1.1 Competing in a Data-Driven Age
Every time we send an email or a message, visit a website, tap an icon on a smartphone, or post and share comments, photos, and video in social media, we generate digital data. But even when we perform actions in the analog world we generate digital data: buying products at the supermarket, driving our connected car, taking the train or the bus, watching an on-demand movie, or simply taking a walk with our geo-localized smartphone in our pocket or using our credit card creates a huge amount of data. Data is considered as the new raw material of the twenty-first century (Berners-Lee and Shadbolt 2011), and its use through analytics is dramatically changing the basis of competition. The volume of available data has grown exponentially in recent years due to the increasing number of individuals, devices, and sensors that are connected by digital networks, along with the development of more sophisticated algorithms, and the improvements of computational power and data storage (McKinsey Global Institute 2016).
Big data analysis is generating significant value across sectors, enhancing the competitiveness of companies. The more data-driven a firm is, the more value it generates in terms of knowledge, higher productivity, profit, and market value (BARC 2015; Brynjolfsson et al. 2011). Several classes of value have been associated with the use of big data (Davenport 2014; Lee 2017):
- Improvements in decision-making. Companies can use sophisticated analytics and develop algorithms to optimize their decision processes, such as the automatic fine-tuning of inventories and pricing in response to real-time in-store and online sales, as well as to minimize risks. For instance, through the use of optimization techniques it is possible to identify the price of a product that is more likely to generate high profitability or the level of inventory that is more likely to avoid stock-outs (Davenport and Kim 2013).
- Increase in process efficiency. The use of sensors and data analytics favors cost savings in operations and improves companies’ reaction time to issues in the supply chain, such as better demand forecasts, optimized distribution network management, transportation, and routing (Sanders 2016). For instance, in the fashion industry, the Prada Group is using Oracle technology to analyze historical data and market demands across its global retail network of 634 stores in order to optimize the merchandising process and detect trends as well as for performance analysis, inventory management, and allocation.
- Enhancement of customer experience. Granular data, namely detailed data for each single customer, allows organizations to implement specific market segmentations and to tailor products and services to meet specific customers’ needs. For instance, major retailers analyzing preferences and sentiments data can deliver personalized product/service recommendations and promotional offers, whereas financial companies exploiting social media data are able to assess the credit risk and financial needs of potential clients and provide new types of financial products.
- Innovation of business models, products, and services. Data on customers’ purchase decisions and social feedback mechanisms can be complemented with digital payments and transaction data to delve deeper into innovation and product adoption. The use of big data can also promote the introduction of new business models in traditional industries, as in the case of Nike, which from a shoes manufacturer became a digital platform owner for data-driven fitness services, or Under Armour, which from solely a sports apparel company partnered with IBM Watson to apply artificial intelligence to create UA Record, an app that provides evidence-based coaching around sleep, fitness, and nutrition.
- Improvements in customer service. Data on the same customer is integrated from multiple channels, allowing service personnel to better understand problems and address them quickly. Moreover, big data analytics can be used to monitor transactions in real time and detect fraudulent activities.
Besides the economic value mentioned above, big data analysis may also generate social value, enhancing transparency, preventing frauds and crimes, responding to natural disasters. Improving national security, increasing transportation safety, and supporting the well-being of people through better education and health care (Günther et al. 2017).
Organizations are still struggling to capture the full potential of big data. As underlined in the Future of Jobs Report released by the World Economic Forum (2018), by 2022 85 percent of the surveyed companies are likely to invest in user and entity big data analytics and 75 percent are likely to increase the use of Internet of Things and app- and web-enabled markets. Likewise, machine learning and cloud computing are receiving considerable attention: respectively 73 percent and 72 percent of the surveyed companies indicated their intention to adopt these technologies.
In addition to spurring the rate of technological advancement and its related adoption in organizational contexts, big data is profoundly changing the job-relevant skills requested in the labor market. Indeed, while the investment in big data technologies is becoming paramount, at least as important is to attract those professionals with the skills profile relevant to use these technologies effectively (Davenport and Patil 2012). Among the most in-demand digital professional roles to emerge are those of data analysts and scientists, artificial intelligence and machine learning specialists, and big data specialists (World Economic Forum 2018).
But what is big data, and how can it be leveraged to create value?
The term “big data” was coined in the mid-1990s but became widespread after 2011 (Gandomi and Haider 2015; Mishra et al. 2017). In providing definitions of big data, academics and practitioners have tried to highlight the properties that characterize information in the digital era. Specifically, big data has been conceptualized as information assets characterized by a combination of volume, variety, and velocity (the so-called Three Vs) that creates an opportunity for organizations to gain competitive advantage in today’s digitized marketplace (Chen et al. 2012; Kwon et al. 2014; De Mauro et al. 2016).
The size or the magnitude of data (“Volume”) is the first dimension that comes to mind when defining data as “big.” Currently, exabytes (1 million terabytes) or zettabytes (1000 exabytes) qualify high-volume data, even if bigger units of measure are occasionally developed, since – as the data storage capacities continue to increase – the property “volume” is relative and varies by time. One of the most important fuels of the increased volume of data in recent years is the phenomenon of the Internet of Things (IoT), namely the pervasive presence of a variety of objects – phones, sensors, Radio-Frequency Identification (RFID) tags, and actuators, among others – which can communicate and interact with each other, over the Internet, and can be remotely monitored and controlled. Data volume is expected to exponentially grow in the next years due to the increasing number of Internet users and the billions of connected devices and embedded systems that create, collect, and share data every day.
The second dimension, velocity, refers to the frequency of generation of data and the speed at which it is analyzed. The diffusion of digital devices, like smartphones and sens...