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Easier, Smarter, Better: What Does The 2019 Data Landscape Look Like?

Data & Analytics have played a larger role in 2018 than ever before. A key talking-point, this year saw the Cambridge Analytica scandal, resulting in increased pressure on the biggest names in tech. With Mark Zuckerberg being summoned before various governments worldwide to answer privacy and security concerns, it’s unlikely that we’re going to stop talking about the impact of Big Data any time soon.  Fortunately, 2018 also appears to have been a turning point. Whilst we’ve seen a greater number of threats from Data, we’ve embraced the technology it supports more than ever before. As a result of this, 2019 is looking brighter than ever for the Data & Analytics landscape.  Easier Access to Data As businesses start to look at keeping their data closer to home, access has become more important than ever. Edge Computing has dramatically increased in popularity, and it doesn’t look like we’re going to see it slow down any time soon. BI Intelligence are estimating that, by the end of 2019, there will be 5.6 billion business-owned devices that access data in this way.  A result of the speed at which technology is advancing, Edge Computing is also seen as a more secure and private way to hold data. Whilst it was initially feared that the ‘Edge Will Eat the Cloud’, we’ve actually seen both perform strongly enough to stand on their own two feet. Far more than just being a stepping stone as we move away from Data Centres, we’ve seen Edge Computing play a role in everything from the Internet of Things, to everyday fuel efficiency. And, in 2019, we can expect it to play an even bigger role, as platforms emerge that will combine edge, multi-cloud and hybrid technologies. Smarter Tech Than Ever Before Whilst we all know that next year will introduce us to smarter technologies, they’re a few places where we can already some 2019 standouts. Natural Language Processing (NLP) has leapt forward over the past year and will continue its acceleration in 2019. Advances in Machine Learning (ML) will see BI tools feel the biggest benefit from this. In particular, the ability to ask programs questions around Data Visualisations and improve our insights will play a huge role.  Off the back of this, we’re likely to see Automated ML play a huge role. Falling between cognitive APIs and custom ML platforms, Auto ML will offer analysts the flexibility to deal with complex issues without having to go through the typical ML training process.  AI and ML will also revolutionise the DevOps world as 2019 sees AIOps become mainstream. This multi-layered technology will automate and streamline significant amounts of IT operations as the speed, and demand, of data continues to increase, but accountability remains the same.  Becoming a Better Society with Data Unsurprisingly, GDPR has had a huge impact on the Data & Analytics landscape in 2018. And, with the USA and India looking to embrace similar regulations in the new year, we’ll continue to feel the repercussions of this in 2019.  Companies are now no longer being assessed on the black and white of the legality of their practices, but how ethically they use the data they have access to. As a result of issues that have arisen over the past few years, how social media giants handle their data is now a leading discussion point during every major election. Whilst Facebook are already taking some steps to address this, expect to see the pressure on them and their contemporaries continue to increase in 2019.  It’s not only major networks that are having to adapt either. The number of leading businesses who now have their own data code of ethics has dramatically increased in the past few years and shows no signs of slowing. Plus, new technologies mean new risks and new principles. For example, as Facial Recognition technology becomes more and more prevalent, public concerns will lead to both fresh regulations but also increased responsibilities for firms.  However, it’s not all rules and regulations. The rise in the amount of Data we have has led to a rise in the possibility to utilise it for social good. Enterprises like Orange and The Hutch Data Commonwealth are looking at how we can share data and insights to achieve common goals and make the world a better place. Despite an increase in risks, 2019 is also offering a plethora of opportunities to use data for good.  If you’re looking to embrace the new and take on a fresh challenge in 2019, we can help. Take a look at our latest roles or get in touch with one of our expert consultants to find out more. 

Sophie ten Berge

11. December 2018

Opportunity Knocks: Media Trading Desks Move In-House

It’s anticipated that by 2022, 80% of the advertising process will be automated. With the remainder of the process made up of elements that rely on human drivers, such as brand value and storytelling, we will have reached peak automation. Programmatic is playing a huge role in this transition, dominating the majority of mobile display and TV advertising. With the promise of more targeted ads, more and more marketers are pointing their budgets in this direction. The consequences of this, however, could have a lasting impact. Larger agencies are already introducing their own Programmatic teams, whilst Adobe believe that 62% brands will bring their media buying in-house by 2022, opening the door for an array of new opportunities. Moving Home There are several reasons that brands are choosing to bring their media buying in-house. First and foremost, with more and more budget directed towards Programmatic, the ability to automate their buying has a significant appeal. With the technology now available to do so, keeping this in-house has a number of benefits: Control: Brands can have greater control over how they spend their budget, giving them more autonomy over every stage of the process. Transparency: By owning their media buying, brands can gauge a better understanding of their ROI. Engagement: Customers continuously move from channel to channel. Keeping buying in-house helps brands keep up. Leverage: Brands can leverage their first-party data to create and execute in-house strategies. The last of these is particularly important. Following the introduction of GDPR, brands are under a significant amount of scrutiny regarding how they use customer data. By keeping this in-house, brands can have more control over how their data is both stored and used, without sharing it with third parties. Making the Investment Naturally, this change to the advertising landscape is already having implications across the wider industry. Sir Martin Sorrell, formerly of WPP, believes that brands moving in-house will be a “short lived trend” brought on by a reaction to “serious economic conditions”. However, this may not necessarily be the case. In addition to the benefits listed above, the significant investment required to move Programmatic in-house means that brands are likely to look to this as a longer-term solution.This not only involves investment in a DMP and the right technology but, more crucially, in building the right team. Without this team, any in-house venture is unlikely to succeed, regardless of technological investment. On Your Doorstep The good news is that this provides several new opportunities for Digital Analytics professionals. With 39% of Marketing Executives believing that a skills shortage is responsible for holding back Programmatic growth, there is a huge demand for the right talent, both permanent and contract. And, with this skills shortage, there is opportunity for Web and Marketing Analysts to expand their skillsets and move into the position of Media or Audience Analyst. By upskilling in media trading platforms such as AppNexus and DoubleClick, digital analysts can further enhance their expertise. When combined with the ability to visualise using Python or R they find themselves well positioned for some of the most in-demand roles around. If you’re looking for the opportunity to play an instrumental role in growing an in-house team, we may have a role for you. Take a look at our latest jobs, or get in touch by calling us on +44 20 8408 6070 or emailing info@harnham.com. This article was originally written for London MeasureCamp in September 2018. 

