Chapter 10
Creating a Big Data Strategy
In This Chapter
Understanding the importance of starting with strategy
Breaking your big data strategy down into six manageable steps
Making a robust case for using big data in your business
We now live in a world in which the amount of data being generated each second is staggering. While some companies are leveraging data very well to generate mouth-watering competitive advantages, many are barely scratching the surface, dipping into data in an arbitrary way without any real underlying strategy.
The truth is, those companies that can turn big data into valuable insights are the ones that will thrive. The companies that continue to merely dip their toes in the big data and analytics pond will be left behind. And those that ignore big data altogether will wither away.
Whether you’re a big data giant like Facebook or Google, or a small, family-run business, all smart business starts with strategy. Therefore, every company, big or small, in any industry, needs a solid big data strategy. I find that many companies are either raring to get started and want to dive straight in or they’re so overwhelmed that they just don’t know where to start. Wherever you sit on the spectrum, you can start thinking strategically about big data by following some very simple steps.
In this chapter, I look at what you need to consider when creating your big data strategy. If you’re not yet up to speed on the big data basics, circle back to Part II for more information before you tackle your big data strategy.
Deciding How to Use Big Data
The really good news is that, initially, it really doesn’t matter what data you have access to already, what you have the potential to generate and collect or even what data is available out there in the universe. Whether your business has tons of analysis-ready data or not is unimportant at this stage. It doesn’t change the fact that you need to start with strategy. And your strategy shouldn’t be determined by what you have or can get your hands on – it’s about what you want to achieve in your business.
So, for now, set aside the data itself and focus on how using data could help you achieve your business goals. There are millions of ways data can help a business but, broadly speaking, they fall into two categories: one is using data to improve your existing business and how you make business decisions; the second is using data to transform your whole business operations or business model. I look at each in turn in the next sections.
You might want to focus on decision making for now and see if the data leads to any operational opportunities farther down the line. Not too many companies set out with the definitive focus on the second (operational) aim but if that’s your goal then it’s perhaps wise to skip the decision-making aspect for now – transforming your operations is a bigger and trickier exercise, so you’ll need to focus fully on that if it’s what you really want to do. In practice, most companies start out wanting to improve their decision making and take it from there.
Using big data to improve your business decisions
This is the goal for most clients I work with, and I think it’s something that all business should be working towards. Whatever you want to do – whether you want to better understand your customers, target new customers, make your supply chain more efficient or so on – you need to make smarter business decisions. Data provides the insights needed to make those decisions.
Even if you run a very traditional company and can’t yet imagine how data can help you improve your business, that doesn’t mean it can’t. I firmly believe data can help businesses of all sizes and across all industries.
I am working with a bus and coach company that had very traditional views and didn’t think data mattered to it. Now it’s collecting and analysing telematics data from its vehicles to improve driving behaviour as well as to optimise routes and maintenance intervals. It has also started to better understand its customers by collecting and analysing time and location stamped data on ticket purchases. All this new data is helping the company make better decisions for the future.
To ensure your decision making becomes more fact based, you need to identify your company’s priorities and unanswered business questions. You then use data to help you answer those questions, gather insights and communicate insights across the company – thereby helping everyone in the company make better decisions.
At first, you may choose to focus on one very specific area of your business, such as understanding the customer response to a particular product, but the very idea of basing decisions and ongoing business strategy on what data tells you should become a company-wide thing. So, even if you run a short, neat data project to find an answer to a specific question, it’s likely the data possibilities will extend far beyond that initial question to other areas of the business. Having a clear big data strategy helps you identify your key questions and prioritise them, so that you’re using your time and resources in the best way possible.
I talk through the detailed process of using data to improve decision making in Chapter 11. For information on building a company culture that supports data-based decision making, turn to Chapter 13.
Using big data to transform your business operations
This is a bigger step than improving business decisions and therefore not something that all businesses do. In this process, you use data to challenge your whole business model: not how you do business and how well you do it, but the very nature of what you do. Data can throw up some surprising insights that can have big ramifications for what your business does.
