Decision Support Systems For Business Intelligence – Decision making is often routine and can be done easily, but when in a position to make a new decision, managers often need information to support the rationale for their choice. Here are some of the issues data management faces when making decisions:
Information overload – This is defined as “too much irrelevant data”. Data access/storage is now easy for n’ days; data is everywhere. While this can be very valuable, it can also be a setback because too much information is available to many managers that is not relevant for decision making. This can cloud the decision-making process.
Decision Support Systems For Business Intelligence
Data Quality – Poor data quality can also negatively impact management decisions, some examples include:
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Certain factors such as the nature of the transaction, costs and organizational needs must be considered when choosing a processing method. For example, WestJet processes transactions instantly because one seat cannot be sold to two different people, which is inefficient for the organization.
OLAP “provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data.” The report is dynamic, allowing viewers to change the structure of the report online.
“A system that informs better decision-making. BI systems differ in their characteristics and capabilities and in the ways they promote competitive advantage.” Here is a summary table of “Characteristics and Competitive Advantage of BI Systems” from the Experience MIS textbook:
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The purpose of a data warehouse “is to extract and clean data from operating systems and other sources, and to store and catalog data for processing by BI tools,” while a database “is a collection of data created to meet a specific business function, problem or opportunity.
Data mining is “the application of statistical techniques to discover patterns and relationships among data, and to classify and make predictions.” This includes: To make strategic decisions about which products to display in our store, we need to carefully analyze sales and click data. This type of data analysis is a form of business intelligence.
If there’s one thing that’s abundant in the world today, it’s data. At the heart of any information system is a database that captures transactional data. For example, who bought what, when, how much it cost, etc. It is useful to understand the architecture of the trading system so that how the data is captured is not a complete mystery.
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However, it is extremely important to understand how captured data is extracted and analyzed to make management decisions. For example, after aggregating thousands of records, we might find that a product sells particularly well among women of a certain age group who live in a certain region. Meaningful information can be actionable as far as supply chain and marketing programs are concerned.
If anything, the world today has too much data. Distilling data into meaningful information is a critical skill. There are many tools available to perform data analysis. These include spreadsheet programs such as Excel and database systems such as Access. Learning to use these tools will increase your competitiveness in the market.
Many information systems projects are conceived in a life cycle that proceeds in phases from analysis to implementation. The diagram below shows the stages we covered in this chapter:
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To illustrate the power of aggregate data, we’ll first show how it can be used as a marketing tool for your website. Impressive statistics help encourage repeat business. The same marketing principles apply even to non-profit organizations.
Kiva is a website that allows you to provide small loans (usually under $500) to entrepreneurs in developing countries. The field of microfinance, known as microfinance, provides very small loans (usually less than $500) to entrepreneurs in developing countries. Most loans are repaid within six months to a year. Microfinance institutions are an extremely important resource in helping Third World citizens escape poverty. Surprisingly, repayment rates for the world’s poor are between 95% and 98%, much higher than loan repayment rates in the United States. Over 80% of Kiva’s loans are to women entrepreneurs. They invest the profits back into the business and improve their family’s lives.
Kiva works by pooling resources, for example 50 people can borrow $10 each for a total of $500. As part of its marketing efforts, Kiva maintains fast facts about its activities to date. For example, they report that they have nearly 500,000 lenders who have lent a total of $161 million over the past three years. These fast facts are gathered from the website’s database after scanning millions of records and constitute business intelligence. This information is not only used for marketing purposes, but also serves as an internal scorecard to track the progress of Kiva’s mission and influence decision-making.
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Kiva’s Facts and History page is a business intelligence report. Note the sentence that appears under “Latest Stats”, which states that the statistics are updated nightly (between 1am and 3am). This is a typical business intelligence system. Searching millions of records can be so resource-intensive on the system that these activities are typically performed during off-peak hours.
Kiva examples are a form of business intelligence that delivers accurate, actionable information to the appropriate decision maker in the required timeframe to support effective decision making. .Business Intelligence (BI) is the delivery of accurate, actionable information to the appropriate decision makers within the required time frame to support effective decision making.
By this definition, everything we do with Excel qualifies as business intelligence because our results contain accurate and useful information to support effective decision-making. However, business intelligence is generally understood to involve extracting and analyzing large data sets, such as those found in corporate databases. Retrieving and analyzing information stored in databases is the subject of this chapter. You will likely be asked to perform this type of analysis at various points in your career.
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Business intelligence is part of the big picture architecture of the information system. Most existing systems can be classified as enterprise systems, collaboration systems, or business intelligence systems. Enterprise systems (such as order taking) provide their data to a data warehouse, which is then queried to support business intelligence.
For example, suppose our goal is to develop a clothing business that produces high-quality products while keeping costs low. We also decided that we would measure product quality by the percentage of products rejected by inspectors at each site. (Think of the inspector 99 tags you find in the pocket of your new clothes. The clothes you wear are accepted by the inspector.) A relatively high rejection rate is a red flag that management needs further analysis. Is this an overzealous inspector? Is there a policy for rejected products? Does one station in the factory generate more waste than other stations?
Suppose our analysis found that the high scrap rate came from only one plant in Southeast Asia. We are reporting the issue to management. They sent a team to check the factory. The audit found child labor, abuses and low morale at the factory. The dire situation was quickly reversed and rejection rates returned to average levels.
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The business intelligence part of information systems architecture. Keep in mind that business intelligence systems typically operate out of a data warehouse—the company’s data warehouse. Every enterprise system contains one or more databases. The contents of these databases are routinely copied to the data warehouse to enable BI analysis. The copying process is called extract, transform and load (ETL).
Static Reports The form of BI reporting we are most familiar with is the summary report that is distributed on a regular basis. is the most common form of business intelligence. Most businesses compile standard reports that are designed and printed to support management decision making. For example, colleges use admissions reports to gauge which departments may need to hire more faculty. Credit card companies will require reports on people with high credit scores to target credit card promotions. Likewise, these companies can target students with good future earning potential. A marketer can look at sales data from different stores and regions to determine where there are opportunities for promotions.
Dynamic reports look like standard reports, but they are quite different. They are interactive, allowing users to drill down to discover aggregate number sources. It looks similar to static reports, but online and interactive. A manager is curious about the location of a summary number on his scoreboard. A view of the data from a high level of management – sometimes depicted with dials and needles similar to the dashboard of a car. In a car, you probably don’t need to know the exact rpm, but you do need to know if you’re redlining. Similarly, top management may not need to know exact sales figures, but they do need to know whether sales
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