03
In order to crystallize your goals, get the most out of data and get buy-in for your activities, you need to make a clear business case for data-driven HR. In practice, this means mapping out a clear HR data strategy that links to wider operational objectives and demonstrates how HR will contribute to those objectives; as well as identifying HR-specific objectives and how those objectives can be achieved through data and analytics. In this chapter I explore why it is so important to have a data strategy and why it needs to be linked to wider organizational objectives. I then set out the process of creating a ‘smart strategy board’ or ‘plan on a page’ to help you crystallize your objectives, and work out how you want to use your data. After that, I delve into the process of creating a strategy for intelligent or data-driven HR, including understanding the four layers of data and the six critical questions that form the basis of any good data strategy.
Everything starts with strategy
As we saw in Chapter 2, the explosion in data is affecting almost every area of our lives, including work. We now live in a world in which the amount of data being generated every day – even every second – is, frankly, astonishing. And when it comes to what we should do with all these data, I have found that many companies, or functions within organizations, fall into one of two camps: some are so eager to ride the data train, they dive in and start collecting all kinds of data simply because they can, with no thought as to how those data benefit the business, while others prefer to bury their heads in the sand, often because they are so overwhelmed they do not know where to start. This is where a data strategy comes in.
Understanding the data you really need
It is never a good idea to start collecting huge amounts of data that you do not really need; and this is especially true with a lot of HR data, because it is so personal in nature. Collecting people-related data just because you can may lead to mistrust or morale problems, as people feel that Big Brother is monitoring what they do with no clear sense of why or how it benefits them and the company as a whole. (As we see in Chapter 6, trust and transparency are vital when it comes to utilizing data successfully.)
I always say that the power of data is not in the impressive amount that can be collected, or the super-cool analytics that can break down the data in a myriad of ways. The power of data lies in how you use them. It is about how you use the insights that you glean from the data to improve decisions, better understand your employees, optimize operations and add value to the company. Therefore, you need to be very clear about what it is you want to achieve and, specifically, what kind of data will help you to achieve that aim.
Why ‘big’ is not always better with data
Ideally, you should collect, store and analyse the smallest amount of data possible to achieve your goals. I once did some consultancy work with one of the world’s largest retailers and, after my session with the leadership group, the CEO went to see his data team and told them to stop building the biggest database in the world and instead create the smallest database that could help the company to answer its most important questions. This is a great way of looking at data. Despite the hype concerning ‘big’ data, small is something to aim for. Keeping your data as small as possible means you are keeping a tight focus on where you want to go and which data will help you to get there.
Of course, big data giants like Google and Facebook collect everything they possibly can and never throw data away because potentially it could be valuable in the future. Google even captures misspelled words in Internet searches, using those data to create the world’s best spellchecker. While Google has the manpower, expertise and budget to cope with enormous amounts of data, most companies do not, which is why it is better to collect only the data that are absolutely necessary for you to reach your goals. Creating a robust data strategy helps you to develop and maintain a laser-like focus on which data are best for your department. Plus, having a strong data strategy in place will help to ensure the whole process runs more smoothly, as well as preparing the HR team and others in the organization for the journey ahead.
Where to start: linking your HR strategy to wider organizational objectives
Which data you gather and how you analyse them will depend entirely on what you are looking to achieve, so you need to consider this as the very first step in creating your data strategy. The best kind of HR data strategy is directly linked to the organization’s wider objectives and, in effect, should cascade down from those corporate objectives to create HR-specific objectives that will help to fulfil the corporate goals. Therefore, a good place to start is not with HR at all, but the company-wide strategic plan. In an ideal world, the organization’s strategic plan would be a concise, simple document that anyone in the organization can read and understand – something like a plan on a page that clearly sets out where the organization needs to go – however, this is not always the case and I recognize that some organizational strategic plans are overly long and complex, making it difficult to determine what actions need to be taken. Whatever your company’s strategy looks like, it should set out intended outcomes for the company, including financial and non-financial objectives, and (hopefully) the core activities and enablers that will lead to those outcomes being achieved. If you struggle to understand this from the company’s strategic plan, have a discussion with your leadership team before going any further, as it is vital you understand exactly where the business wants to go.
With the company’s strategic objectives in mind, you can begin to create your own HR plan that links to those objectives and identifies what you need to achieve in order to contribute to the company’s success. Say, for example, one of the corporate objectives is to reduce operating costs over the next three years, this will clearly influence your HR-specific objectives and, in turn, the kind of data you will want to work with.
