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Juan Mateos-Garcia, Nesta P&R NEMODE PDW BAM Conference 9-11 September, 2014

The profile of the management (data) scientist: Potential scenarios and skills for B/SMD-based Management research. Juan Mateos-Garcia, Nesta P&R NEMODE PDW BAM Conference 9-11 September, 2014. Organisational + personal context.

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Juan Mateos-Garcia, Nesta P&R NEMODE PDW BAM Conference 9-11 September, 2014

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  1. The profile of the management (data) scientist: Potential scenarios and skills for B/SMD-based Management research Juan Mateos-Garcia, Nesta P&R NEMODE PDW BAM Conference 9-11 September, 2014

  2. Organisational + personal context • Nesta: The UK’s innovation foundation., with a mission to help people and organisations bring great ideas to life. • Doing research on data skills for BIS data capability strategy in partnership with RSS and Creative Skillset • Doing some ‘big’ data work myself • I used to do management research (CENTRIM). Draw on all this to reflect on the implications of big data for management research, focusing on skills.

  3. 1. Definitions More online activity, digital processes, better hardware. Generated at faster velocities More varieties of data Larger volumes of data New applications Data-driven (automated, personalised) products, processes and services. New formats for data communication

  4. More complexity

  5. New opportunities for researchers • Coverage: Large samples • Revelation: Make the invisible visible, reveal preferences, run experiments. • Granularity: High level of resolution (temporal + dimensional). • Cheap! £££

  6. 3. MOR examples I looked at abstracts of 103 papers in last three issues of [1] AOMJ, [2] BJM, [3] Management Science. No ‘big data’ papers in [1] and [2]. 11 in MS (8 in a ‘Business Analytics’ special issue)

  7. Technical skills required, or the profile of the management data scientist Access data Get data: Web scraping/API programming skills Run experiments: Experimental designs Manage and process the data: Database management Clean the data:‘wrangling’ (and patience). Model data Initial visualisation: Exploratory data analysis Dimension reduction: Cluster analysis, PCA. Model selection, estimation, evaluation: Econometrics/statistics/machine learning Data Pipeline Present findings Display findings visually + interactively:Data visualisation

  8. Challenges (not all technical) Access data Obtain proprietary data Manage anonymity and ethical issues (including experimental research cf. Facebook infamous RCT). Model data Ask the right questions: “The best dimension reduction tool that there is.” Be careful with biases: N = All? Rarely. It is important to understand the (administrative and organisational) processes that generated the data. Requires theory and domain knowledge Data Pipeline Present findings Dealing with false positives bound to happen with large samples and multiple tests. Encouraging consilience through reproducibility and relating finding to wider bodies of knowledge

  9. Institutional solutions • People with technical skills and domain knowledge are rare -> Unicorns. • Supply push + Demand pull to increase MOR big data capabilities. • Internal dialogue within the discipline and with other disciplines (Computer Science, Information Systems) • Acknowledge big data limitations for looking at important issues (power, perceptions, structural change.)

  10. THANK YOU Juan.mateos-garcia@nesta.org.uk @JMateosGarcia

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