Big data is characterized as huge datasets generated at a fast rate, in unstructured, semi-structured, and structured data formats, with inconsistencies and disparate data types and sources. The challenge is having the right tools to process large datasets in an acceptable timeframe and within reasonable cost range. So, how can social media big datasets be harnessed for best value decision making? The approach adopted was site scraping to collect online data from social media and other websites. The datasets have been harnessed to provide better understanding of customers' needs and preferences. It's applied to design targeted campaigns, to optimize business processes, and to improve performance. Using the social media facts and rules, a multivariate value creation decision model was built to assist executives to create value based on improved “knowledge” in a hindsight-foresight-insight continuum about their operations and initiatives and to make informed decisions. The authors also demonstrated use cases of insights computed as equations that could be leveraged to create sustainable value.
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Social media gives everyone - not only B2B companies but also consumer brands, consultants, non-profits, and even rock bands, churches, and colleges - a tremendous opportunity to reach people and engage them in new and different ways. Now we can earn attention by creating something interesting and valuable and then publishing it online for free: a YouTube video, a blog, a research report, photos, a Twitter stream, an e-book, a Facebook page. Those measurements, which seemed so great in an offline world, are wholly inadequate online. But what should we do instead? A debate has raged in recent years. On one hand, people tried to adapt old (but successful) offline measurements to the social media world. David Meerman Scott, (culled from the Foreword of (Sterne, 2010, p. x))
The above quote can be said to be a synopsis of this book chapter on Leveraging on New Media for Value Creation. New Media and its associated infrastructure of Information Systems play crucial roles in businesses today. Businesses have become dependent on Information systems, and indeed, the survival of businesses is hinged on the importance placed on information systems.
As technology becomes more pervasive, flexible and easier to use, the issues of globalization affect every business (Piccoli, 2013). The market place has expanded from hitherto local economies to a global space. To compete effectively and efficiently, decision-makers require secured, relevant and accurate information or data. The size of data, as well as its mostly unstructured complex nature, and coupled with the rate at which data is generated – Big Data – is the object of this study.
Traditionally, in industrial economies, there have been three (3) factors of production until in recent times. The Financial Times, in its December 27, 2012 edition, posited that “Big Data” has assumed the 4th factor of production, considering its pivotal role in business decision-making (Jones, 2012). Big data appeals to corporate executives and business leaders, with such intuition and creative thinking – that are unexpectedly emanating from data patterns (Dunlop, 2015). In terms of value creation, Big Data could be harnessed for value creation or competitive advantage, especially when an increased range of data sources are employed. Jones (2012) posits that Big Data can be harnessed for value creation by employing both subjective and objective decision-making approaches, i.e. “intuitive and analytical thinking”.
Big Data can be used to manage the limited government resources to the right people at the right time. For example, Predictive Policing uses data to predict where crimes might occur, so that police can deploy its limited resources efficiently (Dunlop, 2015). That is, Big Data is used to identify people and locations at risk of crime.
Big Data is basically “large datasets (with vast amounts of data) with an irregular structure” involving high storage of data. Big Data is about the customer behavior, rather than the transactions carried out (Chen, Mao, & Liu, 2014). Big Data finds various uses from social media user generated data (Baker, 2013) (Dunlop, 2015) (Cerrato, 2012).
Big data is usually multivariate in nature – and in enterprises, must, should and ought to fit into the key processes and more importantly, to create value for the business. Big Data – should, must and ought – NOT to be:
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Disorganized;
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Dysfunctional and unusable;
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Disconnected from business strategy; and
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Denying business leaders the needed intelligence to make key informed decisions.
In discussing Big Data value creation, Preator (2018) avers that there needs to be organizational clarity and alignment with the business strategy. In essence, Big Data value creation must, necessarily: