News

Categories
243 Posts found

Weekly News Digest: 5th - 9th April 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.    The Drum: How data visualisation turns marketing metrics into business intelligence Gathering data is just one part of a marketer’s job but having the ability to turn this data into something visually stunning, informative and easy to use is another skill completely.  Marketers, on the whole, are extremely visual learners along with around 65 per cent of the population. Most of us are able to absorb data more effectively if the information being presented to us is done in such a way that is pleasing to the eye. And this is why Data Visualisation exists; it allows us to group, organise and represent data sets in a way that allows us to analyse larger quantities of information, compare findings, spot patterns and extract meaningful insights from raw data. Not only does Data Visualisation allow us to learn more effectively, but we can then turn this understanding into much broader and deeper Business Intelligence.  To read more on the positives of Data Visualisation and how to translate this into meaningful Business Intelligence, click here.  ZDNet: The five Vs of customer data platforms According to ZDNet, Customer Data Platforms (CDPs) are the hottest marketing technology today, offering companies a way to capture, unify, activate, and analyse customer data. Research done in 2020 by Salesforce showed that CDPs were among the highest priority investments for CMOs in 2021. If you’re planning to invest in a CDP this year, what five critical things do you need to think about when developing a successful strategy? ZDNet tells all.  Velocity - Your systems need to manage a high volume of data, coming in at various speeds.Variety - Every system has a slightly different main identifier or "source of truth," and the goal is to have one. This starts with being able to provision a universal information model, or schema, which can organize all of the differently labelled data into a common taxonomy. Veracity - Companies must ensure they can provision a single, persistent profile for every customer or account.Volume - It has been theorized that, in 2020, 1.7MB of data was created every second for every person on Earth. If you want to use those interactions to form the basis of your digital engagement strategy, you have to store them somewhere. Value - Once you have a clean, unified set of scaled data – now’s the time to think about how to derive value from it.  To learn more, read the full article here. Towards Data Science: How to Prepare for Business Case Interview Questions as a Data Scientist When you think of Data Science, the first thing that comes to mind will be technical knowledge of coding languages and fantastic statistical ability; softer skills such as communication and exceptional business knowledge may be overlooked. However, this is where many budding Data Scientists trip up. It is these softer skills and business acumen that sets brilliant candidates apart from others.  But how, when not usually taught at university, do you gather the business knowledge that will set you apart from the competition and showcase it in interview? Towards Data Science shares a few key pointers. Build a foundation – Brush up on your business basics. Research project management methodologies, organisational roles, tools, tech and metrics - all are crucial here. Company specifics – Research your company and its staff. Make sure your knowledge is tailored to the company you’re interviewing for. Products – This is where you’ll stand out above the rest if you get it right. The more you can know the ins and outs of products and metrics at the company, the more prepared you will be to answer business case questions. Read the full article here.  Harnham: Amped up Analytics: Google Analytics 4 Joshua Poore, one of our Senior Managers based in the US West division of Harnham, explores Google’s new and improved data insight capabilities, predominantly across consumer behaviours and preferences.  This exciting new feature of Google was born in the last quarter of 2020 and has now fully come into its infancy, and it’s an exciting time for Data & Analytics specialists across the globe. Joshua explores four key advantages of Google Analytics 4.0. Combined data and reporting - Rather than focusing on one property (web or app) at a time, this platform allows marketers to track a customer’s journey more holistically. A focus on anonymised data - By crafting a unified user journey centred around machine learning to fill in any gaps, marketers and businesses have a way to get the information they need without diving into personal data issues.Predictive metrics - Using Machine Learning to predict future transactions is a game changer for the platform. These predictive metrics for e-commerce sites on Google properties allow for targeted ads to visitors who seem most likely to make a purchase within one week of visiting the site. Machine Learning driven insights - GA4 explains it “has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms.” Machine Learning-driven insights include details that elude human analysts.  To read Joshua’s full insights on GA4, click 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 Life Science Analytics Innovating For A Post-Pandemic World?

