Strategies to utilize and unleash the exponential power of data in the digital age for increasing effective decision making and competitiveness both in public and private organizations
Keywords:Strategy, Data utilization, Effective decision making, Competitiveness
The development of advanced technology has dramatically produced a large amount of data. The achievements of the world's leading organizations – such as Uber, Amazon, Netflix, Rolls-Royce, Google, IBM, Microsoft, and so on – have shown clearly that the data combined with technological progress is revolutionizing the ways or practices of organizations in both the public and private sectors in creating competitive value. However, data utilization has still faced a variety of limitations, such as knowledge and understanding about the use of data, drowning in data, data management, data quality, potentiality of data users, data context, and so on. Utilizing the data to drive the organizations is still a great challenge for the organizations. This article presents administrative strategies to take advantage of the data to make effective decisions and increase competitiveness. These nine strategies are the result of integrated synthesis of relevant literature, such as empirical researches, academic works, books and survey results from world-class organizations. The strategies will help organizations survive and thrive in the transition period of humanity as a result of advanced technology development.
Berners-Lee, T. (2006). Linked data-design issues. Tech. rep., W3C. Retrieved November 2, 2019 from http://www.w3.org/DesignIssues/LinkedData.html
Bertot, J.C., Jaeger, P.T., Munson, S., & Glaisyer, T. (2010). Social media technology and government transparency. Computer, 43(11): 53-59.
Blauer, B. (2018). Building the data city of the future. ANNALS, AAPSS, 675: 151 – 165.
Borzacchiello, M. T. & Craglia, M. (2012). The impact on innovation of open access to spatial environmental information: A research strategy. International Journal of Technology Management, 60(1–2): 114–129. http://dx.doi.org/10.1504/ijtm.2012.049109.
Brynjolfsson, E., Hitt, L.M., & Kim, H.H. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?. http://dx.doi.org/10.2139/ssrn.1819486
Cai, L & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14(2): 1–10, DOI: http://dx.doi.org/10.5334/dsj-2015-002
Chatterjee, D., Grewal, R., & Sambamurthy, V. (2002). Shaping up for e-commerce: Institutional enablers of the organizational assimilation of web technologies. MIS Quarterly, 26(2): 65–89.
Chawda, V. (2017). Big data boom drive need for new chief data officer role. In KPMG, At Gov Inspiring innovative government: Data driven government. Belgium: KPMG Central services. Retrieved July 5, 2019, from https://home.kpmg/xx/en/home/insights/2018/06/building-trust-in-governments-use-of-data.html
Christensen, C.M. (1997). The innovator’s dilemma: when new technologies cause great firms to fail. Boston, Massachusetts: Harvard Business Review Press. Retrieved July 5, 2019, from http://library.globalchalet.net/Authors/Startup%20Collection/%5BChristensen,%201997%5D%20The%20Innovator's%20Dilemma%20-%20When%20New%20Technologies%20Cause%20Great%20Firms%20to%20Fail.pdf
Cohen, G. (2010). Agile excellence for product managers: A guide to creating winning products with agile development teams. San Jose, CA: Super Star Press.
Colonescu, C. (2018). The effects of Donald Trump’s Tweets on US financial and foreign exchange markets. Athens Journal of Business and Economics, 4(4): 375–388. DOI: 10.30958/ajbe.4-4-2.
Corcelli, G., Dolan, R.J., & Sirigu, A. (2007). Brain, emotion and decision making: the paradigmatic example of regret. TRENDS in Cognitive Sciences, 11(6): 258–265.
Cotton, B. (2015). Smart city as a service: Using analytics to equip communities for data driven decisions. Mountain View, CA: Frost & Sullivan.
CrowdFlower. (2016). 2016 Data science report. Retrieved July 5, 2019, from https://visit.figure-eight.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf
Damasio, A. (1994). Descartes error: emotion, reason and the human brain. Great Britain: Picador.
Danneels, L., Viaene, S., & Van den Bergh, J. (2017). Open data platforms: Discussing alternative knowledge epistemologies. Government Information Quarterly, 34:
Dawes, S.S., Vidiasova, L., & Parkhimovichc, O. (2016). Planning and designing open government data programs: An ecosystem approach. Government Information Quarterly, 33: 15–27.
Darmody, J. (2018). How to reskill for the future of work and boost your career. Retrieved July 5, 2019, from https://www.siliconrepublic.com/advice/reskill-future-of-work-upskill
Desouza, K. C., & Jacob, B. (2017). Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043–1064. DOI: 10.1177/0095399714555751
Diaz, A., Rowshankish, K., & Saleh, T. (2018). Why data culture matters. In McKinsey. (2018). McKinsey quarterly: Data culture opening the flow of analytic insight. New York: McKinsey & Company.
