Business Intelligence: Compelling Topics

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.



  • Ahlemeyer-Stubbe, Andrea, Shirley Coleman. (2014). A Practical Guide to Data Mining for Business and Industry, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from
  • 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
  • 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:
  • 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
  • Padhy, N., Mishra, D., & Panigrahi, R. (2012). The survey of data mining applications and feature scope. arXiv preprint arXiv:1211.5723.  Retrieved from:
  • Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest L. Rev., 49, 393

Business Intelligence: OLAP

Within a Business Intelligence (BI) program online analytical processing (OLAP) and customer relationship management (CRMs) are both applications have strategic uses for the company and are dependent on the data warehouse to help analyze multidimensional datasets stored in them to provide data-driven solutions to queries. They are both systems that require data analytics to turn all the multidimensional data into insightful information. OLAP’s multidimensional view of the data warehouse data sets can occur because it is mapped onto n-dimensional data cubes, where data can then be easily rolled up, drilled down, slice and dice, and pivot (Conolly & Begg, 2014). OLAP can have many applications outside of customer relationships.  Thus, OLAP is more versatile compared to CRMS, because CRMs are more targeted/focused with their approach, analysis of the customer relationship to the company/product.  CRMs main goal is to analyze internal and external data stored in the data warehouse, to come up with insights like “predicted affinity to buy” of a customer, the “cost or profit” of a customer, “prediction of future customer behavior”, etc. (Ahlemeyer-Stubbe & Shirley, 2014).  The information gained from the CRM can empower employees at the company on a customer’s affinity towards a product to either sell similar items or items of the result in a market basket analysis.

OLAP is the online analytical processing application, which allows people to examine data in real time from different points of view in aid to driving more data-driven decisions (McNurlin et al., 2008).  With OLAP, computers can now make what-if analysis and goal-based decisions using data. The key ability of OLAPs systems are to help answer the “Why?” question, as well as the typical “Who?” and “What?” questions (Conolly & Begg, 2014).  Connolly and Begg (2014) further explain that OLAP is a specialized implementation of SQL. Unfortunately, data queried is assumed to be static and unchanging.  Hence, the low volatile aspect of a data warehouse, with multidimensional databases is ideal for OLAP apps.  They value of the data warehouse does not come from just storing the right kind of data, but through making and conducting analysis, to solve queries that will in the end help make data driven decisions that are the best for the company.  According to Conolly & Begg (2014), OLAP tools have been used in studying the effectiveness of marking campaigns, product sales forecasting, and capacity planning.  However, it is of the opinion of Conolly & Begg (2014) that data mining tools can surpass the capabilities of OLAP tools.

CRMs, on the other hand, focuses a wide range of concepts revolving how companies store, capture and analyze customer, vendor, and partner relationship data. Information stored in CRMs could be interactions with customers, vendors or partners, which allow the company to gain insights based on previous interactions and could even be grouped/associated into different customer segments, market basket analysis, etc. (Ahlemeyer-Stubbe & Shirley, 2014). CRMs can assist in real time with making data-driven decisions with respects to a company’s customers (Mcnurlin, Sprague, & Bui, 2008).  The goal is to use the current data, to help the company build more optimal communications and relationships with it customers, vendors or partners.  Both internal and external data of the company is usually added to the data warehouse for the CRM. Through the use of the internet, companies can study more about their customers and their noncustomers, to aid a company to become more customer centric (McNurlin et al., 2008).  McNurlin et al. (2008) stated a case study with Wachovia Bank purchasing a pay-by-use CRM system from  After the system was set up within six weeks, sales reps had 30 more hours to use on selling more bank services, and managers can use the data that was collected by the CRM to tell the sales reps which customers would have the highest yield.


Business Intelligence: Zero Latency & Item Affinity

Types of data (based off of Laursen & Thorlund, 2010)

  • Real-time data: data that reveals events that are happening immediately, like a chat rooms, radar data, dropwindsonde data
  • Lag information: information that explains events that have recently just happened, like satellite data, weather balloon data
  • Lead information: information that helps predict events into the future based off of lag data, like regression data, forecasting model output, GPS ETA times

Everyone can easily find lag data, its old news, but what is interesting is to develop lead information from real-time data.  That has the biggest impact to any business trying to gain a competitive edge against its competitors.  When a company can use data mining and analytical formulas on real-time data they have a head start into generating lead information (Ahlemeyer-Stubbe & Coleman, 2014), which would allow for a company to make data-driven decisions much faster.  To do so, one needs to fully automate the modeling towards a predictive target, in an efficient manner (which is of particular importance when dealing with Big Data).  An example of zero latency (real-time) data analysis is seen through the production line on any manufacturing plant (i.e. Toyota, Tesla, etc.), data is stored in an enterprise resource planning (ERP) system (Carter, Keith, Farmer,  & Siegel, 2014).  Speed is vital, thus zero-latency means a manufacturing plant can meet its demands without incurring additional costs, and therefore keeping their profit margins up and their manufacturing programs in the black.  Carter et al. (2014) claim that General Electric could extract $150 billion of unrealized efficiencies just by analyzing their data.  They could get to that much faster if they drove down their latency to zero.  But, there is a caveat, the data must be not only real-time but accurate (Carter et al., 2014).

Item affinity (market basket analysis) uses rules-based analytics to understand what items frequently co-occur during transactions (Snowplow Analytics, 2016). Item affinity is similar to the current method to drive more sales through getting their customers to consume more.  But, to successfully implement the market basket analysis, the company like must implement real-time (zero-latency) analysis, to find those co-occurrence items and make suggestions to the consumer.  As the consumer adds more and more items into their shopping cart, Amazon in real-time begins to apply probabilistic mining (item affinity analysis) to find out what other items they would like to purchase in conjunction with their primary purchase (Pophal, 2014). For instance, buyers of a $40 swimsuit also bought this suntan lotion and beach towel.  Item affinity analysis doesn’t only impact the online shopping experience but also impacts shopping catalog placements, email marketing, and store layout (Snowplow Analytics, 2016).