As COVID-19 unfolded, the Life Science discipline was thrust into the spotlight. The pandemic has shown the extent of the Life Sciences industry’s ability to innovate and collaborate. When facing a new disease, Life Sciences adapted quickly. The rate at which pharmaceutical companies successfully developed COVID-19 vaccines was unprecedented. Approaches that may have previously been labelled risky, were implemented to manage changing demand and deliver increased throughput. Embracing digitisation and innovation enabled organisations to adapt and accept constant change. The pandemic has shown just how well the Life Science industry is able to innovate and develop according to changing demands. As the world looks to the future, how can Life Sciences continue to remain dynamic?  Cloud data The cloud is becoming a CEO agenda item for Life Sciences. The cloud has the potential to enable more effective and profitable ways of doing business throughout the life science industry. It offers a powerful, secure platform for innovation and collaboration, with immense transactional power and data throughput. The cloud is necessary for creating data enablement, ensuring the right data is in the right place at the right time. It enables companies to innovate faster, work at a greater scale and increase collaboration.  Virtual communication According to Accenture, sixty-one per cent of healthcare professionals now communicate more with pharmaceutical sale reps than before the pandemic. 87 per cent now want either purely virtual or a blend of in-person and virtual meetings post-pandemic.  New means of virtual communication have created new opportunities in the industry. Digitisation allows for increased communication with trial participants and new opportunities to educate people about their conditions and care. There was already a growing trend for virtual healthcare interactions, but the pandemic has shifted this is into becoming the new normal. Collaboration ecosystem COVID-19 has led to increasing collaboration between companies. The race for a vaccine has seen cooperation evolve at an extraordinary pace. Companies who usually compete are now coming together to share data and cooperate. Organisations have created collaborative agreements in a matter of weeks; partnerships that pre-pandemic would have taken years to create.  The industry is now seeing the value of ecosystem partnership. The success of organisations post-pandemic relies on this continued collaboration.  AI and blockchain technology COVID-19 has increased the focus on AI in Life Sciences. Yet, Life Sciences have only scratched the surface of AI capabilities. AI has the potential to transform the industry; it can design novel compounds, identify genetic targets, expedite drug development and improve supply chains. The use of AI in Life Sciences is expected to continue to grow and organisations will need to focus ever more on merging human knowledge and AI capabilities.  Blockchain is also becoming increasingly trusted in Life Sciences. Its ability to create tamper-proof records makes it a key resource in increasing patient trust in remote clinical trials. As more of the industry understands the skills needed to use blockchain and increases collaboration, blockchain has the potential to become ubiquitous in Life Sciences. The pandemic has shown the importance of digital technology in Life Sciences. Digitisation increases efficiency and, collaboration, and also helps create a framework for future scientific discoveries. As we look towards a post-pandemic world, a successful Life Science industry must continue to embrace this mindset of innovation, collaboration and dynamism.  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.

Weekly News Digest: 29th March - 1st April 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.    Forbes: 5 converging trends driving the need to know and trust your users online Over the past 12 months, digital transformation has boomed. In this new remote era, more and more people are taking to their phones or laptops to undertake traditionally ‘offline’ tasks. This uptake of digital is evidenced through a recent study by J.D Power which states that nearly a third of new account opening, such as bank accounts, are done via a website of mobile app, a 50 per cent increase compared to only a year ago.  While efficient and much faster than consumers having to go into a physical bank, there are many dangers that lay in wait for both brand and consumer. From data breaches to the dark web, privacy regulations to fraud, businesses much ensure they have strong identity orchestration capabilities to keep users and their identities safe online.  Read more on this article here. TechCrunch: Facebook makes it easier to view a non-algorithmic news feed Any social media platform’s algorithm is the bane of any users’ experience. The way in which news feeds are shaped are purely shaped by the company’s own algorithms which, very often, are not reflective of what we want to see. Businesses have also struggled for many years to get themselves seen and heard by the correct audience on sites like Facebook due to the ever-changing and, frankly, confusing algorithm it employs.   However, it was announced this week that Facebook have finally created a feature that allows users to see a version of their news feed that isn’t algorithmically moulded. This includes aspects such as a ‘favourites’ view that displays posts from up to 30 of your favourite friends and pages, as well as a ‘most recent’ view which shows posts in chronological order. Read more on this here. Builtin: Nine SMS marketing tools to know  Many brands choose to use SMS Marketing over email, as it has been shown to be more effective. While email open rates usually sit around 20 per cent, SMS open rates beat this three-fold with usual open rates of around 82 per cent. Additionally, SMS marketing is a much more human, intimate way of talking to consumers, an important aspect of communication emails still miss.  This informative article by Builtin helps those new to SMS marketing by guiding them through nine of the best tools to use to get started. PostscriptAttentiveSMSBumpSlickTextSimpleTextingOctane AIKlayviyoOmnisendSubtext While all are used to send texts and facilitate relationships between brand and consumer, each is designed differently in order to best serve the brand’s needs and wants from this style of marketing.  To learn about each platform, click here.   Praxis: How to prepare for a Data Engineering interview Preparation for any interview is key. The more learning and research you have undertaken, therefore the more knowledge you can take to the table, the more likely your interviewer is to sit up and listen. So, what exactly should you focus on if you’re heading into an interview for a Data Engineering role? This brilliant article from Praxis gives eight great pointers, here a few examples:  Programming and coding skills – Ensure you know how to use optimal data structure and algorithms to handle potential data issues. Know your tools – Get acquainted with popular open-source libraries such as Spark, Pandas, Hadoop and Kafka. Languages are important too, knowledge around Java, Python, HTML, CSS and JavaScript will set you apart from the competition.Be a pro at SQL – SQL is one of the most essential skills for any Data Engineer, make sure you know it inside out.  To learn what else you need to know to be best prepared for your Data Engineer interview, 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.  

