Enabling self-service BI: A methodology and a case study for a model management warehouse
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The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI, but prior attempts at developing modeling languages for non-technical audiences have not been widely implemented. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor. The paper concludes by outlining several areas for future research.
KeywordsBusiness intelligence Model management Analytics Modeling Self-service
- Box, G. E. P., & Draper, N. R. (1987). Empirical model building and response surfaces. New York: Wiley.Google Scholar
- Brooke, A., Kendrick, D., & Meeraus, A. (1988). GAMS: a user's guide. Redwood City: Scientific Press.Google Scholar
- Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach (2nd ed.). New York: Springer.Google Scholar
- Cox, D. R. (1995). Comment on “model uncertainty, data mining and statistical inference”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 158(3), 455–456.Google Scholar
- Cunningham, K., & Schrage, L. (2004). The LINGO Algebraic Modeling Language. In J. Kallrath (Ed.), Modeling languages in mathematical optimization (pp. 159-171). Kluwer Academic Publishers.Google Scholar
- Davenport, T. H. (2013). Telling a Story with Data. Deloitte Review, 12.Google Scholar
- Davenport, T. H., & Kim, J. (2013). Keeping up with the quants. Harvard Business Review Press.Google Scholar
- Guazzelli, A., Zeller, M., Lin, W-C., & Williams, G. (2009). PMML: an open standard for sharing models. The R Journal, 1(1), 60. http://journal.r-project.org
- Hampton, J. (2011). SEMMA and CRISP-DM: data mining methodologies. JessHampton.com http://jesshampton.com/2011/02/16/semma-and-crisp-dm-data-mining-methodologies. Accessed 21 July 2016.
- HBR Analytic Services (2012). The evolution of decision making: how leading organizations are adopting a data-driven culture. Harvard Business Review. https://hbr.org/resources/pdfs/tools/17568_HBR_SAS%20Report_webview.pdf. Accessed 18 September 2015.
- Henschen, D. (2014). IBM Watson analytics goes public. InformationWeek. http://www.informationweek.com/big-data/big-data-analytics/ibm-watson-analytics-goes-public/d/d-id/1317887. Accessed 14 February 2015.
- Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 77–105.Google Scholar
- Kimball, R. (1997). A dimensional modeling manifesto. Database Magazine, 10(9), 59–78.Google Scholar
- Logi Analytics (2014) State of Self-Service BI Report. http://images.learn.logixml.com/Web/LogiAnalyticsInc/%7B7c21cd62-221c-44af-9ecd-a35265bc8e34%7D_LogiAnalytics-2014StateOfSelfService-Artwork-1028.pdf. Accessed 8 February 2015.
- Maslow, A. H. (1966). The psychology of science. Chicago: J. Dewey Society.Google Scholar
- McMurtrey, M. E., Grover, V., Teng, J. T. C., & Lightner, N. J. (2002). Job satisfaction of information technology workers: the impact of career orientation and task automation in a CASE environment. Journal of Management Information Systems, 19(2), 273–302.Google Scholar
- Object Management Group (2003). Common Warehouse Metamodel (CWM) Specification. http://www.omg.org/spec/CWM/1.1/PDF/. Accessed 24 April 2015.
- Pack, D. J. (1987). A practical overview of ARIMA models for time series forecasting. In S. G. Makridakis & S. C. Wheelwright (Eds.), The handbook of forecasting: a managers guide (pp. 196–218). New York: Wiley.Google Scholar
- Pechter, R. (2011). PMML conformance progress report – five years later. In Proceedings of PMML’11 (pp. 6–15). New York: ACM Press.Google Scholar
- Powell, S. R. (2015). Summary of state models. Unpublished working paper. New Brunswick, NJ: John J. Heldrich Center for Workforce Development.Google Scholar
- Rohanizadeh, S., & Moghadam, M. (2009). A proposed data mining methodology and its application to industrial procedures. Journal of Industrial Engineering, 4, 37–50.Google Scholar
- SAS Institute (1998). Data Mining and the Case for Sampling. SAS Institute Best Practices Paper, Carey, NC.Google Scholar
- Sottara, D., Mello, P., Sartori, C., & Fry, E. (2011). Enhancing a production rule engine with predictive models using PMML. In Proceedings of PMML’11 (pp. 39–47). New York: ACM Press.Google Scholar