02
The HR function in any organization is particularly rich in data. Personal employee data, recruitment data and key performance indicators (KPIs) are just a few examples of the kinds of data a typical HR team collects in the normal course of their work. And this has been true for many years, long before the phrase ‘big data’ came about. Yet, a lot of the time, having all these data did not lead to a significant increase in the number of performance-related insights being gleaned from them. Hence, a lot of HR teams could be described as ‘data rich’ but ‘insight poor’. In this chapter, I explore how the rise of big data and analytics techniques has helped to change this situation, enabling HR teams to become as rich in insights as they are in data, and giving rise to the term ‘intelligent HR’. But to fully understand these developments, I will first look at the explosion of data in today’s world, introduce the Internet of Things (IoT) and delve into advanced analytics capabilities like artificial intelligence (AI) and machine learning. Later in the chapter, we will take a look at the emergence of what I call ‘super-intelligent HR’, which refers to the dramatic increase in our ability to automate many of the HR team’s day-to-day activities. Finally, I will address whether HR teams are still needed in this big data age.
The explosion of data
Eric Schmidt, executive chairman of Google’s parent company Alphabet, famously claimed that every two days we create the same amount of data as we did from the beginning of time until 2003.1 Think about that for a second: the amount of data that humans managed to generate from the dawn of civilization to 2003 is now being created in just two days. While some people have taken issue with Schmidt’s claim, everyone agrees that the amount of data we are creating is increasing rapidly, and will continue to do so. By 2020, the amount of digital information created worldwide is estimated to hit 44 zettabytes,2 and I have seen some estimates that say we will be creating 180 zettabytes by 2025.3 To put that in context, in 2013, we created just over 4 zettabytes.
Nowadays, everything we do creates data
Let us think about what this means in everyday life. Almost everything we do these days creates a digital trail. Whether you are browsing online (even if you do not buy anything), searching for a local business on your phone, paying for a coffee with your credit card, touching in with your travelcard as you board a bus, taking a photograph, reading an online article or playing a video game, all this (and much more) generates data. When people talk about ‘big data’, they are referring to the collection of all these data, as well as our increasing ability to gather insights from these data and use those insights to our advantage, particularly in a business context.
Of course, the ability to gather data is not new in itself. Going back even before computers, we still used data to track actions and simplify processes – think of paper transaction records or personnel files. What has changed is our ability to work with these data. Computers – particularly the early spreadsheets and databases – finally gave us a way to easily store and organize data on a large scale, and make it easy to access those data, rather than poring through a load of archive files.
Working with new types of data
I mention spreadsheets and databases because, for a long time, that was the limit of what we could do with data. For data to be easy to store, access and interrogate, they had to be neat and orderly. They really had to fit into columns and rows, and this comprises what are known as ‘structured data’. Anything that could not be organized into columns and rows (‘unstructured data’) was too difficult, or just too expensive, to work with. That all changed with the massive advances in computing power and data storage that we have seen in recent years. Today we can capture, store and interrogate very many different types of data, including all kinds of unstructured data (there is more on the different data types in Chapter 4). As a result, ‘data’ now can mean anything from a database or spreadsheet, to sensor data, written text (such as social-media posts), photographs, videos and sound recordings. It is these advances in computing power and analytics that allow Amazon to track how you move around its online store, what you look at and what you eventually choose to buy, and then promote other products that it thinks you will like.
Much is made of the sheer volume of data we are generating each day, week, month and year, and it certainly does make for some impressive statistics. But I prefer to focus on the value of data rather than volume. Data bring incredible opportunities to better understand our world and how we live in it, which is why, when I work with businesses, I always advise them to focus on the value they can get from data. When it comes down to it, it does not matter what volume of data you have, or what type of data they are, all that matters is whether you are using those data successfully to drive performance.
