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Many organisations have made cloud migration a priority during the pandemic. According to Gartner, worldwide spending on public cloud services was expected to grow by 18.4% in 2021. Cloud migration provides a host of benefits, particularly regarding the remote working and digital transformation that has provided a lifeline for organisations during the pandemic. However, that doesn’t make the transition any easier. Being aware of the potential solutions — and how to solve them — is key to a successful cloud migration. Problem: Security A major overhaul to any system inevitably opens up security vulnerabilities. Misconfiguration of cloud services is the most common reason for security issues and is even more likely to occur in hybrid set-ups. However, even technologically reputable organisations can fall victim to misconfiguration—Facebook suffered a server breach that resulted in 540 million records being exposed by a third-party provider on an Amazon cloud-computing server. Solution: Responsibility and DevOps It should go without saying that choosing a reputable cloud provider with built-in security is essential. However, as demonstrated by the Facebook breach, systems fail. In addition, there is usually a shared responsibility towards cloud security (with the provider being responsible for the security inside the cloud, and the users being responsible for the security of the cloud). To tackle this issue, upskilling your security team is essential. In addition, establishing a DevOps approach, where security is a shared responsibility across teams and development stages, has proved an invaluable tool to bolster security.   Problem: Compliance & Regulations GDPR is considered the most stringent data protection policy in the world. Although cloud providers should be fully GDPR compliant, the nature of cloud computing can cause potential compliance issues. For example, under GDPR, data cannot be kept for longer than the pre-defined purpose. However, data is often stored with multiple providers, in multiple jurisdictions and locations, making efficient deletion of data difficult. Location raises a further challenge, as it is not always clear where the data is stored, and therefore it can be difficult to determine which privacy laws apply. Solution: Avoid over-reliance on providers To avoid potential compliance issues, avoid over-reliance on cloud providers (even when they are GDPR compliant). Take initiative in maintaining a good overview of your data, and ensuring you have the proper access to data at all times. You can also limit the amount of data put into the cloud in the first place—ensure its only essential data that has been cleared for purpose. Again, a DevOps approach can be useful here to ensure GDPR concerns are a company-wide responsibility, and not an afterthought.  Problem: Cost and complexity Cost is a common complaint with cloud migration. According to the Flexera 2020 State of Cloud Report, 23% of companies are over their cloud budget and 30% believe they are wasting their cloud spend. Migration costs include investment in new staff and tools, training, rewriting application architecture, bandwidth costs and more. In addition, cloud migration often turns out to be more complex that anticipated, so organisations may have to spend more fixing performance issues. Solution: Strategy Solid change and cost management plans are vital to keeping costs down. Taking the time to research cloud providers — and combine the cheapest options from multiple providers — is an often recommended method to save on costs. In addition, managing your migration in incremental steps can avoid disruption to your existing business, avoiding productivity losses. There are even exciting new tools, such as machine learning start-up, Device42, which can help you understand your organisation’s infrastructure and recommend the optimal (and most cost-effective) cloud migration strategy.  Cloud migration shows no signs of slowing down because despite the challenges, the long-term benefits are worth it. In our digitally dependent world, cloud technology is essential. However, attempting to get migration right will save you plenty of time and expense along the way.  

Any good digital marketing strategy must constantly adapt to the changing landscape. The upcoming ePrivacy regulation, growing digital fatigue and policy changes from major technology players such as Google, are going to create a host of challenges in 2021. Here is a brief overview of what to expect, and the potential opportunities that come with them. First-party data  The upcoming ePrivacy regulation (also known as the ‘cookie law’) will tackle online privacy issues not currently covered by GDPR. The focus on curtailing third-party tracking has made many digital marketers apprehensive. Google has also announced that they will be phasing out the use of cookies and have no plans to introduce alternative third-party tracking methods. These changes will mean a shift towards first-party data in 2021. However, this isn’t necessarily a bad thing. Leading auto part and accessories retailer, CarParts.com, found that by establishing the right customer data platform (based on first-party data) click-through rates increased by up to 400% and email revenue doubled. Reducing reliance on third-party data requires ongoing interaction with customers to build a quality first-party dataset. While this transition shouldn’t be underestimated, the long term benefits of this approach includes a rich behavioural dataset built on trust and consent. Analytics  Once you have a reliable source of first-party data, what should you do with it? Many organisations struggle to generate insights from their data. According to a study conducted by Forrester, companies that used artificial intelligence or machine learning technologies to analyse their data reported a better insight into their customers and were able to use this to inform strategy. For example, analytics tools can be used to alert marketers to a fall in click-through rates and respond in real-time. Digital marketing platform, Mapp, have suggested that companies are benefiting less from staff training and education, and more from technology that supports insight-led marketing. Insight-led marketing utilises personalisation to improve customer experience and engagement. In a recent survey by Salesforce, 80% of customers said the experience they had with a company was as important as its products and services. Analytics tools are not new, but they are growing in sophistication. Given that customers now expect such a seamless, personalised experience from their online interactions, analytical tools are quickly becoming essential. User experience On a related note, providing a positive customer experience is now likely to directly impact search engine optimisation results (SEO). SEO is a hallmark of successful digital marketing and continues to deliver ROI in 2021. The May 2021 update to Google’s algorithm, core web vitals metrics, will now measure load speeds, interactivity and content stability, and integrate this data into SEO. To maintain SEO results, brands must invest in user experience to ensure their online offerings meet their customer’s needs. Google previously introduced ‘mobile-first ranking’ for SEO. As more customers use devices with voice-recognition software (and devices such as Google Home and Alexa), it’s expected that Google will also introduce voice-search into existing SEO algorithms. So, ensuring that your SEO is optimised for user experience across devices and platforms is a logical preparatory step to future-proof your strategy. Ad blockers Despite revenues dropping and budgets being cut during the pandemic, digital advertising surged. According to the Digital Marketing Institute, 28% of surveyed organisations have been spending 90% of their marketing budget on digital strategies. Consequently, the digital advertising space has become cluttered. It’s now harder to get noticed and many users are turning to ad blockers to cut down on the noise.  Ad blockers significantly reduce the reach of a campaign, so one strategy is to focus on targeting a small, niche market instead. Place adverts in locations where your target market will see them as relevant information, and not as distracting mass advertisements. Or take this strategy further and don’t let your advertisement be a distraction at all. Even without ad blocking software, the human brain is extremely proficient at filtering out irrelevant distractions, especially in a cluttered visual display. Instead, utilise channels where the advertisement isn’t disrupting the user’s task, such as social media and email. Creating original content that customers truly want to see, rather than block, means they will be seeking out your content in a positive way.  The digital marketing landscape remains an exciting one, with upcoming changes providing as many opportunities as challenges. As always, those who embrace and prepare accordingly are most likely to reap the benefits. If you need support finding the right Digital Marketing Talent for your business, get in touch with our specialist Marketing Recruitment Team.  

The UK is already a global leader in data-derived products and services, securing 33% of European technology investment in 2019. Recently, the government announced their National Data Strategy, which aims to advance the UK’s position by facilitating further digital transformation and unlocking value from data-driven insights. Let’s take a look at the opportunities and challenges likely to arise from the strategy. The National Data Strategy mission statements 1. Unlocking the value of data across the economy: making data usable and accessible across organisations, while protecting privacy concerns. 2. Securing a pro-growth and trusted data regime: meaning that a rigorous system is established that does not place too great a responsibility on small to medium sized businesses (SMEs) 3. Transforming government’s use of data to drive efficiency and improve public services: creation of best practices and standards to facilitate use of government data. 4. Ensuring the security and resilience of the infrastructure on which data relies: protect businesses, public services and other organisations from disruption. 5. Championing the international flow of data: ensure that best practices are maintained at home and across international borders. Opportunities According to an analysis by innovation consultancy, Ctrl-Shift, personal data mobility (a world where data flows safely and efficiently) could increase the UK’s GDP by £27.8billion. Data mobility also has a wider impact on digital innovation (for example, the development of new technologies such as artificial intelligence), which could have a significantly greater effect on GDP. The National Data Strategy aims to increase usability and accessibility of data, providing a wealth of business and employment opportunities.  Brexit will create plenty of challenges, but it also provides the chance to improve existing systems. The UK’s existing data regulation standards are generally approved of by the EU, so it’s vital to maintain this high standard and commit to sharing data internationally. However, this increased independence provides the opportunity to listen to domestic concerns. For example, recent research conducted by FTSE 100 software company, Sage, found that ‘Complex and expensive processes, exacerbated by lack of trust between institutions’ was a major factor holding back business performance of SMEs. The National Data Strategy promises to prioritise SMEs and hypothetically the goverment should be able to use this increased independence to do so. The National Data Strategy can also support other government initiatives. For example, within the healthcare sector, data can improve care, treatment and give patients better control over their information. The Department of Health’s new strategy ‘Data saves lives’ aims to further the use of data within healthcare. In addition, the government has set a target to be ‘net zero’ by 2050. Using data for climate risk management, environmental monitoring and the creation of new solutions (particularly those that businesses can implement to reduce carbon emissions), will help to achieve this net zero goal. Challenges Concerns have been raised over the lack of specific targets in the National Data Strategy and the need for a defined action plan. For example, the strategy report emphasises the need to close the ‘cyber skills’ gap, without setting out how this will be addressed. Software company, Kainos, and AI innovator, Faculty, have suggested introducing data science into the school curriculum as a concrete step the Government could take in solving this problem. Data privacy is another issue. The strategy mentions both its aim to increase data accessibility, but also the importance of public trust. Lack of public trust can seriously undermine the efficiency of data-driven solutions — COVID-19 contact tracing systems were seriously hampered by this in several countries. It’s vital any upcoming regulatory changes outline how data privacy will be protected. Overall, the National Data Strategy has been positively received as it highlights the importance of data for both economic and social growth. However, the impact of the strategy will depend upon its execution and consideration of issues such as data privacy. The government is expected to outline further actions in due course, the details of which will be eagerly awaited by the technology industry.  

