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.

 

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

Business Intelligence: Predictions Followup

  • Potential Opportunities:

o    Health monitoring.  Currently, smart watches are tracking our heart rate, steps, standing time, climbing stairs, siting time, heart beats, workouts, biking, sleep, etc.  But, what if we had a device that measured daily our chemicals in our blood, that is no longer as painful as pricking your finger if you are diabetic.  This, the technology could not only measure your blood chemical makeup but could send alerts to EMT and doctors if there is a dangerous imbalance of chemicals in your blood (Carter et al., 2014).  This would require a strong BI program across emergency responders, individuals, and doctors.

o    As Moore’s law of computational speed moves forward in time, the more chances are companies able to interpret real-time data and produce lead information which can drive actionable data-driven decisions. Companies can finally get answers to strategic business questions in minutes as well (Carter et al., 2014).

o    Both internal data (corporate data) and external data (competitor analysis, costumer analysis, social media, affinity and sentiment analysis), will be reported to senior leaders and executives who have the authority to make decisions on behalf of the company on a frequent basis.  These issues may show up in a dashboard, with x number of indicators/metrics as successfully implemented in a case study of a hospital (Topaloglou & Barone, 2015).

  • Potential Pitfalls:

o    Tools for threat detection, like those being piloted in New York City, could have an increased level of discrimination (Carter, Farmer, & Siegel, 2014). As big data analytics is being used to do facial recognition of photographs and live video to identify threats, it can lead to more racial profiling if the knowledge fed into the system as a priori has elements of racial profiling.  This could lead to a bias in reporting, track higher levels of a particular demographic, and the fact that past performance doesn’t indicate the future.

o    Data must be validated before it is published onto a data warehouse.  Due to the low data volatility feature of data warehouses, we need to ensure that the data we receive is correct, thus expected value thresholds must be set to capture errors before they are entered.  Wrong data in, means wrong data analysis, and wrong data-drove decisions.  An example of expected value thresholds could be that earth’s temperature cannot exceed 500K at the surface.

o    Amplified customer experience.  As BI incorporates social media to gauge what is going on in the minds of their customer, if something were to go viral that could hurt the company, it can be devastating for the company.  Essentially we are giving the customer an amplified voice.  This can be rumors of software, hardware leaks as what happens for every Apple iPhone generation/release, which can put current proprietary information into the hands of their competitors.  A nasty comment or post that gets out of control on a social media platform, to celebrity boycotts.  Though, the opportunity here lies in receiving key information on how to improve their products, identify leakers of information, and settle nasty rumors, issues, or comments.

  • Potential Threats:

o    Loss of data through hackers, which are aiming to steal someone’s identity.  Firewalls must be tighter than ever, and networks must be more secure than ever as a company goes into a centralized data warehouse.  Data warehouses are vital for BI initiatives, but if HR data is located in the warehouse, (for example to help HR identify likelihood measures of disgruntled employees to aid in their retention efforts) then if a hacker were to get a hold of that data, thousands of people information can be compromised.  This is nothing new, but this is a potential threat that must be mitigated as we proceed into BI systems.  This can not only apply to people data but company proprietary data.

o    Consumer advertisement blitz. If companies use BI to blast their customers with ads in hopes to better market to people and use item affinity analysis, to send coupons and attract more sales and higher revenues.  There is a personal example here for me:  XYZ is a clothing store, when I moved to my first house, the old owner never switched their information in their database.  But, since they were a frequent buyer and those magazines, coupons, flyers, and sales were working on the old owner of the house, they kept getting blasted with marketing ads.  When I moved in, I got a magazine every two days.  It was a waste of paper and made me less likely to shop there.  Eventually, I had enough and called customer service.  They resolved the issue, but it took six weeks after that call, for my address to be removed from their marketing and customer database.  I haven’t shopped there since.

o    Informational overload.  As companies go forward into implementing BI systems, they must meet with the entire multi-level organization to find out their data needs.  Just because we have the data, doesn’t mean we should display it.  The goal is to find the right amount of key success factors, key performance indicators, and metrics, to help out the decision makers at all different levels.  Complicating this part up can compromise the adoption of BI in the organization and will be seen as a waste of money rather than a tool that could help them in today’s competitive market.  This is such a hard line to walk on, but it is one of the biggest threats.  It was realized in the hospital case study (Topaloglou & Barone, 2015) and therefore mitigated for through extensive planning, buy-in, and documentation.

 

Resources:

Business Intelligence: Decision Support Systems

Many years ago a measure of Business Intelligence (BI) systems was on how big the data warehouse was (McNurlin, Sprague,& Bui, 2008).   This measure made no sense, as it’s not all about the quantity of the data but the quality of the data.  A lot of bad data in the warehouse means that it will provide a lot of bad data-driven decisions. Both BI and Decision Support Systems (DSS) help provide data to support data-driven decisions.  However, McNurlin et al. (2008) state that a DSS is one of five principles of BI, along with data mining, executive information systems, expert systems, and agent-based modeling.

