Departments are currently organized in a silo. Thus, their information is in silo systems, which makes it difficult to leverage that information across the company. When we employ a data warehouse, which is a central database that contains a collection of decision-related internal and external sources of data, it can aid in the data analysis for the entire company (Ahlemeyer-Stubbe & Coleman, 2014). When we build a multi-level Business Intelligence (BI) system on top of a centralized data warehouse, we no longer have silo data systems, and thus, can make a data-driven decision. Thus, to support data-driven decision while moving away from a silo department kept data to a centralized data warehouse, Curry, Hasan, and O’Riain (2012) created a system that shows results from the hospital centralized data warehouse at different levels of the company, as the organization level (stakeholders are executive members, shareholders, regulators, suppliers, consumers), the functional level (stakeholders are functional managers, organization manager), and the individual level (stakeholders are the employees). Data may be centralized, but specialized permissions on data reports can exist on a multi-level system.
The types of data that exist and can be stored in a centralized data warehouse are: Real-time data: data that reveals events that are happening immediately, Lag information: information that explains events that have recently just happened; and Lead information: information that helps predict events into the future based off of lag data, like regression data, forecasting model output (based off of Laursen & Thorlund, 2010). All with the goal of helping decision makers if certain Target Measures are met. Target measures are used to improve marketing efforts through tracking measures like ROI, NVP, Revenue, lead generation, lag generations, growth rates, etc. (Liu, Laguna, Wright, & He, 2014).
Decision Support Systems (DSS) were created before BI strategies. A DSS helps execute the project, expand the strategy, improve processes, and improves quality controls in a quickly and timely fashion. Data warehouses’ main role is to support the DSS (Carter, Farmer, & Siegel, 2014). Unfortunately, the talks above about data types and ways to store data to enable data-driven decisions it doesn’t explain the “how,” “what,” “when,” “where,” “who”, and “why.” However, a strong BI strategy is imperative to making this all work. A BI strategies can include, but is not limited to data extraction, data processing, data mining, data analysis, reporting, dashboards, performance management, actionable decisions, etc. (Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Padhy, Mishra, & Panigrahi, 2012; McNurlin, Sprague,& Bui, 2008). This definition along with the fact the DSS is 1/5 principles to BI suggest that DSS was created before BI and that BI is a more new and holistic view of data-driven decision making.
But, what can we do with a strong BI strategy? Well with a strong BI strategy we can increase a company’s revenue through Online profiling. Online profiling is using a person’s online identity to collect information about them, their behaviors, their interactions, their tastes, etc. to drive a targeted advertising (McNurlin et al., 2008). Unfortunately, the fear comes when the end-users don’t know what the data is currently being used for, what data do these companies or government have, etc. Richards and King (2014) and McEwen, Boyer, and Sun (2013), expressed that it is the flow of information, and the lack of transparency is what feeds the fear of the public. McEwen et al. (2013) did express many possible solutions, one which could gain traction in this case is having the consumers (end-users) know what variables is being collected and have an opt-out feature, where a subset of those variables stay with them and does not get transmitted.
Reference:
- Ahlemeyer-Stubbe, Andrea, Shirley Coleman. (2014). A Practical Guide to Data Mining for Business and Industry, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781118981863/
- Carter, K. B., Farmer, D., & Siegel, C. (2014-08-25). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781118920657/
- Curry, E., Hasan, S., & O’Riain, S. (2012, October). Enterprise energy management using a linked dataspace for energy intelligence. In Sustainable Internet and ICT for Sustainability (SustainIT), 2012 (pp. 1-6). IEEE.
- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. Retrieved from: http://www.aaai.org/ojs/index.php/aimagazine/article/download/1230/1131/
- Laursen, G. H. N., & Thorlund, J. (2010) Business Analytics for Mangers: Taking Business Intelligence Beyond Reporting. Wiley & SAS Business Institute.
- Liu, Y., Laguna, J., Wright, M., & He, H. (2014). Media mix modeling–A Monte Carlo simulation study. Journal of Marketing Analytics, 2(3), 173-186.
- McEwen, J. E., Boyer, J. T., & Sun, K. Y. (2013). Evolving approaches to the ethical management of genomic data. Trends in Genetics, 29(6), 375-382.
- McNurlin, B., Sprague, R., & Bui, T. (09/2008). Information Systems Management, 8th Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781323134702/
- Padhy, N., Mishra, D., & Panigrahi, R. (2012). The survey of data mining applications and feature scope. arXiv preprint arXiv:1211.5723. Retrieved from: https://arxiv.org/ftp/arxiv/papers/1211/1211.5723.pdf
- Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest L. Rev., 49, 393
Dr. Skylar (Sky) Hernandez 