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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Dan started his career in recruitment as part of Harnham’s 2013 graduate scheme as a member of the recently formed Digital team. He has helped grow the team, and progressed through to Manager level, where he is now looking to continue Harnham expansion in the Digital Analytics and Optimisation space.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of data and analytics. The FinTech Times: Google Cloud Partners with Twitter to Improve Data Insights and Analytics At the end of this week, we learned that Google Cloud has announced a new partnership with Twitter. From now on, Twitter plans to move its offline analytics, data processing and machine learning workloads onto Google’s Data Cloud. For Twitter to be able to process and create correct algorithms based on users’ likes, retweets and comments, a huge amount of data needs to be stored; we’re talking hundreds of petabytes of data and trillions of events. By partnering with Google, Twitter’s machine learning processes and data-informed decisions will not only be much faster and more efficient, it will also allow the company to create much deeper machine learning innovation. Read the full article here. Think with Google: Top five Lunar New Year observations from the past five years As we geared up for this year’s Lunar New Year, Think with Google used Google Trends Data to explore what piqued our interest before the big celebration last year. The top five findings were: Singaporeans love Lunar New Year – Singapore’s searches around the Lunar New Year are the highest in the world!Promotions, not sales, please – In Singapore, interest in Chinese New Year retail promotions isn’t just restricted to the festive period — it happens all year long.Tasty trends – Yusheng is the top food of choice at the time of the Lunar New Year in Singapore. Feng Shui is important in Vietnam at this time, they’re more likely to be banking on higher powers for the Lunar New Year. It starts a lot earlier than we might think – searches of Lunar New Year begin at least 8-10 weeks before the big event! Read the full article here. Analytics Insight: Artificial intelligence redefining and innovating the textile industry AI is becoming a key part of most industries, and the textiles sector is no different. Automation and AI is being used not only for product creation and transformation, but across manufacturing processes and customer service, too. Statista has reported that the textiles industry is set to grow from a value of $1.5 trillion currently to $2.25 trillion in 2025. This rapid increase in demand for textiles will undoubtedly mean the need for increased labour within the industry but, to ensure companies remain profitable, automation and robotics are most likely going to be put in place to reduce workforce costs. In this insightful article from Analytics Insight, we learn exactly how the adoption of AI in the industry is helping strengthen and innovate service offerings. From pattern inspection to pattern making, merchandising and smart apparel, this technology is redefining the industry for good. Read the full article here. Techiexpert: Software engineering trends to look for in 2021 As the capabilities of technology reach further than ever before and businesses invest heavily in software engineering to rapidly accelerate productivity, there’s no doubt that the sector has a lot to look forward to this year. Techiexpert explores eight key trends we should expect to see from software engineering in 2021. From more businesses embracing the abilities of cloud computing to the domination of Python, the full throttle of the Internet of Things and the further growth of blockchain, the landscape is set to continue evolving and continue adding huge value to all businesses. Read the full list of trends for the industry here. We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at firstname.lastname@example.org.
12. February 2021
This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics. Raconteur: Implementing AI successfully The pandemic has undoubtedly accelerated the implementation of AI systems across a vast range of companies worldwide. Many business leaders and senior teams are aware of, and excited by, the huge scope of opportunities AI will bring into the workplace, especially when it comes to decision making. However, teams are still concerned and frustrated by the lack of understanding of the technology involved and the inadequate amount of reliable Data. With the help of AI and Data specialists, Raconteur explores how senior leaders can solve these issues; from the increasing the investment of both time and money in boosting employee skills training, creating high-quality Data silos or dispersing the workload across the whole team, Data Analysts have a key role to play. There’s a lot of golden nuggets of advice in this piece, a great resource for Data teams looking to make their AI more effective and efficient in 2021. Read the full article here. Tech Crunch: Amazon Web Service (AWS) adds natural language search service for business intelligence from its data sets Andy Jassy, CEO of Amazon Web Services, recently delivered a keynote speech at event AWS re:Invent where he discussed Amazon Web Services' latest upgrade – QuickSight Q. In 2016, AWS launched QuickSight, a business intelligence service allowing business users to have access to product information and customer information, usually something only developers were privy too. But at the time, the natural language processing tools simply weren’t good enough for this to happen. Fast forward four years, and AWS has finally cracked it; QuickSight Q was born. In this article by Tech Crunch, Jonathan Shieber explores how QuickSight Q will work and how the natural language processing in AWS is a direct challenge to several business intelligence startups. Read the full article here. ZDNet: Amazon unveils Amazon HealthLake, Big Data store for Life Sciences Taking centre stage at this week’s Amazon Web Service event, AWS re:Invent, Vice President for AI at AWS, Matt Wood, unveiled an all-new service, Amazon HealthLake. The new AI system will be revolutionary for the healthcare and Life Sciences sector, especially when it comes early detection of illnesses such as diabetes. As said by Wood, “[Currently] the work in these fields is hampered by the way that data is spread throughout numerous repositories in different formats, requiring weeks or months to prepare, stage, transform data.” This overwhelming abundance of data in healthcare can make it much harder for specialists to diagnose and treat patients efficiently. We know that AWS HealthLake will be a way to store, transform, and analyse health and life science data in the cloud at petabyte scale, making the whole process a lot more streamlined and effective. One to keep an eye on. Read more on HealthLake here. The Drum: What brands need to know about consent and Data This year, data has played a role like never before in both our day-to-day personal and professional lives. With the quick and unexpected move to online and remote working as a direct impact of COVID-19, keeping on top of and up to date with technology, privacy and data consent has been an ever-growing challenge for brands, businesses and agencies worldwide. Those businesses that have been putting data safety and consent first, over the past nine months especially, will get ahead of the competition. Those who haven’t are in danger of coming under fire from their own customers, and it may cause irreparable damage to their brand. At the Drum Digital Summit last month, a panel of data specialists including Niall Hogan, Siva Jayaraj and Colleen Ngo, discussed exactly what brands need to know about data and most importantly data consent moving forward. The panel agreed that the biggest issue lay in the education of data consent, and more should be done to raise awareness from a young age. Click here to read more and to listen to the full panel discussion. The Data & Analytics market is one full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at email@example.com.
