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Weekly News Digest: 22nd - 26th Feb 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.  Search Engine Journal: 4 ways call tracking is changing (and why it’s a good thing) Call tracking is no longer about a customer seeing an ad, calling up the company, telling them how much they loved the ad and then deciding to purchase goods. This is a positive thing really because it wasn’t the most effective way for businesses to track how well adverts were doing anyway - who really remembers where they saw a billboard that took their interest, or what time of day an advert popped up on the TV? As call tracking technology becomes more advanced, call analytics have become much more accessible for all. Not only have they been able to transform how businesses of all shape and size advertise and track their success, but also how they market to potential audiences and track their sentiment.  This article from Search Engine Journal looks at the evolution of call tracking and call analytics from its most basic form, how it works now and what the future of this crucial set of analytics will look like in the future.  Read more on this here.  Towards Data Science: Data Science Year Zero Skills or qualifications in Data Science are becoming incredibly sought after by many employers, but the knowledge of how to break into the sector is still a little unclear for potential candidates. In this article by Towards Data Science, they break down the crucial elements of how to successfully enter the industry in four easy steps.  What the author, Bala Vishal, lacked when he started and how you can set off on a better footing.The most important skills and tools to have under your belt.Which skills should you home in on first.How to thrive in the workplace. This incredibly insightful piece should be a ‘must-read’ for any budding Data Scientist looking to break into Data in 2021 and beyond.  Read more here.  KD Nuggets: 10 Statistical Concepts You Should Know for Data Science Interviews This article is perfect for anyone in the Data Science industry. Whether you’re new to the game or looking to take the next step on the career ladder, make sure you brush up on these crucial statistical concepts you should know inside out before entering interview.  A few, in no order, include: Z tests vs T tests An invaluable piece of knowledge that will be used daily if you are involved in any statistical work.Sampling techniques Make sure you’ve got the main five solidified in your knowledge bank - Simple Random, Systematic, Convenience, Cluster, and Stratified sampling.Bayes Theorem/Conditional Probability One of the most popular machine learning algorithms, a must-know in this new era of technology.  Want to know about the other seven? Read more here. Forbes: 48 per cent of Sales Leaders Say Their CRM System Doesn’t Meet Their Needs. The Good News Is That This Is Fixable. This article by Gene Marks explores why teams aren’t happy with their current CRM systems, and how this can be remedied. New research from SugarCRM found: 52 per cent of sales leaders reported that their CRM platform is costing potential revenue opportunities.50 per cent of the companies said they cannot access customer data across marketing, sales and service systems.Nearly one-third complained that their customer data is incomplete, out of date, or inaccurate. While damning statistics, Marks then goes into how this worrying situation can be fixed for good. He says: “Like just about all problems in business, this problem comes down to two factors: time and money. The blunt fact is that most companies are not willing to spend the necessary time or money needed to enable their CRM systems to truly do what they’re designed to do. CRM systems are not just for sales teams. And they're not just for service teams. For a CRM system to be effective, a company must adapt it as its main, collaborative platform.” Read more on this 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 info@harnham.com.

How Are Data & Analytics Professionals Mapping COVID Trends With Data?

