12
I hope this book has given you a good idea of the current and emerging data and analytics possibilities and how they are beginning to transform people management. I also hope I have sparked your interest and excitement. I certainly believe this is an exciting time for businesses and the people who work in them; however, as you have probably guessed, the technology is changing fast – faster than even I would have anticipated 10 or even 5 years ago – so it is likely that how HR teams function in just a few years’ time will be quite different again. Anyone who says they know with absolute certainty where data-driven HR is going is, quite frankly, lying. There are technological advancements coming that we cannot even imagine right now, but it is possible to make some predictions about what might be coming our way in the future based on some emerging trends. Therefore, in this chapter, I set out the potential data-driven HR landscape of the future, including the challenge facing tomorrow’s HR teams, how the digital transformation will affect all our workplaces, and the key data and technology trends every HR professional should be aware of. I finish by returning to the importance of a robust data strategy and how this should be your first step on the journey ahead.
The challenge facing the HR teams of the future
The ‘datafication’ of our world and the proliferation of Internet of Things (IoT)-enabled devices is only going to continue, and this will continue to impact on the way in which HR works, just like every other area of the business.
Finding HR’s unique role
The challenge for HR teams is to find a balance between technology (specifically, the increasing automation of work) and the human role in the organizations of the future. I believe the biggest challenge facing HR teams going forward is not keeping up with technology and learning new skills like data analysis; it is finding the uniquely human place in the organization and within the HR team itself. Yes, naturally there will be greater automation in the future, both in HR tasks and across the business, but if absolutely everything is automated and performed by robots, what need is there for HR? Therefore, the HR teams of today need to be thinking about what HR will look like in the future, and this includes what exactly can be automated and what cannot. They need to figure out HR’s contribution to the workplace of the future. Personally, I do not believe we will ever reach a point where the HR function is entirely redundant. But the role of HR will undoubtedly shift away from the more administrative tasks concerning people management (which can be easily automated in the future) to activities that help the organization to meet its goals. It is vital that HR delivers real value and unique benefits that cannot be delivered by any other function in the organization.
Should HR be restructured?
This is why, in Chapter 1, I argued for a two-team approach to HR: one team to focus on people analytics and one on people support. The people analytics team looks at people more scientifically and supports the company with insights and analytics, answering questions such as: ‘What are our talent gaps?’ ‘What makes a good employee in our company?’ and ‘Which employees have got the most potential?’ (as we will see later in this chapter, this does not mean HR people need to become data scientists). The people support team then focuses on supporting everyone in the company, from the frontline to the senior leadership team. This includes helping employees with their development, ensuring staff engagement, identifying issues with morale and generally looking after the wellbeing of the people in the business. Anything outside of these two functions, like administrative, bureaucratic tasks, should be either outsourced or, more probably, automated.
How the digital transformation will change all of our workplaces
Across every industry and almost every job role, the nature of work is changing. Politicians like President Trump may vow to bring jobs back to communities, but have they really considered how automation could throw a spanner in the works? Aside from reading the odd attention-grabbing news story about how robots will take everyone’s jobs, most people give little thought as to how their workplaces will change in the future. But now, more than ever, it is vital that those charged with ensuring their organization is appropriately skilled to navigate the choppy waters of digital transformation understand how data, artificial intelligence (AI) and automation are going to impact on the workplace.
Essential skills for surviving the fourth industrial revolution
The fourth industrial revolution is here (see Chapter 2), and it is completely transforming the way in which we live and work. Our world is now fuelled by data and Internet-connected devices that are capable of collecting and processing ever-growing amounts of information. It is important for everyone, in every job and every industry, to consider the implications of this new transformation and how it will change their job and employment prospects over the coming years. This does not just apply to HR teams, but to most people in any typical organization. For HR teams, however, it is particularly pertinent because not only are HR professionals finding their own way in this new world, but also they have to equip the people in their organization with the essential skills for helping the business succeed in the future. The following are my top three tips for HR professionals wondering how to navigate these changes on a personal level and boost their own development.
