessary.   Research has shown that, in the UK, these tools have been able to predict crime around ten times more accurately than the police.   However, above and beyond the geographical and socioeconomic trends that define these predictions, advances in AI have progressed things even further.   Often, after a mass shooting, it is found that the perpetrators had spoken about their planned attack on social media. The size of the social landscape is far too big for authorities to monitor everyone, and often just scanning for keywords can be misleading. However, IBM’s Watson can understand the sentiment of a post. This huge leap forward could be the answer to the sincere, and fair, policing of social media that we’ve yet to see. Man vs Machine  Whilst our social media posts may be in the public domain, the question remains about how much of our data are we willing to share in the name of public safety.   There is no doubt that advances in technology have left us vulnerable to new types of crime, from major data breaches, to new ways of cheating the taxman. So, there is an argument to be had that we need to surrender some privacy in order to protect ourselves as well as others. But who do we trust with that data?  Humans are all susceptible to bias and AI inherits the biases of its creators. Take a program like Boulder, a Santa-esque prototype that analyses the behaviour of people in banks, determining who is ‘good’ and who is ‘bad’. Whilst it can learn signs of what to look for, it’s also making decisions based around how it’s been taught ‘bad’ people might look or act. As such, is it any more trustworthy than an experienced security guard?  If we ignore human bias, do we trust emotionless machines to make truly informed decisions? A study that applied Machine Learning to cases of bail found that the technology’s recommendations would have resulted in 50% less reoffenders than the original judges’ decisions. However, whilst the evidence suggests that this may be the way forward, it is unlikely that society will accept such an important, life-changing decision being made by a machine alone.  There is no black and white when it comes to how we use data to prevent and solve crime. As a society, we are continuously pushing the boundaries and determining how much technology should impact the way we govern ourselves. If you can balance ethics with the evolution of technology, we may have a role for you.   Take a look at our latest roles or contact one of our expert consultants to find out how we can help you. 

What does it take to be a Chief Data Officer?

By Noam Zeigerson Noam Zeigerson is a Data & Analytics Executive and entrepreneur with over 16 years’ experience delivering Data solutions. What does the role of the CDO entail and how can we succeed? Researchers at Gartner estimate that 90 per cent of enterprises will have a ‘Chief Data Officer’ (CDO) in place by the end of 2019. It also predicts that by then only half of CDOs will have been successful. So, what does the role of the CDO entail and how can we succeed? The rise in the use of data in the enterprise to inform business decisions has led to a recent phenomenon - the Chief Data Officer. Organisations will have a CDO in place to handle the many opportunities and responsibilities that arise from industrial-scale collection and harnessing of data. Unfortunately, it is rare to be successful, due to a number of challenges. As a new role, the CDO need to be in a position to increase business efficiencies and improve risk management, especially since the General Data Protection Regulation (GDPR) came into effect in May 2018. This puts the CDO in a position where business expectations will be high, and we have to make tough and potentially unpopular decisions, because the CDO’s role sits at the crossroads of IT and business. We typically responsible for defining the data and analytics strategy at our organisation. The CDO becomes instrumental in breaking down siloed departments and data repositories, which makes information easier to find and also have ramifications for the IT team. As Gartner notes, many CDOs have faced resistance, but the successful ones are working closely with their Chief Information Officer (CIO) to lead change. To be a key part of any organisation’s digital transformation, the CDO need a wide range of skills. The skills required of a Chief Data Officer The role of the CDO is multifaceted. For this reason, CDOs need to be able to combine skills from the areas of data, IT, and business to be successful. Data skills: A background in data science is crucial. A passion for statistics and a clear understanding of how to interpret data to glean insights is core to the role of the CDO. The CDO then needs to be able to communicate what those insights mean in a business context and make information easily available to all. A knowledge of data security is also critical. In the UK, the Information Commissioner’s Office (ICO), whose job it is to enforce GDPR in the country, recommends the creation of a Data Protection Officer (DPO) at each organisation. This should fall within the remit of the CDO. The value of sharing data at a senior level is recognized by UK organisations, by and large. Further down the authority chain the picture is different, with about three-quarters of executive teams and nearly half of front-line employees actually need to have access to detailed data and analytics. The CDO needs to ensure that those who need data to further inform decision making can do so and are sufficiently trained to gain business insights from that data. IT skills: Understanding how information flows is an advantage as the CDO is well placed to recommend and implement technology to democratise and operationalise data, as well as improve security. The CDO will need to manage expectations across the enterprise, so appreciating what technology can deliver is the key. Artificial Intelligence (AI) and machine learning are going to feature heavily of UK data projects, so many CDOs need to get to grips fast with this technology. Business skills: Strategic business logic is essential to success as a CDO. If the expectation of the CDO is to influence strategy based on data, then consulting experience will be valuable. Project management skills is at the forefront of the CDO’s day-to-day role. Being able to bring siloed groups together and get them striving for the same common goal is a vital skill for any CDO. It’s clear that data analytics is only going to be deployed more heavily throughout the enterprise, so the CDO’s role is only going to become more influential and pivotal within organisations as different business units seek to gain insights to improve the business further. Making a success of the CDO role Every organisation will have different objectives and expectations of their CDO. Gartner estimates that four in every five (80 per cent) CDOs will have revenue responsibilities, meaning we will be expected to drive new value, generate opportunities, and also deliver cost savings. No pressure! Given those expectations, it’s no wonder that Gartner expects only half of CDOs to succeed. The core responsibilities of the CDO includes data governance and quality, and regulatory compliance. The CDO must also address the way that technology is deployed to address these issues. The CDO needs leadership and team building skills, as we are the chief change agent in the organisation for creating a data-driven culture. This means first-class communications skills will be valuable.The Chief Data Officer is going to be essential in delivering digital transformation. Organisations who create a CDO role must support that individual and make sure that they are integrated across departments, not isolated in a silo. The C-suite must lead from the front on this and, as we saw earlier, the support of the CIO will be critical. Harnham are the global leaders in Data & Analytics recruitment.  Take a look at our latest roles or get in touch with one of our expert consultants to learn more. 