  • 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; and McNurlin et al., 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.
  • 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).  The three components of a DSS are Data Component (comprising of databases, or data warehouse), Model Component (comprising of a Model base) and a dialog component (Software System, which a user can interact with the DSS) (McNurlin et al., 2008).

McNurlin et al (2008) state a case study, where Ore-Ida Foods, Inc. had a marketing DSS to support its data-driven decisions by looking at the: data retrieved (internal data and external market data), market analysis (was 70% of the use of their DSS, where data was combined, and relationships were discovered), and modeling (which is frequently updated).  The modeling offered great insight for the marketing management.  McNurlin et al. (2008), emphasizes that DSS tend to be defined, but heavily rely on internal data with little or some external data and that vibrational testing on the model/data is rarely done.

The incorporation of internal and external data into the data warehouse helps both BI strategies and DSS.  However, the one thing that BI strategies provide that DSS doesn’t is “What is the right data that should be collected and presented?” DSS are more of the how component, whereas BI systems generate the why, what, and how, because of their constant feedback loop back into the business and the decision makers.  This was seen in a hospital case study and was one of the main key reasons why it succeeded (Topaloglou & Barone, 2015).  As illustrated in the hospital case study, all the data types were consolidated to a unifying definition and type and had a defined roles and responsibilities assigned to it.  Each data entered into the data warehouse had a particular reason, and that was defined through interviews will all different levels of the hospital, which ranged from the business level to the process level, etc.

BI strategies can affect supply chain management in the manufacturing setting.  The 787-8, 787-9, and 787-10 Boeing Dreamliners have outsourced ~30% of its parts and components or more, this approach to outsourcing this much of a product mix is new since the current Boeing 747 is only ~5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial it is to capture data on fault tolerances on each of those outsourced parts.  Other things that BI data could be used is to make decisions on which supplier to keep or not keep.  Companies as huge as Boeing can have multiple suppliers for the same part, if in their inventory analysis they find an unusually larger than average variance in the performance of an item: (1) they can either negotiate a lower price to overcompensate a larger than average variance, or (2) they could all together give the company a notice that if they don’t lower that variance for that part they will terminate their contract.  Same things can apply with the auto manufacturing plants or steel mills, etc.

Resources:

 

Business Intelligence: Data Warehouse

A data warehouse is a central database, which contains a collection of decision-related internal and external sources of data for analysis that is used for the entire company (Ahlemeyer-Stubbe & Coleman, 2014). The authors state that there are four main features to data warehouse content:

  • Topic Orientation – data which affects the decisions of a company (i.e. customer, products, payments, ads, etc.)
  • Logical Integration – the integration of company common data structures and unstructured big data that is relevant (i.e. social media data, social networks, log files, etc.)
  • Presence of Reference Period – Time is an important part of the structural component to the data because there is a need in historical data, which should be maintained for a long time
  • Low Volatility – data shouldn’t change once it is stored. However, amendments are still possible. Therefore, data shouldn’t be overridden, because this gives us additional information about our data.

Given the type of data stored in a data warehouse, it is designed to help support data-driven decisions.  Making decisions from just a gut feeling can cost millions of dollars, and degrade your service.  For continuous service improvements, decisions must be driven by data.  Your non-profit can use this data warehouse to drive priorities, to improve services that would yield short-term wins as well as long-term wins.  The question you need to be asking is “How should we be liberating key data from the esoteric systems and allowing them to help us?”

To do that you need to build a BI program.  One where key stakeholders in each of the business levels agree on the logical integration of data, common data structures, is transparent in the metrics they would like to see, who will support the data, etc.  We are looking for key stakeholders on the business level, process level and data level (Topaloglou & Barone, 2015).  The reason why, is because we need to truly understand the business and its needs, from there we can understand the current data you have, and the data you will need to start collecting.  Once the data is collected, we will prepare it before we enter it into the data warehouse, to ensure low volatility in the data, so that data modeling can be conducted reliable to enable your evaluation and data-driven decisions on how best to move forward (Padhy, Mishra, & Panigrahi,, 2012).

Another non-profit service organization that implemented a successful BI program through the creation of a data warehouse can be found by Topaloglou and Barone (2015).  This hospital experienced positive effects towards implementing their BI program:  end users can make strategic data based decisions and act on them, a shift in attitudes towards the use and usefulness of information, perception of data scientist from developers to problem solvers, data is an immediate action, continuous improvement is a byproduct of the BI system, real-time views with data details drill down features enabling more data-driven decisions and actions, the development of meaningful dashboards that support business queries, etc. (Topaloglou & Barone, 2015).

However, Topaloglou and Barone (2015) stressed multiple times in the study, which a common data structure and definition needs to be established, with defined stakeholders and accountable people to support the company’s goal based on of how the current processes are doing is key to realizing these benefits.  This key to realizing these benefits exists with a data warehouse, your centralized location of external and internal data, which will give you insights to make data-driven decisions to support your company’s goal.

Resources