Tip 1: consider the future of your job
Think hard about how much of what you do every day is repetitive and potentially could be done by intelligent robots or computers. Remember, we already have self-driving cars and trucks, and computers that can recognize faces as well as any human. In an HR sense, as we have seen throughout this book, it is possible to completely or partly automate tasks like assessing job candidates, corresponding with candidates, answering simple employee queries like ‘When is the office closed over Christmas?’ and even measuring employee satisfaction. But the areas that computers still struggle with include creativity, problem solving and connecting with people on a human level – all of which I would say are, or should be, vital skills in HR. These are the areas where HR can add real value to the organization, so it makes sense to try to develop your skills in those areas and reshape your job to do more of those things that robots cannot do.
Tip 2: become data savvy
I am not saying HR professionals have to become data scientists, far from it. But it is important to have a good understanding of the possibilities of data and how they can help you to solve problems, run a more efficient organization and make your customers (your employees) happier. Demonstrating that you are able to use data in original ways to solve key problems is a certain path to success in the information age. Being comfortable with using data in your job will only become more important.
Tip 3: make friends with your AI colleagues
AI is being adopted at an incredible rate. We can see that in our own private lives with AI assistants such as Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa becoming increasingly competent at helping us to run our lives. They can manage schedules, proactively let us know about travel delays or breaking news and inform us of forthcoming events that they think will interest us. Increasingly, they can do this without us telling them to do so, all they have to do is monitor our behaviour and they will be able to work out what information is relevant to us. This type of technology is fast becoming commonplace in our workplaces, and that understandably makes people nervous. HR teams need to be leading the way in embracing AI technology and showing how collaborating with AI systems can drive efficiencies and help the business to succeed.
How employees of all kinds will become more data savvy
Throughout this book, we have seen how data and analytics have the potential to create efficiencies, help us better understand business processes and predict problems before they occur. But imagine how much greater the potential for change could be if it could spontaneously emerge anywhere in an organization. This is part of the thinking behind the democratization of data, where everyone across the company has access to whatever data they may need to make improvements (more on this later in the chapter). The idea is that, when everyone has access to the tools, skills and knowhow to harness technology to do their job better, or drive efficiencies, then a workforce looks better equipped to deal with the challenges of the future.
Opening up data and analytics to a wider audience
This thinking is shared by Infor CEO Charles Phillips, and was the driving force behind the software company’s decision to establish its Educational Alliance Program.1 The initiative involves rolling out its training programme into colleges and leveraging its customer and partner networks to supply students with placements and, eventually, jobs. Given the move towards platform infrastructure and software-as-a-service tool sets, it is no longer necessary to have an academic background in computer science or statistics to effectively lead data-driven transformation within an organization. It is just a question of getting that message out there to students or professionals who may be wondering what their next move should be, to ensure they are viably skilled for a place in the workforce of tomorrow. Infor CEO Charles Phillips told me: ‘Our assumption is that pretty much every business is going to become a digital business to some degree. So even if you’re not a computer science or tech major, you need some exposure to learn how to use and apply technology’.1 The course gives students hands-on training with Infor’s enterprise software as it is used by more than 70,000 of its clients, delivered either online or, increasingly, thanks to a growing number of arrangements with US universities, in a formal educational setting. Pushing the course into colleges and universities has been seen as a priority. Phillips admits that initially there was some scepticism from academia. But it has so far managed to get its course into 27 institutions nationwide – 25 of which are colleges and two of which are high schools – and is working towards its target of gaining 50 more by next year.
Data-savvy employees will be more valuable
It is certainly true that there is a growing need for people capable of understanding both business and technology. Those who become best at analysing a business problem and devising how it could be solved most effectively with automation, AI and data are likely to be able to name their price in the workforce of tomorrow, regardless of what industry they are in. As Phillips puts it: ‘Automation can build a car but it can’t tell it where to go. You have to know where you want to go’.1
How other business functions are being impacted by data and analytics
Big data technology, particularly AI and machine learning, is affecting every area of the business, so I thought it might be helpful to highlight a couple of specific functions outside of HR and the kinds of changes they are facing. This not only demonstrates more of the practical changes coming our way, but also equips HR teams with the knowledge to help colleagues elsewhere in the organization. After all, as HR professionals wonder about how their roles will change in this new data-driven world, those questions are echoed right across the typical organization.