From hard-hitting journal articles to conversational podcasts, there is a fantastic range of resources out there for risk management professionals. Here is a selection of some of the best blogs, podcasts, software tools and more, to help you keep up with the latest in the industry and to further your personal development. Comprehensive resources No list about risk management resources would be complete without mentioning Risk.net and the Institute of Risk Management (IRM). Risk.net is the leading resource for everything from climate risk to upcoming regulations and data insights. It should be your first point of call to expand your existing knowledge and keep up with industry news. They also host regular in person events and webinars. Make sure you are making the most of what they have to offer by signing up to their mailing list. If a full membership to Risk.net is out of your price range, why not try IRM. Membership is extremely reasonable, and they have a huge amount of content on offer. As a member, you will have access to topical articles, news items, videos and webinars. They also publish new research, provide training, qualifications and thought leadership. It is a fantastic way to invest in your professional development. Podcasts There are a number of risk management podcasts emerging, but PwC’s ‘Risk & Regulation Rundown’ series is a particularly excellent one. Hosted monthly by PwC Director, Andrew Strange, each episode invites an expert guest speaker to discuss the latest in the industry. Episodes are around twenty-thirty minutes long and have previously covered topics such as ‘Operational resilience – the end of the beginning’ and ‘Re-articulating risk - preparing for the IFPR.’ Another extremely popular podcast is RIMScast. Hosted by Justin Smulison (Business Content Manager at RIMS, the Risk and Insurance Management Society), RIMScast posts new thirty minute episodes every week. The podcast covers a mixture of topical discussions, career conversations and updates on industry news. Previous episodes have covered topics such as ‘Climate Change, Business Interruption, and the 2021 Hurricane Season’ and ‘Your LinkedIn Profile and the Path to the Boardroom.’ Blogs Clear Risk have weekly blog posts on a range of topics. Previous posts have included ‘7 Tips on How to Control Your Organization's Insurance Costs’ and ‘6 Reasons All Organizations Should Embrace the Cloud.’ Their library of white papers is a particularly useful resource, providing free downloads on topics like business continuity planning, ROI of risk and claims systems, product liability risk and more — all of which use practical examples. Finally, a free risk management book aimed at small to medium-sized businesses — all you have to do is provide a few contact details and it is yours! Another interesting blog is Paladin Risk Management. It was set up by Rod Farrar, who turned to risk management after serving twenty years as a military officer. He aims to help people become ‘Risk Gladiators’ through his insights and training courses. The blog is definitely worth following for its entertaining style, insightful tips and thorough analysis of risk cases that have gone wrong. Software tools It is highly likely you already know about ModelRisk, the highly valuable Monte Carlo simulation Excel add-in that can be used to add uncertainty into your modelling. If not, do download this free software tool as soon as possible! Another excellent tool is Probability Management’s SIPmath. All the models you build with SIPmath are native to Excel — this makes it easier for models to communicate with each other and be shared with others. You can also access a generous library of modelling tools to aid your analyses. Finally, best of all, all of your actions are traceable — so no more wondering where your analysis went wrong! Download it for free, here.   If you are looking to expand your team or if you are looking for a new role get in touch with our Risk Team to see how we can help.  

The technology community is brimming with creative people who push the limits of what is possible. To celebrate this continued innovation, let’s look back at some of the pioneers who made our modern, digital world a reality. Hedy LaMarr, the Hollywood Tech Genius All creative people want to do the unexpected. Hedy LaMarr was a woman who became a Hollywood actress to escape her marriage, then conducted scientific experiments in her spare time and quietly developed a technology with world-changing implications — as one does. LaMarr created a frequency-hopping technology in 1941 that was used to cloak Allied torpedoes during WW2. Later, this technology paved the way for wi-fi, GPS and Bluetooth. Unfortunately, LaMarr received little credit, and absolutely no financial reward, for her achievements. It wasn’t until 1997 that she received some recognition when the Electronic Frontier Foundation presented her with a Special Pioneer Award — to which she responded, ‘It’s about time.’ Seymour Cray, Father of Supercomputing One of my guiding principles is don’t do anything that other people are doing. In 1964, Cray created the CDC 6600 — a computer that ran 10 times faster than the average at the time and is often considered to be the world’s first supercomputer. Cray’s innovative design practice focused on ensuring the cooling system ran efficiently, and that electrical signals all arrived at the same time. However, Cray was a controversial character, known for his disdain of bureaucracy and penchant for working at night. He reportedly once used artificial blood as a coolant for his supercomputer. While artificial blood might be a step too far in most offices, perhaps we can all learn from his out of the box thinking style. Grace Hopper, Rear Admiral and Programming Trailblazer A ship in port is safe, but that’s not what ships are built for. Hopper, sometimes known as ‘Amazing Grace’, was a computer scientist and Rear Admiral in the United States Navy. She’s known for developing one of the first computer programming languages, COBOL, during the 1960s, and predicted that computers would one day be small enough to sit on a desk. We also have Hopper to thank for the term ‘bug’ in relation to programming — she coined the term after a moth flew into a computer. Lewis Latimer, Prolific Inventor We create our future, by well improving present opportunities: however few and small they be. Edison may have invented the light bulb, but Latimer wrote the book on it: ‘Incandescent Electric Lighting: A Practical Description of the Edison System.’ Latimer was also heavily involved in helping Alexander Graham Bell patent the telephone. As a black man in the 1800s, Latimer wasn’t given the credit he deserved for his inventions. Besides the light bulb and the telephone, Latimer also invented train bathrooms, installed electric plants in several American cities, won a patent for his safe elevator design and created disinfecting and cooling systems for hospitals.  Chieko Asakawa, Accessibility Champion Accessibility ignites innovation. On a more modern note, one of the most incredible recent developments in technology accessibility is undoubtedly Chieko Asakawa’s IBM Home Page Reader, a voice browser program that gave visually impaired people access to the internet. Asakawa became blind after a swimming accident at age 14 and grew up asking her brothers to read her textbooks aloud. As a result, she began developing digital book technologies. Since then, Asakawa has been working on cognitive assistance technology, which will eventually enable people with sensory limitations (e.g., blind or deaf) to navigate the world independently. Tim Berners-Lee, Founder of the World Wide Web The web is for everyone, and collectively we hold the power to change it. It won’t be easy. But if we dream a little and work a lot, we can get the web we want. Last but not least: the creator of the internet. Berners-Lee came up with the early version of the internet while working at CERN in 1991 to share research materials and data with fellow academics. In 1993, CERN put the World Wide Web in the public domain, and from there the internet flourished. Berners-Lee believes the Internet is a basic right and has continued to campaign for public accessibility.  

It’s no secret that the pandemic has increased the frequency and sophistication of cyber-attacks. And according to a recent government survey, they’re showing no signs of slowing down. Increased cross-sector collaboration has been suggested as a method of combatting the increased threat. The UK’s new self-regulatory body, the UK Cyber Security Council, and Microsoft’s new initiative, The Asia Pacific Public Sector Cyber Security Executive Council, are welcome steps in this direction. Why are these private-public partnerships so important, and can they help create more cyber-resilient organisations? Why does collaboration matter? Cyber-attacks do not distinguish between sectors. One of the highest profile attacks of 2020, the SolarWinds breach, exploited a flaw in the Orion security tool to target industry and government agencies alike. BitSight has estimated the financial impact of the SolarWinds attack to be approximately $90,000,000 in insured losses. Not to mention the unquantifiable effect on national security. Sometimes, lack of collaboration can result in the need for court-ordered cybersecurity intervention, as was the case with Microsoft — legal approval was required for the government to remove compromised web shells in Microsoft Exchange servers. These kinds of actions can cause unnecessary delays and substantial losses. In some time sensitive cyber-attacks, such as medical ransomware, the consequences could be devastating. The benefits of collaboration  Given the significant consequences of a cybersecurity breach, many organisations are calling for greater collaboration — the benefits of which include greater intelligence sharing, a cohesive response to threats and robust international infrastructure. Intelligence sharing According to a study by the Ponemon Institute, organisations with high cyber-resilience were more likely to participate in some form of threat-sharing program (e.g., open source, commercial sources, threat intelligence platforms). Sharing intelligence allows organisations to identify likely threats in their industry and develop appropriate responses based on what similar organisations have tried. Intelligence sharing between public and private sectors is vital because of the distinct perspectives each sector has. For example, government agencies can conduct cyber espionage operations and, therefore, have insight into adversary networks. In contrast, business providers often have greater understanding of cyber-attack victims.  Increased cross-sector talk could vastly improve cybersecurity responses, and even prevent attacks before they occur. Microsoft’s new initiative, The Asia Pacific Public Sector Cyber Security Executive Council, aims to facilitate private-public partnerships, to share information and strengthen government cyber defences. The council plans to meet quarterly going forward.  Consistent threat response Having a clear response to cybersecurity incidents helps to protect organisations against cyber threats — particularly for smaller organisations that may lack expertise and/or resources. IBM have often emphasised the importance of having an incident response process that is consistent, repeatable and measurable, and has worked with organisations across sectors to help develop resilient solutions.  However, there is still remarkable variation in the cybersecurity industry because of the lack of professional regulation. The UK Cyber Security Council plans to correct this issue, bringing private and public sectors together to create regulatory standards in cybersecurity, similar to what already exists in industries such as accounting and finance. This hope is that this will create a set of standards that improves the quality of cyber defence strategies and the efficiency of incident responses. International collaboration Many organisations operate internationally and therefore, so are the attacks. For example, while the impact of the SolarWinds attack was the most severe in the US, at least seven additional countries were impacted (including the UK, Belgium, Spain, Canada, Mexico, Israel and the UAE). However, the response from US allies was far from cohesive, and none matched the impact of the sanctions the US imposed on Russia for their suspected role in the attack. It’s crucial that private-public partnerships are not only encouraged on a national scale, but globally. Participating in global forums, sharing intelligence and developed global frameworks will inevitably improve cyber-resilience. Finally, co-ordinated global responses may deter nation state attacks, and increase trust between co-operating countries. Clearly, many are working hard to facilitate cross-sector collaboration. However, there is much further to go. Cybersecurity is no longer optional — protected digital environments are crucial for organisations of all kinds, so they must work together to secure a cyber-resilient future.