Dunning, D., Fetchenhauer, D., & Schlosser, T. (2017). The varying roles played by emotion in economic decision making. Current Opinion in Behavioral Science, 15: 33–38. http://dx.doi.org/10.1016/j.cobeha.2017.05.006
Esty, D.C. & Rushing, R. (2007). Governing by the numbers: The promise of data – driven policymaking in the information age. Center for American progress.
EY. (2015). Becoming an analytics driven organization to create value: A report in collaboration with Nimbus Ninety. Retrieved July 5, 2019, from https://www.ey.com/Publication/vwLUAssets/EY-global-becoming-an-analytics-driven-organization/%24FILE/ey-global-becoming-an-analytics-driven-organization.pdf
Gartner. (2018). Gartner forecasts worldwide information security spending to exceed $124 billion in 2019. Retrieved July 5, 2019, from https://www.gartner.com/en/newsroom/press-releases/2018-08-15-gartner-forecasts-worldwide-information-security-spending-to-exceed-124-billion-in-2019
Gartner. (2018). Gartner Identifies the Top 10 Trends Impacting Infrastructure and Operations for 2019. Retrieved November 2, 2019 from https://www.gartner.com/
Goldsmith, S. & Crawford, S.P. (2014). The responsive city: Engaging communities through data-smart governance. San Francisco, CA: Jossey-Bass.
Gruen, N., Houghton, J., & Tooth, R. (2014). Open for business: How open data can help achieve the G20 growth target. Retrieved July 5, 2019, from https://www.omidyar.com/sites/default/files/file_archive/insights/ON%20Report_061114_FNL.pdf
Harvard Business Review. (2012). The evolution of decision making: How leading organizations are adopting a data – driven culture. Retrieved November 2, 2019 from https://hbr.org/resources/pdfs/tools/17568_HBR_SAS%20Report_webview.pdf
Hochtl, J., Parycek, P., & Schollhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1-2): 147–169. DOI: 10.1080/10919392.2015.1125187
Hodge, V.J. (2014). Outlier detection in big data. In: Wang, J., & Wang, J., (eds.) Encyclopedia of Business Analytics and Optimization. Encyclopedia of Business Analytics and Optimization (pp. 1762–1771). Hershey, PA: IGI Global. DOI: 10.4018/978-1-4666-5202-6.
Hopkins, B., McCormick, J., Schadler, T., Sridharan, S., Doty, C. A., Little, C., Miller, E., & Vale, J. (2018). Insights-driven businesses set the pace for global growth: The vision report of the insights-driven business playbook. Retrieved July 5, 2019, from https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848#
HP. (n.d.). Michael “MafiaBoy” Calce - Bio”. Retrieved July 5, 2019, from https://www8.hp.com/us/en/images/MafiaBoy_Biography_tcm245_2433189_tcm245_2430162_tcm245-2433189.pdf
Huawei Technologies. (2018). GIV 2025 Unfolding the industry blueprint of an intelligent world. Retrieved from https://www.huawei.com/minisite/giv/Files/whitepaper_en_2018.pdf
IBM. (2015). Data-driven government: Challenges and a path forward. Armonk, NY: IBM Corporation. Retrieved July 5, 2019, from https://public.dhe.ibm.com
IBM-Ponemon Institute. (2018). 2018 Cost of data breach study: Impact of business continuity management. Retrieved July 5, 2019, from https://www.ibm.com/downloads/cas/AEJYBPWA
International Data Corporation (IDC). (2017). Data age 2025: The evolution of data to life – critical, Don’t focus on big data; focus on the data that’s big. Framingham, MA: IDC Headquarters. Retrieved July 5, 2019, from https://www.seagate.com/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf
Jaeger, P. T. & Bertot, J. C. (2010). Transparency and technological change: Ensuring equal and sustained public access to government information. Government Information Quarterly, 27: 371–376.
Janssen, K. (2011). The influence of the PSI directive on open government data: An overview of recent developments. Government Information Quarterly, 28(4):
Jung, K., & Park, H. (2015). A semantic (triz) network analysis of South Korea's “open public data” policy. Government Information Quarterly, 32(3): 353–358.