Introducing the IoT
Why has there been such an explosion in data? A big part of the reason for this is the IoT. The IoT refers to everyday objects – such as smartphones, smart TVs, Fitbit bands etc – that can be connected to the Internet, collect and transmit data, and be recognized by other devices. In the IoT, data are created by things, not humans. Today, there are about 13 billion devices that connect to the Internet, but, by 2020, that number is predicted to rise as high as 50 billion.4 Whether we actually hit that huge 50 billion number remains to be seen, but no one can dispute the fact that the IoT has seen enormous growth in recent years, and that growth is very likely to continue.
Almost everything can be made ‘smart’ these days. Our cars are offering ever-increasing levels of connectivity and, by 2020, it is estimated that a quarter of a billion cars will be connected to the Internet. Everyday devices in the home are now routinely hooked up to the Internet, including TVs, fridges and thermostats. There are even smart versions of products you really would not expect, like yoga mats and frying pans. As for smartphones, it is projected there will be 6 billion smartphone users in the world by 2020:5 that is 6 billion smartphones generating data every day! All these smart devices, from your phone to your TV to your Fitbit, can connect to and share information with each other. This is a crucial part of the IoT: machine-to-machine connections mean that devices can talk to each other and decide on a course of action without any human intervention. For example, in the near future, it is not unreasonable to imagine your refrigerator knowing when your milk is out of date and automatically telling your smartphone to order more in the next online shop.
Getting into machine learning, deep learning and AI
The idea of AI has been around for a long time – the Greek myths contain stories of mechanical men designed to mimic our own behaviour. Very early European computers were conceived as ‘logical machines’ and, by reproducing capabilities such as basic arithmetic and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains. As technology and, importantly, our understanding of how our minds work have progressed, our concept of what constitutes AI has changed. Rather than performing increasingly complex calculations, work in the field of AI has concentrated on mimicking human decision-making processes and carrying out tasks in ever-more human ways. Many of the developments in AI in recent years have come about thanks to machine learning and deep learning.
Understanding the terminology
What is the difference between AI, machine learning and deep learning? Well, AI refers to the broader concept of machines being able to carry out tasks in a way that we would consider ‘smart’, while machine learning is one of the major applications of that concept. Machine learning is based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
While machine learning is often described as a sub-discipline of AI, it is better to think of it as the current state of the art of AI work. It is the field of AI which, today, is showing the most promise. In turn, deep learning is the cutting edge of the cutting edge! Deep learning takes some of the core ideas of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision making. Deep learning focuses even more narrowly on a subset of machine learning tools and techniques, and applies them to solving just about any problem which requires ‘thought’ – human or artificial. IBM’s Watson system is a prime example of this in action. The system ‘learns’ as it processes information, so the more data the system is given, the more it learns, and the more accurate it becomes.6
How does it work?
Essentially, machine and deep learning involve feeding a computer system a lot of data, which it can use to make decisions about other data. So, if we give the computer a picture of a dog and a picture of a stick, and show it which one is the dog, we can then ask it to decide if subsequent pictures contain dogs. The computer compares other images to its training data set (ie the original image) and comes up with an answer. Today’s machine learning algorithms can do this unsupervised, meaning they do not need their decisions to be pre-programmed. The same principle applies to even more complex tasks, albeit with a much larger training set, such as Google’s voice recognition algorithms. The same techniques are used by Netflix and Amazon to decide what you might want to watch or buy next.
Machine and deep learning are responsible for advances in computer vision, audio and speech recognition, and natural language processing. They are what allow computers to communicate with humans (not always 100 per cent successfully, as Microsoft’s widely publicised Twitter bot proved, with its crazy and often racist tweets7) and make Google’s self-driving cars possible. They are also the reason Facebook is able to recognize individuals in photographs to the same level as humans can, automatically suggesting tags for individuals. This technology is already being used in fields as diverse as healthcare, finance and education, as well as, of course, almost all areas of business. Whenever there is a job that requires a large amount of complex data to be processed and analysed in order to solve problems, AI, and specifically machine and deep learning, can help. As computers are more able to think like humans, we are moving into an age where computers can enhance human knowledge in entirely new ways. And, of course, because machine and deep learning technology means that computers learn from the data they have access to, this means computers’ ability to learn, understand and react will increase dramatically in line with the amount of data we are generating every year.
