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1. What are data warehouses and data marts? What is their significance in terms of data organization?
A data warehouse is a set of databases designed to support decision making in an organization. It is structured for fast online queries and exploration. Data warehouses may aggregate enormous amounts of data from many different operational systems. A data mart is a database focused on addressing the concerns of a specific problem (e.g., increasing customer retention, improving product quality) or business unit (e.g., marketing, engineering). Marts and warehouses may contain huge volumes of data. For example, a firm may not need to keep large amounts of historical point-of-sale or transaction data in its operational systems, but it might want past data in its data mart so that managers can hunt for patterns and trends that occur over time.
1. Describe "database," "DBMS," and "SQL."
A database is simply a list or several related lists of data. Firms often create specialized databases for recording transactions, as well as databases that aggregate data from multiple sources in order to support reporting and analysis. Databases are created, maintained, and manipulated using programs called database management systems (DBMS), sometimes referred to as database software. Structured query language (SQL) is by far the most common language for creating and manipulating databases.
1. How are increasingly standardized data, access to third-party datasets, cheap, fast computing, and easier-to-use software collectively enabling a new age of decision making?
A study by Gartner Research claims that the amount of data on corporate hard drives doubles every six months. With this flood of data comes a tidal wave of opportunity. Increasingly standardized corporate data, and access to rich, third-party datasets—all leveraged by cheap, fast computing and easier-to-use software—are collectively enabling a new age of data-driven, fact-based decision making. The phrase of the day is business intelligence (BI), a catchall term combining aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis. Alongside business intelligence in the new managerial lexicon is the phrase analytics, a term describing the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Anyone can acquire technology—but data is oftentimes considered a defensible source of competitive advantage. The data a firm can leverage is a true strategic asset when it's rare, valuable, imperfectly imitable, and lacking in substitutes.
1. Describe how L.L. Bean's Big Data efforts have changed and improved firm interactions with customers.
Automated tools were also developed to leverage this data in real-time (making it not just "big data" but also "fast data"), allowing Bean to serve customers online, on the phone or in stores based on what the firm knows about them. This kind of integrated shopping experience and unified customer view across channels is sometimes referred to as omnichannel. Bean's web and app stores leverage these insights for on-the-fly personalization with a sharp-shooter aim at what the customer wants. Additionally, iPad wielding store staff and info-armed phone reps now have access to a deeply-detailed real-time view of each customer, and these profiles are served up to create more relevant offers, better-received recommendations, and to deliver overall customer delight. Need proof that customers see the benefit? Bean tied with Amazon as best in online retail customer satisfaction.
1. _____ is the general term used to describe the massive amount of data available to today's managers.
Big data
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
2. What is data mining? What are the key areas where businesses are leveraging data mining?
Data mining is the process of using computers to identify hidden patterns in and to build models from large data sets. Some of the key areas where businesses are leveraging data mining include the following: Customer segmentation—figuring out which customers are likely to be the most valuable to a firm. Marketing and promotion targeting—identifying which customers will respond to which offers at which price at what time. Market basket analysis—determining which products customers buy together, and how an organization can use this information to cross-sell more products or services. Collaborative filtering—personalizing an individual customer's experience based on the trends and preferences identified across similar customers. Customer churn—determining which customers are likely to leave, and what tactics can help the firm avoid unwanted defections. Fraud detection—uncovering patterns consistent with criminal activity. Financial modeling—building trading systems to capitalize on historical trends. Hiring and promotion—identifying characteristics consistent with employee success in the firm's various roles.
3. _____ refers to a job title focused on directing, performing, or overseeing activities associated with a database or set of databases.
Database administrator
2. Why do many organizations have data that cannot be converted to actionable information? What are the hurdles faced by firms that attempt to query transactional databases?
Despite being awash in data, many organizations are data rich but information poor. The big culprit limiting BI initiatives is getting data into a form where it can be used, analyzed, and turned into information. Legacy systems are older information systems that are often incompatible with other systems, technologies, and ways of conducting business. Incompatible legacy systems can be a major roadblock to turning data into information, and they can inhibit firm agility, holding back operational and strategic initiatives. The problem can be made worse by mergers and acquisitions, especially if a firm depends on operational systems that are incompatible with its partner. Firms might be under extended agreement with different vendors or outsourcers, and breaking a contract or invoking an escape clause may be costly. Another problem when turning data into information is most transactional databases are not set up to be for simultaneous access for reporting and analysis. When a customer buys something from a cash register, that action may post a sales record and deduct an item from the firm's inventory. But if a manager asks a database to analyze historic sales trends showing the most and least profitable products over time, they may be asking a computer to look at thousands of transaction records, comparing results, and neatly ordering findings, which may bog down the system operation.