The rise of AI and machine learning in sales
It is safe to say that organizations that transform their sales functions due to machine learning and AI will find themselves rising above the competition. Now that we generate and collect vast amounts of data, we are able to give machines the amount of data that are required for them to learn (by using algorithms) to interpret the data and predict outcomes. Of course, there is a personal side to selling that machines will not (at least for a long time) be able to replace. Humans, and exemplary sales professionals in particular, are uniquely suited to listen, convince, negotiate and empathize, as well as explore and answer the very critical question of: ‘Why is this the best product or service for me?’; but the power of machine learning to contribute to successful sales initiatives cannot be emphasized enough and will only continue to grow in importance. The following are just a few of the ways in which machine learning will transform sales.
Interpreting customer data Machine learning helps us to make sense of the data we collect about our customers. Even though many organizations have systems or resources in place to gather and store customer data, machine learning will help to make more effective use of those data in ways that relying on humans alone could not.
Improving sales forecasting Sales colleagues gather all kinds of data on a prospect (company size, stakeholders, solutions they want etc) and machine learning provides the ability to compare those data to historical sales efforts. This means sales teams can connect the dots and better predict what solutions would be effective, the likelihood of the deal closing and how long it will take. This insight helps sales management to better allocate resources and predict sales projections.
Predicting customer needs Business success relies on how well we provide what our customers need. Machine learning can improve how responsive and proactive we are to anticipate the needs of our customers. The better sales teams are at addressing clients’ needs before they get escalated, and at suggesting a solution that could help to make their lives better and easier, the stronger the company’s relationship with customers will be. Machines will not forget to follow up or be too busy to proactively share solutions.
Handling transactional sales According to Harvard Business Review, by 2020, customers will manage 85 per cent of their interactions with an organization without interacting with a human.2 Having machines step in to handle certain sales efforts quickly and effectively will free up the human salesforce to focus on the relationship.
Improving sales communication There probably will be dramatic changes to sales communications as a result of machine learning. If business communication mimics the transformation of consumer communication, the business equivalent of short-form communication such as tweets and text messages will be AI responses. Machines can quickly and easily answer queries about pricing, product features or contract terms. Within the next decade, virtual reality (VR) could allow prospects to tour a factory, ‘join’ in to conferences and meetings with the entire team and see products being manufactured, all without leaving their own office.
Fine-tuning sales processes We get a glimpse into the way in which sales functions will change by looking at the promises of Einstein, the AI solution from the customer relationship management (CRM) company Salesforce. Einstein aids sales personnel by reminding them who to follow up with, suggesting what opportunity should be prioritized because of a high probability of conversion and helping to predict the best product or service for each prospect.
Taking care of the more mundane tasks Machines can take care of transactional sales to free up the human salesforce to build relationships and nurture their leads in ways only humans can. By taking care of mundane tasks for sales staff, machines clear the way for the sales process to be better and more effective. This mimics the way in which HR may change in the future, to move away from more administration-type tasks to focus more on identifying questions and insights and better supporting people throughout the organization.
The rise of AI and machine learning in accounting
Okay, robots are not going to replace your beloved accounting colleagues (not anytime soon, at least). But it is true that white-collar workers who are part of the knowledge economy are beginning to experience what manual labourers have in the past when new technology made their jobs obsolete. Given the improvements we have recently seen in computing, many accounting professionals fear for their future as machines threaten to overtake them. But, rather than fearing the changes that machine learning will make in accounting tasks, it is an opportunity for accounting professionals to be excited. The profession is going to become more interesting as repetitive tasks shift to machines. There will be changes, but those changes will not completely eliminate the need for human accountants; they will just alter their contributions. The following are just a few of the ways in which machine learning will transform accounting.
Handling repeatable accounting tasks Currently, there is no machine replacement for the emotional intelligence requirements of accounting work, but machines can learn to perform redundant, repeatable and often extremely time-consuming tasks, a few of which are included in this list.
Auditing expense submissions Machines could learn a company’s expense policy, read receipts and audit expense claims to ensure compliance and only identify and forward questionable claims to humans for approval; otherwise, machines could handle the bulk of this task.