With so many organisations operating online, digital marketing has never been as important as it is in 2021. It’s a continually evolving field, so marketers have to keep up with the latest trends, tips and tools. Read on to discover six great resources across content marketing, search engine optimisation (SEO), social media and more, to help you stay ahead. The All-In-One Resource Neil Patel is widely acknowledged as the guru of all things digital marketing, and his blog is a fantastic source of information for all marketers. Neil’s website covers topics such as SEO, content marketing, social media, Ecommerce, conversion optimisation, email marketing and more. So, if you’re looking for a single resource to suit all your digital marketing needs — this is the one for you. In addition, the website offers tools such as the SEO Analyser, which provides you with a free analysis of your SEO errors, and Ubersuggest, which gives you information about keywords, content suggestions and backlink data. Some of these features are available for free, and the more advanced options available for a fee. Content Marketing Contently provides advice on improving your content strategy, building your content platform and directing you towards expert content creators. Their blog is always written in a jargon-free and engaging manner, and covers topics such as strategy, digital transformation, storytelling, ROI and trending topics.  Their educational hub, ContentlyU, provides a wealth of free knowledge in the form of research, playbooks, courses and events — fantastic for career progression and developing new skills. Their collection of real-world case studies, which includes organisations such as Walmart, Microsoft, Marriot, are also worth a quick read. Social Media Social Media Today is a great source of news and blog insights. They cover new updates to social media, key industry reports and advice to improve your social media game. Social Media Today also facilitate wider community engagement by hosting regular Twitter chats, SMT Live. Past conversations have covered topics such as ‘Building Brand Trust on Social Media’ and ‘Brands That Inspire Us on Social.’ Water-cooler chats If you are missing the buzz of office life, then look to the ‘Marketing over Coffee’ podcast to fill that void. Each twenty-minute episode takes the format of a casual chat between colleagues, while still providing plenty of useful tips and tricks.  While the podcast covers marketing as a whole, you don’t need to look far into the archives to find some fascinating digital content. Recent 2021 episodes have covered topics such as ‘AI for Marketers’, a conversation with Eric Schwartzman on his new book, ‘The Digital Pivot – Secrets of Online Marketing’, and an episode exploring, ‘What tech will replace cookies for Google?’ Search Engine News The SEM Post is another excellent digital marketing news source, run by industry expert, Jennifer Slegg. They are known for their timely responses to breaking stories and thoughtful commentary for the wider industry. For up to date news about the major players such as Google, Twitter and Facebook, as well as topic-focused insights and wider industry analysis, be sure to look to the SEM Post.  Subscribe to their newsletter for daily and weekly updates, and follow them on twitter to keep on top of the latest stories. Psychology for digital marketing Psychology is key to successful marketing, so why not dig into the science behind what makes people tick? Roger Dooley’s popular podcast, Brainfluence, features conversations with experts across marketing and business. Each episode covers personal stories, discussion of underlying psychological principles and practical tips. Previous episodes have included a conversation with Howard Tiersky about ‘Winning Digital Customers’ and a look at ‘Influencer Marketing’ with Jason Falls.

The International Financial Reporting Standard 15 (IFRS 15) has been one of the most significant changes to financial regulatory standards in recent years. Since the standard took effect in 2018, most organisations have completed implementation, but with varying degrees of success. Here’s a quick look at some common issues encountered under IFRS 15, and why the standard presents a continued compliance challenge to organisations. What is IFRS 15? To briefly recap, the IFRS 15 was introduced to improve clarity and comparability of revenue accounting across industries. According to the International Accounting Standards Board (IASB), “The objective of this Standard is to establish the principles that an entity shall apply to report useful information to users of financial statements about the nature, amount, timing and uncertainty of revenue and cash flows arising from a contract with a customer” (IFRS 15, page A665). Prior to IFRS 15, approaches to revenue reporting varied, but the new standard sets out a clear five-step plan to standardise the procedure. Implementation status The 2019 FRC review found that while IFRS 15 had generally improved clarity of revenue reporting, there were still several issues in the following areas: Performance obligations: the specific nature of these obligations was not always properly defined. For example, whether a company was acting as an agent in providing goods and services. Revenue judgements: descriptions of judgments often lacked clarity and failed to highlight those which had a significant impact on revenue recognition. Quantitative disclosures were often missing for judgments relating to estimation uncertainty.  Variable consideration: the nature of this pricing feature was often not fully disclosed, particularly in regard to assessment methods. Specific policies: many companies disclosed the specific policies used within their system. However, it was often unclear whether these policies complied with IFRS 15 regulations. Key challenges While the FRC highlighted some positives in revenue reports, they emphasised the need for improvement. So, what are some of the key challenges that organisations face in IFRS 15 compliance? Complexity Introducing IFRS 15 was meant to simplify reporting by providing one standard for revenue recognition instead of several (as was the case prior to IFRS 15), and account for issues certain industries faced under the old standards. However, an academic review discovered that both auditors and accountants reported the extensive scope of IFRS 15, and particularly the focus on complex industries, failed to simplify matters and created more work — particularly for small to medium sized companies. The theoretical complexity of the standard made it difficult to apply in practice and increased the risk of technical errors. Before IFRS 15 came into effect, the importance of training employees across departments and functions was highlighted as a key aspect of preparations. However, given that even the technical experts find the standard daunting, widespread organisational understanding remains difficult. Data Even before IFRS 15 implementation, many organisations expressed concern about how much data would be required and whether their existing IT infrastructure could support this increased demand. In particular, the need to identify and satisfy performance obligations within contracts requires more data to be captured and ongoing analysis that was not necessary under previous requirements. Organisations that established a data strategy before IFRS 15 implementation and worked to integrate finance, commercial and legal teams were in a good position to meet the new requirements. However, for others, creating and storing the correct data makes accurate revenue reporting a continued challenge. External environment While organisations have now had time to implement IFRS 15, changes to business practices and the external environment raise further regulatory questions. For example, the pandemic had implications for several aspects of revenue reporting, including variable estimation, changes to selling prices and contract modifications/termination. This highlights that IFRS 15 compliance is an ongoing process, and having an experienced team with industry-specific knowledge is a continued necessity.  While IFRS 15 implementation is now over, the challenges continue. Many organisations are still adapting to the changes in the face of a turbulent external environment, meaning that demand for experienced technical professionals and supporting infrastructure will remain high for years to come.  

IoT refers to the billions of physical devices (besides laptops and smart phones) that connect to the internet — and can include anything from smart lights to jet engines. These devices use sensors to monitor and communicate information, meaning complicated activities can be examined (and responded to) in real time. It’s one of the most highly anticipated trends of 2021, but will it deliver? Why is IoT such a hot topic right now? Iot has gained such momentum because of the accelerated digital transformation caused by the pandemic. According to the department for Digital, Culture, Media and Sport (DCMS), almost half (49%) of UK consumers have purchased one or more IoT devices since the pandemic began. Despite some IoT dependent industries being hit hard during the pandemic, (such as automotive) overall demand for IoT has increased. According to Microsoft’s IoT Signal’s Report, more companies are using IoT for the first time and more users are reliant on IoT as an essential part of their operations — 90% of enterprise leaders describe IoT as critical and 95% expected their reliance on IoT to increase. In addition, 1/3 of enterprises stated they were specifically increasing IoT investment due to the pandemic. A big part of the increased industry need for the technology is driven by desire to increase efficiency and reduce costs. IoT can be used to gather huge amounts of data and monitor it in real time. Digitally-savvy organisations are using IoT alongside AI and other data analytics tools to create digital twins — a virtual representation of real-world observations — and using these insights for everything from streamlining manufacturing to improving customer service Remaining Challenges. While 2021 looks hopeful for IoT adoption, there are several challenges that could impede progress. Security concerns, lack of standardisation and dependence on infrastructure being among them. Security concerns: security has been notoriously lax when it comes to IoT. IT teams have expressed the difficultly of keeping networks secure in the face of numerous IoT vulnerabilities. Digital infrastructure minister, Matt Warman also said, ‘Our phones and smart devices can be a gold mine for hackers looking to steal data, yet a great number still run older software with holes in their security systems.’  However, upcoming legislation may help with this. The UK’s ‘security by design’ law aims to create legally binding security requirements for almost all virtual smart devices. This should force manufacturers to improve security at the device level and may assuage concerns among those still hesitant to adopt IoT. Infrastructure: IoT requires fast and reliable broadband to reach its full potential. Therefore, it is heavily reliant on existing infrastructure, such as 5G. The increased bandwidth and network speed of 5G would enable more IoT devices to be connected and to run efficiently. While 5G roll-out has been delayed by COVID-19, security issues and political factors, there has been renewed government commitment to establishing the UK as a leader in 5G technology. With the number of tech giants also supporting the roll-out, 5G could help to further boost IoT development during 2021. Standardisation: IoT at its full potential should provide a seamless, interconnected experience for the user. However, the lack of industry standardisation on IoT features such as operating systems, development frameworks and architecture holds the dream of a fully integrated network back. Establishing legal security requirements is the first move towards solving this issue, but more regulation is needed so that all devices follow a set of agreed-upon standards. To summarise, 2021 is looking positive for IoT. Many organisations are already capitalising on the opportunities the technology offers and are continuing to invest. However, the extent to which IoT emerges as the hero technology of 2021 depends upon how genuine concerns over security, infrastructure and standardisation are addressed.   