Weekly News Digest - 22nd - 26th March 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.   Silicon Republic: Eight ways to stay productive while working from home This week marked a whole year since the entirety of the UK entered lockdown for the first time as a response to the rapidly spreading coronavirus. The crisis was expected to blow over in a matter of weeks but here we are, a full 12 months on with at least 12 more weeks to go until the country eases back into some sort of normal.  Employees have faced, and coped with, extreme challenges over the past year, but tethers are beginning to wear thin and Zoom fatigue is rife.  In this great article by Silicon Republic, author Lisa Ardill gives eight ways to stay motivated and productive when working from home in 2021; from planning your work schedule to adding greenery to your workspace, keeping in touch with colleagues regularly and taking exercise breaks.  Read up on the full eight tips here to help refresh your remote working strategy.  Analytics Insight: How businesses can benefit from Augmented Analytics Augmented Analytics is an approach of data analytics that combines the use of specialist technologies such as AI, Machine Learning (ML) and Natural Language Processing (NLP) to automate analysis usually done by a data scientist. It simplifies the process and makes it much easier for businesses to understand actionable insights and implement changes where needed.  This article in Analytics Insight explores other key areas where Augmented Analytics can benefit businesses, for example: Boosting online visibility Improving reliability of recommendations Expanding the range of possibilities  And as Augmented Analytics, and the capabilities of Data & Analytics, improves over the coming years, there are countless benefits these technologies could have on businesses in decades to come.  Read more on this here. Econsultancy: How Confused.com rebuilt its brand through customer insight The comparison market is a busy, lively and competitive space, with numerous characters and brand figures all battling for our attention - from meerkats to opera singers and bulls in china shops. However, one company who were struggling to cut through the noise with the right brand character was Confused.com. While it ran its ‘Drivers Win’ campaign, which featured none other than British Actor and American chat show host, James Corden, they just weren’t getting the consumer engagement they needed.  As stated by CMO of Confused.com, Samuel Day; “There was incredible prompted awareness – incredible, in the 90% range, but there was very low spontaneous awareness. The ads had very high recall – but not for Confused.com.” Consumers didn’t put James Corden and Confused.com together.   It took a shift of thinking to fix the problem: a revisit to the brand’s ethos and values, a longer-term strategy and, most importantly, valuable customer insight. From there, the company was able to understand the problem, fix it and make Confused.com the well-known consumer brand it is today.  Read more on this story here.  The FinTech Times: How Data privacy will accelerate digital ID adoption In this article for FinTech Times, Adam Desmond, the UK&I Country Lead at Mitek, shares his thoughts on how data privacy will accelerate digital ID adoption. As our world becomes ever-increasingly virtual, and the majority of our transactions occur online, the risk of cyber breaches grows with it. Only last year, 75 per cent of large companies in the UK reported a data breach – a shocking statistic and a real worry for consumers. Desmond stated that the only way to combat this is for businesses to implement stronger, more robust, Digital ID processes. In the same way that you would be asked for ID when buying alcohol or a signature would be needed in the bank, the same should be happening online.  However, as with anything online, by inputting sensitive data, we open ourselves up to potential fraud risks. But Desmond believes this doesn’t have to be the case.  “Digital identities could actually be a solution to withholding unnecessary data and protect us from fraud – not open ourselves up to it. For digital identity adoption to be successful, consumers need to trust that their data is safe and secure. This all comes down to how we build these digital identities, and who looks after them.” Read more on this 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 Can Your Career In Big Data Help You To Accelerate Change?

