Abstract
In a manner similar to most organizations, BigCompany (BigCo) was determined to benefit strategically from its widely recognized and vast quantities of data. (U.S. government agencies make regular visits to BigCo to learn from its experiences in this area.) When faced with an explosion in data volume, increases in complexity, and a need to respond to changing conditions, BigCo struggled to respond using a traditional, information technology (IT) project-based approach to address these challenges. As BigCo was not data knowledgeable, it did not realize that traditional approaches could not work. Two full years into the initiative, BigCo was far from achieving its initial goals. How much more time, money, and effort would be required before results were achieved? Moreover, could the results be achieved in time to support a larger, critical, technology-driven challenge that also depended on solving the data challenges? While these questions remain unaddressed, these considerations increase our collective understanding of data assets as separate from IT projects. Only by reconceiving data as a strategic asset can organizations begin to address these new challenges. Transformation to a data-driven culture requires far more than technology, which remains just one of three required “stool legs” (people and process being the other two). Seven prerequisites to effectively leveraging data are necessary, but insufficient awareness exists in most organizations—hence, the widespread misfires in these areas, especially when attempting to implement the so-called big data initiatives. Refocusing on foundational data management practices is required for all organizations, regardless of their organizational or data strategies.
- P. Aiken and J. Billings. 2013. Monetizing Data Management: Finding the Value in Your Organization's Most Important Asset. Technics Publications, LLC. Google ScholarDigital Library
- P. Aiken, L. Flory, and A. G. Chin. 2014. Enriching enterprise data models: Incorporating active taxonomies. J. Manag. Stud. 24, 3.Google Scholar
- P. Aiken and M. Gorman. 2013. The Case for the Chief Data Officer (CDO): Recasting the C-Suite to Leverage Your Most Valuable Asset. Morgan Kaufmann. Google ScholarDigital Library
- P. H. Aiken, M. D. Allen, B. Parker, and A. Mattia. 2007. Measuring data management pratice maturity: A community's self-assessment. IEEE Comput. 40, 4, 8. Google ScholarDigital Library
- P. H. Aiken, M. Gillenson, X. Zhang, and D. Raffner. 2011. Data management and data administration: Assessing 25 years of practice. J. Database Manag. 22, 3, 20. Google ScholarDigital Library
- C. Andrew. 2009. Defend the Realm: The Authorized History of MI5. Knopf, New York, NY.Google Scholar
- P. A. Bernstein and L. M. Haas. 2008. Information integration in the enterprise. Commun. ACM, 51, 9, 7. Google ScholarDigital Library
- Y. E. Chan, S. L. Huff, D. W. Barclay, and D. G. Copeland. 1997a. Business strategic orientation, information systems strategic orientation, and strategic alignment. Informat. Syst. Res. 8, 2, 26. Google ScholarDigital Library
- Y. E. Chan, S. L. Huff, and D. G. Copeland. 1997b. Assessing realized information systems strategy. J. Strategic Informat. Syst. 6, 4, 25.Google ScholarCross Ref
- J. Clarence and W. Hempfield. 2011. Data Quality? Thats ITs Problem Not Mine: What Business Leaders Should Know About Data Quality. Pittney Bowes Business Insights.Google Scholar
- Corporate_Executive_Board. 2006. Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results. Washington, DC. White paper.Google Scholar
- DAMA_International. 2009. The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK). Technics Publications, LLC. Google ScholarDigital Library
- P. Drucker. 2006. Did Peter Drucker actually say “culture eats strategy for breakfast”—--And if so, where/when? Retrieved from https://www.quora.com/Did-Peter-Drucker-actually-say-culture-eats-strategy-for-breakfast-and-if-so-where-when.Google Scholar
- W. Eckerson. 2001. TDWI Data Quality and the Bottom Line. TDWI.Google Scholar
- T. Economist. 2010. (February 27, 2010). Data, data everywhere: A special report on managing information. The Economist.Google Scholar
- Economist_Intelligence_Unit. 2013. Fostering a Data-Driven Culture. Retrieved from http://www.tableausoftware.com/learn/whitepapers/economist-fostering-data-driven-culture.Google Scholar
- H. Harris and S. Murphy. 2013. Harris 2013 Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work. O’Reilly Media. Google ScholarDigital Library
- F. Hayes. 2002. The Story So Far. Retrieved from http://www.computerworld.com/s/article/70102/The_Story_So_Far.Google Scholar
- IBM. 2006. (July 2006). The Toxic Terrabyte: How Data-Dumping Threatens Business Efficiency. IBM Global Technology Services.Google Scholar
- B. K. Kahn, D. M. Strong, and R. Y. Wang. 2002. Information quality benchmarks: Product and service performance. Commun. ACM 45, 4, 8. Google ScholarDigital Library
- E. Kavanagh and R. Jozwiak. 2011. A New Awareness: The Age of Architecture Has Arrived. Bloor Groug.Google Scholar
- P. G. W. Keen. 1981. Information systems and organizational change. Commun. ACM 24, 2, 24--33. Google ScholarDigital Library
- J. Ladley. 2010. Making EIM Work for Business: A Guide to Understanding Information as an Asset. Morgan Kaufmann. Google ScholarDigital Library
- J. M. Lattin and M. Rierson. 2007. Capital One: Leveraging Information-Based Marketing. [M-316]. Case Studies and Teaching Materials. Stanford University.Google Scholar
- S. E. Madnick, R. Y. Wang, Y. W. Lee, and H. Zhu. 2009. Overview and framework for data and information quality research. J. Data Informat. Quality 1, 1, 22. Google ScholarDigital Library
- B. Marr. 2015. Where big data projects fail. Forbes/Tech. http://www.forbes.com/sites/bernardmarr/2015/03/17/where-big-data-projects-fail/#7609662264e2.Google Scholar
- A. Maslow. 1943. A theory of human motivation. Psychol. Rev. 50, 4, 370--396.Google ScholarCross Ref
- T. McCabe. 1976. A complexity measure. IEEE Trans. Software Eng. 2, 4, 12. Google ScholarDigital Library
- M. Mecca. 2014. Data Management Maturity (DMM) Model. Carnegie Mellon University/Software Engineering Institute.Google Scholar
- B. Parker, L. Chambless, D. Duvall, D. Satterthwaite, and D. Smith. 1995. Data Management Capability Maturity Model (MP 95W0000088). Retrieved from http://www.paladinintegrationengineering.com/resume.htm.Google Scholar
- A. Perez. 2006. The elusive species of the information age: The data management professional (results of the 2006 DAMA international survey). Unpublished, 6.Google Scholar
- L. L. Pipino, Y. W. Lee, and R. Y. Wang. 2002. Data quality assessment. Commun. ACM 45, 4, 9. Google ScholarDigital Library
- M. Porter. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press, New York, NY.Google Scholar
- T. C. Redman. 2008. Data Driven: Profiting from Your Most Important Business Asset. Harvard Business School Press, Cambridge, MA.Google Scholar
- WEBCPA_Staff. 2010. CFO Tenure Appears to Be Lengthening. Retrieved from http://www.accountingtoday.com/news/CFO-Tenure-Lengthening-53255-1.html.Google Scholar
Index Terms
- EXPERIENCE: Succeeding at Data Management—BigCo Attempts to Leverage Data
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