Clearing invoice payments Today, when customers submit payments that might combine multiple invoices or that do not match any invoices in the accounting system, it is time-consuming for accounts-receivable staff to apply payment correctly without making a call to the client or trying to determine the right combination of invoices. Smart machines could analyse the possible invoices and match the paid amount to the right combination of invoices, clear out short payments or automatically generate an invoice to reflect the short payment without any human intervention.
Handling bank reconciliation Machines can learn how to completely automate bank reconciliations.
Undertaking risk assessments Machine learning could facilitate risk assessment mapping by pulling data from every project a company has ever completed to compare them to a proposed project. This very comprehensive assessment would be impossible for humans to do on this scale and within a similar timeline.
Providing analytics calculations The accounting department is continuously barraged with questions along the lines of ‘What was our revenue for this product in the third quarter last year?’ or ‘How has this division grown over the last 10 years?’ Given the data, machines can learn to answer these questions very quickly.
Automating invoice categorization Accounting software firm Xero is deploying a machine learning automation system that will be able to learn over time how to categorize invoices, something that currently requires accountants to do manually. As accounting departments begin to rely increasingly more on machines to do the heavy lifting of calculations, reconciliations and responding to enquiries from other team members and clients about balances and verifying information, accountants will be able to deliver more value to the organization than ever before. This will allow them to focus less on the tasks that can be automated and more on those inherently human aspects of their jobs.
The key data and technology trends every HR team should recognize
For the rest of this chapter, I want to focus on my top 11 (I just could not whittle it down to 10!) predictions for the next few years (see Figure 12.1). HR teams wanting to stay ahead of the curve will want to ensure they are exploring all of the opportunities indicated by these trends, and fully consider how these developments may impact on their data strategy (see Chapter 3).
FIGURE 12.1 Key HR data and technology trends
Trend 1: smart devices will become well and truly smart
Thanks to machine learning, smart devices such as watches, home appliances and entertainment, and even infrastructure like lighting and wiring, will finally live up to their names. After all, although we have called phones ‘smartphones’ for a decade or so now, it would probably be more accurate (although less catchy) to have called them ‘multi-tasking phones’. Embedding AI such as Siri into the operating system was the first step towards making them truly ‘smart’. And we can certainly expect to see more of this in the next few years. Expect automated personal assistant features to become more proactive and predictive, and IoT devices such as smart lighting, security and air conditioning to become better at adapting to how we want them to behave. From an HR data point of view, all this means that the volume of data we have the potential to gather and extract insights from is going to increase even further.
Trend 2: companies will spend more on data software but less on hardware
Corporate infrastructure spending is increasingly being put into software, as much more functionality becomes available ‘as a service’ through cloud providers. At the same time, hardware spending is falling, partly due to the increase in the number of services carried out off site, and partly due to a preference for cheap, commodity hardware such as servers and storage space over expensive, bespoke, hardware solutions. This is a trend which will continue in the coming years. For HR teams, this boom in software as a service opens up a whole new world of analytics possibilities, without having to invest in costly on-site hardware or an army of in-house data scientists.
Trend 3: we will be spending less time in the real world and more time in virtual ones
Technologically advanced mass-market VR headsets are now readily available as consumer devices from several manufacturers. While VR has been growing in popularity in the technology industry for a few years now, its use mostly has been limited to large companies with budgets to build bespoke systems and software. With mass-market devices becoming available, and a growing amount of open-source software enabling users to design their own worlds and realities, expect its reach (and that of augmented reality (AR)) to extend significantly in the next few years. As we have already seen in this book, VR and AR can provide real benefits to organizations in the areas of employee learning and development, and in boosting your employer brand to recruit the best talent.
Trend 4: more organizations will discover their ‘digital twin’
I talked a bit about digital twin technology in Chapter 10, and I think ‘digital twin’ is a phrase companies can expect to hear more of in the future. Essentially, the thinking behind this technology is that, due to increases in computing power and the affordability and accuracy of sensor technology, most things can be simulated on a computer these days with a high degree of precision. The obvious application for this in an HR sense is in learning and development, but it could go much further. A digital twin could even be built of a whole company, along with data-driven simulations of all its processes, effectively providing a ‘sandpit’ for experimenting with driving change. The data used to build the twin also could serve as the input for advanced predictive analytics, allowing the likely outcomes of changes to procedures and processes to be examined in a safely simulated environment.
