Isys 201 Midterm – Flashcards
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Transactional information
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encompasses all of the information contained within a single business process or unit of work, and its primary purpose is to support the performing of daily operational tasks
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Analytical information
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encompasses all organizational information, and its primary purpose is to support the performing of managerial analysis tasks
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Real time information/systems
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immediate, up-to-date information. Real-time systems provide real-time information in response to query requests
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Integration
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allows separate systems to communicate directly with each other
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Forward vs. backward integration
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A forward integration takes information entered into a given system and sends it automatically to all downstream systems and processes. A backward integration takes information entered into a given system and sends it automatically to all upstream systems and processes.
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Compare the multi dimensional nature of data warehouses (and data marts) with the two dimensional nature of databases
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A relational database contains information in a series of two-dimensional tables. In a data warehouse and data mart, information is multidimensional, meaning it contains layers of columns and rows. For this reason, most data warehouses and data marts are multidimensional databases. A dimension is a particular attribute of information. Each layer in a data warehouse or data mart represents information according to an additional dimension
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Identify the importance of ensuring the cleanliness of information throughout an organization
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If an organization is using a data warehouse or data mart to allocate dollars across advertising strategies (such as in the case of Sega of America), low quality information will definitely have a negative impact on its ability to make the right decision.
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Explain the relationship between business intelligence and a data warehouse
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In modern businesses, increasing standards, automation, and technologies have led to vast amounts of available information. Data warehouse technologies have set up repositories to store this information. Improved ETL has increased the speedy collecting of information. Business intelligence has now become the art of sifting through large amounts of data, extracting information, and turning that information into actionable knowledge.
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Define the systems organizations use to make decisions and gain competitive advantage
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Transaction Processing Systems. Decision Support Systems. Executive Information Systems.
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Describe the three quantitative models typically used by decision support systems
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(1) sensitivity analysis, (2) what if analysis, and (3) goal seeking analysis.
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Describe the relationship between digital dashboards and executive information systems
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EIS systems, such as digital dashboards, allow executives to move beyond reporting to using information to directly impact business performance. Digital dashboards help executives react to information as it becomes available and make decisions, solve problems, and change strategies daily instead of monthly.
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List and describe four types of artificial intelligence systems
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(1) expert systems, (2) neural networks, (3) genetic algorithms, and (4) intelligent agents.
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Describe three types of data mining analysis capabilities
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Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information, which analysts use to develop new business strategies. Analysts use the output from data-mining tools to build models that, when exposed to new information sets, perform a variety of data analysis functions. The analysts provide business solutions by putting together the analytical techniques and the business problem at hand, which often reveals important new correlations, patterns, and trends in information.
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Data warehouse
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is a logical collection of information gathered from many different operational databases that supports business analysis activities and decision making tasks. The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository in such a way that employees can make decisions and undertake business analysis activities. Therefore, while databases store the details of all transactions (for instance, the sale of a product) and events (hiring a new employee), data warehouses store that same information but in an aggregated form more suited to supporting decision making tasks. Aggregation, in this instance, can include totals, counts, averages, and the like. Because of this sort of aggregation, data warehouses support only analytical processing.
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ELT (extraction, transformation, loading)
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a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.
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Data mart
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contains a subset of data warehouse information. Data marts have focused information subsets particular to the needs of a given business unit such as finance or production and operations.
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Multi dimensional analysis
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The information is multidimensional, meaning it contains layers of columns and rows. Most data warehouses and data marts are multidimensional databases. A dimension is a particular attribute of information. Each layer in a data warehouse or data mart represents information according to an additional dimension.
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Cube
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A cube is the common term for the representation of multidimensional information. Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information.
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Data mining
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the process of analyzing data to extract information not offered by the raw data alone.
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Business intelligence
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refers to applications and technologies that are used to gather, provide access to, and analyze data and information to support decision- making efforts.
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Association detection
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reveals the degree to which variables are related and the nature and frequency of these relationships in the information.
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Cluster analysis
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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 relation- ship management systems to help organizations identify customers with similar behavioral traits, such as clusters of best customers or onetime customers. Cluster analysis also has the ability to uncover naturally occurring patterns in information
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Decision support system (DSS)
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models information to support managers and business professionals during the decision-making process. Three quantitative models are typically used by DSSs: (1) sensitivity analysis, (2) what-if analysis, and (3) goal-seeking analysis.
