Big data is high-volume, high-velocity, high-variety, and high-veracity data generated by digital devices. Over the past decade, efforts to harness this data for predictive purposes have increased dramatically in all areas of society, including the private, public, and civil sectors. Big data analytics has largely been used for three main goals: more effective marketing, increased internal connectivity, and enhanced efficiency.
The first of these goals, marketing, is easiest to observe within the ‘Internet data economy’, as defined by Dr Jovan Kurbalija in An Introduction to Internet Governance. The Internet data economy describes the advertising-based business model developed by Silicon Valley companies in the late 1990s, where companies such as Google and Facebook analyse users’ data regarding activities, preferences, and behaviors, to provide targeted advertising. Traditional brick-and-mortar companies have also taken advantage of big data in their marketing efforts. A study by Dr Amir Gandomi and Mr Murtaza Haider, describes how facial recognition technologies allow retailers to ‘acquire intelligence about store traffic, the age or gender composition of their customers, and their in-store movement patterns. This invaluable information is leveraged in decisions related to product promotions, placement, and staffing’.
The private sector is not alone in using big data for marketing. At the 2017 Knowledge for Development Conference, participants shared their successful examples of implementing big data analytics for marketing and knowledge management goals. Francesco Pisano explained how the UN Library is trying to ‘mobilise’ data and transform the traditionally passive library model into an interactive, data-driven forum. This mobilisation involves organising informational pipelines to allow for a flow of specific literary material to its target audience, who can then send feedback about the usefulness of the content. Big data analytics has also been helpful in increasing internal connectivity within organisations through knowledge management, which seeks to provide a network for the employees and staff of a firm to collaboratively share information and act as a more cohesive unit. For example, Mr Ian Thorpe from the United Nations International Children’s Emergency Fund (UNICEF), highlighted how information sharing connects employees in the organisation to build a holistic staff directory that breaks down structural silos. Likewise, Ms Maria Gonzalez Asia, from the World Bank, emphasised the success of the Global Delivery Initiative, which connects practitioners to take advantage of cumulative knowledge. Practitioners can report non-technical issues during the implementation of their projects, which is added to their database and connects the individual to others who had similar problems. Through an analysis of the resulting big data, the initiative is then able to predict the most difficult delivery challenges to streamline future projects.
The Global Delivery Initiative example above highlights another important role for big data: enhanced efficiency. One of the most exciting prospects of big data analytics is its role in increasing productivity and streamlining processes in both the private and public sectors. The results of big data analytics have been largely positive so far. According to a study by Dr Shahriar Akter and Mr Samuel Fosso Wamba, ‘e-commerce firms that use big data analytics (BDA) experience 5-6% higher productivity than competitors, BDA contributes to 10% or more of growth for 56% of firms, and 91% of Fortune 1000 companies are investing in BDA projects as of 2016’. Likewise, statistics from Akter and Wamba and the International Data Corporation indicate optimism for the BDA industry: worldwide revenues for BDA increased from USD$7.3 billion in 2011 to USD$130.1 billion in 2016, and is expected to grow with a compound annual growth rate (CAGR) of 11.7% to reach USD$203 billion by 2020. This widespread adoption includes actors outside the private sector as well: for example, as described at the Knowledge Management Conference, the World Bank uses big data to track its road-building projects in the Philippines. By linking road data to mobile devices and local networks, the World Bank can monitor conditions and ensure accountability within the projects it finances. Another relevant example is the US government, which initiated significant big data projects under the Obama Administration. 93% of federal government respondents with big data projects said that this analysis had improved the ‘quality and speed of decision-making’, and 87% said it improved their ability to ‘predict trends and quantify risk’.
Clearly, successful implementation of big data analytics offers a tremendous opportunity for organisations to improve their operations and meet their objectives more easily.
Dane Burkholder is an undergraduate student at Duke University in North Carolina. He is studying Economics, Finance, and Biology and interned with DiploFoundation in Geneva from April to May 2017.