Trend 5: we will get used to the idea of being in two places at the same time
No, I have not gone mad or taken a detour into science-fiction writing. Telepresence combines ideas from VR/AR and the digital twin concept to effectively enable a human to be in two places at the same time. Drones and remote-control devices increasingly will be used to extend our immediate field of influence outside the range of our own hands and arms. Surgery can be performed remotely by a surgeon controlling a robot, or remote-controlled surgical equipment, and unmanned vehicles and equipment increasingly will be used to access places that are hazardous to our health, or which they are able to reach more quickly than we can. The simplest applications are remote meetings, which take place in a virtual environment (eg Skype) but use technology to give the impression or effect of everyone being present – like the Harvard virtual classroom we saw in Chapter 10. More advanced applications are unlimited, ranging from exploring deep space to the bottom of the oceans in a fully safe but immersive fashion. For businesses, this technology is likely to centre on making operations more effective and efficient by having people complete tasks remotely where it is beneficial.
Trend 6: companies will make greater use of external data
There is no doubt that internal data provide a fantastic competitive advantage because they are so uniquely tied to your business, your industry, your challenges, your customers and your employees; however, companies are increasingly supplementing their internal data with external data. This has been happening in marketing and sales teams for a number of years (eg think of your marketing colleagues making use of Facebook data to target advertising appropriately). Over the next couple of years, HR teams will also increasingly supplement their internal people-related data with external data from the government, data brokers, sites like Glassdoor etc, to look at things like trends in the employment market and general trends in terms of how engaged people are. Just focusing on internal data is a bit like putting blinkers on, so it is important to bring in additional data to put your own data into context. This means HR teams in the future will need to strike more of a balance between their own valuable internal data and the wealth of data that are available outside the company.
Trend 7: HR teams will move from reporting to predicting
Traditionally, HR teams have produced reports that tell us about what happened over the last year, using data that may be just a month or two old or a full 12 months old. Increasingly, we will see HR departments, and other functions within the business, move away from these legacy data to real-time analytics. Much more analysis needs to happen in real time for a company to be truly competitive in this data-driven world, which means understanding exactly what is happening right now and taking action where needed to remedy problems before they take root. A great example of this is the regular pulse employee surveys that are starting to replace the annual employee survey (see Chapters 7, 8 and 9). Over the next couple of years, we will see many more companies move to this approach. And, as we have seen throughout this book, the more data you gather (eg through regular surveys), the greater your predictive capabilities become. Machine learning and AI capabilities mean companies will be able to spot indicators that employee satisfaction may be about to drop, or that a key employee could be about to leave the company, for example.
Trend 8: quantum computing revolution
Today’s smartphones have the computing power of a military computer from 50 years ago that was the size of an entire room; however, even with the phenomenal strides we have made in technology, there remain problems that standard computing just cannot solve. This is where quantum computing may come in: solving complex problems beyond the capabilities of a traditional computer.
How does quantum computing work?
When you enter the world of atomic and subatomic particles, things begin to behave in unexpected ways. In fact, these particles can exist in more than one state at a time and it is this ability that quantum computers take advantage of. Instead of bits, which conventional computers use, a quantum computer uses quantum bits (known as qubits). To illustrate the difference, imagine a sphere. A bit can be at either of the two poles of the sphere, but a qubit can exist at any point on the sphere. So, this means that a computer using qubits can store an enormous amount of information and uses less energy doing so than a classical computer. By entering into this quantum area of computing where the traditional laws of physics no longer apply, we will be able to create processors that are significantly faster (a million or more times) than the ones we use today.