11. December 2020
For the latest episode of The Dialogue, Associate Director Dan Lewis sat down with Andy Dagnall from Contract insurance specialists Kingsbridge to talk all thing IR35. Here's what we learnt: - Understanding your current workforce and who your contractors are should be the first step for any business looking into IR35. - Businesses should be set up better now there has been a delay. Getting IR35 right in the current economic is so much more important than it was the first time round. - Blanket bans on contracting don't factor in the risks of projects not being completed and loss of talent. - The Public Sector tried blanket bans and they didn't work. They've now bounced back by embracing a new way of working. - There are online tools to help determine a contractor's status in regards to IR35 and Harnham have our own tool for this stage of the process. - Collaboration will be key for both businesses and contractors. Working with third parties will help make the process easier. - It is almost essential that contractors take out insurance to mitigate the risk of a wrongful determination. - Status determination can change throughout a project. An inside determination at one stage of a project won't necessarily stay that way for the duration. - Contractors will have more trust in businesses that have a robust process in place. - Contractors can get a working practices review done now so they can approach recruiters or businesses with an understanding of where they will fall with IR35. - The most important thing in a status determination is being honest. - And much, much more. You can watch the full conversation below:
17. September 2020
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 firstname.lastname@example.org. This article was originally written for London MeasureCamp in September 2018.
05. December 2018
The UK analytics market is an ever-shifting landscape, with technical skills that flit in and out of favour more frequently than primary school playground friendships. Recruiting into this market, we at Harnham find the demand for analysts with up-to-date skill sets is a constant. The subtle variants required within these skill-sets coupled with the proficiencies we need to find, seem to also evolve on almost a monthly basis. This means that Harnham need to be as agile in our practices as the candidates we source. This is nevermore clearly illustrated by the fact that one month we can be scouring the country for highly technical Tag Management specialists, and the next, our focus shifts to sourcing 8 new Conversion Rate Optimisation specialists. Moving on to next month, pinning down the elusive and much sought-after Web Analytics / BI / Computer Science / Statistical modelling expert (aka. The mythical “Analytics Unicorn”). The Benefit of Forward Thinking Skillsets The diversity of technical requirements should give tremendous encouragement to any analyst looking to find work – regardless of background and skill-set you possess; there will definitely be no shortage of suitors clamouring for the right expertise. However, it is prudent to be ever mindful, that with this exciting and evolving landscape, there are also risks attached. How often have you heard stories of promised roles not being fulfilled by companies? Or about analysts who join a company to do a specific job, and then have their career development curtailed by lack of long-term strategy, or a lack of knowledge of what to do next for the team? This kind of thing is and does happen regularly. Yet, hope is at hand. If you were to cast your gaze across the Atlantic Ocean to our American cousins, you would find that Analytics is a settled and well established practice, where Chief Data Officers regularly sit on the board and the Analysts control business strategy. Becoming a Unicorn Harnham’s team in New York City have found the lines between offline and online blur when stateside – Web Analysts do advanced statistical analysis and modelling, Stats Analysts measure conversion rates and so on and so on. The US is trying to breed their own “Unicorns”, rather than chase them. As the dust starts to settle in the UK, and the market definition solidifies to become as robust as it is in the States; teams will grow and budgets will increase as the market develops. The senior analysts of today will become the thought leaders and managers of tomorrow.