How Data Is Making Mass Marketing Personal

“Pretend that every single person you meet has a sign around his or her neck that says, ‘Make me feel important.’ Not only will you succeed in sales, you will succeed in life.” – Mary Kay Ash, Founder of Mary Kay Cosmetics From the very first market stall, sales have always relied on convincing individuals that what you’re selling is meant for them. The ability to connect with a person’s instincts, likes, and dislikes, is one of the key skills of any good salesperson. But as sales have moved from the market to the masses, businesses have needed to be increasingly innovative with the ways they target their specific audiences. To do this, they’ve looked to data. However, as customers become increasingly sceptical of targeted ads, just presenting your audience with a tailored advert is no longer enough. We’re having to get creative with data. Speaking to the Masses One approach brands are utilising to be more creative is, rather than using data to target, they’re using it to inform campaigns for a wide-audience. For example, Spotify’s end of year campaigns use data to recap highlights of the past year. These range from broader data about what music performed well, to data highlighting unusual behaviour from individuals.  This tongue-in-cheek approach helped reaffirm Spotify’s position as a brand who represent the zeitgeist. Furthermore, it feels personal even though it isn’t specifically targeted. If users identify themselves as part of a group being discussed they can feel as though the ad is personal to them, even if it’s on a billboard in Time’s Square.  However, there are still some risks to being so transparent with your use of data. Netflix stirred up a minor controversy when using viewing data for a light-hearted tweet. Whilst some saw the funny side, others felt that the post was invasive. Either way, it got people talking and ultimately led to an increase in views of the film they mentioned.  Using Insights to Incite Change Whilst some companies, like Spotify, use data to reaffirm their current brand, others utilise it to help them define their position. This doesn’t have to take the form of a radical change.  Nike’s recent campaign was fronted by a divisive figure within the world of US sports, Colin Kaepernick. Whilst some audiences found the move controversial, Nike’s core audience of under-35s saw this as a principled stand, repositioning one of the world’s biggest companies as a challenger brand. The move paid off and Nike saw their share price rise to an all-time high as a result of the campaign.  Data also has its place in reshaping an actual product. Take Hinge, a dating app that started life with few differentiators from its competitors. In 2017, they relaunched with a revolutionised app informed entirely from insights from their existing userbase.  Their data told them that users were “over the game” of swiping and wanted an app that allowed them to make more meaningful connections. Armed with this information, Hinge re-established themselves as an app led by unique, personal insights through a UX and brand overhaul, and are now a major player in the world of online dating.    Getting Engaged Data-driven advertising is also an excellent way to engage your audience. For example. Snickers brought their ‘You’re Not You When You’re Hungry’ campaign to life in Australia with their ‘Hungerithm’ algorithm.  The algorithm scanned 14,000 social posts across three sites every day throughout a five-week period, searching for users in a bad mood. If they found a post complaining about a traffic jam or the weather, they’d send a personalised promo code for a discounted Snickers to the ‘Hangry’ user. Across the campaign over 6,600 coupons were redeemed, and both sales and online engagement dramatically increased. Additionally, by using data that people had publicly posted, rather than their own stored information, Snickers managed to swerve any controversy.  If you are looking to create personalised ads based upon cookies and profile data, you can engage your audience without appearing too invasive. Animal rescue non-profit, the Amanda Foundation, used data to target groups without appearing too specific.  Fans of staying in and reading books were shown programmatic banner ads suggesting they adopt a cat, whilst athletic types were presented with active puppies. By loosely targeting demographics they created personal adverts that didn’t feel overly intrusive.  If you can creatively interpret data to inform targeting strategies, we may have a role for. From Marketing Analyst opportunities to Campaign & CRM jobs, we work with some of the best agencies around. Get in touch if you’d like to know more. 

