Big Data for Digital Transformation of Public Services

Big Data for Digital Transformation of Public Services

Deepak Saxena
DOI: 10.4018/978-1-7998-8583-2.ch013
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Big data is presently considered integral to the management and strategies for digital enterprise transformation. Beyond being ‘a lot of data', big data can be characterized in terms of seven Vs: volume, velocity, variety, variability, veracity, visualization, and value. Already being applied in private businesses, big data has immense potential for the digital transformation of public services in advancing the e-governance agenda. This chapter explores the nature of big data in public service and discusses its application in areas such as tax administration, transportation, energy, public health, and disaster management. Challenges and concerns are noted in terms of data quality, infrastructure cost, availability of suitable human resources, privacy, and security. Possible solutions such as shared services, cloud computing, open source software, open data framework, and regulatory compliance are noted. The chapter ends by noting future research directions to realize the full potential of Big data application in digital transformation of public services.
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Digital enterprise transformation may be defined as end-to-end value creation for the enterprise via management and application of digital technology aligned with business strategy. Big data is presently considered integral to the management and strategies for digital enterprise transformation. However, it is not entirely a new term. In the context of data and storage management, the term Big data was already in use in computing circles (e.g. Bourgoin & Smith, 1995; Mashey, 1997) even before the dawn of the new millennium. However, the term became well-known at the start of the decade when popular business research outlets such as McKinsey Global Institute (Brown, Chui, & Manyika, 2011) and Harvard Business Review (McAfee & Brynjolfsson, 2012) started publishing about it. By 2015, Gartner dropped the term from the hype cycle, noting that Big data has become so prevalent that it is now part of multiple emerging technologies (Heudecker, 2015) such as business intelligence, data science, social media analytics, enterprise information management or machine learning. The central role of Big data in digital enterprise transformation reflects in wide availability of technological solutions such as Apache Hadoop, Spark, Microsoft HDInsight, NoSQL, or Hive. In fact, Apache Hadoop has emerged as a best in-class Big data ecosystem, supporting data processing capabilities and system connectivity across heterogeneous databases and systems. However, since the focus of this chapter is on management and strategies for the transformation of digital enterprises, specific technologies are not discussed in this chapter. The interested reader is directed towards some recent works (Luengo, García-Gil, Ramírez-Gallego, García, & Herrera, 2020; Rao, Mitra, Bhatt, & Goswami, 2019) for a discussion on Big data ecosystem, technology and tools.

The key role of Big data in digital enterprise transformation and value creation may be inferred from the fact that KPMG / Harvey Nash CIO survey (2019) identifies Big data analytics as the most important (and the scarcest) skillset required in organizations. Big Data capabilities are found to be an important predictor of value creation and firm performance (Wamba, Gunasekaran, Akter, Ren, Dubey, & Childe, 2017; Mikalef, Boura, Lekakos, & Krogstie, 2019), especially in the private sector. However, the scope of Big data is not just limited to the private sector firms. Big data is frequently noted (Joseph & Johnson, 2013; Gaardboe, Svarre, & Kanstrup, 2015; Patel, Roy, Bhattacharyya, & Kim, 2017; Shukla & Mathur, 2020) as a key element in the management and strategies of digital public service transformation. This chapter explores the potential of Big data in advancing the e-Governance agenda via digital transformation of public services. Hence, the research question for this study is: How does Big data contribute towards the digital transformation of public services?. The research approach is exploratory in nature and thus relies on secondary data and studies. The remainder of the chapter is as follows. The next section provides a general background to Big data and outlines seven Vs that underpin the Big data revolution. This is followed by a discussion on how seven Vs are applicable to Big data in public service. Key application areas from the public service that may be transformed by Big data applications are discussed in detail. Thereafter. challenges and concerns associated with the development of Big data capabilities in the government are also noted. This is followed by offering solutions and recommendations to the problems raised earlier. Future research directions are noted before concluding the chapter.

Key Terms in this Chapter

E-Governance: Application of digital technology for the purpose of efficient, effective, and responsive public governance.

Variability: Variation in the meaning when processing natural language data.

Big Data: Datasets characterized by Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value.

Strategy: A long term direction outlining the value proposition and growth of the enterprise to attain its objective.

Management: The set of principles and actions geared toward implementing the strategy to attain the objectives of the enterprise.

Enterprise: An organizational entity with a definite objectives, scopes, and operational rules. An enterprise focus may be conducting a business or providing public services.

Digital Enterprise Transformation: End-to-end value creation for the enterprise via the management and application of digital technology aligned with business strategy.

Visualization: Visual interpretation of data and analysis to assist in decision-making.

Value: Intended outcomes, for instance efficiency or reputational gains, made possible by Big data analytics.

Volume: Amount of data generated.

Velocity: The speed with which data is generated.

Variety: Various forms of data in disparate formats.

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