The coronavirus pandemic has impacted industries across the globe. There’s no ignoring that simple fact. This disruption (most notably) caused devastating effects in two strands: to our health and to business operations. As the virus spread, the health and wellbeing of people in society worsened, and businesses felt the strain of projects being placed on hold, and work slowing or completely grinding to a halt. As of the 24th February 2021, the disease has infected more than 112,237,188 people, with 2,487,349 reported deaths. For Data & Analytics professionals, it soon became evident that they could use their skills to help. Using the mass of data available, professionals and researchers turned to big data analytics tools to track and monitor the virus’s spread, along with a variety of trends. Here’s how: Genomics and sequencing Life science is a significant application within Data & Analytics and explores the study of all living things on earth. One particular section of this study looks at the concept of genomic sequencing.  Genomic sequencing is significant as it allows us looks at the entire genetic code of a virus – in this case, COVID-19. Most importantly, the technique means that researchers and analysts can identify dangerous mutations and track movements of specific variants. We know that the UK has the most advanced system for tracing covid variants too. Last year, Britain launched one of the world’s largest coronavirus sequencing projects, by investing £20 million in the Covid-19 Genomics UK consortium. In a group that included NHS researchers, public health agencies, academic partners and the Wellcome Sanger Institute, they set out to map the genetic code of as many strains of the coronavirus as possible. And the buy-in paid off. It took the US approximately 72 days to process and share each genetic sequence, compared with 23 days for UK researchers, according to figures compiled by the Broad Institute with data from Gisaid. Tech giants stepping in Ultimately, your organisation is more agile than you think it is. Regardless of the size of the business, or the industry in which it operates, the sector’s response in applying analysis and data to track the coronavirus was nothing short of miraculous. Google introduced a series of features such as popular times and live busyness, COVID-19 alerts in transit, and COVID checkpoints in driving navigation in order to keep their one billion (and growing) app users safe. They also introduced the COVID layer in Maps, a tool that shows critical information about COVID-19 cases in a given area, allowing their customers  to make informed decisions about where to go and what to do. Apple also released a mobility data trends tool from Apple Maps. This data was shared in order to provide insights to local governments and health authorities so that they could support mapping specific covid trends. These first-hand examples indicate the influence and power of using data to better our understanding of the virus. Before the coronavirus pandemic, professionals, businesses and industries alike worked in siloes. What we have witnessed since has been very much the opposite, as experts quickly came together to begin mapping out data requirements and supporting the world’s focus to improve the public’s health and get businesses back on their feet. Without Data & Analytics, none of this would be possible. If you're looking to take the next step in your career or build out a diverse Data & Analytics team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Weekly News Digest: 15th - 19th Feb 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.  KDnuggets: 10 resources for data science self-study If you are interested in getting into data science, there are two basic pathways that you can opt for. There’s the traditional college degree route or the self-study option, the latter of which is growing in popularity among aspiring data scientists. This informative article from KDnuggets provides some insightful tips for data science self-study, grouped into three main categories: Resources for building fundamental knowledge; resources for data science practice; and resources for networking and continuous studies. Resources for building fundamental knowledge:Massive Open Online Courses (MOOCs)Learning from a TextbookYouTubeKhan AcademyResources for Data Science practice:KaggleInternshipsResources for networking and continuous studies:MediumLinkedInKDnuggetsGitHub Find out more here. Analytics India Magazine: How machine learning streamlines risk management Abhaya K Srivastava, SVP at Northern Trust Corporation, recently spoke at the Machine Learning Developers Summit 2021. Srivastava delved into how different sectors including financial, healthcare and retail are making use of emerging technologies like AI and Machine Learning. One of the main takeaways from the speaker session was discussions around how Machine Learning can support how organisations streamline their Risk Management. Srivastava stated, “It is essential for us to establish the rigorous governance processes and policies that can quickly identify when the model begins to fail.” He continued, “The terms of AI are not new, but businesses and organisations have started using these technologies in a different way. We have noticed the influence of machine learning in business applications, ML is playing an important role in Risk Management and there has been a constant focus on how risks are being detected, reported, managed.” There are a range of different machine learning techniques that can be applied to support risk management. It is the role of organisations, and their partners to discover how these processes can be applied. Read more on this here. Information Week: 3 Ways to Empower Female Software Engineers on Your Team We think this is a great article from Information Week that acknowledges the importance of establishing greater diversity and inclusion within software engineering, in particular to empower women in the industry. The article focuses on three areas: Create an inclusive team:Building an inclusive team is a strategic process and should include making sure everyone has a voice and that the workplace is a safe place to take risks.Provide a support system:Support establishes trust and shows a commitment to the well-being of your people. When leaders support their employees, it can significantly affect job satisfaction and performance.Enable women to inspire othersThe first thing to do is make sure the women in your organisation have a seat at the table; they should have a say in the decision-making process. Even if you have a good understanding of these, it’s important to keep educating yourself and the wider team in order to implements processes and strategies that make for a truly inclusive team. Read more on this here.  TechRepublic: 8 must-read leadership books recommended by tech titans and innovators Are you looking for your next read to help you elevate your visibility and skill as a leader in the tech industry? Look no further, as TechRepublic have put together a list of leadership books recommended by notable leaders from within the industry. Here are a few: The Ride of a Lifetime (Robert Iger) - Recommended by Bill GatesDrop the Ball (Tiffany Dufu) - Recommended by Sheryl SandbergMindset (Dr Carol S. Dweck) - Recommended by Satya NadellsTrailblazer (Marc Benioff) - Recommended by Susan Wojcicki It’s valuable to have insight from leaders that are already leading the way for tech innovation in their field, inspiring and supporting future leaders to achieve great things too. Click here to read the full list of recommended leadership books from Bill Gates, Satya Nadella, Sheryl Sandberg, Tim Cook, and other notable industry leaders. We've loved seeing all the news from Data & 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 info@harnham.com.