Being Human: How the Interview Process is Evolving

Should we make our interview processes more like a talent show? That’s what a job centre in France thought when they introduced ‘This Is The Job’, an interview more in the style of ‘The Voice’ than a traditional Q&A session. Complete with spinning chairs and buzzers, this ‘technique’ has been swiftly brought to an end following public outcry.  But whilst it might not be the best idea to base the recruitment process on a popular TV show (let’s not use ‘Bodyguard’ as an inspiration for problem-solving tasks), we are seeing an evolution in how interviews are conducted. Here’s a look at some of the most popular trends we’re seeing businesses apply to their recruitment processes.  Cultivating a culture  Perhaps the most significant change we’re seeing at the moment is the increased prominence placed on cultural fit. No longer an afterthought, this is now a make or break factor for most employers.  Interview panels are looking for a candidate’s personality to come through when they discuss previous projects they’ve worked on. They’re keen to know that they can explain their findings to a wider audience. This includes being open about where they can improve and showing a level of humility. We’ve seen candidates rejected for being overly-defensive when receiving critiques of their technical work.  Alongside this, businesses are adapting their interviewing techniques to reflect this more human approach. First-round telephone interviews are being replaced by video calls, offering an experience closer to face-to-face. Agencies, in particular, are taking interviews out of the office and into coffee shops, with the ambition of creating a more social interaction.  All of these changes should mean that both businesses and candidates have a better understanding of what they’re signing up for before an offer is made or accepted.  Ironing out the creases  Given the fast pace of working life, finding time to dedicate to an interview process is a challenge for businesses and candidates alike. Fortunately, we’re seeing processes streamlined.  Whereas we had seen a trend for employers sending out time-consuming tasks to thoroughly test people’s abilities, the amount time required led to delayed processes and candidates dropping out. As a result, businesses are now including technical screenings within the interview itself, alongside short demos, presentations of work and online coding sessions.  By keeping things simple, we’re now seeing less candidate drop off early in the process. This, alongside combining technical and competency questions, has resulted in a more concentrated, yet just as detailed, way of assessing an applicant’s suitability for a role.  Getting hands on Employers have always looked for someone who can walk the walk, as well as talk the talk. Now they’re taking matters into their own hands by requiring candidates to react to real world examples. Most commonly, we’re seeing these take the form of case studies, generally falling into one of two categories: Quantitative, where a relevant business situation and data are provided and need to be addressed.  Conceptual, where there are no figures, and the interviewer is trying to gain an insight into the candidate’s approach and thought processes. On top of this, we’re beginning to see new methods introduced that test applicants even further. Job Auditions are becoming an increasingly popular way of assessing how well a candidate can perform in a real-world situation. There’s even talk of introducing Virtual Reality to push this idea even further within a controlled simulation.  Regardless of what the future may hold, companies are clearer than ever with what they’re looking for in the interview process. Don’t be surprised if we continue to see innovations that offer more depth into a candidate’s true behaviour, personality and working styles.  If you’re on the lookout for a new role, we can support and guide you through the interviewing process. We have a variety of roles in both Junior and Senior positions, both Contract and Permanent.  Take a look at our latest roles or get in touch with one of our expert consultants to see how we can help you progress you career. 