COVID-19 has demanded our attention for the past year, so you could be forgiven for thinking that climate change has slipped down the agenda. However, the UK remains committed to achieving net zero by 2050. A hard transition to a low carbon economy poses significant risks to the finance industry and beyond. As such, climate risk will become an integral aspect of all risk management. Companies that do not prioritise the shift to greener operations could incur hefty losses in the years to come. Climate Risk The science is clear: we will see a multitude of climate change effects in the coming decades. Climate risk is the process of estimating and evaluating the financial costs of these changes and using this information to guide future decisions. Two of the main climate change risks are physical and transitional.  Physical climate risk: refers to the devastation caused by volatile weather, storms, floods, fires, wind and rising sea levels. For example, the average global sea level may rise by 29-59cm by the end of the century. However, according to recent research, even a much smaller rise of 11cm could cause additional annual losses of $1.4 trillion. One of the biggest reasons rising sea levels poses such a threat is the number of facilities in flood zones or coastal regions. Transitional climate risk: refers to the processes associated with transitioning to a low carbon economy. This can include upcoming policy decisions, such as the anticipated carbon pricing system. However, this can also include other external pressures such as consumer demand or technological change. Fossil fuels are an example of an industry heavily impacted by transitional risk. According to the Commodity Futures Trading Commission (CFTC), an estimated $1-4 trillion in global fossil fuel wealth could eventually be lost. Targets and Regulation The UN climate talks are to be held in Glasgow in November 2021. Ahead of this, there has been revived effort to establish the UK as a global leader in sustainable finance. For example, the launch of government backed initiative, the Centre for Greening Finance and Investment (CGFI) in 2021, to advise on how to build a greener economy. The HM Treasury recently stated that the UK was working towards a system where ‘every financial decision takes climate change into account’ and the Chancellor encouraged the Financial Conduct Authority to consider the UK’s climate change commitments in upcoming regulations. This year, the Bank of England and the European Central Bank are conducting climate change stress tests — an in depth evaluation of how the portfolios of banks and insurers would perform against increasing physical and transitional climate risk. The climate stress test should clarify the level of financial risk these organisations face and will undoubtedly feed into upcoming regulations. Future action Those that fail to properly assess their level of climate risk and reconfigure their portfolios accordingly could incur major losses in the long-term. Being unprepared for upcoming regulations increases the chance of being forced to face a hard transition. This demonstrates the need for organisations to not only appoint a climate risk specialist among senior management, but for all risk management staff to prioritise climate concerns in their strategy.  Companies also need to appreciate the difficultly of modelling climate change risk. Unlike market or credit risk, there’s little historical data to assess the impact of climate change. Models are also attempting to predict effects that may take place decades in the future, rather than in the next few years. While stress-tests are a good start, assessing climate risk is an ongoing challenge. The need for talented data scientists, analysts and engineers has never been higher. 2021 is an important year for climate change risk. The outcome of the UN climate talks, and the finance industry stress-tests will provide much needed direction for forward-thinking companies. However, it’s clear that renewed global interest in the transition to a low carbon economy will bring further regulation sooner rather than later. The message is clear: organisations need to be ready for the climate storm to come.  

Artificial Intelligence (AI) makes big promises, but does it deliver? According to a recent study conducted by the BCG GAMMA, the BCG Henderson Institute, and the MIT Sloan Management Review, just 10% of companies report a significant financial impact of implementing AI. Despite this low figure, adoption of AI in business continues to increase. AI is perceived as a strategic advantage and a way to manage risk in increasingly complicated economic times. So why have so many struggled to glean financial benefits from AI, and what are those 10% doing right?   What do we mean by AI? AI can now be used in a wide variety of processes — from low-level automation, augmenting human experts and producing novel insights. For example, Alibaba uses AI for everything from predicting-commerce purchasing decisions, reducing traffic jams in smart cities and maximising agricultural efficiency by monitoring crops. The challenge According to a survey by Fountech Solutions, 36% of firms were concerned about their level of AI progress compared to competitors. Media hype and the huge number of AI-powered tools that make big claims contribute to the pressure to implement AI quickly. However, with so few companies achieving a worthwhile return on investment, is it time to stop the AI hype? The most obvious challenges to AI adoption are the most fundamental. Accessing the right quantity and quality of data has always been an issue, and the skills gap in the data science industry continues — with demand far outstripping supply. However, according to the BCG GAMMA, the BCG Henderson Institute, and the MIT Sloan Management report, even when companies got the basics right, just 20% of them saw significant financial benefits (calculated according to company revenues). A somewhat disappointing figure given the cost and time involved in AI implementation. The key to success: organisational learning According to the report mentioned above, ‘Learning with AI’ was the most important factor influencing the financial impact of AI — the likelihood of achieving significant benefits increased to 73% in these businesses. This organisational learning had three key elements including: Continuous learning: businesses that facilitated multiple methods of mutual learning (AI teaching humans, humans teaching AI, and independent AI learning) were five times more likely to earn significant financial benefits than those that used a single method. An example of this mutual learning can be seen from how AI is used by traders in financial institutions. In this environment, the AI first observes trader behaviour to learn which data to prioritise, then the AI autonomously creates a set of rules based on what it has learnt, and finally the trader learns from the AI-derived insights. Context dependent human-AI interaction: the weighting of human-AI interaction varies from the AI autonomously making decisions with minimal human supervision to human employees making the decisions and only using AI to gain further feedback on likely outcomes. Using a variety of different methods is more successful because the appropriateness of each method depends upon the wider context. Generally, in simple environments where the AI has all the appropriate data, then the AI can be given more control. In contrast, in more complex environments that require human creativity and interpretation to understand the likely outcome, AI should be relegated to a lesser role. Structural and cultural change: one of the biggest challenges to implementing AI is underestimating the difficulty of the transition. Businesses that extensively changed many processes across their organisation were five times more likely to achieve significant financial benefits than those who only made small changes. Making sure employees are practically able to use AI technology and understand both the advantages and limitations of such tools is vital. It’s clear that to reap the financial rewards of AI, companies must commit to an ongoing culture of organisational learning. If this culture represents a radical change from the status quo, then AI adoption may be challenging in the short term. However, discomfort is a small price to pay in order to secure the competitive advantages of AI and thrive in our increasingly digital world.  

Financial fraud is a top concern for banks in 2021. The pandemic caused a surge of fraudsters and hackers engaging in financial crime, resulting in heavy costs and damaged reputations for many banks. One potential solution is machine learning, which the finance industry has been steadily adopting in recent years, using the technology to identify fraudulent transactions. So, is machine learning the answer to the pandemic-related surge in financial fraud? The problem According to Forrester, fraud rates across financial products increased by 33% in April 2020. This trend is expected to increase in 2021. In addition, a high number of fraudulent COVID-19 loans will come to light. This problem has emerged due to both the increased number of scams and attacks, alongside the cybersecurity vulnerabilities caused by rapid digital transformation. Besides the money stolen by fraudsters, an incident results in further costs for financial service organisations through the time and effort put into the investigation, and the potential legal fees and fines. The solution Machine learning (ML) is a subset of artificial intelligence (AI). The terms are often used interchangeably, but ML is most commonly used to refer to statistical algorithms with the potential to learn from datasets without specific, ongoing programming. ML provides huge value in financial services because it’s able to flag potential fraud more quickly and causes less false positives than existing methods. For example, KPMG created a ML tool for HSBC, which eliminated 80% of the false positives created by the bank’s previous transaction alert system. The potential of ML to combat financial fraud means that experts in this area are in high demand. According to Bloomberg, financial services job listings requiring skills in data science, AI and ML increased by 60% in 2018. Demand has steadily increased ever since. A more recent report from the Bank of England found that half of surveyed banks expected the importance of machine learning and data science to increase because of the pandemic. Clearly, demand for AI and ML is increasing. It’s one of the most promising tools to fight financial fraud in years. However, other issues will emerge as usage grows. What are they and how can they be mitigated? Bias A common concern about the use of AI and ML techniques is bias. There have been several high-profile cases of gender discrimination in AI-enabled finance, such as the husband who received a credit limit twenty times higher than that of his wife, despite her having the better credit score. Building models to spot fraudulent transactions could suffer from similar bias in what constitutes risk and unusual activity. Such issues often result from the subconscious bias of their creators, so seeking employees who prioritise ethics in ML and those from a diverse range of identities will help organisations to implement ML ethically. Transparency Lack of transparency about how algorithms operate can make it difficult to establish whether they meet regulatory standards. To increase public trust and industry compliance, organisations need to explain how data is being used and why. To do this, it will be vital to employ skilled communicators able to breakdown the technical aspects of machine learning to a wider audience. Data According to a Bank of England survey, 35% of banks reported the pandemic has negatively impacted ML model performance. It’s harder for ML models to detect unusual activity because the pandemic has changed our behaviour so drastically.  The role of a data scientist must change to combat this risk, spending more time continuously monitoring and validating models, so that unexpected data patterns can be flagged early. Sharing data between banks could help machine learning models become more sophisticated. There is also a role for external organisations (e.g., the UK’s industry funded Dedicated Card and Payment Crime Unit with the Police) and professionals with regulatory and/or legal experience to help create a central pool of information from which better risk assessments can be made. McGregor Boyall Support The teams in Compliance, Financial Crime, Data, Analytics, Change and Legal are all available to discuss the above and have the talent available on both Permanent or Interim contracts to help provide you with both commercial and technical solutions. Paul Geist Head of Practice, Regulation, Legal, Compliance and Financial Crime McGregor Boyall Associates Ltd  t: +44 (0)207 422 9030 m: +44 (0)7791 806 434 e: PGeist@mcgregor-boyall.com   