Creating a plan on a page or smart strategy board to inform your data strategy
I cannot recommend strongly enough that you keep this objectives phase simple. Do not be tempted to create a list of 100 HR objectives that cover everything you could possibly want to achieve. Instead, focus only on core objectives. After all, you cannot create a robust data strategy if you are not crystal clear on what exactly you need to achieve and, in turn, what areas or activities you need to focus on to achieve those aims. A list of 100 nice-to-have objectives will lead to a very muddled (probably very expensive) data strategy that delivers little real value. To clarify your objectives and activities, it is a good idea to create an HR plan on a page, or what I call a ‘smart strategy board’. This is divided into six simple sections, as outlined below, and each section should be developed with the overall organizational objectives firmly in mind.
The HR purpose
Here you set the scene and provide an overarching context for your strategy by laying out, in simple terms, exactly what the HR department is aiming to achieve. A good way to do this is to include your purpose and vision statements. Your purpose (or mission) statement should be a brief, simple statement that neatly encapsulates why your team exists. As the name suggests, it answers the question: ‘What is our purpose?’ Your vision (or ambition) statement defines your purpose, but with the focus on what you want the HR function to look like in the future. It should set out your ambitions in an inspiring way, including your values and what behaviours the HR team adheres to.
Your customers
For any HR team, its primary customers are the organization’s employees. Therefore, this section is about understanding the company’s people:
· what you already know about them;
· what you do not yet know;
· what you need to find out if you are going to meet your goals successfully.
As with each section, remember to tie this into the larger organizational objectives and how they relate to the organization’s employees.
The finances
In this section you need to clearly set out any financial goals and ambitions as they relate to the organization’s strategy. Yes, part of this may be about cutting costs, but it should also be about creating additional value for the company, such as by boosting your brand as an employer and attracting the best talent. We know that finding, training and keeping hold of good talent costs organizations a lot of money, so many of your financial goals and ambitions may centre around this area. For example, one of your goals might be to streamline training and onboarding by moving to online training modules, while, at the same time, demonstrating greater value from that online training (ie using data and analysis to demonstrate a clear link between training, performance and even retention).
HR operations
Here you need to carefully consider your operations and any changes you need in order to deliver your goals. For example, do you need to partner with external providers and, if so, do you already have a relationship with those partners or do you need to build that relationship? Also, look at internal competencies and whether there are any gaps you need to fill in the team (and, if so, how will you fill them?). Your HR systems and processes also will come under this section.
HR resources
The aim here is to define exactly what resources you need in order to achieve your objectives. This covers IT systems, infrastructure, people, talent and cultures, and value and leadership. Clearly, there is a lot to consider concerning data and how they impact on IT resources, but you do not need to go into lots of detail at this stage. Remember, this plan on a page is about clarifying what you want to achieve and what you might need in order to do that. There is more on the systems needed to turn data into insights in Chapter 6.
Your competition and risks
In this section, you should consider what competition you will be facing as you work to deliver your strategy and what risks you may face along the way. Ask yourself: ‘Who is the main competition (such as external HR services) and why?’ Also consider what external factors may threaten your success, such as market, regulatory or people-related risks. And what are the internal financial, operational or talent risks you face? Being aware of these threats before you move forward is the best way to mitigate them.
Working out how best to use data
Having created your plan on a page, you should have a firm idea of where the HR team needs to go in the future, how you can add value to the organization and what areas you may need to develop. Next, before we delve into the data strategy itself, it is worth thinking about how best to use data. As we saw in Chapter 1, there are many ways in which data are used in business, but, in their broadest sense, these uses boil down to four categories:
· improving decisions;
· optimizing operations;
· understanding customers (or, in the case of HR, employees) better;
· monetizing data.
In this section we look at each usage again in relation to your HR strategy. I recognize that every organization is different, and you may feel some of these options do not apply to you. For example, some HR teams may face greater operational challenges, while other companies are suffering significant morale problems and need to develop a better understanding of their people, fast. When it comes to monetizing data, this is particularly problematic for people-related data; however, I still recommend you look at all four areas in turn before deciding how best you can use data. It is likely your core focus will lie in one or two of these categories, but it is best to consider all four before you start to get your data strategy down on paper.