Vision 2020: Challenges of Today and the Skills of Tomorrow

Yesterday is history. As businesses race to stay competitive and relevant in today’s world, they face unprecedented changes to the way business works. The increasing speed at which digital advancements transform our ways of working has forced all of us, from entry-level to CEO, to adapt. If business leaders can’t add digital leadership to their expertise, they’re in danger of being left behind.  Key Characteristics of a Data & Analytics Strategy Data is driving business and it is increasingly important to build an effective Data & Analytics strategy. To do this, companies need the right people in place. They’ll need to get familiar with pressing topics and trends such as GDPR, AI, and Blockchain. Though GDPR is currently only within the EMEA region, it’s important for all businesses to adopt worldwide as part of their ongoing strategy.  There are three key characteristics businesses will need to bear in mind when formulating their strategy: Trust Robust Capabilities Insights By engaging with these characteristics, business can help secure their enterprise for the long term.  Skills to Have for the Future of Work While technologies such as AI can take over routine tasks allowing human employees more time to solve complex problems, we all need to review what other skills we can contribute.  Whether you’re in school or looking for your next opportunity, here are a few skills which can help you rise above the competition in the next year or two. According the World Economic Forum’s 2018 Future of Jobs Report, these are the skills employers will need, whether they know it yet or not: Cognitive flexibility and critical thinking. This involves logical reasoning, problem sensitivity, and creativity.   Negotiation skills. This applies to every industry from Data Analysis and Software Development to those in commercial and industrial Art and Design fields. Service to others. Are you known for helping those on your team, your supervisors, and those in your industry? These skills will be more important than ever. Judgement and decision making. Getting buy-in from a colleague and offering a strong suggestions to managers and executives at the right moment.  Emotional Intelligence. Can you gauge someone’s reaction by their body language or the slight hesitation before they answer a question or make comment? This skill will become increasingly important as the workforce of the future begins to blend robot and human.  Coordinating and collaborating with others. The ability to adjustable, flexible, and be sensitive to others’ needs. People Management. For managers and higher, it will be crucial to choose the best people for the job, motivate them, and help them develop their talents and skills. Creativity. Employers will need people who can think creatively and not only apply it to new products and services, but also to discern new ways to solve a problem. Critical thinking skills coupled with creativity just may be the one-two punch needed for the workforce of the future. Complex problem-solving. Humans who can analyse data results and have intelligent conversations with the employees who need them will be highly sought after in 2020.  One thing that’s important to note in the list above is the prominence of ‘soft skills’. Though Data & Analytics roles remain the top technical arena, what employers need in the future is individuals with highly developed social skills too. As robots and AI take on mundane, routine jobs, employers will need people who can be, well, human.  Can you bring a group of people with diverse opinions together? Can you morph from cold analytical numbers to warm greetings? Can you explain complex topics in varying degrees to people at different levels – graduates, managers, the boardroom? Continue to harness these skills and you will be even more valuable by 2020.  Have you got 2020 vision for the future? We may have a role for you. We specialise in both Junior and Senior roles.  To learn more, check out our current vacancies or get in touch. 

How to Succeed in Self-Service BI

Business Intelligence, along with Business Analytics and Big Data, is one of the terms often associated with decision-making processes in organisations.  However, there is little discussion around the importance of what skills decision makers in your organisation need to use the technology efficiently.  In recent years, the development of user-friendly tools for BI processes, Self-Service BI are increasing. Self-Service BI is an approach to BI where anyone in an organisation can collect and organise data for analysis without the assistance of data specialists. As a result of this, many businesses have invested in comprehensive storage and information processing tools. However, many are beginning to find that they are not able to realise the gains of these investments as they were expecting, may often due to underestimating the difficulties of introducing these systems into the current processes and transforming existing knowledge into actual actions and decisions.  In a worst-case scenario, if left unplanned, Self Service BI can sabotage your successful BI deployment by cutting mass user adoption, impairing query performance, failing to reduce report backlogs, and increasing confusion over the “single truth”. To prevent this from happening, here are our top three tips for ensuring the right implementation of SSBI in your company: UNDERSTAND YOUR USERS’ NEEDS There are three major user areas for analytics tools: strategic, tactical and operational. The strategic users make few, but important decisions. The tactical users make many decisions during a week and need updated information daily. Operational users are often closest to the customer, and this group needs data in its own applications in order to carry out a large number of requests and transactions.  Understanding the different needs of each group is necessary to know what information should be available at each given frequency to help scale the BI solution.  HARNESS THE POWER OF ADVANCED USERS To ensure a successful BI deployment, utilising advanced users is key. Self-service BI is not a one-size fits all approach. Casual users usually don’t have the time to learn the tool and will often reach out to ‘Power Users’ to create what they need. Hence, these users can become the go-to resource for creating ad-hoc views of data. Power Users are the ideal advocates for your business’ self-service BI implementation and should be able to help spur user adoption.  UPGRADE INTERNAL COMPETENCIES  Our final tip for a successful implementation is to communicate the new tool thoroughly to the users.  It is highly unlikely that employees who have not been involved in the actual development project will immediately understand what the tool should be used for, who needs it, and what it should replace. By upgrading internal competencies, you can avoid becoming dependent on external assistance. Establishing a cross-organizational BI competence centre of 5-10 members, who meet regularly to share their experiences will help drives and prioritise future use of the tool. The added benefit of a successful implementation is that it will generate new ideas from users for how the organisation can use data to make better decisions. If you have the skillset to implement Business Intelligence solutions, we may have a role for you.  Take a look at our latest opportunities or get in contact with our team. 

