Mis Chapter 15
1. Data mining is leveraged by some firms to determine which products customers buy together, and how an organization can use this information to cross-sell more products or services. This area of application of data mining is referred to as: a. market basket analysis. b. expert systems. c. customer churn. d. customer segmentation. e. vertical integration.
A
1. E-discovery refers to: a. identifying and retrieving relevant electronic information to support litigation efforts. b. something a firm does not account for in its archiving and data storage plans. c. older information systems that are often incompatible with other systems, technologies, and ways of conducting business. d. collecting and reselling data. e. rewards and usage incentive, typically in exchange for a method that provides a more detailed tracking and recording of consumer activity.
A
1. In database systems, a _____ refers to a list of data. a. file b. column c. field d. row e. record
A
1. Inventory turnover ratio is: a. the ratio of a company's annual sales to its inventory. b. the ratio of a company's variable cost to its inventory. c. the ratio of a company's fixed cost to its inventory. d. the ratio of a company's annual cost to its inventory. e. the ratio of a company's fixed assets to its inventory.
A
1. _____ refer to databases focused on addressing the concerns of a specific problem or business unit. a. Data marts b. Dashboards c. Hadoop d. Data aggregators e. Data analytics
A
1. A(n) _____ is an AI system that examines data and hunts down and exposes patterns, in order to build models to exploit findings. a. Hadoop b. canned report c. data aggregator d. neural network e. e-discovery
D
1. In database systems, a row is also known as a _____. a. table b. column c. key d. record e. field
D
1. Why do firms need to create separate data repositories for their reporting and analytics work? a. Most firms store their data assets offsite to insure themselves against the possibility of data damage through natural disasters. b. Maintaining huge databases can be a cost-sink for most firms. c. Most organizations need to differentiate data derived in-house and from data aggregators. d. Running analytics against transactional data can bog down a TPS. e. Reporting and analytics are two separate functions, each requiring its own separate database specifically formatted to the needs of the management team.
D
1. _____ is a method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube. a. Ad hoc reporting b. E-discovery c. Data aggregation d. Online analytical processing e. Data adjacency
D
1. _____ is the term used to describe raw facts and figures. a. information b. knowledge c. analytics d. data e. intelligence
D
1. _____ refers to software for creating, maintaining, and manipulating data. a. Extranet b. ROM c. RAM d. DBMS e. Internet 2
D
1. A _____ is a system that provides rewards and usage incentives, typically in exchange for a method that provides a more detailed tracking and recording of consumer activity. a. sugging report b. canned report c. dashboard d. legacy system e. loyalty program
E
1. A(n) _____ refers to a heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics. a. interstitial b. embassy c. canned report d. prediction interface e. dashboard
E
1. Firms that collect and resell data are known as: a. data barons. b. data mongers. c. knowledge consultancies. d. data miners. e. data aggregators.
E
1. If a customer pays a retailer in cash, he is likely to remain a mystery to the retailer because his name is not attached to the money. Retailers can tie the customer to cash transactions and track the customer's activity if they can convince the customer to use a _____. a. transaction processing system b. point-of-sale terminal c. data cube d. dashboard e. loyalty card
E
1. In database systems, a _____ defines the data that a table can hold. a. row b. key c. record d. file e. field
E
1. _____ are model building techniques where computers examine many potential solutions to a problem, iteratively modifying various mathematical models, and comparing the mutated models to search for a best alternative. a. Expert systems b. Ad hoc reporting tools c. Iterative mutations d. Sampled alliterations e. Genetic algorithms
E
1. _____ is by far the most popular language for creating and manipulating databases. a. XML b. HTML c. PHP d. Java e. SQL
E
1. _____ is the process of using computers to identify hidden patterns in and to build models from large data sets. a. Data harvesting b. E-discovery c. Optimization d. Report canning e. Data mining
E
1. Systems that can absorb any type of data, structured or not, from any type of source are often called schema-less.
True
1. The data a firm can leverage is a true strategic asset when it is valuable, rare, imperfectly imitable, and non-substitutable.
True
1. Data are raw facts that must be turned into information in order to be useful and valuable.
True 1.
1. Computer-driven investment models can be very effective when the market behaves as it has in the past. However, in terms of historical consistency, they are vulnerable to failure in the face of: a. brute force attacks. b. black swans. c. zero-day exploits. d. calendar rivalry metrics. e. distributed denial of service.
B
1. Data becomes _____ when it is presented in a context so that it can answer a question or support decision making. a. knowledge b. information c. a database d. wisdom e. a relational language
B
1. Most transactional databases are not set up to be simultaneously accessed for reporting and analysis. As a consequence: a. navigational databases are being preferred over transactional databases. b. data is not efficiently transformed into information. c. firms prefer to outsource data mining operations to third-party firms. d. analysts must also become transactional specialists. e. most firms incur additional expenditure to effectively record transactions.