Perhaps Eric Ladizinsky, co-founder of quantum computing company D-Wave, explained the differences between a regular computer and a quantum computer best when he spoke at the WIRED 2014 conference.3 He said to imagine that you only have five minutes to find an X written on a page of a book among the 50 million books in the Library of Congress. In this scenario, you would be a regular computer and you would never find the X. But, if you had 50 million parallel realities and you could look at a different book in each of those realities (just like a quantum computer), you would find the X. A quantum computer splits you into 50 million versions of yourself to make the work quick and easy. We are venturing into an entirely new realm of physics and there will be solutions and uses we have never even thought of yet. But when you consider how much classical computers revolutionized our world with a relatively simple use of bits and two options of 0 or 1, you can imagine the extraordinary possibilities when you have the processing power of qubits that can perform millions of calculations at the same moment. We do not actually know all the possibilities of quantum computing yet, but what we do know is that it will be game-changing for every industry. It is no coincidence that some of the world’s most influential companies, such as IBM and Google, and the world’s governments are investing in quantum computing technology. They are expecting quantum computing to change our world because it will allow us to solve problems and experience efficiencies that are not possible today.
What are the applications of quantum computing?
One area that will be greatly impacted by quantum computing is AI. The information processing that is critical to improving machine learning is ideally suited to quantum computing. Quantum computers can analyse large quantities of data to provide AI machines with the feedback required to improve performance. Quantum computers are able to analyse the data to provide feedback much more efficiently than traditional computers and therefore the learning curve for AI machines is shortened. Just like humans, AI machines powered by the insights from quantum computers can learn from experience and self-correct. Quantum computers will help AI to expand to more industries and help technology become much more intuitive very quickly.
There are many other anticipated uses for quantum computing, ranging from improving online security to making better weather and climate-change predictions. But, for me, what is most exciting about quantum computing is, instead of troubleshooting issues bit by bit as we do now with classical computers, quantum computers tackle the entire problem at once. This opens the door for amazing developments in every field from financial services to our national security. By the very nature of this cutting-edge field, there will be discoveries, innovations and solutions we have never dreamed of yet, but it is clear that quantum computers give us the ability to solve complex problems that are beyond the capabilities of classical computers.
Trend 9: greater democratization of HR data
We have seen a new wave of data democracy in recent years and there is every indication this will only continue. For the bulk of the last five decades, data were ‘owned’ by IT departments and used by business analysts and executives to drive business decisions. As organizations became inundated with data and bottlenecks increased due to volume, it became apparent that more business users needed to have access to those data to explore them on their own without IT being a gatekeeper. Now, organizations are allowing more business users access to data to expedite decision making, influence sales and customer service, and uncover opportunities. This increased access is what is known as data democratization (sometimes referred to as data socialization).
Why data democratization matters
Expanding the pool of people who can analyse and develop meaningful business actions from data is critical to gaining a competitive edge for a business, seeing the big picture and, in some cases, could ensure its survival. Ultimately, the goal of data democratization is for the people in your organization to be able to quickly and easily get to the business insights they need without help, essentially so the right people have the right data at the right time. It has become apparent that embedding data and analytics throughout an organization, and ensuring their effects can be measured on every process, is often a more productive approach than attempting to impose data directives in a top-down, centralized manner. The applications of this range from giving shop-floor sales staff instant access to personalized (but not necessarily personal – the analytics might be done using anonymized data sets) insights about customers, to allowing engineers to know when an essential machine is likely to fail.
What this means for HR teams
The ‘datafication’ of our world and the increase in the number of IoT devices mean that HR teams will have more data available to them to enhance their decisions and operations. But these data also have relevance and use outside of the HR team. Line managers, for instance, need information on how their people are performing and how satisfied they are. Data democratization means HR teams should ensure their data are increasingly relevant, useful and available to those who need them, whenever they need them, regardless of whether those people are inside or outside the HR team. This does not mean that everyone in the organization needs to become data scientists. In the future, as technology progresses, data democratization will be less about analysing data to extract critical insights and more about asking the right questions of the data. Rather than HR professionals and managers throughout the company reading and interpreting data, advancements like natural language processing and chatbots mean it will be possible to have a conversation with your data analytics tools and ask questions like: ‘Who in my team may be about to leave the company?’