Using data to improve your decision making
Making better business decisions is the goal for the majority of clients that I work with, and data increasingly are fuelling decision making at all levels in organizations, from multinational corporations to small start-ups. So much of a company’s success comes down to making better, more informed business decisions, and data are providing the insights needed to make those decisions. There are two key strands when it comes to making better HR-related decisions. The first is the HR team itself making better decisions that address the organization’s and HR team’s objectives, as well as critical people-related challenges. The second is about the HR team helping others, ranging from the leadership to other functions right across the business, to make better decisions using people-related data. There has been a strong move towards democratizing data, and giving wide (often real-time) access to data in order to aid decision making across businesses. Therefore, every HR team should be thinking about how it can make relevant HR data available to those who need it, in real time where necessary. Increasingly more companies are building cultures of data-based decision making, as opposed to basing decisions on gut feeling or how things have always been done. We have already seen examples of this in Chapter 2 (Google’s data-based decisions on managers) and Chapter 1 (Xerox creating a data-based profile of the ideal call-centre worker). The HR function potentially provides a wealth of data that can make a valuable contribution to this data-based culture.
Using data to optimize your operations
This use of data is about optimizing HR processes and everyday operations in order to make improvements, generate efficiencies and deliver a better service. As we saw in Chapter 2, increasingly this is about automating as much as possible, putting internal systems in place that allow you to automatically make use of people-related data. Automation is something HR teams can no longer afford to ignore. But this category is not all about automating processes and replacing HR professionals with, say, chatbots. In its broadest sense, it is simply about looking at key processes and activities, understanding what the HR team spends its money and time on and looking at how to make those processes better. I delve into specific HR functions and activities in Chapters 7–11.
Using data to better understand your customers
This is one of the most common uses of data in business, with examples ranging from Amazon and Netflix using data to make helpful recommendations on what to buy or watch next, to high street stores tracking how customers move around a shop and which displays catch their eye. This category is all about understanding your customers – where they come from, what matters to them, what they like and do not like etc – as well as wider trends in the market.
Clearly, as an HR professional, your customers are the organization’s employees (and, to some extent of course, the leadership team). The more you can understand about your customers, the better you can serve them. There is both an internal and an external side to this category. Internally, the HR team can use data to better understand employees and the organization’s culture, including how happy they are, how engaged they are, how safely they are working etc. With the many different analytical techniques available today, such as text analytics and video analytics, not to mention the boom in wearable technology, it has never been easier to gain critical insights into how your people are feeling and performing. Social media also has made it easier than ever to build up a rich picture of customers. Externally, data can help the HR team look beyond the organization and understand your employer brand (using platforms like LinkedIn and Glassdoor). Data can give valuable insights into your company’s perception from the outside, and how to attract the kind of talent you need to succeed. This category has a lot of crossover with making better decisions. Armed with a better understanding of your customers, you can make much smarter decisions on how best to serve them – decisions rooted in data, rather than hunches or assumptions – which is why this category and the first often go hand in hand.
Monetizing data
Data are valuable, and companies are increasingly being bought on the basis of the data they have. Microsoft’s purchase of LinkedIn for US $26.2 billion, for example, gave Microsoft access to the professional network’s more than 400 million users, and the data they generate.1 These data will be integrated with Microsoft’s collaboration and productivity tools, potentially allowing greater personalization within Microsoft’s products, which could help the company become more competitive in the enterprise market.
The ability to sell data to third parties is a growing area for many businesses; take Facebook, for example. The social network is free to users, but it has created a revenue stream from that user data by making certain data available to other businesses, for a fee.2 Amazon, too, has commercialized its data on an impressive scale (and, unlike Facebook, Amazon’s data relate to how we spend our hard-earned money, which makes them especially valuable to businesses). This makes the company now a head-on competitor with Google, with both online giants fighting for a chunk of marketers’ budgets.3 And, with the launch of Amazon’s home voice assistant Alexa, and Google’s Google Home, both companies will continue to fight it out for the best, most valuable, user data.