Data & Analytics is fast becoming a core business function across a range of different industries. 2.5 quintillion bytes of data are produced by humans every day, and it has been predicted that 463 exabytes of data will be generated each day by humans as of 2025. That’s quite a lot of data for organisations to break down. Within Gartner’s top 10 Data & Analytics trends for 2021, there is a specific focus on using data to drive change. In fact, business leaders are beginning to understand the importance of using data and analytics to accelerate digital business initiatives. Instead of being a secondary focus — completed by a separate team — Data & Analytics is shifting to a core function. Yet, due to the complexities of data sets, business leaders could end up missing opportunities to benefit from the wealth of information they have at their fingertips. The opportunity to make such an impact across the discipline is increasingly appealing for Data Engineers and Architects. Here are a just a selection of the benefits that your role in accelerating organisational change could create. Noting the impact In a business world that has (particularly in recent times) experienced continued disruption, creating impact in your industry has never been more important. Leaders of organisations of a range of sizes are looking to data specialists to help them make that long-lasting impression. What is significant here is that organisations need to build-up and make use of their teams to better position them to gather, collate, present and share information – and it needs to be achieved seamlessly too. Business leaders, therefore, need to express the specific aim and objective they are using data for within the organisation and how it’s intended to relate to the broader overarching business plans. Building resilience Key learnings from the past year have taught senior leaders around the globe that being prepared for any potential future disruption is a critical part of an organisation’s strategic plans. Data Engineers play a core role here. Using data to build resilience, instead of just reducing resistance or limiting the challenges it presents, will ensure organisations are well-placed to move into a post-pandemic world that makes use of the abundance of data available to them. Big Data and pulling apart and understanding these large scale and complex data sets will offer a new angle with which to inform resilience-building processes.  Alignment matters An organisation’s ability to collect, organise, analyse and react to data will be the thing that sets them apart from their competitors, in what we expect to become an increasingly competitive market. Business leaders must ensure that their teams are part of the data-driven culture and mindset that an organisation adopts. As this data is used to inform how an organisation interacts with its consumers, operates its processes or reaches new markets, it is incredibly important to ensure that your Data Engineers (and citizen developers) are equipped and aligned with the organisation’s visions. Change is a continuous process, particularly for the business community. Yet, there are some changes that are unpredictable, disruptive and mean that many pre-prepared plans may face a quick exit from discussions. Data professionals have an opportunity to drive the need for change, brought about by the impacts of the pandemic, in a positive and forward-thinking way. In understanding impact, resilience and alignment, this can be truly achieved. Data is an incredibly important tool, so using this in the right way is absolutely critical. 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.

Weekly News Digest: 15th - 19th March 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.  Forbes: How to hire a Data Scientist Writing for Forbes, Joel Shapiro discusses a role within the Data & Analytics industry that continues to surge in both popularity and demand – a Data Scientist. Often, high-performing data scientists have software engineering and computer science backgrounds—they know how to program and code. Yet, another interesting point to make about this article is that Shapiro notes that the role of a Data Scientist requires professionals to look beyond technical expertise to secure a career in this specialism. These could include: Problem-spotting: All Data Scientists are expected to be “problem solvers,” but data science teams, as a whole, must help identify problems across the organisation.Critical thinking: Data teams must be able to draw parallels to their prior work and identify how approaches used in the past might apply to a new problem.Flexibility, adaptability, patience, perseverance: More important than broad knowledge of technology is the ability to learn and adapt to new environments.Know the stakeholders: Data Science teams must be able to identify the different stakeholders for any given project.Listen and communicate: Listening to the needs of the stakeholders and communicating clearly with them increases the impact of Data Science projects. Learn more about the work of a Data Scientist here. TechRepublic: Dell closes the STEM gap with Girls Who Game Within STEM (and Data) disciplines, there is a long-standing gender gap, with men typically holding the line share of roles across all areas. With this in mind, there needs to be a concerted effort to level the playing field. Enter, Dell Technologies. Dell Technologies Girls Who Game (GWG) is a program designed for young girls and underserved students across North America "to learn more about gaming and the use of Minecraft as a learning tool, while developing their global competencies, such as communication, collaboration, critical thinking and creativity," said Dell's GWG website. TechRepublic break down the core aims and ambitions of the project, with a primary goal being to engage female students in learner-driven experiences that broaden their knowledge, skills and dispositions within STEM-related fields. GWG does this by promoting computational thinking skills and coding within a game-based learning environment. It increases awareness and improves "global competencies by providing experiences for students to develop and practice their collaboration, communication, creativity, self-regulation, citizenship and critical thinking and problem-solving skills”. Find out more about the exciting programme here. DigiDay: ASOS is building a programmatic ads business as it chases an emphasis on first-party data You’re familiar with the fashion company ASOS, right? Perfect. Well, the team at ASOS has built a global business out of selling clothes, and now it wants to do the same for ads...! Current job vacancies at the retailer outline its plan to make money from programmatic advertising. Like many of their other competitors in the clothing and fashion industry, ASOS wants to sell space around content on its site and mobile app in online auctions managed by ad tech. Pretty clever stuff. It has been selling ads this way since October, though the latest spate of vacancies suggests this will accelerate over the coming months. The company has recognised that they do not need whole teams or departments to facilitate this shift but can instead rely on programmatic executives to oversee the campaigns. The rise in online sales during stay-at-home orders is dictating where advertisers spend their money too. As one of the largest fashion retailers, ASOS is well-positioned to capitalise on this shift. Definitely one to watch! To read more about how ASOS is planning to use data in its marketing plans, read the full article. The Good AI: The 12 Best Communities for Women in Tech and AI According to the World Economic Forum report, women represent 22 per cent of Artificial Intelligence (AI) professionals in the industry. Interestingly too, our own research highlights how just 27 per cent of Data & Analytics roles in the US are held by women. The Good AI has compiled a list of the best communities for Women in Tech and AI, and best of all, it features Women in Data, an organisation we have recently formed a partnership with! The full list includes: Women in DataWomen in AIWomen in Data ScienceWomen in Machine Learning & Data ScienceBlack Girls CODEWomen Who CodeGirls in TechAda’s ListAnita Borg Institute for Women and TechnologyGirl Develop It11. Women in TechWomen in Technology International (WITI) To learn more about any of these incredible organisations or find out how you can get involved in their work, click 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.