Data Science For Business Decision Making

All strong and successful businesses are built and run upon well-informed decision-making, which derive from a mix of leader experience, industry knowledge and, more recently, the regular implementation and use of advanced Data Science teams.  While the use of data has been around for many years, it’s hard to believe that it is only in the last five years or so that we have seen the adoption of such technology and skills really take off. Five years ago, the importance and demand for Data Scientists sat at a very meagre 17 per cent, whereas in 2019, we saw exponential growth of over 40 per cent – a number that is expected to continue growing as we move forward.  Within Data & Analytics, Data Science is a crucial arm within many businesses of all shapes and sizes. Through the collection and analysis of certain datasets, Data Science teams can delve into an organisation’s pain points, any potential obstacles and future predictions; crucial elements which, if looked at and planned for in advance, can be the making of a business.  So, how else can Data Science influence the decision-making process and make a positive impact on a business and its bottom line? The removal of bias and the increase of accuracy As humans we are innately susceptible to bias, conscious and unconscious, and this can be a hindrance on our ability to make informed yet impartial decisions. By relying solely on facts and figures instead of our own opinions, we are not only removing bias, but we are in turn making the decision-making process more accurate.  Accuracy within decision-making will remove the potential risk of mistakes and the need to re-do tasks, therefore saving precious time, resource and money, unequivocally a benefit for any business’s bottom line.  Efficiency There are elements of all businesses that require trial and error for example, hiring practices. People who look great on paper and perform exceptionally well in first interview may turn out to be utterly the wrong fit six months down the line. However,  collecting and recording data of those employees who do fit well into the business, compared to those who don’t, can help to reduce the chance of choosing the wrong candidate. This in turn improves staff retention rates, helps create a positive work culture and, of course, positively impacts profitability.  Considering the cost for hiring one person for a company is around £3,000, Data Science is of huge benefit to any company, large or small, in reducing the risk of high staff turnover.  Mitigating risk All businesses at some point in their lifetime will come up against potential obstacles and risks that, if not managed properly, can be potentially lethal. The implementation of Data Science will allow senior leaders to learn from past mistakes and create evidence-based plans to better tackle, or completely avoid, similar problems in the future.  This could be for either organisational risk or strategic risk, both of which can be extremely damaging if not prepared for. Organisational risk entails problems occurring within daily business tasks such as fraud, data loss, equipment and IT issues and staff resignations. Strategic risk relates to events that cannot be planned for in advance; those sudden and unforeseeable changes - a great example being the current COVID-19 pandemic.  However, with both risk groups, Data Scientists can help to mitigate these risks through learnings and observations made from reams of previous data, as well as real-time intelligence. This allows senior leaders to act fast where needed, and plan where possible.  Data & Analytics, and especially Data Science, has been, and will continue to be, a key driver in the evolution of many industries worldwide. As we move forward, we will undoubtedly see an even larger uptake of the available technologies as business leaders everywhere begin to see the influential value of data-driven decision-making. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Weekly News Digest: 8th - 12th Feb 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 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 info@harnham.com.