Disruption is the buzzword of the technology industry. Its association with innovation and soaring profit makes disruption a highly sought after label — entrepreneurs dream of creating it and investors dream of finding it. However, some experts have criticised the technology industry’s obsession with the term and believe that it’s equally important to consider the negative impact a culture of disruption can cause. What is disruption? Disruption refers to a technological development that significantly alters the way things are done in an industry. For example, Apple disrupted the communication and camera industries. Netflix disrupted the film and television industries. Amazon disrupted the entirety of retail. Disruption refers not only to a single technological breakthrough or organisation, but broader change. For example, the pandemic has caused widespread disruption by accelerating digital transformation. Many organisations won’t return to previous methods of operating, as shown by mass closures of physical locations, meaning this disruption will irreversibly change demand for associated employment and services. The dark side of disruption Whenever disruptive change takes place, someone loses out. For example, the rise of digital media is putting traditional platforms out of business. It’s generally assumed disruption is a good thing for the technology industry — but is this always the case? High turnover Disruption goes hand in hand with innovation and, as a result, we expect skyrocketing growth from disruptive companies. However, what goes up must come down. Over the past twenty years, only four companies have continuously held their position on the list of highest-valued technology companies: Microsoft, Intel, Cisco and Oracle. The rest of the list varies year on year, with newcomers shooting up, only to be knocked off the top spots by the next start-up. The lesson is clear: if you operate in a disruptive industry, you are also more likely to be disrupted. Culture The pressure to be disruptive can cause some companies to take on new initiatives (such as AI, DevOps, etc.), because they think they should and not because it’s the right move for their business. Cultural clash is often cited as the reason these initiatives fail. In addition, constantly having to adapt to new technology and processes is tiring. The pressure to ‘be disruptive’ can contribute to burnout — an enormous concern in the technology industry which, in a recent survey, 57% of people were said to be suffering from. The high demand the technology industry places on its members, including the need for constant growth and industry disruption, can create an extremely stressful environment for employees. Ethical issues Mark Zuckerberg famously encouraged his employees to, ‘move fast and break things.’ Since then, Facebook (and other social media platforms) have been under fire from everything to data privacy breaches, impact on mental health, exploitative advertising and harassment. Moving quickly may be profitable, but breaking things can create long-term damage to an organisation’s brand and action from regulators.  There’s also the temptation for companies to over-promise on what their technology can deliver to raise start-up investment. The most scandalous example of which is Theranos, the company that raised over $600 million for blood analysis technology that didn’t exist. While fraud at this level is unusual, pressure to be disruptive encourages start-ups to make big claims. A culture of disruption can be a positive thing, leading to innovation and excellence in technology. However, it’s important to know that this isn’t all disruption leads to. Pressure to deliver explosive growth may hinder slower, potentially more meaningful, change. And, at its most extreme, disruption may have a negative impact on both employee well-being and organisational ethics.

Data doesn’t lie, but people make mistakes. Analysts are only human and, therefore, are susceptible to errors and bias that can result in misleading conclusions. Given the weight that is now given to data-driven insights, it’s vital to minimise potential errors as much as possible. Here are a few common challenges analysts face, and how to avoid making costly mistakes.   Data validation Once you have been working as a data analyst for some time, it’s easy to forget the basics. Validating your data should never be rushed, because there is nothing worse than conducting your entire analysis, only to find the numbers do not add up. This is especially important if someone else has worked on the dataset before passing it over to you — make sure you request all the information you need, such as summary reports. A row count and key column summaries are usually a good way to start off data validation, but the exact steps will depend on your dataset. The most important thing is to have a clear plan of how you will validate your data before you even touch it — and continue to validate your data each time you make a major change to ensure the results are as you expected. Documenting your steps in this way will save you countless hours of trying to find the moment your analysis went wrong. Theory overload Many early career data analysts spend too much time on theory, overloading on trendy statistics and algorithms. While it’s important to keep learning the latest industry techniques, this can be a waste of time if you’re not applying them, because it’s extremely difficult to master new skills without consistent practice. Accept that you cannot learn everything and prioritise techniques that will provide you with the highest return on time-investment for the projects you are currently working on. It’s much better to master a smaller number of useful everyday techniques that have a broad but vague theoretical understanding of advanced concepts that you rarely use. Understanding your data Many data analysts are overly focused on applying the model, and as a result they rush some of the early stages of analysis. However, taking the time to understand the data by exploring it and applying visualisation techniques can improve the quality of your models, so it’s not a step that should be overlooked. You will also be more likely to catch unusual distributions of data, outliers, inconsistencies and/or missing values. At the most extreme end, failing to  understand your data could bias the model and lead to an incorrect conclusion. This conclusion could then be used to guide future business decisions, so it could become a very costly mistake. Looking beyond the data If you really want to establish yourself as someone who provides cutting-edge insights for their business, then you need to be more than just an analyst. Data doesn’t exist in a vacuum, and the conclusions you draw should take context into account. To improve your insights, try to understand key industry-specific concepts, research the wider market and stay aware of trends and developments in your area. This will improve the way you communicate the importance of your analysis to business stakeholders and help you stand out from the competition. In addition, having industry knowledge can help you avoid incorrect data conclusions. For example, ‘correlation is not causation’ is a guiding principle of statistics — meaning that you cannot assume two associated relationships are causally impacting each other. Take this very simple example: an analyst notices a correlation between ice cream sales and sunburn and incorrectly concludes that sunburn drives ice cream sales. They lack the industry specific knowledge to realise that hot weather is driving both trends. Obviously, the factors driving industry trends are many and complex, but all this means is that mistakes of this nature are much easier to make.  

In the second instalment of our series “The importance of Technical Accounting in the Modern World”, I caught up with Derek Yasumatsu, Head of Technical Accounting and Revenue Recognition at Nokia, to get his take on what makes technical accounting so crucial to businesses in technology. Why is Technical Accounting so important in the world of modern accounting? I think that it is very important for all types of Accountants to have at least some basis in technical accounting. In my experience, to understand the numbers and to explain to management, it is not enough to just obtain an understanding of the underlying business transaction or economic event that drive the numbers, you also need to have an equally strong understanding of the accounting methods applied and why they are appropriate. Technical accounting gives you this knowledge of the accounting whether you are a business controller, financial planner or transactional accountant. What are the advantages of having experts in this space in an organisation? The advantages of having skilled in-house technical accounting experts is extremely important to an organisation. In my view, to be a good technical accountant, you need three things in order of importance: Knowledge of the underlying business/industry  IFRS/GAAP technical expertise Knowledge of the company (processes, stakeholders, etc) Of these three, typically only in-house accountants are able to deliver on 1) and 3). Without an understanding of the company’s business nor understanding how to access needed information (i.e. company knowledge), an external consultant is at a big disadvantage. After all, accounting is meant to faithfully represent the economic substance of the underlying transactions – how can one achieve this when you lack the basic understanding of that substance? I see that use of external consultants often results in companies getting generic cookie-cutter solutions that don’t take into account the company’s specific fact pattern. Only in-house experts are able to develop this most crucial skill and they are also able to use their network of contacts within the company to get the information they need. When they combine this with sufficient technical accounting expertise, it creates significant synergy that I believe drives value in technical accounting. Without an understanding of the company’s business nor understanding how to access needed information (i.e. company knowledge), an external consultant is at a big disadvantage. What are the risks of not having expertise in this space? Companies that rely extensively on external support tend to lose control of their own policies. Often, such organisations also do not have sufficient expertise in-house to fully understand the accounting positions created by external consultants. This is a big risk for a company both in terms of being able to manage an efficient finance function as they rely exclusively on expensive external support for all actual as well as planned transactions. It is also difficult for management to evidence that it has met its stewardship responsibility over the financial statements of the company. Companies that rely extensively on external support tend to lose control of their own policies. What are future challenges/themes that will arise in this space? I expect that companies’ accounting policies and positions will come under more frequent and detailed challenge as a result of the latest mandatory audit firm rotation requirements. Many companies have had decades long relationships with their auditors where a deep understanding of the companies’ policies and practices have developed.  Companies that are not able to develop, explain and defend their policies and practices may experience big challenges including internal controls issues when they introduce a new auditor.   *The views that Derek has expressed are personal and should not be deemed to be those of Nokia.

How can you be sure the person accessing sensitive information is who they say they are? Authentication has become much more complicated during the pandemic. Having the correct account credentials is no longer enough to confirm a user’s identity. So, what are organisations doing to improve information security, and how is the field of authentication developing? The problem The threats to information security have surged during the pandemic; this coupled with the challenge of keeping remote workers secure has led to several high-profile breaches. While IT teams have been working hard to ensure that data is encrypted, anti-virus systems are installed and firewalls are configured, none of this really matters without effective authentication. It’s the information security equivalent of leaving the front door open. Poor authentication processes make your organisation an easy target for hackers. A hacker can gain access to a username and password via social engineering, phishing, social engineering, malware and more — all of which have increased during the pandemic. 95% of all web application attacks are because of weak or stolen credentials.   While malicious attacks are the primary concern, authentication is also important to maintain compliance with data privacy regulations. With the boundary between home and work becoming blurred, more and more employees have been accessing sensitive information on personal devices and some have even been sharing devices with family members. Multi-factor Authentication (MFA) Throughout some sectors, such as finance, MFA is already widely used. With the need for secure authentication of remote workers, other industries are now following suit. The reason being that MFA is extremely effective — according to Microsoft, only 0.1% of comprised accounts were those using some form of MFA. MFA significantly increases the difficulty of a successful hack. Therefore, potential attackers are deterred unless the incentive is worth the time investment. Most commonly, MFA works like this: you login to a system using your username and password, which triggers a second authentication process before you are granted access. The second process could be a text message to your mobile or a smart card/USB key.  Removing Passwords According to the 2020 Verizon Data Breach Investigations report, 80% of data breaches were due to compromised or ineffective passwords. Therefore, the strongest MFA approaches have been removing passwords completely. This includes secondary authentication methods that rely on a ‘shared secret’ such as one-time passwords or SMS codes, as these can be vulnerable to channel-jacking (a hacker taking over the channel which authentication attempts are sent through). Given that strong, unique passwords are often difficult to remember and manage, removing passwords also improves usability. Biometric authentication methods such as fingerprints, facial recognition and retina scanning are often held up as the gold standard. These are easy to use and cannot be stolen like a smart card or USB key can be (although biometrics are still vulnerable to coercion). However, some employees may object to sharing private biometric data and providing devices with biometric authenticators can be expensive. Continuous and Dynamic Authentication Although MFA is very effective, authentication is typically conducted once at the start of a session. MFA assumes that the user remains the same throughout the session. Some organisations with particularly sensitive data are adopting continuous authentication, where a benign background software monitors for changes in location, device or behaviour to trigger further authentication processes. Also, MFA does not usually consider the device in the authentication process. Dynamic authentication is being used to verify device identity and health by looking for factors such as unexpected screen resolution, suspicious IP addresses and CPU speed. This information will be continuously combined with predictions about user behaviour risk (and organisations can have input into defining what constitutes risk). Overall, it seems the simplest answer to the authentication challenge is implementing MFA. It’s an inexpensive method that radically improves security without significantly decreasing usability. While developments in continuous and dynamic authentication offer added protection, attacks on systems using MFA are rare, so this more advanced approach is only necessary for organisations at high-risk of a serious breach.