Computers can even understand our emotions
The technology has advanced to such an extent that it is now possible for computers to recognize and respond to human emotions. Known as ‘affective computing’, this technology analyses facial expressions, posture, gestures, tone of voice, speech and even the rhythm and force of keystrokes to register changes in a user’s emotional state. Leading organizations like Disney and Coca-Cola are already using this technology to test the effectiveness of advertisements and assess how viewers react to content.8 The BBC uses it to measure viewers’ responses to TV programmes during trials.8 In one such experiment, a number of viewers in Australia were monitored as they watched a trailer for a season premiere of Sherlock. The trial showed researchers that viewers who went on to rate the show favourably showed a greater reaction to on-screen events that were tagged as ‘surprising’ or ‘sad’, as opposed to ‘funny’. This led Sherlock’s producers to include more dark, thriller-type elements in the show, in favour of less comedy.9
What this means for HR
The explosion in data applies to the world of work just as much as it does to every other area of our lives. Almost everything in a business context generates data, from an employee sending an e-mail to the sensors on production-line machinery. This means businesses have more data to mine for insights than ever before, and the HR function is at the very heart of this.
More data than ever before
I said at the start of this chapter that HR is especially rich in data, and that is correct. HR teams have recruitment data, career progression data, training data, absenteeism figures, productivity data, personal development reviews, competency profiles and staff satisfaction data. In the past, these data mostly went unused or, if they were used, they were put into charts and tables for something like a corporate performance pack. Now, in the era of big data and analytics, companies are turning their data into insights, such as predicting when employees will leave, where to recruit the most suitable candidates from, how to identify those people and how to keep them happy. In addition to traditional HR data sets, companies now can collect so many more data – data that were not available before – including things like capturing employees on CCTV, taking screenshots when staff are using company computers, scanning social-media data, analysing the content of e-mails and even monitoring where they are using the data from geo-positioning sensors in corporate smartphones. We have seen mind-boggling improvements in our ability to store and analyse all sorts of data. What is more, we now have big data analytics tools that allow us to compute huge volumes of data, which enable us to combine the analysis of traditional structured data with the analysis of unstructured data, such as written text and images.
All this means that the data and analytics revolution has some significant implications for HR teams. In the last couple of years, according to a 2015 report from the Economist Intelligence Unit, 82 per cent of organizations planned to either begin or increase their use of big data in HR between 2015 and 2018.10 This has given rise to ‘intelligent HR’ as a bit of a buzz phrase. In theory, intelligent HR boils down to this: the more information we have, the better decisions we can make. Despite this, HR data are not always used in the most intelligent way. Research has shown that only 23 per cent of companies have HR systems that can always provide sufficient data to measure the execution of their business strategy.11 The application of HR analytics is still too often ad hoc, and not always used with driving performance in mind. Truly intelligent HR focuses HR data and analytics on the goal of adding value and driving performance across the organization all the time, not just every now and then or on specific projects.
Crunching the wrong HR data
With all these data, the challenge is to establish which HR data are really going to make an impact on the company’s performance. One big problem I have seen is that most HR departments simply start crunching the masses of quantitative data they have in the hopes of finding something. This is like looking for the proverbial needle in a haystack. As with any big data or analytics project, the key is not in the quantity of data points, but in the quality of the questions being asked about the data. What HR departments should be doing much more is embracing qualitative data and analysis. Instead of analysing the masses of structured numeric data, like accounting and finance departments might do, HR departments need to build on their strengths and capture more qualitative data. The less quantitative and more qualitative side is what HR departments have traditionally been good at and it is where big data offer huge opportunities. For example, instead of counting training hours per employee and crunching the data to death, one of my clients is now using ethnography-type data collection to gain powerful insights about training effectiveness using qualitative methods. The company is now collecting and analysing data using written training journal logs, video analytics and performance impact assessments. The company realized it needed to look beyond the simple existing metrics to answer the real business questions.