Here’s an example of a company that changed its business model thanks to data, moving from a manufacturer to a big data company. Jawbone is the company that manufactures the fitness tracking band that I wear every day. It tracks my activity levels, calorie consumption and sleep levels. With millions of users, Jawbone gathers an incredible amount of data. The company soon realised that all the data it collected was more valuable than the device itself! It’s no wonder the company is now transitioning to a big data company. It still manufactures the products, as they’re the vehicle for continuing to collect data, but the data itself is now the company’s primary focus. Analysing the collective data brings insights that can be fed back to users and sold to interested parties.
In Chapter 12, I talk through the detailed steps involved in using data to transform your business operations.
If you feel this stage isn’t relevant to your business, that’s fine, but it’s a good idea to stay open to operational opportunities that your data may point to. You may be surprised at what you find: You may spot exciting new business opportunities, or ideas for new products and revenue streams or the data may lead to a fundamental change in your business model.
Transforming your business operations is a big step, and it’s likely to require a total mindset shift. For it to work, you need to build a company culture that’s open to opportunities that data highlights. For more on how to build such a culture in your business, check out Chapter 13.
Small Can be Beautiful: How Not to Collect Everything
Remember being a kid and desperately wanting that piece of cake or pie that was far too big? You either couldn’t finish it or you felt sick from squeezing it in. In my family we called this having eyes bigger than your belly. A similar thing applies to big data. You need to remember what your business can, well, stomach.
It’s easy to get dazzled by big data opportunities. The sheer volume of data available and the amazing things that companies are doing can be overwhelming. But trying to replicate what the big companies are doing in your own business is stressful, expensive and completely unhelpful.
Or maybe you’re so dazzled that you feel almost paralysed by the possibilities. The same advice applies: Don’t worry about what the big guys are doing; focus on what’s best for your business.
Big data giants never throw data away; every tiny piece of data may be valuable to them to some extent or other. Everything is captured and analysed because it can potentially offer unique and powerful insights for business development. Even errors are captured and analysed. Take misspelled words and names in Internet search queries – you’d think those could be discarded, but you’d be wrong. Instead of discounting incorrect entries, Google captures that data and uses it to create the world’s best spell checker!
For big data giants like Google, Tesco and Amazon, every tiny piece of data may well be valuable. But that’s because they have the expertise, money and technology to cope with massive datasets. They have the storage capacity, manpower, analytical know-how and software to mine all that data for insights. Also, they have the best talent gagging to work for them. (Each year, a staggering two million people apply to work at Google but only around 5,000 get hired!)
I’d guess that 99.9 per cent of all the companies in the world will never be in that position, nor do they need to be. For most business leaders, the idea of collecting and storing everything is terrifying. They already have a mountain of dusty archive material lying around, let alone dealing with all the new data that is generated every day.
Most businesses will never have the time, money, expertise or even inclination to collect and crunch data like the big guys do. And that’s a good thing. Rather than trying to collect as much data as possible, your aim should be to collect only the data that you really need to meet your goals. Aim for the smallest, most focused dataset that you can possibly get away with in order to answer your key business questions. To keep your dataset small and focused, you need a really clear strategy.
The Key Steps in Creating Your Big Data Strategy
After you have an idea of how you want to use big data (improve business decisions, transform operations or both), you’re ready to create your big data strategy. Before embarking on any big data project, you must start with strategy. For example, what data you gather and how you analyse it depends entirely on what you’re looking to achieve – so you need to have considered this at the outset. A good, strong strategy helps the whole process run more smoothly and prepares you and your people for the journey ahead.
In the next sections, I break down the big data strategy into six components or steps. There’s more of the nitty-gritty on how to implement these steps in Chapters 11 and 12, but here the focus is on understanding what you want to do. I also look at why it’s so important to make a good case for data in your business.
Keep in mind that, like any business improvement process, things may shift or evolve along the way. While it’s important to know what you want to achieve at the outset, nothing in business is ever set in stone. Some strategic questions lead to neat and tidy one-time data projects that do exactly what they say on the tin. Others need to be monitored over time and strategies tweaked depending on what the data indicates.
You may find that your data points to interesting new questions that you want to explore or leads to modifications to your existing data strategy. If that happens, simply revisit your strategy, re-evaluating each of the points in the next sections in turn.
The six components of your big data strategy
These components form the basis of your big data strategy, helping you understand and focus on what you want to do. They should be considered in the order set out in the next sections.