Data privacy compliance has been all about GDPR for the last few years. Now it seems that the ePrivacy Regulation is finally coming into force, so it’s time to switch focus. Here’s a brief update on the situation and a few ideas on how to prepare. What is ePrivacy? The ePrivacy Regulation is essentially an extension of the GDPR. Both were drafted to protect personal data, but ePrivacy specifically relates to personal online presence and activities. ePrivacy will address some limitations of the previous ePrivacy Directive and will extend data protection legislation to overlooked technologies such as the Internet of Things. Key aspects of the ePrivacy Regulation include changes to unsolicited marketing and use of cookies. For the former, marketers will now be unable to send emails, texts or social media communications to the user without their consent. For the latter, the rules surrounding cookie consent will be simplified (e.g., some cookies, such as those used for security, will be exempt from the consent ruling).  ePrivacy has been in the works for quite some time, leading some to conclude that it might be scrapped due to repeated delays. However, a recent report from the European Council suggests we may finally see some movement, as the EU member states have agreed on a negotiating mandate for revised rules on ePrivacy — talks can now begin to develop the final text. Lessons from GDPR Given that the details of the ePrivacy Regulation have not yet been finalised, we can’t prepare for the specifics. However, considering the significant overlap between the ePrivacy Regulation and GDPR, we can glean insights from the latter. The announcement of GDPR resulted in soaring demand for analysts, consultants, project managers and DPO’s with a strong understanding of the legalisation. In fact, reports showed that the majority of businesses intended to hire permanent or at least temporary staff to handle the transition. GDPR also caused an explosion of specialist data protection officer (DPO) roles.  Given the consequences of a GDPR infringement — up to a maximum of 4% of worldwide revenue or €20 million (about £18 million) — it’s hardly surprising that so many organisations took preparation seriously. Despite this, the GDPR transition was hugely challenging. In the year after GDPR came into force, an estimated third of European businesses were still not GDPR compliant. In 2020, both the number and size of regulation breach penalties increased, and so far, penalties imposed by the EU total £242.5 million. Preparing for ePrivacy Regulation Like GDPR, hiring a DPO with a strong technical background will help you prepare for the ePrivacy changes. GDPR didn’t stipulate the exact qualifications for what made a good DPO, but emphasised experience and knowledge. DPO’s can be sourced externally or internally, but the latter can raise challenges as GDPR stipulates that the DPO must operate independently. In larger companies, it’s vital that new regulations are understood across teams. For example, professionals working in Marketing will be particularly affected by the ePrivacy Regulation changes to unsolicited communication and cookies. Therefore, it’s crucial to hire data privacy experts with excellent communication skills, who are experienced at working with inter-departmental teams and are committed to spearheading data protection throughout the organisation. Overall, the coming ePrivacy Regulation presents a challenge similar to that of GDPR a few years ago. Those who were well-prepared experienced minimal disruption and protected themselves from potentially staggering fines. Approaching the ePrivacy Regulation in the same manner is likely to have positive results. If you wish to discuss this in more detail or your own firm plans, please reach out at your convenience. Paul Geist, Head of Practice, Regulation, Compliance and Financial Crime t: +44 (0)207 422 9030 m: 07791 806 434 e: PGeist@mcgregor-boyall.com    

The world is changing and with that businesses have to keep up with the progression in regulation and reporting requirements such as IAS, IFRS and GAAP. With this in mind we thought it would be useful to gain some insights into why Technical Accounting is so crucial to businesses today and the challenges it faces. For the first insight in this series I caught up with Kalpesh Shingala, Senior Director of Global Accounting Advisory and Policy at Cognizant, to find out their thoughts on why this area is so important.   Why is Technical Accounting so important in the world of modern accounting? The way companies do business has changed significantly over the last three decades. An exponential rise in the use of internet, innovative technologies, e-commerce, complex financial risk management and modern business strategies have changed the way commercial transactions are structured. Global organisations operate under various different regulations and have disparate financial reporting requirements. Although US GAAP and IFRS have been the two most comprehensive accounting frameworks in the world today, they are not fully converged and, as a result, differences in these accounting principles still exist. Accounting standard setters of many countries use IFRS as their initial reference point but they are required to make changes to the original standards as issued by the International Accounting Standards Board (IASB) to render them suitable for use within their local business environment. This introduces challenges that were not foreseen when we were living in a world with a handful of accounting standards. The U.S. Financial Accounting Standards Board (FASB) and the IASB have issued several new, and modified existing, accounting standards in the last two decades to keep up with the pace of a rapidly changing business landscape. Some of these standards are quite lengthy and complex. Also, several business transactions require application of more accounting standards than one. Accounting firms spend hundreds of hours in writing interpretive guidance on specific accounting topics for their clients and, understandably, many of their publications run into several hundred pages. This complexity and the regulators’ focus on consistent application of accounting standards necessitated accounting firms setting up specialist teams with subject matter experts (SMEs). Likewise, companies were required to expand their financial reporting teams by hiring new or dedicating existing resources who could focus on acquiring in depth knowledge of these accounting standards and who could run the new standards’ adoption projects across the organisation in an efficient manner. Global organisations having to prepare and file financial statements of their subsidiaries or legal entities operating in different countries for local/regional compliance face an added challenge of having to establish teams who have knowledge of local accounting requirements too. These specialist teams and SMEs came to be known as an Accounting Policy Team or Technical Accounting Group or Accounting Advisory and Policy Group etc. and assumed a critical role in ensuring timely adoption, policy documentation, consistent interpretation and application, and an overall compliance with the organisation’s diverse financial reporting requirements.     What are the advantages of having experts in this space in an organisation? Over time, technical accounting teams have gained significant importance within the finance function of organisations as commercial teams often need to engage with these experts to ensure that their planned transactions do not trigger any unexpected or undesired accounting and reporting implications. In fact, business teams even partner with these SMEs early on in the structuring of their transactions such that they achieve a desirable financial reporting outcome. These opportunities involve cross-functional engagement with other global functions such as Tax, FP&A, M&A, Corporate Development and other senior stakeholders, and provide a platform to technical accounting teams in playing an influential role in the decision-making process. They develop great insights into business activities of the company and thus build deep relationships within the organisation fairly quickly. This, in turn, creates several opportunities for these SMEs to take up senior roles within the finance organisation ranks. Given their subject matter expertise and a higher organisational profile, technical accounting champions command huge respect and are rewarded well within the finance organisation. And for these reasons, this team usually sits directly under the Chief Accounting Officer or Chief Financial Officer. The unique skill set possessed by these team members arm them with an ability to easily eliminate competition in their niche market. That said, a relatively higher compensation aspect has constrained many companies to upskilling their existing resources to take up additional technical accounting responsibilities rather than setting up an independent full-fledged technical accounting function. However, this has helped several SMEs take up consulting roles that offer steep per day rates and flexibility usually not available in permanent roles.       What are the risks of not having expertise in this space? Misinterpretation and non-compliance are the biggest risks companies face if they under-invest in a robust technical accounting function. As noted above, the length, complexity and number of accounting standards in the recent years have increased manifold. Adoption of many of these new standards requires companies to launch a multi-year adoption strategy and make significant changes to their existing systems and processes. Adoption of a new accounting standard also involves several hours of training, consultation and new policy documentation. Many companies fail to recognise this challenge and expose themselves to delays, inaccurate application or non-compliance. In several countries, the CFO and CEO have to certify completeness and accuracy of the financial statements filed with the regulator and in many countries directors even face personal liability for non-compliance or wrong decisions (e.g. declaring dividend in excess of distributable reserves in the U.K.). In the U.S., the Securities and Exchange Commission’s (SEC) reviews result in many public companies having to restate and reissue their historical financial statements due to an incorrect application of the accounting standards. Hence, the contribution of a robust technical accounting function cannot be over-emphasised.  Organisations with global presence face further challenges. Having to comply with different GAAP frameworks in multiple jurisdictions require careful planning and strategy such as alignment of accounting policies to minimize differences between the framework used for local reporting vs the GAAP used in the entity’s consolidated financial statements. Differences in local and consolidation GAAP also introduce several operational challenges such as different chart of accounts, financial reporting systems and tailored audit documentation. Most transactions also need to consider impact of local tax and company law requirements. Treasury transactions such as dividend distribution, foreign currency risk management and hedging often cause financial reporting volatility in local statutory financial statements as most of these strategies are planned to mitigate these risks at the global level in their consolidated financial statements. Lack of an experienced technical accounting team who can help decision-makers navigate through these multiple layers of responsibilities may result in companies missing out on such structuring opportunities.   What are future challenges/themes that will arise in this space?  Strong technical accounting skills take years to develop and require a good balance of passion, dedication and opportunities/exposure. Having a solid foundation and conceptual understanding of various accounting topics is an essential ingredient in developing subject matter expertise across several topics. Companies usually cannot afford to dedicate resources focusing only on a single topic and, as such, one has to develop a good grasp of several topics. Knowledge of available technical reference guides – both paid an unpaid – and innovative research capabilities go a long way in developing skills in this area. Continuing professional development plays a key role in keeping abreast of the changes in the accounting world. Although there are several free online resources available to assist with this, more interactive paid programs are, sometimes, more relevant in developing best practices in an organisation. Technical accounting specialists are also known to lack a key soft skill: ability to simplify complex accounting concepts while presenting them to senior stakeholders. Many organisations fail to support teams in developing these skills due to the cost considerations. A typical pitfall for technical accounting experts is over-confidence on their own analyses and conclusions. This leads to their lack of willingness or inability to identify situations when a wider consultation may be warranted. Many areas of accounting are extremely complex, require hours of research and could often lead to different interpretations. “Sleeping over” own findings and discussing with other members of the team or external advisors assist in arriving at conclusions that are more supportable from an audit perspective. Well-documented processes, techniques, checks and audit documentation are key to creating a healthy internal control environment. A mature technical accounting function uses tools, checklists and process documents to assist in an efficient and effective consultation. With the advent of Artificial Intelligence, innovative and sophisticated tools are being developed to assist with data-intensive analyses. While these technology-assistive tools can deal with data more effectively, they are a long way away in replacing human judgements required in interpreting and applying increasingly complex accounting standards.      *The views that Kalpesh has expressed are personal and should not be deemed to be those of Cognizant.

