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:

 

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Business Intelligence: Multilevel BI

Annotated Bibliography

Citation:

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.

Author’s Abstract:

“Energy Intelligence platforms can help organizations manage power consumption more efficiently by providing a functional view of the entire organization so that the energy consumption of business activities can be understood, changed, and reinvented to better support sustainable practices. Significant technical challenges exist in terms of information management, cross-domain data integration, leveraging real-time data, and assisting users to interpret the information to optimize energy usage. This paper presents an architectural approach to overcome these challenges using a Dataspace, Linked Data, and Complex Event Processing. The paper describes the fundamentals of the approach and demonstrates it within an Enterprise Energy Observatory.”

 

My Personal Summary:

Using BI as a foundation, a linked (key data is connected to each other to provide information and knowledge) dataspace (a huge data mart with data that is related to each other when needed) for energy intelligence was implemented for the Digital Enterprise Research Institute (DERI), which has ~130 staff located in one building.  The program was trying to measure the direct (electricity costs for data centers, lights, monitors, etc.) and indirect (cost of fuel burned, the cost of gas used by commuting staff) energy usage of the enterprise to become a more sustainable company (as climate change is a big topic these days).  It covered that a multi-level and holistic view of the business intelligence (on energy usage) was needed.  They talked about each of the individual types of information conveyed at each level.

My Personal Assessment:

However, this paper didn’t go into how effective was the implementation of this system.  What would have improved this paper, is saying something about the decrease in the CO2 emission DERI had over the past year.  They could have graphed a time series chart showing power consumption before implementation of this multi-level BI system and after.  This paper was objective but didn’t have any slant as to why we should implement a similar system.  They state that their future work is to provide more granularity in their levels, but nothing on what business value it has had on the company.  Thus, with no figures stating the value of this system, this paper seemed more like a conceptual, how-to manual.

My Personal Reflection:

This paper doesn’t fit well into my research topic.  But, it was helpful in defining a data space and multi-level and holistic BI system.  I may use the conceptual methodology of a data space in my methodology, where I collect secondary data from the National Hurricane Center into a big data warehouse and link the data as it seems relevant.  This, should save me time, and reduce labor intensive costs to data integration due to postponing it when they are required.  It has changed my appreciation of data science, as there is another philosophy to just bringing in one data set at a time into a data warehouse and make all your connections, before moving on to the next data set.

A multilevel business intelligence setup and how it affects the framework of an organization’s decision-making processes. 

In Curry et al. (2012), they applied a linked data space BI system to a holistic and multi-level organization.  Holistic aspects of their BI system included Enterprise Resource Planning, finance, facility management, human resources, asset management and code compliance.  From a holistic standpoint, most of these groups had silo information that made it difficult to leverage across their domains.  However, this is different than multi-level BI system setup.  Defined in Table II in Curry et al (2012), in the multi-level set up, the data gets shown to the organization (stakeholders are executive members, shareholders, regulators, suppliers, consumers), functional (stakeholders are functional managers, organization manager), and individual level (stakeholders are the employees).  Each of these stakeholders has different information requirements and different levels of access to certain types of data. Thus, the multi-level BI system must take this into account.  Thus, different information requirements and access mean different energy metrics, i.e. Organizational Level Metrics could be Total Energy Consumption, % Renewable energy sources, versus Individual Level Metrics could be Business Travel, Individual IT consumption, Laptop electricity consumption, etc.  It wouldn’t make sense that an executive or a stake holder to look at every 130 staff members Laptop electricity consumption metric when they could get a company-wide figure.   However, the authors did note that the level organization data can be further drilled down, to see where the cause could be for a particular event in question.  Certain data that the executives can see will not be accessed by all individual employees. Thus, a multi-level BI system also addresses this.  Also, employee A cannot view employee B’s energy consumption because of lateral level view of the BI system data may not be permissible.

Each of the different levels of metrics reported out by this multi-level BI system allows that particular level to make data-driven decisions to reduce their carbon footprint.  An executive can look at the organizational level metrics, and institute a power down your monitors at night initiative to save power corporate wide.  But, at the individual level, they could choose to leave to go to work earlier, not to be in traffic too long and waste less gas, thus reducing their indirect carbon footprint for the company.  Managers can make decisions to a request for funding for energy efficient monitors and laptops for all their teams, or even a single power strip per person, to reduce their teams’ energy consumption cost, which is based on the level of metrics they can view.