(05) Debating big data: A literature review on realizing value from big data. Flashcards
Work-practice debates
(1) Inductive and deductive approaches to big data analysis
The article argues that induction and deduction are, in practice, two ideal approaches that intertwine and complement each other, which implies the need to balance them.
> “Are we equipped to be able to do both?”
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(2) Algorithmic and human-based intelligence.
The article states that both types of intelligence have strengths and weaknesses, and in many cases, still augment each other. Therefore, it is proposed that organizations need to devise ways to meaningfully balance them.
Organizational Debates
(3) Centralized VS. decentralized big data capability structures
When designing for analytics, organizations may assess the synergistic benefits of centralized capability structures, as well as recognize the need for specific expertise associated with decentralizing. Hybrids between centralized and decentralized structures have been suggested as a viable compromise.
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(4) Incremental v.s. Radical
Big data-driven improvement and innovation implies that organizations can leverage big data while generally continuing to function in the same manner, only more effectively and efficiently: ‘‘incremental enhancements to established business models through increased digitization and big data analytics may replace less efficient business models (and thereby companies) in the long run.
Organizations can also innovate their business
models when big data leads them to develop whole new value propositions, target different
customers, or interact with customers in different ways.
Supra-organizational Debates
(5) Controlled v.s. open access to big data
Organizations can be reluctant to share or exchange data with network partners due to, for example, privacy and security concerns, or when data analytics is considered a source of competition and sharing endangers the organization’s unique strategic position. On the receiving end, organizations can control data access by choosing to collect data from specific channels only, that is, those that are considered most valid, reliable, and robust.
Open modes of access are emerging in which data are made publicly available to organizations and consumers. Commercial organizations and governments can choose to open up data to, for example, stimulate innovation and provide transparency.
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(6) Social v.s. Economic
Minimizing and neglecting the social risks of big data value realization at the supra-organizational level, several stakeholders, including organizations, their competitors, partners, and customers, can mutually benefit from data being shared and combined.
Data governance, that is, the arrangements around policies, procedures, and formal control, generally plays an important role when multiple stakeholders are involved.
Portability and inter connectivity as social-technical features
Drawing on the six debates discussed in the previous section, the article formalizes two socio-technical features that shape how organizations realize value from big data:
portability and interconnectivity.
(1) With portability the article refers to the possibility to transfer and remotely access digitized data from one context of application to be used in other contexts. In essence, organizations ‘‘now have access to essential data needed to solve problems or gain insights that was not possible to collect before” as data can be transferred and remotely accessed across technological platforms and organizational boundaries.
(2) With interconnectivity the article refers to the possibility to synthesize data from various big data sources. In essence, sophisticated technologies increasingly allow actors to integrate heterogeneous sources of data and extract insights from their combination. This enables organizations to go beyond the pre-existing templates of tapping isolated data sources by correlating and combining them in new ways.
