Plug In18 BI – Flashcards

Cluster analysis

Association detection

. Analyzes website information B. Analyzes checkout scanner information C. Detects customers' buying behavior D. All of the above*

Time-stamped information collected at a particular frequency

Forecasting

Predictions made on the basis of time-series information

Whirlpool

Using tactical business intelligence

Data rich, information poor

Retail and sales BI decisions

Banking BI decisions

Operations management BI decisions

Insurance BI decisions

A. Law enforcement industry B. Airline industry C. Broadcasting industry D. All of the above*

A. Reliable B. Understandable C. Easily Manipulated D. All of the above*

Reliable

Consistent

Understandable

Easily manipulated

Why

Operational BI

Tactical BI

Strategic BI

Operational BI

Tactical BI

Operational BI

Strategic BI

The time duration to make data ready for analysis and loading the data into the database

The time from which data are made available to the time when analysis is complete

he time it takes a human to comprehend the analytic results and determine an appropriate action

Up-to-the minute information for everyone

Quantifiable benefits

Quantifiable benefits

Unpredictable benefits

Intangible benefits

TRUE Improving the quality of business decisions has a direct impact on costs and revenue.

TRUE Quantifiable benefits, indirectly quantifiable benefits, unpredictable benefits, and intangible benefits are the four primary categories of BI benefits.

FALSE Analysis latency is the time from which data are made available to the time when analysis is complete.

FALSE The three forms of BI include operational, tactical, and strategic.

TRUE Data is a strategic asset for a business and if the asset is not used the business is wasting resources.

Basket

Statistical

Forecasts

(1) Cluster analysis is a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. (2) Association detection reveals the degree to which variables are related and the nature and frequency of these relationships in the information. (3) Statistical analysis performs such functions as information correlations, distributions, calculations, and variance analysis.

The issue most organizations are facing today is that it is next to impossible to understand their own strengths and weaknesses, let alone their enemies, because the enormous amount of organizational data is inaccessible to all but the IT department. Please note that organization data includes far more than simple fields in a database, it also includes voice mails, customer phone calls, text messages, video clips, along with the numerous new forms of data organizations are storing. The Problem: Data Rich: Information Poor. The Solution: Business Intelligence. In every organization, employees make hundreds of decisions each day. They can range from whether to give customer X a discount, whether to start producing part Y, whether to launch another direct mail campaign, whether to order additional materials, etc. These decisions are sometimes based on facts, but mostly based on experience, accumulated knowledge, and rule of thumb. That poses a problem because experience, knowledge, and rule of thumb can take years to develop. Some employees never acquire them. Those who do may still fall prey to decision traps or biases in judgment. Improving the quality of business decisions has a direct impact on costs and revenue. For instance, giving a customer a discount may or may not help the bottom line, depending on the profitability of the client over the duration of the relationship. To improve the quality of business decisions, managers can provide existing staff with BI systems and tools that can assist them in making better, more informed decisions. The result creates an agile intelligent enterprise.

The more common forms of data-mining analysis capabilities include Cluster analysis a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. Cluster analysis is frequently used to segment customer information for customer relationship management systems to help organizations identify customers with similar behavioral traits, such as clusters of best customers or one-time customers. Association detection reveals the degree to which variables are related and the nature and frequency of these relationships in the information. Maytag's warranty analysis tool, for instance, uses statistical analysis to automatically detect potential issues, provide quick and easy access to reports, and perform multidimensional analysis on all warranty information. This association detection data-mining tool enables Maytag's managers to take proactive measures to control product defects even before most of its customers are aware of the defect. The tool also allows Maytag personnel to devote more time to value-added tasks such as ensuring high quality on all products rather than waiting for or manually analyzing monthly reports. Statistical analysis performs such functions as information correlations, distributions, calculations, and variance analysis, just to name a few. Data-mining tools offer knowledge workers a wide range of powerful statistical capabilities so they can quickly build a variety of statistical models, examine the models' assumptions and validity, and compare and contrast the various models to determine the best one for a particular business issue.

Richard Hackathorn of Bolder Technologies developed an interesting graph to demonstrate the value of operational BI. Figure B18.5 shows the three latencies that impact the speed of decision making. These are data, analysis, and decision latencies. Data latency is the time duration to make data ready for analysis, i.e., the time for extracting, transforming, and cleansing the data, and loading it into the database. All this can take time depending on the state of the operational data to begin with. Analysis latency is the time from which data is made available to the time when analysis is complete. Its length depends on the time it takes a business to do analysis. Usually, we think of this as the time it takes a human to do the analysis, but this can be decreased by the use of automated analytics that have thresholds. When the thresholds are exceeded, alerts or alarms can be issued to appropriate personnel, or they can cause exception processes to be initiated with no human intervention needed. Decision latency is the time it takes a human to comprehend the analytic result and determine an appropriate action. This form of latency is very difficult to reduce. The ability to remove the decision making process from the human and automate it will greatly reduce the overall decision latency. Many forward thinking companies are doing just that. For example, rather than send a high value customer a letter informing them of a bounced check (which takes days to get to the customer), an automated system can simply send them an immediate email or voice message informing them of the problem.

A practical way of breaking down these numerous benefits is to separate them into four main categories: Quantifiable benefits: Quantifiable benefits include working time saved in producing reports, selling information to suppliers, etc. Indirectly quantifiable benefits: Indirectly quantifiable benefits can be evaluated through indirect evidence— improved customer service means new business from the same customer, and differentiated service brings new customers. Unpredictable benefits: Unpredictable benefits are the result of discoveries made by creative users and a few Intangible benefits: Intangible benefits include improved communication throughout the enterprise,