Mitigating Risk In The Financial Services Sector With Machine Learning

Data & Analytics is an industry that is constantly evolving and is always using the latest technology to innovate its services and capabilities. More recently, these advancements have moved in areas such as Artificial Intelligence (AI) and Machine Learning (ML). Machine Learning is a method of data analysis, under the branch of Artificial Intelligence, that allows systems to learn from data, identify patterns and ultimately make decisions with little to no human intervention. Used across a vast range of sectors, this arm of Data & Analytics has become widely popular, especially within highly-advanced industries such as Finance.  Since the 2008 financial crash, at the top of the agenda for many Financial Institutions (FIs) was, and still is, the need to protect business, increase profitability and, possibly most importantly, address the abundant inadequacies of risk management. This includes risks posed by consumers such as liquidity, insolvency, model and sovereign, as well as any internal process and operational risk the FIs may also be facing through any failures or glitches.   Machine Learning has played a crucial role in improving the quality and precision of FIs risk management abilities. In HPC Wire, it has been reported that the use of AI and ML within the financial sphere to mitigate risk, improve insights and develop new offerings may generate more than $250 billion for the banking industry.  How does ML work? By using incredibly large data sets, drawn (with consent) from consumers, ML can learn, and predict, patterns in consumer behaviour. This can be done in one of two ways: through supervised learning tools, or unsupervised learning tools.  Explained by Aziz and Dowling; “In supervised learning you have input data that you wish to test to determine an output. In unsupervised learning, you only have input data and wish to learn more about the structure of the data.” How do banks use ML to mitigate risk? In FIs, a mix of the two ML tools are used. Most commonly, we can expect to see learning systems such as data mining, neural networks and business rules management systems in play across a lot of banks. These models work in tandem to identify relationships between the data given from the FIs and their consumers – from their profiles to their spending habits, credit card applications to recorded phone calls – which then build ‘character profiles’ of each individual customer. The process can then begin, spotting signs of potential risk factors. This may include debt, fraud and/or money laundering.  Here we break down two key examples.  Fraud Thanks to ML, customers have become accustomed to incredibly quick and effective notification of fraudulent activity from their banks. This ability from FIs comes from large and historical datasets of credit card transactions and machines which have been algorithmically trained to understand and spot problematic activity. As stated by Bart van Liebergen; “The historical transaction datasets showcase a wide variety of pre-determined features of fraud, which distinguish normal card usage from fraudulent card usage, ranging from features from transactions, the card holder, or from transaction history.”  For example, if your usual ‘character profile’ is known by ML tools to spend between £500 - £1000 per month on your credit card, and suddenly this limit is overtly exceeded, fraudulent activity tags will be alerted, and the freezing of your account can be done in real-time.  Credit applications When borrowing from a bank or any other FI, consumers must undertake a credit risk assessment to ensure that they have a record of paying back debt on time, and therefore not adding greater cost, and risk, to the lender.  Traditionally, FIs have approached credit risk with linear, logit and probit regressions but, serious flaws were found in these methods, with many applications being left incomplete. In this space, the evidence for the effectiveness of ML is overwhelming. Khandani et al. found that FIs using ML to analyse and review credit risk can lead to a 25 per cent cost saving for the FI involved.   These ML models come in various shapes and sizes, with the most common being instantaneous apps or websites which allow users and their banks to have access to real-time scoring, data visualisation tools and business intelligence tools.  The risk of risk management with ML Like with any AI or ML application or tool, there will always be cause for concern and real need to always remain vigilant. While ML has shown to be an invaluable tool across lower risk areas, the complexity of more statistical areas of banking, such as loans, has proven to be an Achilles heel for the technology. This usually stems from bias, a perpetual problem for AI and ML across all industries.  Technology Review notes that “There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.”  Data, analytics, AI and ML are notoriously non-diverse working sectors. The person behind the screen creating learning algorithms tends to be white and male, and very unrepresentative of the whole society that the machine learning tool will serve. Over the years, we have heard numerous accounts of unfair and unjust machines, which have learned from a very narrow and unrepresentative dataset, which stems from the lack of diversity amongst the employees within the Data & Analytics industry. For example, Microsoft’s racist bot and Amazon’s sexist recruitment tool, both clear examples that ML and AI are not ready to be used on their own, and humans still need to play an integral part in decision making.  Banks and FIs must be aware of the, potentially lethal, consequences that bias in ML may present. Lenders must be careful to ensure they are working within the guidelines of fair lending laws and that no one group of people are being penalised for no reason other than issues within the technology and its algorithms. It is vital that the humans behind the technology don’t rely on ML to provide them with an answer 100 per cent of the time but, instead, use it to aid them in their decision making when it comes to risk mitigation.  If you’re looking for a role in Data & Analytics or are interested in finance or Risk Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.  