LGBT History Month: Reflections On Alan Turing

Alan Turing was a pioneering mathematician and data scientist. His contributions to artificial intelligence and data science undoubtedly shaped the data professions we know today. But his influence doesn’t stop there. Turing is also an admired role model within the LGBT+ community. Each February in the UK, LGBT history month is celebrated to further encourage education into LGBT+ issues, understand the history of the gay rights movement, and promote an opportunity to drive action in creating a more inclusive society. In observing lesbian, gay, bisexual and transgender history, we can continue to build communities that recognise the remarkable contributions of these individuals both in society and within our workplaces. Although laws during the 1950s made it illegal for Turing to be openly gay, he did not shy away from his sexuality. Instead, he continued to live his life, as an out gay man, both in social circles and his profession. It is his commitment to authenticity and equality that makes Turing’s achievements that much more remarkable to us today. It is only since his death that his contributions to advancements in data science and his impact on changes to attitudes towards the LGBT+ community have been appreciated though. Turing received a posthumous pardon in 2013, along with thousands of other gay and bisexual men in 2016 under the ‘Alan Turing law’. More recently, Turing has been announced as the face of the new £50 note. With this in mind, how can we understand Alan Turing’s influence in Data & Analytics today? Conceiving the Turing machine In the mid 1930s, Turing devised a mathematical model of computation, which could solve complex calculations. It was this idea of a universal machine, that could decode and action a set of instructions, that became a practical plan for computing. This became known as the Turing machine, and precedes the digital computer as we know it today. Turing is often noted as the ‘founder of modern computing’, and his published works are documented as the foundation of computer science. Without this theorised approach to collating and unpacking data, such swift advancements in computing may not have been possible for many more years. Deciphering the enigma code Perhaps Turing’s most notable piece of work in the public eye was his role in cracking the enigma code used in German naval communications in World War II. He joined the code-breaking department of the government and quickly became a crucial contributor in successfully decoding German messages. These codes were often referred to as being unbreakable. The Bombe – designed and created by Turing – was capable of deciphering codes on a scale so significant it has been said to have shortened the war by as many as two to four years. It is this achievement that sees Turing remembered as a pivotal wartime leader. Paving the future of artificial intelligence In the 1950s, Turing published a paper based on this principle of the ‘imitation game’ whereby computers can mirror the outputs of a human. The Turing Test is at the centre of discussions about artificial intelligence (AI). Once more, Turing’s work has been so impactful, it is hailed as being instrumental in shaping our approach to AI today. Professionals working within AI may still refer to the Turing Test as a way to map the progress made in the field. Turing’s work here was recognised by the opening in 2015 of The Alan Turing Institute, a national centre for research in data science and AI. Fast-forward to the present and, across a range of industry verticals within Data & Analytics (and beyond), employers have a responsibility to support access and opportunities for LGBT+ professionals. This is not only in contributing to and progressing within an organisation, but also in creating an environment and culture where LGBT+ individuals can be their authentic selves and drive innovation and success. Diverse teams perform better, it’s a fact. Alan Turing once said, "We can only see a short distance ahead, but we can see plenty there that needs to be done"; and it’s a powerful reminder of where innovation and pragmatism join hand in hand. If you're looking to take the next step in your career or build out a diverse Data & Analytics team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Weekly News Digest - 1st-5th Feb 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 and analytics.  TechRepublic: Analysts predict Bezos' move to the board won't slow the pace of cloud innovation at Amazon On Tuesday, we learnt that Amazon CEO, Jeff Bezos, is stepping down from his position to take on the role of Executive Chairman on the board. Taking his place in the top spot will be Andy Jassy, who is currently the head of Amazon Web Services. This migration of leadership brings about anticipated antitrust issues, according to Analysts. However, this change in leadership isn’t thought to mark the start of a decline in innovation and growth of their cloud platforms as Bezos will be on hand to guide Amazon’s overall strategy. Jassy’s experience in the cloud arm of the business shouldn’t go unnoticed too. Interestingly, Glenn O'Donnell, Vice President and Research Director at Forrester, said Jassy's time at AWS proved he has the executive chops to take the reins of the bigger Amazon machine. This is definitely something to keep an eye on! Read the full article here. Forbes: The Race To Outsmart The Competition: Adobe Unveils New AI Tools For Retailers More people are shopping online than ever before, and consumers are demanding much more from their online experiences. As a direct result of the pandemic, the e-commerce sector has skyrocketed and Adobe Analytics data showed that on Christmas Day 2020, the mobile share of revenue exceeded desktop for the first time, at 52 per cent. With such high