Naturally, this category of data usage presents significant challenges for HR because so many HR data are personal and sensitive. It is therefore unlikely you will be able to create a new revenue stream from employee data (and even if you could, would you want to?). In the United States there have been stories of agencies collecting pay-related data from companies, including Fortune 500 companies, and providing access to those data to interested parties like collection agencies.4 On the whole, this practice is largely unheard of; however, in terms of adding value to the company, HR data certainly do have a monetary value. When HR data are used to improve decisions, make employees happier and optimize processes, of course they add value to the company. For me, this usage is about seeing HR data as comprising a key business asset that can deliver significant value for the business. But, as your HR data become a core asset, the need for careful data governance becomes even more pressing. One of the biggest concerns regarding data, particularly personal data, is privacy and governance, and with good reason. I talk more about data security, privacy and governance in Chapter 6.
Which usage is best for you?
Having looked at all four areas, and with your HR objectives in mind, you can now begin to figure out where the biggest opportunities lie for data-driven HR in your organization. For example, say your company has a corporate objective to become a top-three provider of specific consultancy services within the next three years. That will translate into various HR-related actions such as assessing and optimizing your employer brand in order to attract the best talent. This will mean the biggest opportunities for data-driven HR are likely to lie in better understanding your customers and making better decisions. Then, once you have a sense of where the biggest opportunities lie and how you might want to use data, you can begin to pull everything together into your HR data strategy.
Understanding the four layers of data
Now we are ready to start delving into the data strategy itself. But, when starting to pull together your data strategy, it is important to understand the four layers of data, because a good data strategy should clearly map across these four layers (see Figure 3.1).
FIGURE 3.1 The four layers of data
1 Data sources layer
This is where the data arrive with the HR team. This layer includes everything from your sales records (for key performance indicator (KPI) purposes), customer feedback, employee surveys and feedback, e-mail archives, personnel files and any data gleaned from monitoring or measuring aspects of your operations. Data could also arrive from outside the organization, through data collection tools like Google Analytics, or social-media networks. One of the first steps in setting up a data strategy is assessing what you already have and measuring this information against what you need in order to answer the critical questions you want help with. You might have everything you need already, or you might need to establish new data sources.
2 Data storage layer
This is where your data live once they have been gathered from your sources. In line with the general explosion in big data, sophisticated but accessible systems and tools have been developed to help with this task, such as the Apache Hadoop computing software. As well as a system for storing data that your computer system will understand (the file system), you will need a system for organizing and categorizing these data in a way that people will understand (the database).
3 Data processing/analysis layer
When you want to use the data, you need to be able to process and analyse them. One common method is by using a MapReduce tool, which selects the elements of the data that you want to analyse, and puts them into a format from which insights can be gleaned. These days, there are many proprietary tools and systems you can use to query data and many of them are designed to be used by non-data scientists.
4 Data output layer
This is how the insights gleaned through the analysis are passed on to the people who need them, whether that is inside the HR team, the company’s leadership team, or other functions in the company which can use the data. This output can take the form of reports, charts, figures and key recommendations. Whatever format it is presented in, the information needs to be clear and concise, making it as easy as possible to identify critical actions.
Creating your data strategy: asking the right questions
With the four data layers in mind, an HR data strategy can be easily broken down into clear sections or questions. Keep in mind that there is more on how to implement each section later in the book. Here your focus should be on understanding what you want to do. You may need some expert help to pull your strategy together and put it into practice. Depending on the size of your company and the expertise within it, it is clearly a good idea to involve your IT team in this process. For those smaller companies which lack the data knowledge and expertise in-house, there are many data consultants who will be able to help you determine the best course of action for your needs. As illustrated in Figure 3.2, the following six questions will help you to understand and really clarify what you want to do, and they form the basis of any good data strategy. Answer all six questions in the order set out below, rather than skipping over various sections.
FIGURE 3.2 Creating your data strategy: asking the right questions
Question 1: what questions do we need to answer or what problems do we need to solve?
Many of the organizations and functions I work with tend to ask for as many data as possible, not because they plan to do very clever analytics, but because they do not know which data they really need; however, it is far better to start by returning to your strategic objectives, rather than thinking about the data themselves. After all, why bother collecting data that will not help you to achieve your goals? Remember the plan on a page we talked about earlier in the chapter? Here you start by identifying the key questions that relate to that plan. So, having set out what it is you want to achieve, you now need to pin down the big unanswered questions you need to answer if you are going to deliver that strategy. Some of the questions will have been identified already as you worked on the plan on a page, while others will need careful thought at this stage. Defining these questions helps you to identify exactly what you need to know. And by making sure your questions are linked to your company’s priorities, you can ensure they are the most strategically important questions, rather than asking every little ‘nice to know but not essential’ question.