Kent, S., Potok, N., & Wilmer, J. (2018). White House hosts roundtable discussion on leveraging data as a strategic asset. Retrieved July 5, 2019, from https://www.whitehouse.gov/articles/white-house-hosts-roundtable-discussion-leveraging-data-strategic-asset/
Khayyat, Z., IIyas, I. F., Jindal, A., Madden, S., Ouzzani, M., Papotti, P., Quiane-Ruiz, J-A., Tang, N., & Yin, S. (n.d.). BIGDANSING: A system for big data cleaning. Retrieved from https://core.ac.uk/download/pdf/145230737.pdf
KPMG. (2017). At Gov Inspiring innovative government: Data driven government. Belgium: KPMG Central services. Retrieved July 5, 2019, from https://assets.kpmg/content/dam/kpmg/be/pdf/Markets/at-gov-brochure.pdf
Kumar, R., Misra, V., Walraven, J., Sharan, L., Azarnoush, B., Chen, B., & Govind, N. (2018). Data science and the art of producing entertainment at Netflix. Retrieved from https://medium.com/netflix-techblog/studio-production-data-science-646ee2cc21a1.
Lane, J. (2018). Building an infrastructure to support the use of government administrative data for program performance and social science research. ANNALS, AAPSS, 675: 240 – 252.
Liu, Q., Bai, Q., Ding, L., Pho, H., Chen, Y., Kloppers, C., McGuinness, D., Lemon, D., de Souza, P., Fitch, P., & Fox, P. (2011). Linking Australian government data for sustainability science-a case study. In D. Wood (Eds.). Linking Government Data (pp. 181–204). New York: Springer. http://dx.doi.org/10.1007/978-1-4614-1767-5_9.
Liu B., Fan W., & Xiao T. (2013). A fast outlier detection method for big data. In Tan G., Yeo G.K., Turner S.J., & Teo Y.M. (eds). Asian Simulation Conference 2013: Communications in Computer and Information Science, 402 (pp.379 – 384). Heidelberg, Berlin: Springer. https://doi.org/10.1007/978-3-642-45037-2_38
Manyika J. et al. (2013). Open data: Unlocking innovation and performance with liquid information. San Francisco: McKinsey & Company. Retrieved July 5, 2019, from https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/open%20data%20unlocking%20innovation%20and%20performance%20with%20liquid%20information/mgi_open_data_fullreport_oct2013.ashx
Manzoni, J. (2017). Big data in government: the challenges and opportunities. Retrieved July 5, 2019, from https://www.gov.uk/government/speeches/big-data-in-government-the-challenges-and-opportunities
Marr, B. (2015). Big data: Using SMART Big data, analytics and metrics to make better decisions and improve performance. New Jersey: Wiley.
Marr, B. (2015b). The amazing ways Uber is using big data. Retrieved November 2, 2019 from https://www.datasciencecentral.com/profiles/blogs/the-amazing-ways-uber-is-
Marr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. West Sussex: John Wiley and Sons.
Marr, B. (2016b). Why investments in big data and analytics are not yet paying off. Retrieved July 5, 2019, from https://www.forbes.com/sites/bernardmarr/2016/06/27/why-investments-in-big-data-and-analytics-are-not-yet-paying-off/#64ca62ad7963
Marr, B. (2017). Data strategy: How to profit from a world of big data, analytics and the internet of things. New York, NY: Kogan Page Limited.
Maurino, A., Spahiu, B., Batini, C., & Viscusi, G. (2014). Compliance with open government data policies: an empirical evaluation of Italian local public administrations. Information Polity, 19(3-4): 263 – 275.
Mayer-Schonberger, V., & Cukier, K. (2017). Big data: The essential guide to work, life, and learning in the age of insight. London: John Murray.
McKelvey, N., Curran, K., & Toland, L. (2016). The challenges of data cleansing with data warehouses. In Singh, M. K., & Kumar, D. (2016). Effective Big Data Management and Opportunities for Implementation, (77–82). DOI: 10.4018/978-1-5225-0182-4.ch005.
McKinsey. (2018). McKinsey quarterly: Data culture opening the flow of analytic insight. New York: McKinsey & Company.
McKinsey. (2018a). Retraining and reskilling workers in the age of automation. Retrieved July 5, 2019, from https://www.mckinsey.com/featured-insights/future-of-work/retraining-and-reskilling-workers-in-the-age-of-automation
MIT. (2016). Lessons from becoming a data driven organization. Retrieved July 5, 2019, from https://sloanreview.mit.edu/case-study/lessons-from-becoming-a-data-driven-organization/
New Vantage Partners. (2019). Big data and AI executive survey 2019: Executive summary of sindings. Retrieved July 5, 2019, from http://newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings-Updated-010219-1.pdf
Nickerson, D., & Rogers, T. (2014). Political campaigns and big data. Journal of Economic Perspectives , 28(2): 51-74.