demand and interest from customers, the tech giant has announced new tools designed to use artificial intelligence and data to alert retailers when something goes wrong in the digital experience, and to offer personalised experiences and promotions to the right customers. Personalisation is becoming increasingly important to consumers as they shop across different channels. Adobe bringing new and emerging technology to the retail industry positions them well for continued growth. Read more on this here. KDnuggets: Build Your First Data Science Application We think this is a great article from KDnuggets that provides a clear and concise overview as to how you can make your first data science application a success. There are seven Python libraries that you should learn to make your first application: PandasNumpySciKitLearnKeras or PyTorchRequestsPlotlyIpywidgetsJupyter Notebook and Voila Even if you have a good understanding of these, it’s important to keep your skills and knowledge up to date, to ensure that you stay valuable in an increasingly competitive market. Keep learning, and you will be sure to stand out. Read more on this here.  Campaign: The winning formula for analytics in 2021 Do you know the winning formula for analytics in 2021? Well, look no further, as Campaign spoke to Alexander Igelsböck, CEO & co-Founder of Adverity and Forbes Technology Council member, on how augmented analytics has changed the media and entertainment industry. Igelsböck defines how media businesses that have successfully adapted to future-proof themselves through data analytics and innovation are embracing three key marketing analytics trends including: The new focus for data-driven marketingMaximum results with minimal resourcesTransforming challenges into opportunities Businesses across a range of industry verticals will need to improve performance and decision-making to make it faster and easier. Making use of complex datasets and simplifying them into actionable insights and tasks is the key to success in the year ahead. Click here to read more on the challenges and opportunities for analytics in 2021. We've loved seeing all the news from Data & 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 info@harnham.com.

National Storytelling Week: Telling A Story Through Data

A story is a lot more than just words on a page. It’s a combination of interesting language, images, colour and, perhaps most importantly, a brilliant narrator.  This is no different in Data Analytics. Like any story, the beginning of any data report starts out as numbers and figures on a page which, let’s face it, isn’t the most interesting read. To ensure the data reaches its full potential and entices an engaged audience, a good Data Analyst will wind and weave them into a compelling story.  So, how might you go about doing this? Know your audience How your story is crafted will be completely dependent on who will be reading it. It’s important to consider your audience’s age, knowledge and expertise. For example, if you were reporting to a junior team, the information given will be simplified, and specific language and jargon should be broken down to include explanations, making the data accessible. The story may also be a lot longer than usual to ensure all areas of information are covered, with room for questions if need be. This is crucial if you want your data, and your story, to benefit the learning and development of the team as well as to encourage their interest and curiosity in the topic.  On the other hand, if you were telling your data story to a group of expert professionals, the explanations will be a lot more top line and the story much pithier and succinct. The depth should instead lie in the narrative of how the data impacts them and their company, providing solutions to problems or providing compelling ideas for innovation and change.  Choose an engaging narrative Undoubtedly, your data will have thrown up all sorts of storylines, from the mundane to the thrilling. When you’re creating your presentation or report, if the data is relevant, opt to design your story around the most exciting dataset. Your aim is to keep your audience engaged and wanting to know more, not to bore them with too many, or figures that are not relevant or provide further guidance.  Be creative No matter how electrifying your data may be, there's only so much information an individual can take in. Your story needs visuals to bring what you are reporting on to life. Typography, font and font size, colour, images, graphs and tables are all valuable assets to include to help stimulate your audience’s imagination.  Of course, in this day and age, these visuals don’t have to be limited to static pictures either. Don’t be afraid to play around with movement and interactivity to get your audience involved and engaged. That being said, it’s important to find a good balance of static and interactive. Be an appealing narrator If you’re having to present your data, you’ve got an extra challenge on your plate. Your story is only as good as you are. No matter how visually fantastic your report is, or how apt it is for your audience, if you are bored, unengaged and uninterested by the information you are presenting, you will pass all these feelings onto your audience.  Not only is it important you know the story you’re telling inside out, but you should be excited by the data you are presenting. Don’t be afraid to inject personality into your data, make it characteristic and make it feel human. If you are passionate about your data and your story, then your audience will be too.  Data doesn’t just have to be statistics on a page. It can be thrilling, it can be colourful, it can be loud, and it can be enticing. You, as a Data Analyst, are that brilliant narrator.  