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Digital dashboards
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integrate information from multiple components and tailor the information to individual preferences. Digital dashboards commonly use indicators to help executives quickly identify the status of key information or critical success factors.
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Drill-down
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enables users to get details, and details of details, of information. Viewing monthly, weekly, daily, or even hourly information represents drill- down capability.
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Executive information system (EIS)
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a specialized DSS that supports senior- level executives within the organization. An EIS differs from a DSS because an EIS typically contains data from external sources as well as data from internal sources. Consolidation, drill-down, and slice-and-dice are a few of the capabilities offered in most EISs.
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Consolidation
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involves the aggregation of information and features simple roll- ups to complex groupings of interrelated information. Many organizations track financial information at a regional level and then consolidate the information at a single global level.
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Goal-seeking analysis
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finds the inputs necessary to achieve a goal such as a desired level of output. Instead of observing how changes in a variable affect other variables as in what-if analysis, goal-seeking analysis sets a target value (a goal) for a variable and then repeatedly changes other variables until the target value is achieved.
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Sensitivity analysis
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is the study of the impact that changes in one (or more) parts of the model have on other parts of the model. Users change the value of one variable repeatedly and observe the resulting changes in other variables.
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Slice-and-dice
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is the ability to look at information from different perspectives. One slice of information could display all product sales during a given promotion. Another slice could display a single product's sales for all promotions.
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Statistical analysis
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performs such functions as information correlations, distributions, calculations, and variance analysis. 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.
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What-if analysis
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checks the impact of a change in an assumption on the proposed solution. For example, "What will happen to the supply chain if a hurricane in South Carolina reduces holding inventory from 30 percent to 10 percent?" Users repeat this analysis until they understand all the effects of various situations.
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CRM (customer relationship management)
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CRM is a business philosophy based on the premise that those organizations that understand the needs of individual customers are best positioned to achieve sustainable competitive advantage in the future.
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RFM (recency, frequency, monetary value)
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How recently a customer purchased items (recency). How frequently a customer purchases items (frequency). How much a customer spends on each purchase (monetary value).
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Reporting
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CRM reporting technologies help organizations identify their customers across other applications.
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Analyzing
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CRM analysis technologies help organizations segment their customers into categories such as best and worst customers.
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Predicting
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CRM predicting technologies help organizations make predictions regarding customer behavior such as which customers are at risk of leaving.
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Operational CRM
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Operational CRM supports traditional transactional processing for day-to-day front-office operations or systems that deal directly with the customers.
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Analytical CRM
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Analytical CRM supports back-office operations and strategic analysis and includes all systems that do not deal directly with the customers. The primary difference between operational CRM and analytical CRM is the direct interaction between the organization and its customers.
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Personalization
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Personalization occurs when a website can know enough about a person's likes and dislikes that it can fashion offers that are more likely to appeal to that person.
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CRM success factors
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Understanding customer behavior and preferences, then realigning product and service offerings and related communications to make sure they are synchronized with customer needs and preferences
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Key performance indicators (KPIs)
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measures that are tied to business drivers
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Benchmarks/benchmarking
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Benchmarking is a process of continuously measuring system results, comparing those results to optimal system performance (benchmark values), and identifying steps and procedures to improve system performance.
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Types of metrics (IT, supply chain, CRM, etc.)
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A metric is nothing more than a standard measure to assess performance in a particular area. Metrics are at the heart of a good, customer-focused management system and any program directed at continuous improvement.
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Balanced scorecard
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a management system, in addition to a measurement system, that enables organizations to clarify their vision and strategy and translate them into action. It provides feedback around both the internal business processes and external outcomes in order to continuously improve strategic performance and results. When fully deployed, the balanced scorecard transforms strategic planning from an academic exercise into the nerve center of an enterprise.
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Simple
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Measurements that are easy to understand
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Precisely definable
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Clearly defined measures so that they can be applied and evaluated consistently.
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Objective
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Two or more qualified observers should arrive at the same value for the measure
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Valid
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The metric should measure the property it is intended to measure
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Robust
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Measures should be insensitive to insignificant changes in the process or product.
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Units of measure
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Most appropriate unit of measure
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Precision/materiality
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How precise the count should be. Materiality refers to knowing when it becomes irrelevant to be more precise with your measurement because it will have a negligible impact on the meaning of the measure or the impact of the information on the decision.
