Big Data Drivers

Technological developments such as personal computers, data communications, and collaboration tools allow both organisations and individuals to create local and global networks. These networks enable data sharing, social communications and emphasise the importance of individuals as change drivers. The uprisings in the Arab countries in 2011 were called “Facebook-revolutions” due to the centric role of social media platforms and unofficial, dynamic networks. People used their mobile phones and apps to communicate, collaborate and organise the events, as well as to pass around data such as text, photos and videos. They made decisions based on the information they shared and gathered in real-time using social media. Technology enables new, sometimes unpredicted ways of doing things.

Connectivity, IoT and mobility are commonly identified, e.g. (Davenport 2014; Mui &

Carroll 2013; Van’t Spijker 2014), as key trends behind the data generation. Information

2.1 Introduction to Big Data 33 technology has enabled enterprises and people to connect for decades. The invention of the personal computer and the Internet have had a big influence on this. Enterprises integrate their processes with others. For example, many companies have created seamless logistics processes with their suppliers. A recent phenomenon is the exploitation of various social platforms. These enable individuals to easily connect with others, create unofficial networks that reach well over company boundaries. Van’t Spijker (2014) underlines that this changes the ways employees communicate as well as the ways clients communicate. These new ways of communicating shape the businesses. At the same time these interactions create huge amounts of data.

The Internet of Things –concept (IoT) is another driver behind the data deluge. Among many others, Van’t Spijker (2014) states that the IoT makes it possible to create products and services that have never before been possible. Energy companies now invoice their clients using real-time data from smart meters. However, the same data offers more possibilities. For example, companies could create energy consumption profiles of households and produce energy-savings information for their clients. Since each electrical device has a unique energy consumption profile, they could even analyse the data to see whether a client’s washing machine wastes energy. Based on the data and analytics the company could then suggest a new machine that meets the customers’ needs but saves energy and thus money. Smart meters as well as many other IoT applications are capable of generating detailed data in real-time. No human input is required; the IoT concept enables computers to sense the surrounding world by themselves. This autonomy, combined with the fact that IoT implementations are rapidly increasing is leading to the exponential growth of data from IoT sources.

The third change driver is mobility, e.g. (Davenport 2014; Mui & Carroll 2013; Van’t Spijker 2014). Mobile devices, such as smart phones and tablets have had an enormous impact on peoples’ lives. Mobility changes both individuals’ lives and their way of working. Smart phones are easy to use, they have access to the Internet, and they have several sensors, e.g. cameras and GPS-sensors. Documenting a planning draft from a flip board is easy, just take a picture and send it to your colleagues. Moreover, humans are social beings; we want to share things with others. Foursquare⁴ is a social sharing app that uses location data and helps you find restaurants etc. based on the suggestions of other people. Connectivity and mobility are closely related. With mobile devices, we are always connected and available – to home, to work, or to social platforms. This not only creates data, but also a new kind of dynamics regarding what we do and how we act.

Services enabled by change drivers create value for their members, e.g. pieces of information, cumulated knowledge or just pure fun, but they also emphasise the role of platforms in value generation. The platform cumulates the data the members create.

Moreover, every time a member visits the platform, a pile of digital breadcrumbs is left behind. The owner of the platform has access to members’ digital trails as well as to the

4 https://foursquare.com/

data the members have created. LinkedIn⁵ has more than 546 million members and probably knows more about career paths than most HR departments.

The resulting data volumes are huge. Figure 6 shows an example. This illustration presents, how Internet users (3.7 billion people in 2017) produced data using some popular services and platforms. The volumes are from 2017 and it should be noted that the numbers in the illustration are per minute.

Figure 6. An exemplary illustration of Internet data volumes⁶.

5 LinkedIn is a business-oriented social networking service (https://about.linkedin.com/)

6 The infographics was created by Domo, see: https://www.domo.com/learn/data-never-sleeps-5

2.1 Introduction to Big Data 35

Moreover, the growth shows no signs of slowing down. On the contrary, in many areas (such as IoT), the trend of the growth is exponential. The number of people who have Internet access is growing, the number of smartphones is increasing, and the IoT concept is emerging, just to name a few factors. Thus, the amount of data is growing at an ever-increasing pace. Figure 7 displays a forecast of annual IP traffic globally. Compound annual growth rate (CAGR) is 23 % per year.

Figure 7. Global IP traffic growth forecast⁷.

The volumes and growth numbers are impressive. However, an enterprise must look deeper in order to understand which data might be of value for its business. The next chapter takes a different perspective to clarify, where the data comes from.