Weekly News Digest: 8th - 12th March 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.  BBC: International Women’s Day: Illustrating the COVID-19 pandemic This incredible article from the BBC reflects on the volatility of the past year and the aftershocks all of us have suffered as a direct response to the COVID-19 pandemic. One huge issue faced by many is the sheer volume of information we are given daily around the pandemic, and how overwhelming it has become for many of us to take in.  Author, Dhruti Shah, speaks to three female pioneers in the fields of science, health and technology who have been using their artistic talents to help us understand and battle coronavirus in a simple, digestible and visual way. For example: “Avesta Rastan, 25, is a visual science communicator currently living in California. At the start of the pandemic, she realised there weren't many infographics revealing how Covid-19 directly affected the human body. So, the artist, who is of Iranian and Canadian heritage, and is a member of the Association of Medical Illustrators, saw a unique opportunity to use her skills and her training in pathological illustration (the drawing of disease) to help the wider public." "I saw lots of illustrations and 3D models of the virus itself and its protein, but I didn't really see what it did to us," she explained.” To read more on this, click here. Open Access Government: How to tackle the gender gap in artificial intelligence There’s no denying that Data & Analytics is an incredibly male-dominated industry, especially across areas such as Machine Learning and AI. Reports show that 26 per cent of the Data and AI industry is made up by women, with the findings going on to say that the lack of representation in senior roles is a real ‘turn off’ for any women considering entering the industry.  In this article by Open Access Government, five key areas are highlighted that must be implemented if we are to challenge and change the stereotypes in the industry currently and encourage a more equal workforce.These include: Fix the STEM gap to reduce bias in development: “With women representing a percentage of only 20-25 per cent of the sector, technological developments will be skewed,” Andrea Mandelbaum, President and CEO of Mc-Luhan says.The battle for diversity starts in education: Dr. Angela L. Walker Franklin struggled to find mentors who had the same lived experience as herself, “despite many years of talk of diversifying leadership in higher education.” It’s time to start practicing what we preach from school.  Defying gender roles and expectations is key to career success: Liliana Mantilla who works as a Cognitive Delivery Manager at Amelia, an IPsoft Company, describes previous roles in which she felt she “had to work twice as hard as fellow male colleagues.” Men need to get involved in the conversation to help create change.  Read more on this here. KDNuggets: 9 skills you need to become a Data Engineer KDNuggets gives some fantastic career advice to the next generation of Data Engineers, of which there is a growing number. As the industry becomes more competitive, there are 9 key areas candidates need to focus on to clinch that dream job. SQL - “Strong SQL skills allow using databases to construct data warehouses, integrating them with other tools, and analysing that data for business purposes.”NoSQL - “Examples of NoSQL include Apache River, BaseX, Ignite, Hazelcast, Coherence, and many more others. You’ll definitely get across them during your data engineer job search, so knowing how to use them would be a huge advantage.”Python - “Data engineers are expected to be fluent in Python to be able to write maintainable, reusable, and complex functions.”Amazon Web Services (AWS) - “If you’re interested in learning AWS, you might want to try online courses or Amazon’s own tutorials"Kafka - “60 percent of the Fortune 100 companies use Kafka for their applications.” For the next four key skills you must know to become a Data Engineer, read the full piece here.  AdExchanger: Inside Disney’s plan to automate half its ad business within five years Disney has announced very ambitious plans to automate over half of its ad business over the next five years. As part of this plan, the animation giant has created a programmatic exchange, also known as Disney Real-Time Ad Exchange (DRAX), which will allow any potential buyers to compete for all Disney ad impressions.   Lisa Valentino, Disney Ad Sales EVP of client solutions and addressable enablement said: “Automation and data is really the underpinning of the Disney Platform, that is a new way of clients doing business with us.” This need to move to a much more automated way of working comes after Disney’s multitude of partnerships which have manifested over the past few years, including Hulu, the Trade Desk and Google.  Valentino continues: “We’re democratizing the client pool and allowing clients to better plan for access to Disney.” Read more on this story 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.

What’s Keeping Women Out Of Data Science?