While I don’t expect you to carve your big data strategy in stone and swear blood allegiance to it, it’s a good idea to create a formal strategy document. It doesn’t have to be a masterpiece, but it will be helpful to have your goals and requirements on paper – that way, the strategy can be shared, referred back to and updated as necessary.
Be sure to involve the important people in your big data strategy. Getting the key company players and decision makers involved now can help you create a more robust strategy overall. Getting key people to buy in at this crucial early stage means they’ll be more likely to put all that data to good use later on.
What do I need to know or what business problem do I need to solve?
Many of the companies I work with tend to ask for as much data as possible – not because they plan to do incredibly detailed analytics, but because they don’t know what to ask for, so they ask for everything. Rather than starting with the data (for example, what you already have, what you might be able to get access to, or what you would love to have), it’s much better to start with company strategy. After all, why bother collecting data that won’t help you achieve your business goals?
Think about the strategic priorities you have laid out for the coming months or years. Define what it is you want to achieve and then think about the big unanswered questions you need to answer to deliver that strategy. Throughout the book, you’ll often see me refer to these as strategic questions. Work out what it is you need to achieve through the analysis of data. Are you looking to reach more customers, better understand your current ones or determine the best locations to provide your service?
Defining these questions helps you identify exactly what you need to know. And by making sure your questions are linked to your company’s priorities, you can ensure they’re the most strategically important questions, rather than asking every little ‘nice to know but not essential’ question.
For more detailed information on how to identify your questions, turn to Chapter 11. Once you’ve identified your business priorities and strategic questions, then you can look at the data you need to solve those problems.
What data do I need to answer my questions?
Most companies get caught up in collecting data on everything that walks and moves, simply because they can, rather than collecting the data that really matters.
This might sound paradoxical, but when it comes to big data is it even more important to think small. I recently worked with one of the world’s largest retailers and, after my session with the leadership group, the CEO went to his data team and told them to stop building the biggest database in the world and instead create the smallest database that helps the company to answer its most important questions. This is a great way of looking at big data.
Stay focussed. Don’t get lost in the background noise from all the data out there that isn’t relevant to your business goals.
Look at each question you’ve identified and then think about the ideal data you want or need to answer that question. You may be able to find the data from within the company, but you could also look to the millions of new sources out there: structured and unstructured, conversation data, video data and so on (I talk about the different kinds of data in Chapters 4 and 5.)
After you define the ideal data, look inside the organisation to see what data you already have. Then look outside and establish what data you may have access to. At this point you can then decide whether you can use existing internal data, bring in existing external data or create new data collection mechanisms. But remember, only by knowing what data you need will you know where to look for it and how to collect it.
How will I analyse that data?
When you’re clear about your information needs and the data required, you need to define your analytics requirements. For example, how you will turn that data into insights. Here you define how the data will be analysed to ensure the raw data is turned into valuable insights that help you answer your questions and achieve your business goals.
Traditional data collection and analysis is one thing – like point of sale transactions, website clicks and so on – but where much of the promise of big data lies is in unstructured data, like email conversations, social media posts, video content, photos, voice recordings, sounds and so on. Combining this messy and complex data with other more traditional data, like transactions, is where a lot of the value lies, but you must have a plan for the analysis.
How will I report and present insights?
All the fancy datasets and cool analytics don’t mean anything if they aren’t presented to the right people in the right way in order to help decision making. Making good use of data visualisation techniques and taking pains to highlight and display key information in a user-friendly way will help ensure that your data gets put to good use.
So, in this step you need to define how the insights will be communicated to the information consumer or decision maker. You need to think about which format is best and how to make the insights as visual as possible. You also need to consider whether interactivity is a requirement. For example, do the key decision makers in your business need access to interactive self-service reports and dashboards?
Keeping your target audience in mind is perhaps the most important thing to remember at this stage. The target audience may be you as the business owner or manager, or it may well be other key decision makers in your business, depending on what your strategic questions are.
Why do you need to think about this before you’ve even gathered a scrap of data? Well, quite simply, it may impact your overall strategy and big data infrastructure. For instance, if you know that the managers in your company will need access to real-time reports and dashboards, then that is going to have a big impact on your software and hardware requirements.
What software and hardware do I need?