Daniel Lewis

05. December 2018

From Idea to Impact: How Charities Use Data

It’s that time of year again. As the festive season draws near and we pull together wish lists, many of us also begin to think about how we can give back. Given that the UK spent over £7 billion this Black Friday and Cyber Monday weekend, it’s not surprising that the idea of Giving Tuesday is becoming more and more popular.  But with 160,000 registered charities in the UK alone, institutions are turning to data to find new ways to stand out and make a greater impact.  Far from just running quarterly reports, charities are now utilising the insights they gain from data to inform their strategies, improve their services and plan for the future.  IDEAS Given that not every charity is lucky enough to go viral with an Ice Bucket Challenge style video, there is a need to find other ways to stand out in such a crowded market. As such, many are looking to the data they have collected to help create a strategy. Macmillan Cancer Support, one the UK’s biggest charities, wanted to see more success from one of their main fundraisers, ‘The World’s Biggest Coffee Morning’. The event, which sees volunteers hold coffee and cake-fuelled gatherings across the country was revolutionised by data. By engaging with their database and researching what motivated fundraisers, they refocused their marketing around how the occasion could create an opportunity for people to meet up and chat, such as swapping ‘send for your free fundraising pack’ for ‘order your free coffee morning kit’. Whilst these amends may seem superficial, they had a major impact increasing funds raised from £15m to £20m.  Some brands have taken this idea even further, using Data & Analytics tools to engage with potential donors. Homelessness charity Cyrenians’ data told them that there were a number of misconceptions about rough sleepers, including 15% of people believing that they were homeless by choice. To counter this they created an AI chatbot, named Alex, that allowed users to ask questions they may not have been comfortable asking a real person.  Another charity using data tools to counter common misconceptions is Dyslexia Association. Their Moment of Dyslexia campaign saw them utilise facial recognition technology; the longer a person looked at their digital poster, the more jumbled up the words and letters became. By harnessing both insights and the technology made possible by data, they were able to offer an insight into what dyslexia is like for people who previously didn’t understand.  INDIVIDUALS A big issue facing a number of charities is trust. Following a series of recent scandals, the public are more sceptical than ever of how charities are run, and their use of data is no exception. This ‘trust deficit’ has resulted in vast amount of potential donors staying away, with recent research highlighting that only 11% of people are willing to share their data with a charity, even if it means a better service.  Whilst charities with effective Data Governance are able to use their vast amount of data to enhance those business, those who mismanage it are likely to suffer. Following a cyber-attack that exposed the data of over 400,000 donors, the British and Foreign Bible Society were fined £100,000. As hackers were able to enter the network by exploiting a weak password, this serves as a timely reminder that our data needs not only to be clean, but secure.  Financial implications aside, improper data usage can also do irreversible damage to a charity’s reputation. St Mungo’s has faced criticism for passing information about migrant homeless people to the Home Office, putting them at risk of deportation. Whilst they were cleared of any wrongdoing by the ICO, this controversial use of data has had a negative impact on the charity’s image. With a decline in the number of people donating to charity overall, anything that can put people off further is bad news.  IMPACT Whilst there is more demand than ever for charities to share their impact data, there is also more opportunity. With Lord Gus O’Donnell urging charities to make data an ‘organisation-wide priority’, many are going beyond publishing annual reports and fully embracing a culture shift. Youth charity Keyfund have been able to justify how the spend their funds based on their impact data. Having heard concerns from fundraisers regarding whether their leisure projects were effective they looked at the data they had gathered from the 6,000 young people they were helping. What they found was that not only were their leisure projects effective, they had an even more positive impact than their alternatives, particularly for those from the most deprived area. This allowed them to continue to support these programs and even increase funding where necessary. Going one step further are Street League, a charity that use sports programmes to tackle youth unemployment. Rather than share their impact data in quarterly, or even annual, reports they moved to real-time reporting. Interested parties can visit an ‘Online Impact Dashboard’ and see up-to-the-minute data about how the charity’s work is impacting the lives of the people it is trying to help. This not only allows for the most relevant data to be used strategically, but also supports the business holistically, gaining donor both attention and trust. To stand out in the charity sector institutions need to take advantage of data. Not only can this be used to generate campaigns and streamline services but, when used securely and transparently, it can help rebuild trust and offer a competitive edge.  If you want to make the world a better place by harnessing and analysing data, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to see how we can help you. 

Kirsty Garshong

29. November 2018

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. 

Henry Rodrigues

15. November 2018