Data Science, the extraction of data to provide meaningful knowledge and insight, is experiencing a surge in growth within Data & Analytics. It is a fast-growing specialism, and talent in this area is in demand, with there being a 650 per cent increase in data science jobs since 2012. Simply put, pretty soon Data Science is going to play a fundamental role in every industry across the globe. Organisations have to adapt and make use of a range of Data Science tools and techniques or they will simply be forced out of business. LinkedIn recognised in their Emerging Jobs report that the role of a Data Scientist sits in the top three in the US, citing significant advancements in the emphasis on using data for this growth. Comparatively in the UK, this role lands within the top 10 at number seven.  Yet, our research tells us that in the UK, 25 per cent of female professionals work within Data Science, with this number dipping to just 20 per cent in the US. So, how can we support more women to enter the specialism? Encourage access to opportunities  Organisations need to continue to hire highly skilled technical talent to keep up with the growth that we are witnessing in the Data Science specialism. Yet, time and time again, working in Data Science can be seen to be an unattractive career proposition – in particular to women. To counteract this, business leaders need to make the role and rewards of becoming a Data Scientist visible within their organisation. Showcasing the range of projects and campaigns that are available, as well as providing opportunities for women to accelerate their careers and follow a pathway that suits them is critical. Education of STEM roles from a young age In order to see more women moving into roles within Data Science, industry leaders from within STEM fields need to take control and lead the way in educating women on the array of opportunities available. Through supporting, organising or hosting workshops, webinars and conferences, organisations can introduce women at entry-level to what careers in Data Science actually look like. This week for example in the UK, we’re currently in the middle of British Science Week. It is initiatives like these that build upon the education that is needed to promote roles in technical fields. Building up communities In the past year, we’ve all come to rely on our connections to provide insight and support during this period of uncertainty and change. This should be a continued focus moving forwards, building communities, networking and sharing knowledge in order to create an informed, educated and engaged workforce that attracts (and retains) female professionals. Within female-focused networks and groups, organisations can support women in advancing their careers, advocating for themselves and acting as a platform to showcase the opportunities that are available to women looking to move into a role in Data & Analytics. The consequence of ignoring these actions is a lack of diversity. We know that diverse teams perform better, and so welcoming in and making the Data Science specialism an attractive career consideration for women is critical. As the industry continues to advance and demand for skilled professionals grows, there will be plenty of opportunity for top talent to make their mark. 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 March 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.  Analytics Insight: Top 10 analytics and business intelligence buzzwords in 2021 If you are a fan of both buzzwords and analytics, then look no further, this article from Analytics Insight is for you! The team at the global publication identify and explore the top buzzwords that are being used to define business intelligence and analytics techniques across the industry in 2021. A few of these include: Predictive AnalyticsEmbedded AnalyticsCognitive ComputingData ScienceX Analytics Ultimately, there are a whole host of buzzwords and key terms being used in Data & Analytics at the moment, but professionals should keep up to date with the core (and most influential) technologies and insights in their area of expertise. Find out more about the top 10 buzzwords and view their definitions here. Forbes: Diversity is what you see, Inclusion is what you do Writing for Forbes, Paolo Gaudiano discusses how to really examine the values and culture of an organisation: you need to change the way in which you think about – or approach – understanding the unique contributions of your team. “Being forced to think about recreating an organization from the inside out, i.e., by actually thinking from the point of view of the individual employees and their experiences, helped us to clarify how we can think about—and define—diversity and inclusion.” It is in considering diversity and inclusion in two separate approaches, that an organisation can truly make this a core area of focus. Afterall, Gaudiano highlights that, “Diversity is a measure of how an individual’s personal characteristics differ from those of the normative majority of an organisation; inclusion is the act of ensuring that people’s experiences within an organisation are not impacted negatively as a result of their personal characteristics.” We need to provide more support, education and tools to ensure that our companies can sustain a growing level of diversity, and to enjoy an inclusive environment. Read more on this here. Computer Weekly: It’s now or never for UK fintech, government told The contributors at Computer Weekly have this week reported on how a Treasury-commissioned review of the UK’s future in financial technology (fintech) has told the government that it must urgently introduce effective policies in five key areas if the fintech industry is to continue to thrive. Policy & Regulation To include creating a new regulatory framework for emerging technology.Skills To include retraining and upskilling adults in support of UK fintech.Investment To include introducing a global family of fintech indices.International To include driving international collaboration and delivering an international action plan for fintech.National Connectivity To include accelerating the development and growth of fintech clusters. The review was comprehensive and provides a startling call to action for senior government officials. Read more on the future growth of the UK’s fintech sector here.  AdAge: First-party data strategies for advertisers and publishers in the age of privacy Can brands partnering with publishers discover better Consumer Insights? That’s the question that this insight from AdAge explores, as brands consider shifting their marketing strategies to align with privacy regulations (and maintain or regain consumer trust). Some of the ways brands are responding are by looking at: First-party Data Strategies Marketers need to pinpoint the right customers to target for different messages. One way to do this is by partnering with publishers and other companies that can offer granular consumer insights built from first-party data in a privacy-safe way.Direct Relationships With Publishers By creating ties with publishers, through trusted networks or direct relationships, brands will be operating in a premium environment, thanks to the data and insights the publishers can provide.Creating A New Digital World Marketing’s priorities now need to shift to first-party data strategies and building trusted networks of first-party data owners. This gives brands an opportunity to not only gain more control over their advertising but also to rebuild consumer trust in advertising. To find out more about how brands and marketers are working together in a digital world, take a look at the article in full 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.