If you're looking to take the next step in your career or build out your Data & Analytics, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Weekly News Digest - 25th-29th Jan 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. Entrepreneur – How to Identity Proof in an Increasingly Virtualised World The past year has shifted our life online at a rapid pace. This migration has meant that not only do our technologies need to be more effective and efficient than ever before, but our identity proofing needs to be more sophisticated and secure too.  Even before the pandemic, identity proofing has never been an easy task. Customers expect security but without any hassle. Being bombarded by multiple authentication factors, for example, is not ideal and will quickly turn a customer off from using your service.  In this article by Entrepreneur, four key factors are explored in detail which aim to assist companies in creating a secure but accessible identity proofing system. This includes evaluating the potential for fraud, learning from Government trends, focusing on being proactive and the need for constant innovation.  Read the full article here.  KD Nuggets - The top 5 reasons why machine learning projects fail There is a clear increase in the number of machine learning projects being developed and perfected globally. Yet, at the same time, an equal number of projects are failing to take off the ground.  This article from KD Nuggets highlights the five main reasons why start-ups implementing machine learning projects are failing, as well as offering solutions. The article hopes new businesses will take these points on board and plan their future projects better, hopefully avoiding too many, potentially lethal, pitfalls.  The most common mistakes made are:• Insufficient data or not enough access to ‘clean’ data. Machine Learning Models not being synchronized with legacy systems.A lack of qualified Data ScientistsDifficulty with innovation and updatingLack of leaders’ support Read the full article here.  The FinTech Times: 10 FinTech Predictions for 2021 In this article from The FinTech Times, we hear from 10 key experts in the field who give insight into what they think 2021 will hold in store for FinTech. Following a year like no other, the overall consensus is that this year will be unlike any other for the industry.  For example: Commercial banking will continue to digitise and experience a subscription revolution to change the way companies of all sizes approach their banking relationships - Matt Cox, EY Americas Corporate, Commercial and SME Banking Consulting Leader. Online payments still have significant room to grow - Sudeepto Mukherjee, Managing Partner, Financial Services at Publicis Sapient. More people will trust in Bitcoin - Eleesa Dadiani, Founder and CEO of the Dadiani Syndicate, a firm that brokers the sale of cryptocurrencies and commodities to high net-worth individuals. Read the full article here.  Silicon Republic – Branching out into tech? Here are 30 top skills to consider.  As a direct result of the pandemic, the tech sector has boomed. This in turn has created a wealth of roles. This article from Silicon Republic suggests 30 key skills for anyone looking to enter the world of tech as their next career move. Roles covered include Digital Marketing, Social Media and Development.  Top three required skills for Social Media Executives: FacebookInstagramTwitter Top three required skills for Digital Marketers: AnalyticsSEOGoogle Analytics Top three required skills for Developers: AgileJavaJavaScript Read the full article here.  We've loved seeing all the news from Data & 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 info@harnham.com.

How Are Digital Marketing Analysts Disrupting Traditional Marketing Functions?

In the age of disruption, the traditional marketing function of organisations across the globe is changing. No longer tied to traditional product-based marketing models and removed from siloed ways of working, digital marketing analysts are changing perceptions of marketing. Successive waves of disruption, from remote working and the shift to cloud-based operations, to developments and advancements in automation, mean that digital analysts are being kept on their toes. In fact, recent research has indicated that worldwide IT spending is set to reach $3.9 trillion in 2021 as digital projects get back on track. Digital Marketing Analysts therefore play a critical role in how organisations adapt their marketing strategies to encompass the use of data and digital to enhance their offer. Through monitoring online marketing trends, analysing statistics and developing campaign reports, these professionals also prepare and share this strategy with colleagues and clients. Here are a few ways in which this is happening. Utilising more and more data Analysing data is one of the most important functions a Digital Marketing Analyst should focus on. We’re all familiar with the value of Big Data to a firm’s operating procedures and applying this to how marketing is completed should have no less value. Looking at complex data sets that can’t be processed through traditional methods, utilising past data and insight to inform future campaigns and channelling this through Cloud systems such as Google Analytics has never been more useful to a marketing team, regardless of size or industry. Targeting a bigger audience Finding, targeting and growing your audience is likely to be a goal set for marketing teams across the globe. Digital marketing functions provide huge scope in reaching a greater number of consumers than just through traditional means alone. Omnichannel marketing, for example, is becoming a key part of a Digital Marketing Analyst’s core role, and campaigns using 3 or more channels are known to have a 90 per cent higher retention rate than single-channel efforts. What needs to be kept in focus though, is