Decision making with Delphi

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The Delphi method brings subject matter experts with a range of experiences together in multiple rounds of questioning to arrive at the strongest consensus possible on a topic or series of topics (Okoli & Pawlowski, 2004; Pulat, 2014). The first round is typically used to generate the ideas for subsequent rounds’ weighting and prioritizing, by way of a questionnaire. This first round is the most qualitative of the steps. Subsequent rounds are more quantitative. According to Pulat (2014), ideas are listed and prioritized by a weighted point system with no communication between the subject matter experts. This is meant to avoid confrontation (Dalkey and Helmer, 1963). Results and available data requested by one or more experts can be shown to all experts, or new information that is considered potentially relevant by an expert (Dalkey & Helmer, 1963; Pulat, 2014). 

While Delphi begins with and keeps a sense of qualitative research about it, traditional forecasting utilizes mostly quantitative methods, utilizing mathematical formulations and extrapolations as mechanical bases (Wade, 2012). Using past behavior as a predictor of future positioning, a most likely scenario is extrapolated (Wade, 2012; Wade, 2014). This scenario modeling confines planning to a formulaic process much like regression modeling. Both Delphi and traditional forecasting utilize quantitative methods, the difference being to what degree. A key question in deciding which method to use is what personalities are involved. Delphi methodology gives the most consideration to big personalities and potentially fragile egos, avoiding any direct confrontation or disagreements.

References

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467.

Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, 42(1), 15-29.

Pulat, B. (2014) Lean/six sigma black belt certification workshop: Body of knowledge. Creative Insights, LLC.

Wade, W. (2012) Scenario Planning: A Field Guide to the Future. John Wiley & Sons P&T. VitalSource Bookshelf Online.

Decision Support Systems, Data Warehouses, and OLAP Cubes

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As Tembhurkar, Tugnayat, & Nagdive define it, BI is “a collection of tools and techniques [that] transforms raw data into significant information useful for analyzing business systems” (2014, p. 128). BI has evolved from the earlier incarnations of Decision Support Systems, which served the same purpose(s) but were much more rudimentary compared to today’s implementations. These DSS solutions were often comprised of data warehouses (DWs) and online analytical processing (OLAP) engines. Both components worked together to serve the business needs: ETL and storage being handled by the data warehouse, and the front-end analysis handled by the OLAP system.

The data warehouse serves as the central repository for multiple systems of record, often heterogenous and disparate in the beginning. Data is typically replicated and stored in subject-area schemas (e.g., sales or employee data), most typically in fact and dimension tables as part of a SQL-backed relational database. The data warehouse itself can offer views and data marts pre-packaged. It supports the OLAP system. Like the OLAP system in its original form, the data warehouse is starting to be eclipsed by data lakes in enterprise environments that deal with a large amount of heterogenous data that often includes unstructured data. The difference between the two, for purposes of this comparison, is where the “T” (transformation) falls in ETL or ELT. In a data warehouse, the transformation happens before loading into the warehouse, as its purpose is to serve as a central common repository. In a data lake, the transformation happens after loading, as the lake does not impose any schemas or restrictions in order to achieve any kind of homogenous state.

The OLAP system is multi-dimensional, not unlike a three-dimensional spreadsheet. It is not a relational database but enables the analysis of the data in the data warehouse. The OLAP system enables what we typically understand as slicing and dicing the data. While these were sufficient in the early days of BI, the shift towards a DevOps culture and the proliferation of machine learning, predictive analysis, dashboarding, and envelope-pushing analytics capabilities have required more from a BI solution than rigid OLAP cubes.

References

Felzke, M. (2014). Data warehouse vs. OLAP cube. Retrieved from https://www.solverglobal.com/blog/2014/04/data-warehouse-vs-olap-cube/

Harris, D. (n.d.). ETL vs. ELT: How to choose the best approach for your data warehouse. Retrieved from https://www.softwareadvice.com/resources/etl-vs-elt-for-your-data-warehouse/

Tembhurkar, M. P., Tugnayat, R. M., & Nagdive, A. S. (2014). Overview on data mining schemes to design business intelligence framework for mobile technology. International Journal of Advanced Research in Computer Science, 5(8).