Question 2: what data do I need to answer those questions or solve those problems?
I have mentioned it a couple of times now, but it is something I see time and time again: too many companies or departments get caught up in collecting data on everything that walks and moves, simply because they can, rather than collecting the data that really matter. When creating your data strategy, it is vital you stay focused and do not get caught up in exciting data possibilities that are not relevant to your goals.
Look at each question you identified in question 1 and then think about which data you need to be able to answer those questions. Many of those data will come from within the company itself, but you also may need to make use of external data providers, particularly when it comes to recruitment. Establish which data you already have access to, and which you do not yet have access to. For the data you do not have access to, do you need to partner with an external provider or can you set up new data collection methods to gather the data internally? You can read more about key sources of HR data in Chapter 4.
Question 3: how will we analyse those data?
Having pinned down your information needs and the data you require, next you need to look at your analytics requirements, ie how you will analyse those data and turn them into valuable insights to help you answer your questions and achieve your goals. When it comes to analytics, much of the promise of data lies in unstructured data, like e-mail conversations, social-media posts, video content, voice recordings etc. Combining these messy and complex data with other more traditional data, like KPIs or sales data, is where a lot of the value lies. There is more on this in Chapter 5.
Question 4: how will we report and present insights from the data?
Data and analytics, and all the interesting insights gleaned from data, are absolutely useless if they are not presented to the right people in the right way at the right time, so that the right actions can be taken. Options for reporting and presenting insights vary from fancy dashboards with real-time access to data through to simple reports with key insights presented as visuals. Keeping your target audience in mind is perhaps the most important thing to remember at this stage. Therefore, you need to define who the audience is for your data and work out how best to get that information to them. The HR team itself may be the largest audience, but no doubt you will also need to present insights to others elsewhere in the organization. Indeed, this is a critical part of HR teams adding greater value to the organization. So consider now who exactly might need access to the information, and how you intend to provide it. Why do you need to think about this now? Because your method(s) for presenting data may have critical implications on your data infrastructure requirements. Which leads us to the fifth question.
Question 5: what are the infrastructure implications?
Having defined which data are needed, how they will be turned into value and how they will be communicated, the logical next step is working out the infrastructure implications of these decisions. Essentially this comes down to what software and hardware will be needed to be able to capture, store, analyse and communicate insights from the data you have identified. For example, if you are looking at gathering significantly more performance data, is your current data storage technology up to the task of storing all those new data, or do you need to supplement them with other solutions? What current analytical and reporting capabilities do you have and what else do you need to get?
Question 6: what action needs to be taken?
Having answered the five questions above, you are now ready to define an action plan that turns your HR data strategy into reality. Like any action plan, this will include key milestones, actions and owners of those actions. As part of this step, you will also need to identify training and development requirements to help you put this plan into action, and pinpoint where you might need external help.
Making the business case for data-driven HR
There is no doubt that getting the leadership team and key decision makers involved will help you to create a more robust data strategy. Not only that, but getting leadership’s buy-in at this crucial early stage means they are more likely to put your people-related data to good use in their own decision making. Thus, an important part of creating a robust data strategy is making a strong business case for a data-driven HR approach, to help get people (both inside and outside the team) on board with the idea of data-driven HR. The more people are aware of and excited by the possibilities of data, the more likely they are to buy into the idea.
‘Selling’ data-driven HR across the company
This extends across all levels of the company and all functions, not just the company leadership. After all, data-driven HR is about people, and their data. When the people in an organization understand what data-driven HR is all about, and how it benefits the company as a whole and them as employees, they are more likely to be on board with, for example, capturing new kinds of employee data. When the business case for data-driven HR is not communicated properly at every level, it can breed mistrust and have serious negative consequences for the organization’s culture. Making a business case for data-driven HR is a bit like an entrepreneur making a business case (or business plan) for their new venture. So, naturally, you will want to do the same sorts of things as an entrepreneur would in their business plan, including giving a good outline of the data strategy and its goals, ie what you are hoping to achieve with data, as well as the tangible benefits to the business and its employees. It is also vital you are open and realistic about the time frame, likely disruption to the business and costs, especially in discussions with the leadership team. You need to make the best case for data-driven HR, which means it is important not to gloss over these issues.