O’Flaherty, K. & Maloney, K. (2017). Governments seize private sector data solutions to achieve the SDGs. In KPMG. (2017). At Gov Inspiring innovative government: Data driven government (pp. 22 - 23). Belgium: KPMG Central services.
Pollock, R. (2011). Building the (open) data ecosystem. Retrieved November 2, 2019 from http://blog.okfn.org/2011/03/31/building-the-open-data-ecosystem
Radware. (2019). 2018–2019 Global application & network security report: THE TRUST FACTOR Cybersecurity’s Role in Sustaining Business Momentum. Retrieved July 5, 2019, from https://www.radware.com
Reuters. (2017). 2017 Global data visualization market is expected to grow at a CAGR of 9.21%, to reach USD 6.99 billion by the end of 2022. Retrieved July 5, 2019, from https://www.reuters.com/brandfeatures/venture-capital/article?id=5123
Rosenbloom, M.H., Schmahmann, J.D., & Price, B.H. (2012). The functional neuroanatomy of decision-making. J Neuropsychiatry Clin Neurosci, 24(3): 266 – 277.
Sansone, S.A., Rocca – Serra, P., Field, D., Maguire, E., Taylor, C., & Hofmann, O. (2012). Toward interoperable bioscience data. Nature Genetics, 44: 121–126
Schwab, K. (2016). The fourth industrial revolution. Switzerland: World Economic Forum.
Simplilearn. (2018). Reskilling the government workforce for the digital age. Retrieved July 5, 2019, from https://www.simplilearn.com/government-workforce-training-and-reskilling-for-digital-age-article
Solar, M., Concha, G., & Meijueiro, L. (2012). A model to assess open government data in public agencies. In H.J. Scholl, M. Janssen, M. Wimmer, C.E. Moe, & L.S. Flak (Eds.), Electronic Government (pp. 210-221). Heidelberg, Germany: Springer.
Soosalu, G., Henwood, S., & Deo, A. (2019). Head, heart, and gut in decision making: development of a multiple brain preference questionnaire. SAGE Open, 1 – 17. https://doi.org/10.1177/2158244019837439
Souza, A.M.C., & Amazonas, J.R.A. (2015). Big data processing and Internet of Things architecture. Procedia Computer Science, 52: 1010 – 1015. https://doi.org/10.1016/j.procs.2015.05.095
Spavin, C., & Fegan, L. (2017). The data-trust deficit. In KPMG. (2017). In KPMG. (2017). At Gov Inspiring innovative government: Data driven government (pp. 12–15). Belgium: KPMG Central services.
Tang N. (2014). Big data Cleaning. In Chen L., Jia Y., Sellis T., Liu G. (eds). web technologies and applications. APWeb 2014. Lecture notes in Computer Science, 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_2
Trish, B. (2018). Big data under Obama and Trump: The data-fueled U.S. presidency. Politics and Governance, 6(4): 29- 38. DOI: http://dx.doi.org/10.17645/pag.v6i4.1565
Ubaldi, B. (2013). Open government data: Towards empirical analysis of open government data initiatives, OECD Working Papers on Public Governance, No. 22. Paris: OECD Publishing. http://dx.doi.org/10.1787/5k46bj4f03s7-en
Wahyudi, A., Kuk, G., & Janssen, M. (2018). A process pattern model for tackling and improving big data quality. Information System Frontiers, 20: 457 – 469. https://doi.org/10.1007/s10796-017-9822-7
Wang, H.J., & Lo, J. (2016). Adoption of open government data among government agencies. Government Information Quarterly, 33: 80–88.
Wessels, B., Finn, R., Sveinsdottir, T., & Wadhwa, K.(2017). Open data and the knowledge society. Amsterdam: Amsterdam University Press. Retrieved July 5, 2019, from https://oapen.org/download?type=document&docid=625332
White House. (2017). The American technology council summit to modernize government services. Retrieved July 5, 2019, from https://www.whitehouse.gov/articles/american-technology-council-summit-modernize-government-services/
Whitman, M.E., & Mattord, H.J. (2012). Principle of Information Security (4thEdition). Boston: Thomson Course Technology.
World Wide Web Foundation. (2016). Open data barometer global report (4th Edition). Retrieved November 2, 2019 from http://www.opendatabarometer.org
Zuiderwijk, A., & Janssen, M. (2014). Open data policies, their implementation and impact: A framework for comparison. Government Information Quarterly, 31: 17–29.