The Fundamental Review of the Trading Book (FRTB) has been a long time coming. However, it is thought that the European Commission will introduce legislation to trigger a three-year implementation period starting in 2021. With the FRTB finally approaching, now is a good time to review attitudes towards FRTB, and how organisations are preparing. What is FRTB? After the 2008 credit crisis, the Basel Committee on Banking Supervision (BCBS) extended and refined banking standards by creating the Basel III regulation, which includes the FRTB. The FRTB’s primary purpose is to better prepare financial institutions for periods of uncertainty by improving the calculations used to estimate the capital required to cope with losses from market risk. One of the key changes is to create a more objective boundary between the banking book and trading book — intending to reduce the incentive to arbitrage between them. Other changes include a revised standardised approach that encourages greater reliance on risk sensitivities as inputs into capital charge calculations, and a revised internal model approach that replaces value-at-risk with expected shortfall. Challenges and opportunities? The number one complaint about FRTB is the cost. Banks have expressed concern about the amount of capital they will be required to hold, the complexity of the regulations, and the additional staff needed to ensure compliance. The Basel Committee has made several amendments to FRTB since its initial proposal in order to reduce the financial pressure on organisations. However, the financial and logistical challenges of implementing FRTB should not be understated. Despite this, it is also important to recognise the opportunities FRTB offers. Banks that fully embrace FRTB have the chance to rebuild modelling approaches using tools such as big data, analytics, cloud technology and data science. This could provide a competitive edge and offset increased costs of compliance. For example, the FRTB requires enormous amounts of data, which could also be used to improve fraud detection and trend spotting. How are banks preparing? While there are a few banks whose existing infrastructure (i.e. those with bespoke and integrated trading and risk platforms built with advanced technology) facilitates the move to FRTB, for most organisations preparations are extensive. However, despite the pandemic, preparations for FRTB are still going strong. According to Risk.net’s conversation with Bloomberg’s Essan Soobratty, ‘banks have mostly continued along their established pre-Covid trajectories.’ And while there have been some inevitable delays, some banks are even viewing the current situation as an ‘opportunity to continue at a pace that allows for a more strategic implementation.’ These more strategic banks are viewing FRTB as an opportunity to improve their entire risk platform. For example, a key priority includes assessing and upgrading computing and operational systems to support the tasks that will be necessary to comply with FRTB (e.g. P&L testing, expected shortfall and sensitivity analysis). In addition, FRTB requires an unprecedented level of integration across departments. This means that banks are having to re-align roles and responsibilities across Risk, Finance and Trading, and co-ordinate FRTB implementation with other programs (such as BCBS 239) to achieve holistic transformation. This approach is likely to provide long term-gains and avoid the situation of the new system, which has been built on different data and models, conflicting with the existing one. Alternatively, many banks are considering third party platform and data vendors. With the number of infrastructure solutions on offer and the FRTB move towards increased standardisation, McKinsey & Co have suggested that it makes sense for banks to buy in certain solutions while building only the aspects of their infrastructure that provides a competitive advantage.  Clearly, a bank’s approach to FRTB preparations will depend on existing infrastructure, size and culture. However, more and more banks are using the delay caused by Covid-19 to implement deeper structural change. While it may make sense to outsource certain processes, investing in new technology and committing to the integrated culture required by FRTB establishes an organisation as a bank of the future.  

According to PWC’s 2021 survey, half of enterprise executives are now considering cybersecurity in every business decision. This security-first approach can include techniques such as multi-factor authentication, artificial intelligence and more — but DevSecOps is one of the most frequently talked about solutions to increased security threats. So, what is it and is it really the answer? What is DevSecOps? Development Security Operations (DevSecOps) is an approach to security that utilises an Agile framework — breaking down traditional silos to maximise speed and efficiency. Traditional silo-based approaches resulted in a production bottleneck once a completed software had been passed over to security teams, but DevSecOps overcomes this issue. DevSecOps prioritises security from the start, continually testing for vulnerabilities throughout development and automating key security processes. Automated tools such as web application firewalls, open source software governance and intrusion detection systems are commonly used to streamline a DevSecOps approach, while cross-functional teams prevent a production bottleneck. Benefits According to a 2020 survey by Sonatype, DevSecOps teams have fewer open source related breaches, and the majority deployed to production at least once a week. Almost half of DevSecOps teams said that they still didn’t have enough time to spend on security, but had built in more automation, meaning that security was being assessed throughout the process. Finally, the more evolved a DevSecOps team was, the higher their employee satisfaction. As well as being faster and more secure, DevSecOps allowed for the sharing of multi-disciplinary knowledge. By working with development operations, security professionals can gain a better understanding of how the software works. This allows them to better understand the DevOps team’s priorities. For example, security professionals usually advocate for a thorough encryption approach, but DevOps are focused on software performance — which encryption can sometimes reduce. By working together, the teams can strike the perfect balance between contrasting needs. Challenges DevSecOps offers huge potential, so why isn’t everyone using this approach? A commonly cited issue is the culture clash between a high-speed, pressurised DevOps approach and more traditional, cautious security practices. Most commonly, security is integrated with an existing DevOps team, meaning that security professionals are viewed as the outsiders. This, combined with entrenched preconceptions about security — that it is something that happens later or may even stifle innovation — can lead to conflict between teams. Security Compass’s ‘2021 State of DevSecOps’ report also highlighted technical challenges, cost, lack of time and lack of education as issues holding back adoption of DevSecOps. In addition, while automation is an important aspect of the DevSecOps approach, the surveyed participants still felt implementation was insufficient and that they were being slowed down by manual security processes. Maximising potential Clearly, successful DevSecOps adoption doesn’t just happen, it has to be nurtured. Cultural clash can be overcome by appointing a ‘security champion’ within the DevOps team who emphasises the importance of security and facilitates communication between teams. It can also help to have leadership explain the businesses case for improvement, so that teams understand the rewards for working through any teething difficulties. It’s equally vital to invest in automation. It’s a cornerstone of DevSecOps and yet still under-utilised. No matter how hard you work to prevent potential cultural clash between teams, if integrating security into development really does slow down the process, then resentment is inevitable. Using automation to streamline security could make the difference between a successful or failed DevSecOps adoption. Finally, most developers are not taught how to write secure code — according to Forrester, even the top Computer Science courses feature little security training, so it’s important to support education in this area. It’s clear that DevSecOps isn’t a panacea for the climate of increasing security threats. It comes with its own set of challenges and if implemented incorrectly, could end up causing more hassle than it’s worth. However, with the right attitude and commitment, you could end up with a well-oiled DevSecOps team who write secure code as second nature.  

According to the BCS, neurodiversity remains an overlooked issue in the tech industry — employment rates for neurodiverse people remains low and stigma remains. However, a growing number of companies are recognising that it’s not only right to offer opportunities to all, but people who think differently provide a competitive advantage and help to create an inclusive environment for everyone. For example, both Microsoft and Dell have an established autism hiring programme. So, what are the barriers to a neurodiverse tech industry and how can organisations help? What does neurodiversity mean? Neurodiversity refers to the differences in thinking patterns, interests and motivations that naturally occur throughout the population. A neurotypical brain functions in the way that the majority expects. However, an estimated 15% of the UK population are neurodivergent. This is an umbrella term that refers to people who have Autism, ADHD, Dyspraxia, Dyslexia and other neurodevelopmental conditions. Barriers to employment Employment rates vary across conditions. For example, according to research conducted by the National Autistic Society, just 16% of autistic people are in full-time paid work and many are working in a job below their skill level. Worryingly, a recent study found that half of leaders and managers would be uncomfortable hiring a neurodivergent person. The highest level of bias was against people with Tourettes, ADHD or Autism. In addition, the majority of neurodivergent people surveyed felt their workplace was not inclusive to their needs. Up to 40% of employees in the tech industry have not disclosed their neurodivergent traits, meaning that their needs are unlikely to be supported. Benefits of neurodiversity It’s important to firstly point out that stereotypes around neurodivergent behaviour are unhelpful and often cause unrealistic expectations. For example, the idea that autistic people are maths or computer savants. However, there are many benefits that go beyond superficial abilities including: People with Autism: often have strong attention to detail, are good are observing rules, are thorough in their work, possess a direct communication style and a high level of subject expertise due to their special interests.  People with Dyslexia: often highly creative, have a talent for story-telling and possess strong problem solving and communication skills. People with ADHD: often display a passion for their work and thrive when completing urgent or physically demanding tasks. Software and data quality engineering start-up, Ultranauts, is a fantastic demonstration of a company leveraging the power of a neurodiversity. 75% of the workforce are on the Autism spectrum. The small company is now winning contracts from Fortune 100 companies over established global IT consultancies. The company’s founder credits their success to their neurodiverse workforce, saying that, ‘with different learning styles and information processing models, to collaborate and focus on attacking the same problem, we’re just going to be better at it.’ Crucially, Ultranauts also worked hard to create an inclusive culture that supports neurodivergent people. Importantly, hiring neurodivergent people has a positive effect on the entire workforce by fostering a culture of inclusion. Accommodating individual needs is a wonderful thing that everyone can benefit from by encouraging both innovation and empathy within the organisation. How to facilitate a neuro-diverse workplace? Many neurodivergent people will require accommodations in their workspace. For example, Autistic people who suffer with sensory processing disorder may benefit from adjustments in lighting and noise (however, it’s important to highlight that variation exists — one autistic person could be over-sensitive and another under sensitive). ADHD people who experience periods of hyper-fixation accompanied by distractibility may benefit from a flexible schedule. In addition, making interviews neurodiverse friendly will support fair assessment practices and encourage hiring of neurodiverse candidates. Finally, many neurotypical people overestimate their knowledge of conditions such as Autism and ADHD. Awareness training can help build understanding and avoid further workplace barriers being created for their neurodivergent peers.  