that despite the innovation rippling through marketing functions, these audiences still demand personal attention. In fact, 68 per cent are likely to spend more with a brand that treats them like an individual, whatever the channel. Supporting smaller businesses to scale Quite often in big companies digital and marketing functions are operated separately but in smaller businesses traditional marketers are just expected to know about digital methods too, when perhaps their skillset lies elsewhere. As a result, a skills gap can start to open up. Digital Marketing Analysts can come into small business (even on a consultancy or contract basis) to support SMEs to scale and grow their digital campaigns. Interestingly, 76 per cent of small businesses believe their digital marketing efforts are effective, so building on this is crucial. What remains apparent is, with such a high demand for digital transformation across the business community, it is crucial that business leaders can both recruit and retain the best individuals out there to really ensure their marketing function is best placed to maximise all the incredible opportunities and tools available to them. If you're looking for your next Data & Analytics role or are seeking the best candidates on the market, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Weekly News Digest - 18th-22nd Jan 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. TechRepublic: Go back to the office? Some employees would rather quit In the past year, the shift to remote working has seen its fair share of highs and lows. To start with, many organisations were not prepared and did not have the right technology in place to support working away from the office for such a long period of time. However, this quickly changed, and professionals across a range of industries are now working from home in seamless fashion. In this article from TechRepublic, they discuss how employees have become so comfortable working away from the office, that they would be happier (and feel safer), working from their homes moving forward. In fact, according to a recent survey from jobs platform LiveCareer, 29 per cent of employees would quit their job if they were told they were no longer allowed to work remotely. Companies will all be approaching the return to the office differently. Would you like to get back to an office environment? Read the full article here. Forbes: 5 Data Trends That Will Take Your Business Forward In 2021, From Google Cloud Leaders We’re a big fan of this piece from some of the senior leaders at Google Cloud, writing for Forbes. There is immediate recognition that the past year has seen changes that have meant, most notably, that gathering and using data has become essential. Leaning on their experiences and expertise, some of Google Cloud’s Leaders shared what they see come in 2021. Real-time data analytics will help you see the future (Debanjan Saha, VP, Google Cloud) In 2021, you’ll demand more of your databases (Andi Gutmans, VP, Google Cloud) Analytics will no longer be dashboard-driven—they’ll come to you through AI-powered data experiences (Colin Zima, Director of Product Management, Looker) "Location, location, location" matters for data, too: Geospatial data will be key to unlocking enterprise transformation (Jen Bennett, Office of the CTO, Google Cloud) Data lakes will smarten up to support open and multicloud infrastructure (Debanjan Saha, VP, Google Cloud) For data and its relationship with cloud services, this year is anticipated to signal its continued growth and application across a range of industries. Read more on this here. Analytics Insight: UNDERSTANDING DEEP LEARNING VS MACHINE LEARNING We’ve seen a lot of buzzwords flying about of late, but two that are causing some to scratch their heads are machine learning and deep learning. The problem is, they seem to be identical, which is why understanding their differences is so important. Analytics Insight pull apart both of these core functions within data and analytics, exploring the benefits, possibilities and what they actually mean and do. Deep learning is a concept of artificial intelligence (AI) that mimics the functioning of the human brain in data processing and the development of patterns for decision-making use. Machine learning is an artificial intelligence (AI) technology that gives systems the ability to learn and develop from experience automatically without being programmed specifically. Whether you’re starting out in a new career within the market and need to understand these technologies better, or if you’re about to begin working on a new project, this is a handy article to help you understand the difference between the two. Read more on this here.  Marketing Mag: Why marketers should prioritise media optimisation in 2021 We’re now moving quite swiftly through January, a month where, typically, marketers are rethinking and planning for long-term campaigns that will deliver on their ROI. This will often involve exploring a range of different strategies to ensure the best possible results. A few of these, as outlined by Marketing Mag, are: Connecting data for a full pictureOptimise, optimise, optimiseMetrics for verification and viewability One thing we can take from this is that media optimisation is going to be under the spotlight more this year, put in place to deal with unpredictable behaviours and a fast-changing environment. For example, by tracking campaigns in-flight, marketers can take action to capitalise on an effective campaign or mitigate the impacts of one that’s failing. Click here to read more on the challenges and opportunities for media optimisation in 2021. 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 info@harnham.com.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

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