Trend 10: HR data will be more valuable to the business
I wrote an article a little while ago arguing that HR data are now becoming more valuable to businesses than their financial data.4 My reasoning was that, if a business’s people are its most valuable asset, surely it stands to reason that information on people comprise the most valuable data for a business. I believe this is becoming increasingly true in the information age. At many tech-driven companies and start-ups, the humans that make up the company – and the intellectual property that they produce – may literally be its only assets, beyond the commodity hardware and software they need to do their jobs. So, it is no wonder that, across many sectors of industry, employers are increasingly investing in technology capable of measuring and analysing the behaviour and performance of their personnel – at all levels from the shop floor to the boardroom. After all, a company that understands its employees is without doubt better placed to keep them motivated, happy and productive. As increasingly more aspects of business become managed through smart IoT-enabled technology, it is inevitable that management of HR will go the same way. In this way, human information will be just as critical as financial data when it comes to informing business strategies and setting goals. But the most value is likely to be unlocked by organizations that use these data sources together – combining HR data with financial, operational and customer data – for example, matching customers to the representative most likely to get on with them, based on personality profiling. This again shows how the democratization of data will be increasingly important. For HR data to provide maximum value, however, they need to be clearly linked to the wider business context and overarching key performance indicators (KPIs) of the business. Now, more than ever, HR data need to link to business objectives in terms of revenue, profit, customer services, talent acquisition etc, which is the best way to ensure that HR data, and the HR team itself, deliver real value to the business.
Trend 11: intelligent HR teams will become more people-focused rather than data-focused
I have worked with so many different HR teams over the years and, in my experience, the world of data and numbers is not exactly what gets the average HR professional’s heart racing. Most HR professionals go into the job because they are intrinsically people-focused, ie they are interested in human interaction, not analysing data sets. So, if you have read this book and feel like you need to become a data scientist to keep your job, you can relax. In fact, the opposite is true. The great news is that a lot of the data analysis tools coming onto the market will allow us to produce automated analysis and insight generation through AI and machine learning capabilities. Quantum computing will transform computing capabilities, enabling us to perform way more advanced AI algorithms in no time and with little effort.
Is this the end for data scientists?
Based on a survey by online employment analyst Glassdoor, ‘data scientist’ was voted the best job in America in 2016.5 But that may have been a little premature as the role of data scientist could be at risk, as machine learning, AI and big data could make the job obsolete. New machine learning algorithms can autonomously analyse data and identify patterns, even interpret the data and produce reports and data visualizations. In addition, natural language processing technologies can help to break down the barriers to widespread use of data analytics by making complex analytics possible for just about anyone, regardless of their technical ability. IBM, for example, believes that it can offer a solution to the skills shortage in big data by cutting out the data scientists entirely and replacing (or supplementing) them with its Watson natural language analytics platform. IBM’s Vice President for Watson Analytics and business intelligence, Marc Altshuller, explains: ‘With a cognitive system like Watson you just bring your question – or if you don’t have a question you just upload your data and Watson can look at it and infer what you might want to know’.6 In addition, new technologies are emerging that will allow lay people in any field to create detailed infographics and other storytelling devices to help interpret the data such technologies will return. Visualizations are usually used as a layer on the top of data, designed to make those data more digestible. In big data analytics, reporting the insights we have gleaned from analysing large amounts of messy data sets is the crucial ‘last step’ of the process, and it is often a step which causes us to stumble. We may have crunched terabytes of data in real time to come up with our world-changing revelations, but unless we can communicate them convincingly to those who need to take action, they are useless and, worse than that, a waste of valuable time and money.
Programs that can visualize data start with the graphing functions available in Excel and get progressively more complex. But one program, called Quill, takes the trend a step further, producing text-based reports that explain the data clearly and concisely. Think of it as an executive summary created by a computer to explain a set of data – at the click of a button. Combined, these types of technologies mean that the data scientist simply may not be needed in the big data landscape where lay persons can conduct their own analytics at will.
What this means for HR
With this in mind, it is completely reasonable to imagine that in the not-too-distant future an HR manager could sit in front of their computer and ask its analytics software a question about employee engagement, for example, and get an accurate response, report or visualization based on real-time data. In this landscape, data analysis skills for HR teams will become less relevant, not more, which is not to say you do not need to know anything about data and analytics. Of course you need to stay abreast of developments and possibilities to be able to deliver maximum value for your organization. But, rather than retraining to become a data analyst, it is actually much more important that you are able to ask the right questions of your data so that you can get the right insights. Once you understand exactly what it is you need to know, and you have the right data and algorithms in place to answer those questions, you will be able to get at the answers you need without performing any fancy analytics yourself. And this is combined with the fact that many simple administrative tasks will be automated and this will free up the HR teams of the future to focus on the people aspect, which is exactly what they went into the job to do. Right now, I admit that we are in a tricky transition phase, where it feels like data analysis is on top of other work, and systems are not yet in place to automate a lot of tasks. But, looking to the future, with advances in computing and analytics, the HR teams of tomorrow ideally will spend their time focusing on helping the business to deliver its objectives and supporting people throughout the organization as they grow and contribute in their own way to the business’s success.