There is an increasing pressure for QA testers to automate their processes. This is due to the assumption that automation is an easy way to cut costs and speed up testing — which can often be true. However, depending on the application being tested, automation isn’t always the right call. In some cases, it might not be worth the initial time and financial investment. So, what are the pros and cons of automation, and how can you find the right balance? What is automation in QA testing? When a QA engineer tests software manually, they’ll interact with it as if they were a real end user. They will log any bugs and implement a fix. Increasingly, QA engineers are utilising automation tools, which involve running numerous pre-scripted test suites. These tools include AI bots, which explore an application as a person would (in theory), testing functions and generating data about the process. What are the pros? Speed: automated testing is often faster than manual testing because thousands of tests can be run simultaneously, and the data can be generated quickly. In large scale projects, it’s essential to automate at least part of your QA testing process due to the sheer number of manual testers you would need to meet turnaround times. Reduce Human error: people make mistakes — especially on repetitive and monotonous tasks — so automating tests that fall into this category is good for everyone. According to research, organisations that utilise automation in their processes are more likely to be both highly productive and ‘human-friendly’ workplaces. Efficiency: repetitive tasks such as smoke tests and regression tests are ideal for automation. There’s no real advantage to having a person do these manually, and in the long run this will free up the tester’s time to focus on complex tasks, data interpretation and software fixes. What are the cons? Set-up costs: Automation can be expensive at the beginning. Specialist QA testers with a knowledge of automation command a higher salary, and you’ll also have to consider the cost of purchasing and maintaining automation tools. However, in the long-term automation should be cost-saving, as fewer engineers are needed to complete everyday testing tasks. Adaptability: If your product frequently changes, that means the scripts will also need to be updated. Therefore, it may not be worth the hassle of creating automated scripts because you’ll miss out on the time and cost-saving benefits. Context: Automation is simply inappropriate for many QA tests. For example, automated programs like Selenium can’t recognise video controls, so they’re not useful for testing video streaming. In addition, more subjective tasks like UX testing require a creative human input that can’t be automated. How do you find the right balance?  Considering how the pros and cons of automation apply to your application will help establish the right balance of automation in your testing process. For example, if your product is relatively stable then the automated test script is likely to have a long shelf life, meaning that’s its going to be worth the initial investment. Experienced software tester, Shilpa Chatterjee Roy, suggests that selecting appropriate test cases and calculating return on investment (ROI) are key to adopting automation in your testing process. How you do this will depend on your application, but she suggests considering attributes such as the purchasing and licensing cost of the tool, time to develop the scripts, time to maintain the scripts, time to analyse the results manually and automatically, time and cost to train the resources, and management overheads. To summarise, automation provides a host of time and cost-saving benefits, but it’s not a sure fire way to improve software. Automation is not an all or nothing proposition, but a tool to maximise efficiency and allow QA testers to focus on the more complex tasks. Ultimately, you should evaluate your tests on a case by case basis and make the decision to automate on a micro level.  

Several major employers, such as Twitter and Facebook, have announced that remote work will continue indefinitely. For those who enjoy the flexibility and lack of commute that working from home offers, this will be welcome news. For others who thrive in an office environment or who lack a suitable home-working space, a remote future could be a nightmare. There are also growing concerns about what remote work will mean for training, teamwork and sustaining company culture. The hybrid office is being touted as a solution, where employees split their week between their home and the physical office space. However, this comes with its own set of problems. For example, there is concern over a two-tier system arising between office and home workers, and a possible breakdown in communication as a result. Luckily, there are a number of innovative new technologies being designed — could they help build a hybrid office that people want to be part of? Creating togetherness One of these new technologies is Yonderdesk, a custom digital workspace. One of the main issues with a hybrid office is that it lacks the ‘sense of togetherness’ created by physically being in the same space. This means employees miss out on socialising and are less likely to ask their colleagues quick queries. Yonderdesk is a digital floor plan that can mimic the organisation’s actual office space. Employees are given an avatar and a desk, so that it’s easy to see where your colleagues are at (e.g., in meetings, available or working on a task). Digital floor plans have been a key element of online games, such as Habbo, for years because they are fun, engaging and make people feel like they are having a shared experience, so it will be fascinating to see whether ideas like Yonderdesk prove popular. Virtual co-workers On a more tech-heavy futuristic note, there is plenty of development in virtual and augmented reality technology. Digital start-up, Spatial, are working on augmented reality filters that create the illusion that your co-worker is right in front of you (similar to Pokemon Go). The avatar has facial expressions and can even sit down on a chair. It also works on existing virtual reality headsets, but Spatial are particularly excited by the idea of lightweight glasses, which are likely to be far more practical for everyday use. In addition, Spatial allows your avatar to interact with virtual tools. In their words, ‘Your room is your monitor, your hands are the mouse.’ There are plenty of other virtual reality meeting applications, such as the ones on this list, but Spatial is one of the most immersive. Productivity software A more controversial development is the increase of monitoring software, sometimes known as ‘Tattleware’. Some of these products can be used without employee knowledge to spy on emails, software use and more, which can have serious data privacy implications and undermine trust. Given that, on average, people have been working longer hours during the pandemic, it seems unwise to use monitoring software in this way. However, when used ethically and transparently, such tools can provide a rich understanding of employee behaviour that can improve productivity, engagement and prevent fatigue and/or burnout. For example, software like Time Doctor has time-tracking features that can help employees and managers gain a better understanding of how long tasks actually take, which can be fed into future estimates and used to reshuffle schedules. Collaboration tools Last but not least, collaboration tools. If you haven’t done this already, finding and implementing effective collaboration tools is vital to successful remote and hybrid working. You are probably most familiar with services like Slack — instant messaging chat rooms are a great way for employees to show their availability and engage in more casual conversations. Take this further with tools like Donut, a slack channel that makes introductions with a random employee every couple of weeks and encourages virtual or in-person meet-ups. This helps build a cohesive company culture by structuring those random encounters from the pre-pandemic days. Clearly, it will take time to build a hybrid office that suits your organisation. Exploring new tools is a great way to avoid complacency and ensure the hybrid office experience is something your employees want to be part of.  

Data Science has become an umbrella category for a number of roles. It is being increasingly recognised that the ‘unicorn’ data scientist who is a master of all the in-demand skills is largely a myth, and that data science roles need to be further specified to serve business needs. Unfortunately, job titles are still sometimes used inconsistently, which can be confusing if you are looking to pursue a new direction or find a new job. Here is a quick and handy guide to the top job roles, and a breakdown of what each one involves. Data Scientist This is probably the most common overarching job title. A data scientist’s core responsibility will be to provide actionable business insight from a dataset. Stitch Fix’s Director of data science, Michael Hochster, suggests that the roles tend to fall into two camps: analytical or building. The first focuses on the statistical interpretation of data, whereas the latter builds models based on data. The extent you’ll be expected to diversify your talents will depend on the structure and size of the data science team (i.e. your role is likely to become more focused on true data science in a larger team). Regardless, a data scientist will need to be comfortable with a range of machine learning and data mining techniques. Key skills include expertise in programming languages such as R, MatLab, SQL and Python and a strong background in computer science or related field. Business analyst / Data Journalist / Data Translator These titles refer to a number of related roles which all focus on communicating data insights and putting them in context of the wider business goals. People in these roles will need a talent for creating a data story and presenting it to people without data expertise or even without much IT knowledge. The focus will be on understanding how data trends can be leveraged to drive the business forward, whereas a data scientist may want to know the root cause of such trends. Though a statistical and coding knowledge are important, it is communication, a business background and an in-depth industry knowledge that are the key skills to these roles. Data Analyst / Statistician  Now, an analyst and a statistician may rely on similar methods to analyse datasets, but they are very different roles. Harvard Business Review describe the difference as a narrow and deep approach (statisticians) vs wide and shallow (analysts). Statisticians can estimate how data insights might hold up in a variety of circumstances by incorporating error into a model — so they are useful for in-depth insights and minimising the risk of reaching an incorrect conclusion. On the other hand, a data analyst can code at lightning speed and discover potential insights extremely quickly — they can then point statisticians in the right direction. Utilising both a statistician and an analyst can make for a highly efficient system. Data Engineer / machine learning engineer Engineering roles often fall under data science (and these roles can often be combined), but when there is a specific engineering role, these people are likely to work with data at an early stage. As a data engineer, you will build and optimise data and data pipeline architecture. Think of engineering as creating the infrastructure necessary for further analysis. As a result, a technical computer science background and exemplary coding skills are essential to this role. It will also be an advantage to gain experience with big data tools, SQL and NoSQL databases, data pipeline tools and cloud services. Machine learning engineering is an additional specialism, which focuses on identifying and applying appropriate models to big datasets. Data Architect / Data Administrator / Data Manager Big data just keeps growing, and in response many companies are hiring specialists to manage these large datasets. If you are working in one of these roles, you will be expected to create systems that enable integration, centralisation and protection of datasets. You ensure the data engineers and data scientists have an efficient dataset to work with, that it is safely backed up and can be easily recovered. Therefore, you will need to be comfortable in data modelling techniques, data warehousing and security procedures.