B
1. What is Wal-Mart's motivation for sharing data with its supply partners? a. Creating switching costs for suppliers b. Lowering prices of products c. Achieving maturity in the American market d. Countering the accusations of union activists e. Deflecting criticism for ruining local mom-and-pop stores
B
1. Which of the following is not considered an advantage of Hadoop? a. flexibility. b. relational structure. c. scalability. d. cost effectiveness. e. fault tolerance.
B
1. _____ is a class of computer software that seeks to reproduce or mimic human thought, decision making, or brain functions. a. Biometrics b. Artificial intelligence c. Android d. Legacy software e. Intranet
B
1. _____ put(s) users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters. a. Canned reports b. Ad hoc reporting tools c. Dashboards d. Data cubes e. Online analytical processing
B
1. A data cube refers to a: a. secure, cloud-based off-site location used for data storage, analysis, and reporting. b. heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics. c. special database used to store data in OLAP reporting. d. firm that collects data with the intention of reselling it to others. e. combination of fields used to uniquely identify a record, and to relate separate tables in a database.
C
1. Data can potentially be used as a strategic asset, capable of yielding sustainable competitive advantage. Which of the items below is not a characteristic of a potentially strategic asset? a. value b. rarity c. imperfect imitability d. lead time e. non-substitutability
C
1. In database systems, a table is also known as a _____. a. field b. record c. file d. row e. key
C
1. In database terminology, a record represents: a. a list of data, arranged in columns and rows. b. all of the data in a given column. c. a single instance of whatever the table keeps track of. d. a field or combination of fields used to uniquely identify a file. e. one or more keys that relate separate tables in a database.
C
1. Knowledge is defined as: a. raw facts and figures. b. the data presented in a context so that it can answer a question or support decision making. c. the insight derived from experience and expertise. d. a listing of primary data. e. the process of breaking a complex topic into smaller parts.
C
1. Which of the following conditions is essential for data mining to work? a. The data must be collected from proprietary sources and not from data aggregators. b. The organization must leverage standard relational databases as opposed to inferior hierarchical and analytical databases. c. The events in the data should reflect current and future trends. d. The data mining software must necessarily incorporate ad hoc reporting tools and dashboards. e. The data should have passed the Diehard suite of stringent tests for randomness.
C
1. _____ refer to older information systems that are often incompatible with other systems, technologies, and ways of conducting business. a. Data aggregator systems b. Loyalty card systems c. Legacy systems d. Transaction systems e. Mnemonic systems
C
1. _____ refers to the process of combining aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis. a. Logistics b. Queritic modeling c. Business intelligence d. Electronic trading e. Big Data
C
1. Advantages based on capabilities and data that others can acquire are long-lived.
False
1. Data warehouses are composed entirely of proprietary corporate data, while data marts take advantage of data purchased from third-party firms.
False
1. Enterprise software tends to be less integrated and standardized than the prior era of proprietary systems that many firms developed themselves.
False
1. In database systems, a column is also known as a key.
False
1. Logistics is the term that describes the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.
False
1. OLAP technology is primarily used for transaction processing.
False
1. Skittish and untrusting managers should realize that the first findings of analytics always reveal an optimal course of action.
False
1. Tesco is the planet's largest retailer.
False
1. Turning data into useable information is hindered by transactional databases set up to be simultaneously accessed for reporting and analysis.
False
1. Wal-Mart supplements its huge data assets with additional data provided by information brokers like Information Resources and ACNielsen.
False
1. While spreadsheets are popular tools, they cannot effectively be used for "what-if" analysis.
False
1. All SQL databases are relational databases.
True
1. Any data-centric effort should involve input not only from business and technical staff, but from the firm's legal team, as well.
True
1. Conventional database technologies often choke when trying to sift through the massive amounts of data collected by many of today's firms, leading to the rise of Hadoop and other "Big Data" technologies.
True
1. Data obtained from outside sources, when combined with a firm's proprietary internal data assets, can give the firm a competitive edge.
True
1. Dynamic pricing is considered especially tricky in situations where consumers make repeated purchases and are more likely to remember past prices, and when they have alternative choices.
True
1. Firms that base decisions on hunches are said to be gambling, not managing.
True
1. Having too much inventory or insufficient inventory is known as a retailer's "twin nightmares."
True
1. In data warehousing projects, it is not uncommon for spending on consulting and services to cost five times or more than the cost of the technology itself.
True
1. In many organizations, the majority of available data is not exploited to advantage.
True
1. One reason L.L. Bean moved to an unstructured, NoSQL, big data environment was because the firm's customer engagement had evolved to include some 30 different customer engagement channels
True
1. Random occurrences in data mining results can be detected by dividing the data and building a model with one portion and using another portion to verify the results.
True
