Innovation Platforms: Data and Analytics Platforms

Innovation Platforms: Data and Analytics Platforms

DOI: 10.4018/978-1-5225-5457-8.ch003

Abstract

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.
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Introduction

Over the last decade, the business has become more and more focused on data. This trend is a consequence of the success of many organizations that have used the data they collect to drive their business. The data and analytics platform supports the digital business platform. White and Oestreich (2017) argue that a data and analytics platform is not a technology in its self:

Primary it is a set of business capabilities that need to be defined, prioritized, then balanced (investment wise) to sustain the desired maturity level across the broader digital business platform. Once this has been considered and mapped to your digital strategy, maturity and investment in people, process, data and analytics technology need to be adjusted in order to prepare for and assist the transition to digital business.

The emergence of a new wave of data from sources, such as the Internet of Things (IoT), Sensor Networks, Open Data on the Web, data from mobile applications, social network data, together with the natural growth of datasets inside organizations, creates new ways to reuse and extract value from such BD (BD) assets (Manyika et al. 2011; Forbes, 2015; Cavanillas, Curry, Wahlster, 2016; BDVA, 2017).

The ability to effectively manage data, information and extract knowledge is now seen as a key competitive advantage, and many organizations are building their core business on their ability to collect and analyze information to extract business knowledge and insight. BD is related with the emerging digital economy and becomes useful when it enriches decision-making that is enhanced by application of analytical techniques and some elements of human interaction. Digital technologies, including the Internet, company Intranets and other related technologies, are changing the way the companies do business (Peitz, Waldfogel, 2013). BD technology adoption within industrial sectors is an imperative need for most organizations to gain competitive advantage.

BD provides a wealth of potential insight that can transform asset management in ways that help companies get more value from their assets. Nevertheless, conventional maintenance management systems and processes are not enough. Asset managers need ways to turn BD into information, knowledge and insights as well as enable information transparency throughout the entire asset lifecycle and within the ecosystem of operators, equipment manufacturers, service providers etc.

Thus, Internet and related technologies are more than just new digital channels: they are a completely different approach of providing business services and creating new business ecosystems and business models. BD and BD analytics are increasingly seen as a core capability for digital business. This will require organizations to reconsider long-standing process and process modification approaches on the path to intelligent business operations and BD assets management.

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