Learning from Google
Unlike most HR departments, Google’s HR team states the objective: ‘All people decisions at Google are based on data and analytics’.12 Initially, Google’s founders believed that middle management roles were not important, so they did away with these positions.13 When it became obvious that this belief was false, they brought the managers back, but the perception that the managers were not valuable persisted. So, Google turned to data to quantify the value of managers. Through data, the company was able to go from the opinion ‘Managers don’t impact performance’ to proving that great managers had a statistically significant impact on team performance, employee engagement, employee churn and productivity. For this analysis, Google looked at data from performance reviews as well as qualitative interviews and the submissions for a ‘best manager’ award programme. By extracting the insights from that analysis using qualitative methods such as text analytics, Google was then able to identify and articulate what made a great Google manager and what caused less-proficient managers to struggle. These insights were then embedded into Google’s culture through twice-yearly performance evaluations of these factors, which act as an early warning system to detect both great and struggling managers. For those that are struggling, there is access to improved training and support as well as plenty of role models to learn from. And for those who are doing well, there is the Great Manager Award. All this was possible because the company started with the right question, refining that question until it got to practical, verifiable analytics. In order to ask the right questions, you need to be clear about what it is you want to achieve, both in terms of HR objectives and in supporting the business’s wider goals. This is where having a clear data strategy for the HR team comes in, and there is more on this in Chapter 3.
Super-intelligent HR is already here
Chances are you have already heard of the ‘intelligent HR’ buzz phrase. But, just as HR teams are getting to grips with what this means for them, I would argue that we are already moving in to the age of ‘super-intelligent HR’, brought about by the dramatic rise in automation. If you think automation does not apply to HR, think again. In fact, as we will see, it is increasingly possible to automate many HR tasks. Therefore, super-intelligent HR means making use of AI techniques like machine learning and deep learning not only to automate various HR activities, but also to carry them out better, faster and more accurately than a human. It is about HR teams working alongside intelligent machines and systems to make better decisions and make operations more efficient.
Pretty much all administrative tasks potentially can be automated, but super-intelligent HR goes way beyond this into critical functions like recruitment or employee engagement. Machine learning in particular is having a major impact on HR. For example, in application tracking and assessment, machine learning tools help HR teams to track applicants’ journeys and speed up the process of giving feedback to candidates. The Peoplise digital recruitment platform, for instance, calculates a fit score for candidates based on digital screening and online interview results, helping HR professionals and hiring managers to decide on who is the best candidate for them.14 Machine learning also can help to identify the risk of someone leaving the company, for example, by identifying specific risk factors based on responses to an employee survey or analysis of e-mails or social-media posts.
Chatbots facilitate greater automation
A recent survey by the IBM Institute for Business Value found that half of chief HR officers surveyed recognized the power of AI technologies to transform key dimensions of HR.15 One often-cited example is chatbots, which are computer algorithms that mimic human conversation. Chatbots can be used across a number of HR activities, including providing real-time answers to HR questions and personalizing learning experiences; companies like IBM are already targeting so-called ‘intelligent assistants’ (or chatbots) that do exactly this. Chatbots are becoming increasingly common in our everyday lives, and their popularity was cemented when, in 2016, Facebook announced it was incorporating chatbot capabilities into its popular Messenger app.16,17 Many large brands are already using chatbots to interact with customers, mainly by answering questions and giving advice. eBay’s ShopBot, for example, helps shoppers to find and buy eBay items from within the Messenger app. You can even hail an Uber or order a pizza through Messenger using chatbots. So, as we become increasingly more used to interacting with chatbots in everyday life, we can expect to interact with them more in the workplace too. Plus, as our workplaces become more geographically dispersed, and the number of remote workers continues to rise, chatbots can fulfil a vital need for employees who do not have easy access to HR colleagues.
Intelligent assistants also can play a role in talent acquisition, from scheduling interviews to supporting (or even making) decisions about applicants. Talla is one example of a chatbot that is designed to serve as a real-time advisor to HR professionals as they source new hires.18 Talla can provide a set of interview questions based upon the role being recruited for and even conduct a Net Promoter Score survey following the recruitment process.
