Strategic Management of Data and Challenges for Organizations: Strategy Development and Business Value

Strategic Management of Data and Challenges for Organizations: Strategy Development and Business Value

Stephen Andrew Roberts (University of West London, UK) and Bruce Laurie (University of West London, UK)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/978-1-5225-0293-7.ch002
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Abstract

Public, organizational and personal data has never been so much in the forefront of discussion and attention as at the present time. The term ‘Big Data' (BD) has become part of public discourse, in the press, broadcast media and on the web. Most people in the wider public have very little idea of what it is and what it means but anyone who gives it a thought will see it as contemporary and relevant to life as much as to business. This paper is directed towards the perspectives of people working in, managing and developing organizations which are dedicated to fulfilling their respective purposes. All organizations need to understand their strategic purpose and to develop strategies and tactical responses accordingly. The organizations' purpose and the frameworks and resources adopted are part of its quest for achievement which creates value and worth. BD is a potential and actual source of value.
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Data: A New Informing Resource

At the root of typical definitions of data are the ideas of the power to define and inform as well as to represent reality in terms of identity and state. Additionally, data carries the idea of unity, definition and lack of ambiguity. Numerical data maximises these unitary properties with added advantage that they can be manipulated mathematically: we often call this ‘hard data’ / quantitative data. The concept and realization of data can also be used to represent levels of ambiguity and endowed with structure and durability through the use of tools such as coding, categorization, compression and abstraction. The term ‘soft data/qualitative data is used to describe this data. Both types of data can be regarded as valid from a user perspective, but soft data often requires human intervention for its capture and this modifies or even potentially degrades it quality. This becomes more significant when sources of data are created and processed, and similarly may affect its interpretation and integrity.

Big data could easily be called ‘wide data’, or ‘deep data’. One view is essentially that BD is a convenient way of naming the concept of linking one data set of one kind to one or more others of different kinds. It is a metaphor: the core quality is not its ‘bigness’ but its origin, informing quality and the properties required to manipulate and process it. And it is not only a matter of linkage, but also of ambition and concept. What can be linked to what? And, what needs to be linked to what? As a concept it can be regarded as a ‘state of mind’. You have constructed the techniques to identify and link data sets, which then leads to the ‘state of mind’ as processing articulates the informing qualities revealed in the embedded content. The term Big Data then describes the end results of the process as it is revealed through the evidence it provides and comprehended. This definition subsumes the data itself and the processes that have created it: data is a gathered, stored (warehoused, processed, analysed (with specific analytics and tools), accessed and displayed, evaluated and then the results are applied cognitively (an interpretation and decision process) to underpin an action.

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