Remember, it all starts with strategy
Now is the time that every HR team needs to create a robust data strategy. If you are just embarking on the data-driven HR journey, your data strategy will help you to get started and identify the best path for you. And if you are already some way into your data-driven HR journey, now is a good time to review your strategy to ensure it ties in with the business’s wider objectives and that you are taking account of the full range of possibilities open to you. Circle back to Chapter 3 for advice on creating or revising your data strategy, or, if you want more detailed guidance, my book entitled Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things will help.7 Finally, I cannot stress enough how vital it is that your HR data strategy and activities are firmly rooted in the wider business context and what your organization is trying to achieve. In this way, your data strategy should demonstrate exactly how HR will add value by contributing to the business’s objectives and helping to drive the business forward. That, for me, is the mark of truly intelligent HR.
Key takeaways
The following is a reminder of the key learning points from this final chapter:
· The ‘datafication’ of our world and the proliferation of IoT-enabled devices is only going to continue.
· The technology is changing fast and developments like quantum computing and VR will bring advancements and possibilities that we cannot even imagine yet.
· HR teams need to embrace automation where relevant and figure out their uniquely human role in their organization. What value do they deliver that cannot be delivered by technology or other areas of the organization?
· Across every industry and almost every job role, the nature of work is changing and employees of all kinds will need to become more data savvy; however, this does not mean that everyone needs to become data scientists.
· As technology progresses, we will be able to produce more automated analysis and insight generation through AI and machine learning capabilities. Human value will become more about asking the right questions of data.
· Combine this with the fact that many simple administrative tasks will be automated and this will free up HR teams to focus on people, not the data themselves.
· Now is the time to create or review your HR data strategy, ensuring it is firmly rooted in the context of your organization’s wider business objectives.
I hope this book and this glimpse into the future have inspired and motivated you to implement your own intelligent, data-driven HR systems and processes. It is clear to me (and you, hopefully) that we are on the edge of a dramatic change in how we do business. You and your HR colleagues will be pivotal in helping companies to navigate this changing workplace.
Endnotes
1 Marr, B (2017) [accessed 23 October 2017] Digital Transformation and Data Will Change All of Our Workplaces – Are You Prepared? [Online] https://www.forbes.com/sites/bernardmarr/2017/03/24/how-digital-transformation-and-data-will-change-all-of-our-workplaces/#3761d4985e04
2 Baumgartner, T, Hatami, H and Valdivieso, M (2016) [accessed 23 October 2017] Why Salespeople Need to Develop ‘Machine Intelligence’ [Online] https://hbr.org/2016/06/why-salespeople-need-to-develop-machine-intelligence
3 Marr, B (2017) [accessed 23 October 2017] 6 Practical Examples of How Quantum Computing Will Change Our World [Online] https://www.forbes.com/sites/bernardmarr/2017/07/10/6-practical-examples-of-how-quantum-computing-will-change-our-world/2/#42a2f9a11c20
4 Marr, B (2017) [accessed 23 October 2017] Is HR Data Even More Valuable to a Business Than Its Financial Data? [Online] https://www.forbes.com/sites/bernardmarr/2017/03/30/is-hr-data-even-more-valuable-to-a-business-than-its-financial-data/#740396c93789
5 Glassdoor [accessed 23 October 2017] 50 Best Jobs in America [Online] https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
6 Marr, B (2016) [accessed 23 October 2017] Big Data: Will We Soon NoLonger Need Data Scientists? [Online] https://www.forbes.com/sites/bernardmarr/2016/04/27/will-we-soon-no-longer-need-data-scientists/#4e840aee6897
7 Marr, B () Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things