Following on from defining what data is needed, how it will be turned into value and how it will be communicated to the end user, you need to define your software and hardware requirements.
Is your current data storage technology adequate? Should it be supplemented with cloud solutions? What current analytic and reporting capabilities do you have and what do you need to get? There’s more on building a big data infrastructure in Chapter 9 but, for now, you need to consider the overall requirements that will help you meet your goals.
What’s the plan of action?
Having identified the various needs, you’re now ready to define an action plan that turns your big data strategy into reality. Like any action plan, this will include key milestones, participants and responsibilities. After creating your strategy, one of your first steps will be to make a robust business case for big data to the people in your organisation – effectively convincing them of the merits of using big data.
Importantly, you should also identify training and development needs within the company and identify where you might need external help. Chapter 8 has more information on key big data competencies that your company is likely to need.
Making a solid big data business case
Like any major business project, you need to make a strong case (a business plan, if you like) for your big data project. These days, no sane person starts a business without first drawing up a business plan. It’s a process that has filtered down to any key business activity – for example, a company is unlikely to embark on a major expansion project without first making a clear business case for doing so, weighing up the costs and benefits. Setting off without such a plan is a recipe for disaster. It’s the same with big data.
Treat your big data project as any other major business project: Build a robust written business plan and communicate the key elements of that plan to all areas of the business. This is an essential step but one that’s pretty easy to do once you have your completed your data strategy.
Covering the key elements of a business plan
Business leaders, managers and decision makers always want to know up front what a project is going to cost, how it will benefit the business and, basically, whether the benefits outweigh the costs. That’s what you’re looking to set out in your big data business plan.
Your big data business plan should aim to cover the following elements:
- Summarise or outline the project in brief.
- Define your goals – what are you aiming to find out?
- List the tangible benefits to the business. How will data help you improve or transform your business?
- Describe the capabilities needed and whether any skill gaps will be filled through training or bringing in external help.
- Discuss timeframe and likely disruption to the business.
- Be very clear on costs. Most of the costs will be related to getting the right talent on board, storing and maintaining the data and, crucially, keeping it safe. It’s important not to gloss over this; otherwise your business case will not be as robust as it should be.
Shouting it from the rooftops
You’ve built a solid argument for using big data in your business. Now you need to promote the idea and evangelise it across the whole company. I can’t stress enough how important this is – selling big data to your colleagues is a crucial early step on your big data journey.
Communicating your business case effectively instils confidence in your big data project and helps you get buy-in across the company early in the process.
Ensuring your colleagues understand the value of big data in the organisation means they’re much more likely to incorporate data into their decision making further down the line. By making the business case early, you’re sowing the seeds for data-driven decision making in future. There’s more on building a culture of data-driven decision making in Chapter 13, along with some helpful tips on facilitating company-wide buy-in.
How you communicate your plan across the company depends on a number of factors, such as the size of your company and your usual processes for kicking off new initiatives. You may simply want to share your business plan document with colleagues and have an informal discussion. But I think a good way to go about this process is by distilling your big data plan into key points that can be communicated in a short presentation. Not everyone in the company will need to know the ins and outs of big data capabilities and costs, but you want everyone to be in love with the general idea of using data in the business. Distilling the plan down into the key nuggets is a good way to get broader buy-in.
Here are my tips for communicating your ideas:
- Keep the information simple and brief.
- Enthusiasm is infectious, so evangelise data and emphasise the positives.
- Focus on tangible benefits, for example ,what questions the data will hopefully answer and how these insights will add value to your business. Explain how data can help your company gain competitive advantage.
- Use examples to demonstrate the power of big data. It’s particularly helpful if these are related to your industry and demonstrate how others in your field are already using data.
Ultimately, starting with strategy and making a proper business case gives you the best chance of successfully using data in your business – no matter what your goals are or the scale of your ambitions, this is always the place to start.
The steps in this chapter are designed to get you thinking about what you want to achieve with the help of big data. I’ve used this approach with companies and government organisations of all sorts of sizes, across many sectors. I find it a simple and intuitive approach to creating a big data strategy and one that engages the key decision makers in an organisation.
When you’re ready, Chapters 11 and 12 can help you nail down your strategic questions and implement your big data strategy in detail.