The Reliability Of Sleep Trackers For Sleep Data

One in three of us regularly suffer from poor sleep. By this we mean not entering the correct stages of the sleep cycle often enough. During the optimum eight hours of slumber, we should be getting per night, the body should enter three different stages of sleep on a cyclical rotation: light, deep and rapid eye movement (REM). The most important stage of this being deep sleep, of which a healthy adult should be entering for around one to two hours.  Unfortunately, it is often the case, for a vast number of reasons, that many adults struggle to wake up feeling refreshed. From absorbing too much blue light from screens before bed, poor dietary habits or increased levels of stress, there are many factors into why good sleep eludes nearly a third of us daily. Over the past year especially, as a direct result of the pandemic, our sleepless nights have become increasingly worse. It seems anxiety related to COVID-19 has spiked our inability to get good rest. What are the dangers of persistent low-quality sleep? Continual restless nights can have profound effects on both our bodies and our minds. It can place immense stress on the immune system, increasing the risk of becoming seriously ill. Other life-threatening diseases also linked with poor sleep include obesity, heart disease and diabetes.  Our mental state can also be incredibly damaged by consistent poor sleep. Not only does our ability to concentrate reduce, but our susceptibility to mental ill-health, such as depression, increases too.  It is no surprise then that, as a global population, our obsession with the amount of sleep we get per night has skyrocketed in the past few years, consequently seeing the boom of sleep tracking technology. From wearable tech such as the Fitbit and Apple Watches, to other bedside devices and bed sensors, the market for sleep trackers is estimated to reach $62bn in 2021 alone. But is this technology a reliable source of data for our sleep patterns? The problems with sleep trackers Wearable technology can only go so far when it comes to measuring our quality of sleep. Watches especially can usually measure aspects of our body such as heart rate and movement – all of which can be used as indicators of restfulness. However, their consistent accuracy is questionable. According to research, sleep trackers are 78 per cent accurate when it comes to identifying whether we are awake or asleep, which is a pretty good statistic for developing technology, however, this drops dramatically to 38 per cent when estimating how long it takes for users to fall asleep. For true accuracy, sleep should be measured through brainwave activity, eye movement, muscle tension, movement and breathing – all of which can only be looked at through a medical polysomnogram.  Additionally, much like many other sources of technology, sleep trackers have become a troublesome culprit for obsessive behaviour. In 2017, scientists coined the term Orthosomnia, the recognition of a real problem many were, and still are, having with become obsessive, to the point of mental ill-health, around tracking sleep. As stated by neurologist, Guy Leschziner; “If you have a device that is telling you, rightly or wrongly, that your sleep is really bad then that is going to increase your anxiety and may well drive more chronic insomnia." However, sleep trackers aren’t all bad. While not a tool to be used for sleep disorder diagnosis, they can be useful gadgets to help rethink our sleep habits to aim for a better night’s sleep.  The positives of sleep trackers While questions around the accuracy of this technology are prominent, trackers, overall, are pretty good when it comes to recording total sleep time. If used as a guide rather than an aid, sleep trackers can help users get into better sleep habits which in turn will undoubtedly improve their quality of sleep.  If the data is showing that users are only achieving five hours of sleep per night, and they are going to bed very late and rising early, then users may be encouraged to practice better sleep hygiene. From removing any blue light from the bedroom space, to taking an hour before bed to engage in less stimulating activities, such as reading, and practicing methods such as mindfulness or meditation to induce relaxation.  Sleep data from trackers can also be a useful tool to begin conversations with health professionals. Someone who regularly finds themselves groggy in the morning, with the notion that their sleep is badly disturbed, may find solace in sleep tracking data and it may give them the confidence to seek relevant help. While this sort of technology and its data will not be the end point for a diagnosis, it may give both the user and their doctor insight into any potential problems or issues they may be having with sleep.  Ultimately, those using sleep trackers shouldn’t be losing sleep over the data they present. Instead, ensure you are taking the analysis provided with a pinch of salt, and explore this in tandem with how you feel in yourself to assess whether you need to make changes to your sleep routine or seek help for a potential sleep disorder. Data is an incredibly important too, but using this in the right way is absolutely critical. If you're looking for a new role to get you out of bed in the morning or to build up your dream data 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: 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.

243 Posts found