Creating value is the most fundamental purpose of any business. Though this value can be financial or non-financial, based on the business and project you are working on, the sole purpose of most businesses is to create financial value for shareholders. Businesses use various innovative ways and technology to create this value and in the list of these technologies or instruments, Big Data is the latest addition.
In this book, the aim is to cut through the hype around Big Data, understand its less technical and more business-related aspect, but also inform and support those millions of managers that face pressure to invest in and make decisions around Big Data for their organization. Big Data is about technology and how technology has enabled information to be gathered at an unprecedented scale. The good news is that this information source can potentially offer an advantage over the competition or help serve your customers better. However, taking advantage of the technical developments in data and information handling requires investments in money, resources and time. For many business managers, this fast-moving technical environment poses the challenge of knowing where and why an investment in Big Data is justified and can make a return. To make an investment decision in business you need to understand the underlying drivers for how value is created, and in the context of Big Data what will be the likely outcome of your decisions.
It may be possible that you and your team can start and run the most successful Big Data project to achieve a specific goal or address a challenge, but it is not always the case that you are creating short-term or long-term value for the company. So, with this book we are setting out a very practical approach and context for managers who have already started or are thinking about starting the journey of a Big Data project.
We provide tools, techniques and processes for executing Big Data projects on one hand and value-creating process and measurement on the other hand – and we want managers to read, learn and practise both aspects of the businesses so you will make appropriate business decisions for making long-term sustainable value for the company.
From first-hand experience of working with practising managers in different organizations of varied sizes, we have realized that while technologies and inventions are highly valuable for businesses, often non-technical business managers face challenges in understanding the true potential of such technologies and using them for the organization at the right time. At the same time, the web, mobile and social networks and other platforms are producing enormous amounts of unstructured data, which can hold a wealth of market intelligence. Unlock this ‘Big Data’ and you have the power to build sustainable value in several forms.
The purpose of this book is to help managers understand how and in what ways Big Data can be used to generate more revenue, save costs and come up with innovative offerings – but on top of everything else, it can create sustainable value for businesses. It can help managers understand what Big Data is and what it is not. It can also help managers have robust conversations with those inside and outside the organization who propose that Big Data is the answer to business problems but are not specific enough about how their investment can create value.
The purpose of the book is to enable readers and practising managers to understand the potential of Big Data, plan to execute projects where needed but more importantly assess the outcome to ensure that value is created for the organization.
A major take-away is the business model and proprietary C-ADAPT framework that can be used to understand how and where we can execute Big Data projects and value is created using Big Data.
This is a book for managers and not a technical book for techies and IT professionals and students. It is important for managers to have a good overview, not least to be able to understand and discuss with IT professionals and consultants. This does not mean that we have not used some technical words or discussed the evolution of data generation and usage, however we have tried to keep this to a minimum.
This book presents enough technical aspects of Big Data that should be picked up by those aspiring managers who are planning to take a plunge into this ocean of opportunity. We have also not covered Big Data hardware or storage in detail but have focused more on analytics and the intelligence part of Big Data from where most businesses create value.
Although this it not a technical book, Chapter 2
has two sections. The first discusses how some organizations apply Big Data technologies, often resulting in disruption in traditional markets. The second section provides a historical review of the development of data science and some of the technologies that have developed over time. This is an important section as it provides some of the basic knowledge of terminology and the technology that underpins Big Data. In particular, it gives an explanation of the difference between structured and unstructured data. The explosion in unstructured data is from the conversations, blogs and web pages on the internet that can hold important insights on customers, trends and sentiments expressed among millions of internet users. Mining this data is what Big Data is all about.
is a primer for the understanding of value creation. It explains how financial value is calculated and gives examples of how Big Data can be used to create value. As this book is aimed at managers in organizations, a good understanding of the drivers of value creation is essential for any investment decision including Big Data. The chapter is built around the financials of a typical mid-sized business and will use its pro-forma income statement and balance sheet to show the potential impact of investment in Big Data. The aim of this chapter is a good understanding of the drivers of value creation. We expect managers to implement their learning from this chapter in measuring the success of any future project that they may run. We have also suggested a model and framework in Chapter 5
, where value creation will be tested and acted on, in the last stage of the model.
is a description of Big Data technologies. This chapter takes us to the next stage – understanding the data and impact on value creation is associated with the analysis and extraction of insights from the data collected. In this chapter, we will be discussing different techniques of data analytics from old statistical models to the latest predictive analytics and data visualizations. The chapter will get technical at times, but this is inevitable, given that Big Data is underpinned by technology. Our aim is to give an overview and to provide lists of the pros and cons, so that a manager can have an excellent quality discussion with data scientists or technical teams. An important part of Big Data is unstructured data such as emails, blogs and text-based data and the chapter gives an overview of the different analytics techniques that can be used for this type of analysis as well.