In learning and development, chatbots are already being piloted by those who teach massive open online courses (MOOCs) as a way to support students while they learn by answering questions, reminding them of deadlines and providing feedback on work. As the popularity of online learning grows, intelligent assistants increasingly will be used to enhance the role of human teaching assistants. Increasing automation like this makes it easy to provide adaptive, personalized learning that is tailored to each individual learner’s needs, which, in a business setting, can dramatically help to improve the employee experience.
AI and super-intelligent HR
Looking beyond chatbots, AI techniques like machine learning are beginning to make an impact on HR functions in many other ways. Machines being able to ‘think’ and make decisions like humans can benefit every aspect of HR, including recruitment. Software already exists to help identify potential candidates for jobs, or narrow hundreds of candidates down to a select few, but AI means this process can be done a lot more accurately. Some people have concerns that this strips away a lot of the ‘human’ side of HR – after all, HR is all about people. But there are many positives that come from incorporating AI into HR processes. For example, with a faster, more streamlined feedback system in place, handled by AI technology, potential candidates are likely to feel greater engagement with the company, not less. Even if they are unsuccessful in their application, this positive experience is likely to encourage them to try again for future positions. And, when it comes to sifting through applicants, machines can make decisions without any of the potential biases that humans bring to the table. Clearly, much of this recruitment work would be done in conjunction with human HR professionals, rather than just handing the entire process over to a machine, but there is no denying that the technology exists to automate and augment a lot of recruitment processes.
Another example comes from employee retention, where AI techniques can give unprecedented insights into a company’s talent, specifically, how they are really feeling and performing. For example, sentiment analysis of e-mails or social-media posts, which involves mining text for insights into the sentiment behind the words being written, can be used in place of staff satisfaction surveys. Machine learning algorithms make it possible to analyse an employee’s e-mails and social-media posts to assess their level of engagement with the company, how they react in certain situations and how well they fit with the company culture (whether this is ethical or even legal is another matter – turn to Chapter 6 for more on this). This process can help to predict, with far more accuracy than humans alone, whether someone is fed up and about to leave the company, or whether someone is ripe for promotion, for instance.
So, will we still need HR teams?
With all this automation, I am seeing increasingly more articles theorizing that soon we will no longer need HR teams at all. I have even written on the subject myself.19 So, do I think machines will replace HR professionals, managers and directors? No, I do not. I believe it is inevitable that machines and algorithms will take certain tasks away from HR teams, just as they will across all areas of business over the coming years, but I do not believe HR teams will cease to exist altogether. They will simply adapt and refocus.
Embracing new technologies and greater automation
Personally, I strongly believe the increase in automation and advances like chatbots/intelligent assistants are great things for the HR profession as a whole. We all understand that a lot of HR time and resources, at present, are swallowed up by day-to-day administrative tasks. When such everyday, mundane or administrative tasks can be automated, it frees up HR to focus on more strategic things that are critical to the business’s success. HR can then shift its focus to adding greater value to the organization. To me, that is a very positive transition, and it is the idea at the very heart of intelligent HR. Therefore, HR teams need not only to be aware of and prepared for the rise in automation, I would argue, they should even embrace it. The HR profession as a whole needs to get on board with AI, rather than fearing the impact it will have on jobs. HR leaders and professionals should feel encouraged to learn as much about these new technologies as they can. Business is changing dramatically, and many are calling this the fourth industrial revolution.