‘Selling’ data-driven HR is a crucial consideration on the way to intelligent HR. It instils confidence in data, inspires feelings of trust and transparency, and emphasizes the HR team’s value to the company as it works to achieve its goals. Plus, when you want your HR data to be used by other functions across the company, ensuring everyone understands the value of your people-related data means they are much more likely to incorporate those data into their decision making further down the line. By making a business case now, you are sowing the seeds for data-driven decision making and adding value through data in the future.
How to go about this in your company
How you communicate your plan for data-driven HR depends on a number of factors, like how big your company is and the usual process for kicking off new initiatives. One good way to go about it is by distilling your data strategy into key points that can be communicated in a short presentation. Keep it simple and brief (there is no need to go into masses of detail on analytical possibilities or data storage options, for instance) and remember that your enthusiasm for this new age of data-driven HR will be infectious. Use examples to demonstrate how other companies are leading the way in data-driven HR and what this means in practice (it is even better if you can find examples that relate to your specific industry). Hopefully, the examples given throughout this book will help you to do this, and remember to focus on the benefits that data-driven HR will bring, both to the organization as a whole and to the people who work there.
Returning to your strategy in the future
No strategy is ever set in stone. Things change, markets shift, organizational priorities evolve etc. It is therefore very likely that you will need to revisit your data strategy on a regular basis to check it is still in line with the company’s overall priorities. Even if nothing has changed, revisiting the strategy helps you to stay lean and remain focused on the outcomes. Also keep in mind that, as you get further down the road of data-driven HR, significant new opportunities or questions may present themselves. For example, when answering one of your strategic questions, the data may throw up other, more pressing questions that also need to be answered, and this may lead to a slight tweak in your strategy. The technology concerning data and analytics is evolving fast and what will be possible in one or two years’ time may be completely different from what is possible now. While you want to follow through on the actions in your strategy, remember that the point of data-driven HR is to add greater value to the organization and do things in a more intelligent, streamlined way; it therefore makes sense to stay alert to new ways of working.
Key takeaways
I cannot stress enough how important strategy is in making good use of data. And, as we continue to create unprecedented volumes of data, having a clear strategy will become more important than ever. Below is a list of what has been discussed about data strategy in this chapter:
· In this world of ever-expanding data, you need to be very clear about what it is you want to achieve and, specifically, what kind of data will help you to achieve that aim. This is where your data strategy comes in.
· Big is not always better. Keeping your data as small as possible means you are keeping a tight focus on where you want to go and which data will help you to get there.
· The best kind of HR data strategy is directly linked to the organization’s wider objectives. Once you are clear on where the organization is trying to go, you can map out HR-specific objectives to help the company achieve its goals.
· To clarify your objectives and activities, it is a good idea to create an HR plan on a page, or what I call a smart strategy board.
· Having created your plan on a page, you should have a firm idea of where the HR team needs to go in the future, how you can add value to the organization and what areas you may need to develop. This will help you to work out your priorities for using data.
· When starting to pull together your data strategy, it is important to understand the four layers of data: data source, data storage, data processing and data output. A good data strategy should clearly map across these four layers.
· To create your data strategy, answer the six key questions set out in this chapter.
· Finally, an important part of creating a robust data strategy is making a strong business case for a data-driven HR approach, to help get people on board with the idea of data-driven HR.
As we have seen in the first three chapters of this book, there is a huge variety and volume of data available today. Having a strong data strategy will help you to filter out the noise and identify the best types of data for your goals. In the next chapter I explore in much more detail some of the main sources of HR-relevant data.
Endnotes
1 Feller, G (2016) [accessed 23 October 2017] This is the Real Reason Microsoft Bought LinkedIn [Online] https://www.forbes.com/sites/grantfeller/2016/06/14/this-is-the-real-reason-microsoft-bought-linkedin/#695b191cf04a
2 Facebook [accessed 23 October 2017] Advertising and Our Third-Party Partners [Online] https://www.facebook.com/notes/facebook-and-privacy/advertising-and-our-third-party-partners/532721576777729
3 Maverick, J B (2015) [accessed 23 October 2017] How Amazon Competes with Google [Online] http://www.investopedia.com/articles/investing/060215/how-amazon-competes-google.asp
4 Carrns, A (2013) [accessed 23 October 2017] Checking the Data Collected on Your Work and Pay [Online] http://www.nytimes.com/2013/08/31/your-money/exploring-companies-that-collect-more-than-the-standard-credit-data.html