Business Drivers for Big Data
Business Drivers for Big Data
Business Drivers for Big Data
Big Data emerged in the last decade from a combination of business needs and technology innovations. A number of companies that have Big Data at the core of their strategy have become very successful at the beginning o f the 21st century. Famous examples include Apple, Amazon, Facebook and Netflix.
A number of business drivers are at the core of this success and explain why Big Data has quickly risen to become one of the most coveted topics in the industry. Six main business drivers can be identified as below:
- The digitization of society;
- The plummeting of technology computing;
- Connectivity through cloud computing;
- Increased knowledge about data science;
- Social media applications;
- The upcoming Internet-of-Things (IoT).
In this article, we will explore a high-level overview of each of these business drivers. Each of these adds to the competitive advantage of enterprises by creating new revenue streams by reducing the operational costs.
The digitization of society
Big Data is largely consumer driven and consumer oriented. Most of the data in the world is generated by consumers, who are nowadays ‘always-on’. Most people now spend 4-6 hours per day consuming and generating data through a variety of devices and (social) applications. With every click, swipe or message, new data is created in a database somewhere around the world. Because everyone now has a smartphone in their pocket, the data creation sums to incomprehensible amounts. Some studies estimate that 60% of data was generated within the last two years, which is a good indication of the rate with which society has digitized.
The plummeting of technology costs
Technology related to collecting and processing massive quantities of diverse (high variety) data has become increasingly more affordable. The costs of data storage and processors keep declining, making it possible for small businesses and individuals to become involved with Big Data. For storage capacity, the often-cited Moore’s Law still holds that the storage density (and therefore capacity) still doubles every two years. The plummeting of technology costs has been depicted in the figure below.
Figure 1: Historical Costs of Computer Memory, reprinted from McCallum and Blok, 2017
Besides the plummeting of the storage costs, a second key contributing factor to the affordability of Big Data has been the development of open source Big Data software frameworks. The most popular software framework (nowadays considered the standard for Big Data) is Apache Hadoop for distributed storage and processing. Due to the high availability of these software frameworks in open sources, it has become increasingly inexpensive to start Big Data projects in organizations.
Connectivity through cloud computing
Cloud computing environments (where data is remotely stored in distributed storage systems) have made it possible to quickly scale up or scale down IT infrastructure and facilitate a pay-as-you-go model. This means that organizations that want to process massive quantities of data (and thus have large storage and processing requirements) do not have to invest in large quantities of IT infrastructure. Instead, they can license the storage and processing capacity they need and only pay for the amounts they actually used. As a result, most of Big Data solutions leverage the possibilities of cloud computing to deliver their solutions to enterprises.
Increased knowledge about data science
In the last decade, the term data science and data scientist have become tremendously popular. In October 2012, Harvard Business Review called the data scientist “sexiest job of the 21st century” and many other publications have featured this new job role in recent years. The demand for data scientist (and similar job titles) has increased tremendously and many people have actively become engaged in the domain of data science.
Figure 2: Increased knowledge about data science
As a result, the knowledge and education about data science has greatly professionalized and more information becomes available every day. While statistics and data analysis mostly remained an academic field previously, it is quickly becoming a popular subject among students and the working population.
Social media applications
Everyone understands the impact that social media has on daily life. However, in the study of Big Data, social media plays a role of paramount importance. Not only because of the sheer volume of data that is produced everyday through platforms such as Twitter, Facebook, LinkedIn and Instagram, but also because social media provides nearly real-time data about human behavior.
Social media data provides insights into the behaviors, preferences and opinions of ‘the public’ on a scale that has never been known before. Due to this, it is immensely valuable to anyone who is able to derive meaning from these large quantities of data. Social media data can be used to identify customer preferences for product development, target new customers for future purchases, or even target potential voters in elections. Social media data might even be considered one of the most important business drivers of Big Data.
The upcoming internet of things (IoT)
The Internet of things (IoT) is the network of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, actuators, and network connectivity which enables these objects to connect and exchange data. It is increasingly gaining popularity as consumer goods providers start including ‘smart’ sensors in household appliances. Whereas the average household in 2010 had around 10 devices that connected to the internet, this number is expected to rise to 50 per household by 2020. Examples of these devices include thermostats, smoke detectors, televisions, audio systems and even smart refrigerators.
Figure 3: The Internet of things (IoT)
Each of these connected devices generates data that is exchanged over the Internet and which can be analyzed to retrieve value. Similar to social media, the data that is generated through IoT devices is massive in terms of quantity and can provide insights into the behavior of consumers. As such, it is extremely valuable.
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