The fourth industrial revolution, or Industry 4.0
First, steam and the early machines mechanized industry. Then the second industrial revolution came with the invention of electricity. Computers and early automation brought us the third industrial revolution. And now we are entering the fourth industrial revolution, Industry 4.0, in which computers and automation come together in an entirely new way, with robotics and AI systems that can learn, control functions and make decisions with very little input from human operators. If we look beyond the HR team for a moment, as a result of this fourth industrial revolution, automation is already having or will soon have an impact on many people’s jobs, across many different industries. As automation increases, computers and machines will replace workers across a vast spectrum of industries, from drivers and accountants to estate and insurance agents. One estimate based on a study by Oxford University claims that as many as 47 per cent of US jobs are at risk from automation.20
HR professionals therefore need to have the knowledge and resources to deal with the impact of this revolution on their industry and their organization, and the people who work for that organization. HR needs to be very much involved in the company’s discussions concerning preparing for the increase in automation. HR professionals need to become technologically fluent, in order to have these conversations with their organization’s leadership team. Automation is going to impact on many different areas of how we work in the future. And some of these changes will be huge, especially in industries like manufacturing. HR professionals therefore need to keep up to date with the rise of automation and develop an understanding of what it may mean for their business, as well as the HR team itself; because, as the nature of business continues to change, HR teams will be central to answering business-critical questions that the leadership team will have, such as: ‘What types of skills and capabilities do we need to attract in order to work with these automated systems?’ The HR team’s expertise, and the wealth of data HR teams have, can help to answer such questions and prepare the organization for changes that may come about. For me, this is a critical part of intelligent and super-intelligent HR: supporting the business as its needs change and evolve.
Challenges and opportunities
It is clear that data, analytics and even automation bring both major changes and major opportunities for HR teams. The way in which HR teams deliver their service will certainly change over the next few years, as the more repetitive tasks can be fulfilled by computers. From sourcing and hiring talent to supporting employees as they learn and develop, automation will help to save time, increase efficiency and improve the decision-making process. As HR moves away from the more mundane and time-consuming tasks associated with day-to-day people management to focus on wider strategic issues, the HR team itself becomes arguably more valuable to the organization, and more critical to its success. So, while I understand that discussions regarding automation do make HR professionals (and employees and often leadership etc) nervous, I think these developments should be viewed within the wider scope of HR becoming increasingly more intelligent and providing greater value to the organization. Just as almost every other area of life is becoming smarter, from our phones and TVs to the way we shop, so too is the way we work. No one can predict with absolute certainty how the technology will evolve, on what scale and on what timelines, but it is absolutely clear that all this technology is only going to go in one direction: forwards. It is not going to go backwards or become less popular. We are only going to have more data, more intelligent algorithms, better machine learning programs, more sensors, greater automation etc; not less, more. HR teams need to be ready for this transformation. Despite the challenges involved, data, analytics and automation present massive opportunities to improve the way we do business, making employees’ working lives better and increasing HR’s contribution to the organization. For me, that is what is so exciting about intelligent (and super-intelligent) HR.
Key takeaways
It is clear that data and analytics have come a long way since the early computers and humble databases, and the explosion in data is going to dramatically change how HR operates – just like every other area of business. Below is a rundown of what has been covered in this chapter:
· Almost everything we do these days creates a digital trail and we can now capture, store and interrogate many different types of data. The IoT, with its smart, connected devices, has played a key role in this data explosion.
· With all these data, the challenge is to establish which HR data are really going to make an impact on the company’s performance. The key is not in the quantity of data points, but in the quality of the questions being asked about those data.
· Companies are now turning their HR data into value-adding insights, such as predicting when employees will leave or where to recruit the most suitable candidates from.
· Whenever there is a job that requires a large amount of complex data to be processed and analysed in order to solve problems, AI (specifically machine and deep learning) can help.
· We are already moving in the age of ‘super-intelligent HR’, brought about by the dramatic rise in automation taking place across all industries.
· HR needs to embrace new technologies and increasing automation. When everyday, mundane or administrative tasks can be automated, it frees up HR to focus on more strategic things that add greater value to the organization.
In the next chapter we will look at how HR departments should be preparing for this transition by laying the foundations for data-driven HR, starting with creating a robust and smart data strategy that links to wider organizational objectives and creates a clear business case for data.
