‘Datafication’: making sense of (big) data in a complex world

By Mark Geoffrey Lycett. European Journal of Information Systems, 2013

426 citations recorded by the source corpus.

Subfields: Management Information Systems, Sociology and Political Science, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/67ea3cb20608797f5e8798a183ca45a6d345e146

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Citations in the corpus

Constructing a New Asset Class: Property-led Financial Accumulation after the Crisis (2018, methodology): “RentRange demonstrates how, by separating data from the locally and contextually situated material objects it represents, sociospatially fragmented assets may be aggregated “in ways that traditionally have been difficult, overly time-consuming or expensive” (Lycett 2013, 382).”
Big Data consumer analytics and the transformation of marketing (2016, neutral): “Cutting edge technology and algorithms enable researchers to identify patterns mathematically without formal hypotheses (Anderson, 2008; Lycett, 2013).”
Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “IoT and Big Data are united by their emphasis on information that is rich in volume, velocity and variety, and which requires innovative forms of processing (Lycett, 2013).”
Business intelligence & analytics in management accounting research: Status and future focus (2018, background): “…2015; Woerner and Wixom, 2015; Loebbecke and Picot, 2015) and accounting (Bhimani and Willcocks, 2014; Vasarhelyi et al., 2015), its implications for the accounting profession (Warren et al., 2015), individuals, organizations and society are much less understood (Lycett, 2013; Zubo ﬀ , 2015).”
A feel for numbers: affect, data and education policy (2015, background): “Lycett (2013) draws attention to three modes of sense-making involved in commensuration or ‘datafication’: the conceptualisation and codification by which the pre-existing frames, categories and classifications shape the information that is constituted as data and which influence the possibilities…”
Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “These effects primarily pertain to sensemaking, which is the process of deriving knowledge based on information extracted from big data (Lycett, 2013).”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “Data velocity focuses on the relentless rapidity of data generation, which enables firms to provide timely insights (Lycett, 2013).”
Debating big data: A literature review on realizing value from big data (2017, methodology): “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 (Madsen, 2015).”
Big data analytics adoption: Determinants and performances among small to medium-sized enterprises (2020, background): “BDA adoption and market performance BDA applications enable organizations to efficiently manage and utilize the hidden values of big data for better decision-making and innovation activities (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016) and marketing strategies (Lycett, 2013).”
Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market (2020, background): “To be maximally effective, this data has to be rich in volume (there must be large amounts of it available), velocity (there must be a high data flow rate), and variety (it must come from many sources) (Lycett 2013).”
Data science, predictive analytics, and big data in supply chain management: Current state and future potential (2015, positive): “It has been noted that big data analytics facilitates enhanced decision making, increased visibility, better risk management, and overall greater value (Akkermans and Van Was-senhove 2013; Lycett 2013; Chae et al. 2014).”