01. October 2015
Camelot, the operator of the National Lottery, has today launched an out-of-home campaign that matches messaging to live train departure times at railway stations. National Lottery syncs real-time campaign to train timetables The "surprising numbers" billboards explain how more people than ever before are winning with National Lottery scratchcards, according to Camelot. The geo-targeted boards reveal the number of people in the UK who are expected to become scratchcard winners before the next train departs, using data from National Rail and Transport for London, cross-referenced with scratchcard data. They feature an image of people forming a "human counter’ that displays data on their T-shirts. Commuters will see messages tailored to their location and the next departing train, such as, "Winners before the 09:05 to Norwich leaves? 1,562." The campaign was planned and bought by OMD UK, Havas Media and Talon, with production and technology by Grand Visual. The train data is syndicated using OpenLoop, the digital analytics out-of-home campaign management system. It pulls in train data before contextualising messages for each individual site and pushing them live to the billboards. Max Lucas, a media strategy manager at Camelot, said: "Clever use of digital analytics out-of-home technology has really brought this campaign to life. "We have been able to use our data in a unique way, serving up dynamic, targeted, locally-relevant messages that let people know there are more National Lottery Scratchcard winners than ever." The messages will run across screens at rail stations and on the London Underground. Click here for the article on the web.
19. November 2013
A job invented in Silicon Valley is going mainstream as more industries try to gain an edge from big data.The job description “data scientist” didn’t exist five years ago. No one advertised for an expert in data science, and you couldn’t go to school to specialize in the field. Today, companies are fighting to recruit these specialists, courses on how to become one are popping up at many universities, and the Harvard Business Review even proclaimed that data scientist is the “sexiest” job of the 21st century.Data scientists take huge amounts of data and attempt to pull useful information out. The job combines statistics and programming to identify sometimes subtle factors that can have a big impact on a company’s bottom line, from whether a person will click on a certain type of ad to whether a new chemical will be toxic in the human body.While Wall Street, Madison Avenue, and Detroit have always employed data jockeys to make sense of business statistics, the rise of this specialty reflects the massive expansion in the scope and variety of data now available in some industries, like those that collect data about customers on the Web. There’s more data than individual managers can wrap their minds around—too much of it, changing too fast, to be analyzed with traditional approaches.As smartphones promise to become a new source of valuable data to retailers, for example, Walmart is competing to bring more data scientists on board and now advertises for dozens of open positions, including “Big Fast Data Engineer.” Sensors in factories and on industrial equipment are also delivering mountains of new data, leading General Electric to hire data scientists to analyze these feeds.The term “data science” was coined in Silicon Valley in 2008 by two data analysts then working at LinkedIn and Facebook (see “What Facebook Knows”). Now many startups are basing their businesses on their ability to analyze large quantities of data—often from disparate sources. ZestFinance, for example, has a predictive model that uses hundreds of variables to determine whether a lender should offer high-risk credit. The underwriting risk it achieves is 40 percent lower than that borne by traditional lenders, says ZestFinance data scientist John Candido. “All data is credit data to us,” he says.Data scientist has become a popular job title partly because it has helped pull together a growing number of haphazardly defined and overlapping job roles, says Jake Klamka, who runs a six-week fellowship to place PhDs from fields like math, astrophysics, and even neuroscience in such jobs. “We have anyone who works with a lot of data in their research,” Klamka says. “They need to know how to program, but they also have to have strong communications skills and curiosity.”The best data scientists are defined as much by their creativity as by their code-writing prowess. The company Kaggle organizes contests where data scientists compete to find the best way to make sense of massive data sets (see “Startup Turns Data Crunching into a High-Stakes Sport”). Many of the top Kagglers (there are 88,000 registered on the site) come from fields like astrophysics or electrical engineering, says CEO Anthony Goldbloom. The top-ranked participant is an actuary in Singapore.Universities are starting to respond to the job market’s needs. Stanford University plans to launch a data science master’s track in its statistics department, says department chair Guenther Walther. A dozen or so other programs have already been started at schools including Columbia University and the University of California, San Francisco. Cloudera, a company that sells software to process and organize large volumes of data, announced in April that it would work with seven universities to offer undergraduates professional training on how to work with “big data” technologies.Cloudera’s education program director, Mark Morissey, says a skills shortage is looming and that “the market is not going to grow at the rate it currently wants to.” That has driven salaries up. In Silicon Valley, salaries for entry-level data scientists are around $110,000 to $120,000.Others think the trend could create a new area of outsourcing. Shashi Godbole, a data scientist in Mumbai, India, who is ranked 20th on Kaggle’s scoreboard, recently completed a Kaggle-arranged hourly consulting gig, a new business the platform is getting into. He did work for a tiny health advocacy nonprofit located in Chicago and is now bidding on more jobs (he earns $200 per hour, and Kaggle collects $300 an hour). His Kaggle work is part time for now, but he says it’s possible that it could be his major source of income one day.To the data scientists themselves, the job is certainly less sexy than it’s being made out to be. Josh Wills, a senior director of data science at Cloudera, says most of the time it involves cleaning up messy data—for example, by putting it in the right columns and sorting it.“I’m a data janitor. That’s the sexiest job of the 21st century,” he says. “It’s very flattering, but it’s also a little baffling.” Click here for the article on the web.
17. July 2013