Endnotes
1 Siegler, M G (2010) [accessed 23 October 2017] Eric Schmidt: Every 2 Days We Create as Much Information as We Did up to 2003 [Online] https://techcrunch.com/2010/08/04/schmidt-data
2 Turner, V (2014) [accessed 23 October 2017] The Digital Universe of Opportunities [Online] https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm
3 Kanellos, M (2016) [accessed 23 October 2017] 152,000 Smart Devices Every Minute in 2025: IDC Outlines the Future of Smart Things [Online] https://www.forbes.com/sites/michaelkanellos/2016/03/03/152000-smart-devices-every-minute-in-2025-idc-outlines-the-future-of-smart-things/#77d1b5ed4b63
4 Ericsson (2010) [accessed 23 October 2017] CEO to Shareholders: 50 Billion Connections 2020, press release [Online] https://www.ericsson.com/en/press-releases/2010/4/ceo-to-shareholders-50-billion-connections-2020
5 Lunden, I (2015) [accessed 23 October 2017] 6.1b Smartphone Users Globally by 2020, Overtaking Basic Fixed Phone Subscriptions [Online] https://techcrunch.com/2015/06/02/6-1b-smartphone-users-globally-by-2020-overtaking-basic-fixed-phone-subscriptions
6 IBM [accessed 23 October 2017] Watson [Online] https://www.ibm.com/watson
7 Price, R (2016) [accessed 23 October 2017] Microsoft Is Deleting Its AI Chatbot’s Incredibly Racist Tweets [Online] http://uk.businessinsider.com/microsoft-deletes-racist-genocidal-tweets-from-ai-chatbot-tay-2016-3
8 Murgia, M (2016) [accessed 23 October 2017] Affective Computing: How ‘Emotional Machines’ Are About to Take Over Our Lives, Telegraph [Online] http://www.telegraph.co.uk/technology/news/12100629/Affective-computing-how-emotional-machines-are-about-to-take-over-our-lives.html
9 Marr, B [accessed 23 October 2017] How the BBC Uses Big Data in Practice [Online] https://www.bernardmarr.com/default.asp?contentID=710
10 The Economist Intelligence Unit (2015) [accessed 23 October 2017] What’s Next: Future Global Trends Affecting Your Organization [Online] http://futurehrtrends.eiu.com
11 SAP SuccessFactors [accessed 23 October 2017] Global HR Survey Shows Why Employees Want a Single Source of Analytics [Online] https://www.successfactors.com/en_us/lp/global-hr-survey.html?Campaign_ID=21487&TAG=Q413_Global_HR_Survey_EC_LinkedIn&CmpLeadSource=Public%20Relations
12 Sullivan, J (2013) [accessed 23 October 2017] How Google Is Using People Analytics to Completely Reinvent HR [Online] https://www.eremedia.com/tlnt/how-google-is-using-people-analytics-to-completely-reinvent-hr
13 Garvin, D A (2013) [accessed 23 October 2017] How Google Sold Its Engineers on Management [Online] https://hbr.org/2013/12/how-google-sold-its-engineers-on-management
14 Peoplise [accessed 23 October 2017] Superior Talent Experience. Mobile. Faster [Online] http://www.peoplise.com
15 IBM [accessed 23 October 2017] Extending Expertise: How Cognitive Computing Will Transform HR and the Employee Experience [Online] http://www-935.ibm.com/services/us/gbs/thoughtleadership/cognitivehrstudy
16 Constine, J (2016) [accessed 23 October 2017] Facebook Launches Messenger Platform with Chatbots [Online] https://techcrunch.com/2016/04/12/agents-on-messenger
17 Messenger [accessed 23 October 2017] Messenger Bots for Business and Developers [Online] https://messenger.fb.com
18 Talla [accessed 23 October 2017] Never Answer the Same Question Twice [Online] https://talla.com
19 Marr, B (2013) [accessed 31 January 2018] Why We No Longer Need HR Departments [Online] https://www.linkedin.com/pulse/20131118060732-64875646-why-we-no-longer-need-hr-departments/
20 Frey, C B and Osborne, M A (2013) [accessed 23 October 2017] The Future of Employment: How Susceptible Are Jobs to Computerisation? [Online] http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf