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How Big Data Is Shaping Academic & Corporate Research

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How Big Data Is Shaping Academic & Corporate Research How big data is shaping academic & corporate research The pursuit of knowledge is a deeply human preoccupation. Uncovering and interpreting new information, then applying it for our advantage, is one of the foundations of humanity’s evolution; we are hard-wired with curiosity, naturally seeking out information to help us make better decisions. Historically, universities and paced global markets, businesses As the age of big data unfolds, research institutions have been demand access to cutting edge the way academic institutions the engines of knowledge growth market intelligence, technology, and corporations work with it and transfer, where academic and social trends to inform and each other is undergoing a researchers devote efforts to commercial strategies, invigorate transformational shift, bridging making new discoveries that product development, and gain a the traditional gulfs between advance our understanding of competitive edge. academia and industry to the world. The emergence of big data accelerate insight and innovation. Of course, research is not the has democratised research sole preserve of academia. In by lowering barriers to entry increasingly disrupted, fast- and making advanced insights accessible to a global audience. SEG-DaaS-ShapingAcademicCorporateResearch-A4.indd 1 3/2/20 3:26 PM From drought to deluge: The dawning of the data age Quality counts: The difference between big data & Academic research must be Enter the big data age. Vast Industry has embraced the research-worthy data rigorous and painstaking. Before amounts of data are created advanced insights aff orded the information age, it was a and collected every second; by big data analytics and the There are important distinctions is governed by best practice As a result, organisations looking labour-intensive process. Data cloud computing power is commercial opportunities it between generalised big data and guidelines designed to ensure to leverage third-party datasets was relatively scarce, diffi cult to capable of processing it on promises. The potential to apply academic Research data—from that it is valid, ethical and must carefully consider the sources obtain and the results of research an unprecedented scale, and artifi cial intelligence to solve internal sources or third-party reproducible. and quality of the data. Does the were not easy to share. Analysis artifi cial intelligence has business challenges is prompting data-as-a-service providers— Big data, on the other hand, is data come from a trusted provider relied on the ability of the human evolved to the point that it can companies to explore how the is specifi cally obtained with the not generally governed by the in a clean, semi-structured format brain to retain, process and deliver insight in nanoseconds. data science capabilities of goal of proving or disproving a same rules and, in an academic or that is easy to ingest? connect data in order to reach From drought to deluge, data leading academic institutions hypothesis. The collection and corporate environment, this can evidence-based conclusions. is ubiquitous and the power to can benefi t their commercial management of research data raise concerns about validity. The information age put data analyse it is more accessible and objectives. processing power in the hands of aff ordable than ever before. Talking about the potential researchers, revolutionising the This revolution has caused for big data in the fi eld of What is the observer effect? velocity and volume of research academic and corporate Information System research, that could be undertaken. researchers to re-examine their Agarwal & Dhar described it as It’s all about infl uence. As researchers expand the Nevertheless, computing power approach and explore how they “possibly the most signifi cant use of complex sets of data, they must consider the Observer and cost limitations still acted can leverage big data sources into ‘tech’ disruption in business and context they bring to the data as well as how the as a brake on the most data- machine learning and predictive academic ecosystems since the tools used—machine learning algorithms, predictive effects are intensive projects. analytics applications to generate meteoric rise of the Internet and analytics—affect the data. insights across disciplines from the digital economy.”1 medicine to manufacturing. Consider, for example, bias in artifi cial intelligence. a threat to Harvard Business Review notes, “AI systems learn to make decisions based on training data, which can validity in include biased human decisions or refl ect historical or social inequities, even if sensitive variables such of data of data much of as gender, race, or sexual orientation are removed.”5 33ZBin 2018 175ZB in 20252 educational research. more bytes of devices4 The SAGE Encyclopedia of data... Educational Research, B 40X 200 Measurement, and Evaluation6 ...than stars in the observable universe3 1 Agarwal and Dhar, 2014, p.443 quoted in Steven L. Johnson, Peter Gray, Suprateek Sarker, “Revisiting IS research practice in the era of big data.” Information and Organization, March 2019. Accessed at: http://bit.ly/2SosjJK 2 David Reinsel, John Gantz, John Rydning, “The Digitization of the World from Edge to Core,” IDC, November 2018. Accessed at: http:// 5 Manyika, James; Silberg, Jake; and Presten, Brittany. “What Do We Do About the Biases in AI?” Harvard Business Review. October 25, bit.ly/37aijsW 2019. Accessed at: http://bit.ly/3bphUWL 3 “Data never sleeps 7.0.” Domo.com. September 9, 2019. Accessed at: http://bit.ly/37cSEzI 6 Frey, Bruce B. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc., 2018. Accessed 4 “A Guide to the Internet of Things”, Intel.com. Accessed September 9, 2019 at: https://intel.ly/2w2P4LR at: http://bit.ly/2ulGB65 SEG-DaaS-ShapingAcademicCorporateResearch-A4.indd 2-3 3/2/20 3:26 PM The open data revolution: Disruption in academia Clusters & collaboration: The strengthening relationship between academia & industry Academia tends to value the However, several factors These pressures and pursuit of knowledge however have come into play in recent opportunities are prompting long it takes, while business years that have bridged the academic institutions to become prizes the application of gulf between academia and more outward-looking. At the knowledge as fast as possible to corporations. same time, companies keen to seize competitive advantage. Funding squeeze capitalise on the innovations For a long time, these differing As government funding for coming out of universities are priorities acted as a barrier academic research has declined, willing to invest, helping to to effective collaboration universities have felt the sting. bridge the funding gap. This between industry and academic Corporate collaborations help to has created collaborations that institutions. As a result, fill the funding shortfall. offer the best of both worlds— rigorous academic research that companies often carried out their Impact imperative is linked to real-world outcomes own research into new product Institutions have come under delivering commercial and/or and strategy development, greater pressure to demonstrate societal gains. where they could push the speed research impact by showing the and be assured of complete real-world applications of their ownership of the inventions and research. discoveries made. Some corporate Commercialisation opportunities laboratories, such as Xerox and The building of science parks IBM, have become world-leaders and university clusters, where in their fields. A definitive value of big data The challenge for academic This created an understandable incubators and accelerators bid is its accessibility. There are institutions is applying the culture of reticence around to bring research discoveries to thousands of publicly available principles of open data to their sharing research data, leading market in the shorter time scales datasets across an extraordinary own research. Historically, this to siloes that potentially slow is blurring the lines between range of topics—everything from has been difficult. Research the pace of discovery. However, academia and business. public healthcare, to imagery institutions are naturally as the principle of open data from the Landsat 8 satellite, competitive with one another has become more widespread to the human genome project. over the prestige of their in general society, it has All are available to be cross- discoveries and for the funding to increasingly become a research referenced with academically enable further research. They are funding requirement that public sourced research data to offer also concerned about the risks to access is granted to researchers’ deeper insight. their intellectual property created analysis and the data on which it by sharing the data on which is based. This is fostering a more discoveries and innovations are open approach to data sharing in based. academia as universities explore how they can make data available while still keeping control of intellectual property. SEG-DaaS-ShapingAcademicCorporateResearch-A4.indd 4-5 3/2/20 3:26 PM Partnering for performance: Big data partnerships Big data for social science studies Case in point: Leveraging the power While big data applications in analysis tools, or to train machine between academia & industry of third-party data for subjects such as science and learning algorithms, researchers political science research medicine are clear, it is also can conduct
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