is the key take-away of this book. Based on our work and observations from many Big Data studies, we present you with a model that
can help any practising manager to lead Big Data projects. In Chapter 5
the C-ADAPT model
of Big Data value creation offers a systematic model and practical template for managers who are tasked with building strategies for Big Data projects. We introduce the model and framework and explain the different elements and how they are used. The last stage of the model will help you measure the value created to ensure that the project is a sound investment in time and effort or suggest how you can make changes in the next iteration of the project. We also present the main tool to use with the suggested model – the C-ADAPT worksheet
– which will help you during the process of executing the project and will keep you focused and help you drive the project accordingly.
includes a range of case studies around Big Data analytics and value creation. The cases are from technology companies like Intel to agriculture-related companies like John Deere. The reason for including such a variety of cases is to show the reader that Big Data analytics can be used across any industry and different types of businesses. Fundamentally in all the cases, you can see that the C-ADAPT model can be mapped or applied if needed. The authors worked with first-hand experience in some of the cases, but other cases are contributions by other practitioners in different industries. Our intention is to present these cases, so you can identify your own business problem where possible. These cases are also written in a very concise and direct manner – if more details are needed or you would like to discuss anything, then you can reach out to the authors.
The concluding chapter discusses the overall take-away and some of the issues we can see over the horizon on a fast-moving and disruptive technological development that most business managers will need to get their heads around and make decisions about.
If you want to keep your reading light, you can skim-read Chapter 4
– Big Data techniques and solutions. Also, if you feel you are well versed with the concept and process of creating value, then you can skim-read Chapter 3
When you are done with reading this book, we would hope that you will be more confident about the subject and process of value creation.
If you are in a business that produces products or services that no other business does, there are no substitutes for your offerings in the market, there is a growing demand for your offerings and your customers know how to reach out to you, then your business is in a very advantageous position. Mostly this kind of business can be categorized as a monopoly business and the only thing you can hope for is that no other business will start to produce a similar or substitute product in the future. In today’s world, monopoly firms can be formed only under very special circumstances – some examples may be when the government grants a special monopoly status to a firm such as the Post Office,1
but this is not the case with most of the businesses around. For most businesses getting to this advantageous position can seem very unrealistic and difficult, and even if a business is in such a situation, it is very unlikely that it can continue under the same conditions for long.
Competition is the key component of any market – in this world, it is very rare to find any business without competition. Businesses try to create entry barriers by using unique ways and strategies to keep their competitors away from the market they are operating in. Sometime such strategies are also driven by their positioning or market segment. In a healthy competitive environment, competition plays the role of catalyst for innovation and growth in the market.
In the UK, supermarket chains Aldi and Lidl are known for their budget offerings and they attract price sensitive customers to their stores. In the fourth quarter of 2015 these two supermarkets demonstrated sales growth of 13.3% and 18.5% respectively.2
At the same time Waitrose, a supermarket with premium price products, has seen the worst dip in their sales since 2006.3
Then in 2016, Waitrose became innovative and launched a new discount campaign – a ‘Pick your own offer’ where they decided to offer 20% discount on the most frequently used 10 products from their selection of
100 products. This strategy worked very well and the new campaign boosted their sales by 3.7% while other supermarkets like Tesco, Asda and Morrisons faced decline.4
Waitrose’s discount strategy was inspired by the growth of the budget supermarkets’ success and post-recession market sentiments that have affected the buying capacity of the average customer. Coming up with the right discount strategy was fuelled by extensive market research that included market and competition data and user behaviour data analysis and business intelligence. For Waitrose, their ‘Pick your own offer’ was one of the most successful strategies that helped them in securing an initial 700,000 customers within three months of its launch.5
This is one of the many examples where retail businesses have recorded sales data and using analytics have established customer journey and buying behaviours of their customers with a great degree of accuracy. Tesco, a large retail chain in the UK, is known for their introduction of a loyalty card (Tesco Club Card) – they use customer spending and usage data in establishing buying patterns and consumer behaviour of their customers.
In 2008, Brian Chesky and Joe Gebbia launched a company called Airbnb. Airbnb is an open marketplace that enables people to list, find and rent vacation homes/spare rooms for a fee. Airbnb challenged the hotel industry that had been in existence for hundr...