Will Artificial Intelligence Revolutionise Eye Healthcare?

Faced with a rapidly expanding and increasingly older population, Healthcare resources in both the UK and US are facing an unprecedented level of demand. With only limited resource available, conversation is beginning to turn to the potential use of Artificial Intelligence (AI) to ease some of the strain. A recent example already seeing success is the current collaboration between Google’s DeepMind and London’s Moorfields Eye Hospital. But, as the lines begin to blur between human and machine-diagnosis, it’s worth questioning what role AI should actually play.  SEEING THE POTENTIAL IN AI Aside from the increase in population, there are many societal elements that are affecting the healthcare system. An increase in illnesses such as diabetes has led to a rise in eye-diseases and increased demand on optometrists.  Fortunately, AI can speed up the process with new technologies allowing systems like DeepMind to make their own diagnosis. Optical Coherence Technology (OCT) allows optometrists to create a 3D scans of people’s eyes. By bouncing near-infrared light of the interior surfaces of the eye, it can create an image that will reveal any abnormalities. DeepMind has been trained on over 15,000 scans and can now form a likely diagnosis, having used algorithms to find common patterns within the data.  Head of DeepMind, Mustafa Suleyman, says: “ [This could] transform the diagnosis, treatment, and management of patients with sight threatening eye conditions [...] around the world.” However, with an accuracy of just over 94%, there is still enough room for error to cause concern, especially given the potential consequences of an incorrect diagnosis.  LOOKING FOR MISTAKES  This doesn’t mean we should rule out the use of AI altogether. Whilst we may not be able to solely rely on the technology for diagnosis, it can be effective when working hand-in-hand with a human skillset.  In particular, by using AI systems for Triage purposes (determining what order patients should be seen in), as opposed to making a full diagnosis, patients demonstrating more significant symptoms could be reported and seen by a medical professional as priority, potentially leading to a higher chance of recovery.  When AI is used as a driver for patient management, as opposed to being viewed as alternative physician, it can create a faster and more efficient process.  To help continue to improve the results produced by DeepMind, the NHS have been given a validated version to use for free for the next five years. Using real-world applications over this time should streamline both their processes, and the technology itself.  A LONG TERM VISION For the time being, AI’s role within Eye Health is one of evolution, not revolution. With the inconsistency of current technology and the impact of incorrect results on people’s sight, it can only be utilised as a supporting tool.  For now, the skillsets of Data Analysts and medical doctors remain too separate to full work hand-in-hand. Add to this the risks of automation bias (a willingness to blindly trust a machine’s output), and the margin of error is too high.  However, that’s not to say that AI can’t and won’t play a significant part in the future of Healthcare. With the technology to detect eye conditions through the lens of your smartphone camera closer than ever to mainstream use, AI is set to play a huge role in outpatient treatment. At this stage, however, that role will be one of risk predictor, not eliminator.  If you think you have the skillset to help take AI to the next level in Healthcare we may have a role for you. Take a look at our latest opportunities or get in contact with our team. 

Harnham's Brush with Fame

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

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

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

Download our 2018 UK & EU Salary Guides

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

Our Top Five Tips For Telling Stories With Data

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

HOW DEEP LEARNING IS TREATING HEALTH-BASED ISSUES

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

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