5. Changing pricing based on demand conditions is known as ________.
Dynamic pricing
1. How is The EchoNest (part of Spotify) leveraging big data for a better music experience?
EchoNest software can "listen" to music; analyze it to break down its characteristics: pitch, key, tempo, vocals or instrumental, live or studio, energy level, mood, and more. The firms software also constantly scours the Web, "reading" music blogs, news reports, and more—as many as 10 million documents each day. The goal: to do "what a great deejay does, or the friend that you rely on musically: to better understand who you are as a fan, understand all the music that's out there, and make that connection.
1. _____ are AI systems that leverage rules or examples to perform a task in a way that mimics applied human expertise.
Expert Systems
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. 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. Tesco is the planet's largest retailer.
False
2. While spreadsheets are popular tools, they cannot effectively be used for "what-if" analysis.
False
3. In database systems, a column is also known as a key.
False
3. Turning data into useable information is hindered by transactional databases set up to be simultaneously accessed for reporting and analysis.
False
3. Wal-Mart supplements its huge data assets with additional data provided by information brokers like Information Resources and ACNielsen.
False
4. Skittish and untrusting managers should realize that the first findings of analytics always reveal an optimal course of action.
False
6. Advantages based on capabilities and data that others can acquire are long-lived.
False
1. Briefly describe the different sources of enterprise data for firms.
For organizations that sell directly to their customers, transaction processing systems (TPS) represent a source of potentially useful data. Grocers and retailers can link customers to cash transactions if they can convince them to use a loyalty card which, in turn, requires the customers to give up information about themselves in exchange for some kind of financial incentive such as points or discounts. Enterprise software (CRM, SCM, and ERP) is a source for customer, supply chain, and enterprise data. Survey data can be used to supplement a firm's operational data. Data obtained from outside sources, when combined with a firm's internal data assets, can also give the firm a competitive edge. Data that can be purchased from aggregators may not in and of itself yield sustainable competitive advantage since others may have access to this data, too. However, when combined with a firm's proprietary data or integrated with a firm's proprietary procedures or other assets, third-party data can be a key tool for enhancing organizational performance.
2. Walmart uses_____ technology to sift through massive amounts of social media data.
Hadoop
4. The term ____________ refers to a technology that sends messages to smartphones and other devices using a low-energy Bluetooth signal.
Ibeacon
1. _____ refers to the ratio of a company's annual sales to its inventory.
Inventory turnover ratio
3. _______________ refers to type of artificial intelligence that leverages massive amounts of data so that computers can improve the accuracy of actions and predictions on their own without additional programming
Machine learning
2. The category of AI technology used by Google to recognize patterns to improve speech recognition is known as ______________.
Neural networks
2. What are the issues to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts?
Once a firm has business goals and hoped-for payoffs clearly defined, it can address the broader issues needed to design, develop, deploy, and maintain its system: • Data relevance: What data is needed to compete on analytics and to meet our current and future goals? • Data sourcing: Can we even get the data we'll need? Where can this data be obtained? Is it available via our internal systems? Via third-party data aggregators? Via suppliers or sales partners? Do we need to set up new systems, surveys, and other collection efforts to acquire the data we need? • Data quantity: How much data is needed? • Data quality: Can our data be trusted as accurate? Is it clean, complete, and reasonably free of errors? How can the data be made more accurate and valuable for analysis? Will we need to "scrub," calculate, and consolidate data so that it can be used? • Data governance: What rules and processes are needed to manage data from its creation through its retirement? Are there operational issues (backup, disaster recovery)? Legal issues? Privacy issues? How should the firm handle security and access? • E-discovery refers to identifying and retrieving relevant electronic information to support litigation efforts. E-discovery is something a firm should account for in its archiving and data storage plans. Unlike analytics that promise a boost to the bottom line, there's no profit in complying with a judge's order—it's just a sunk cost. But organizations can be compelled by court order to scavenge their bits, and the cost to uncover difficult to access data can be significant, if not planned for in advance.
2. The acronym_____ refers to a technology that is often used to tag objects and inventory items so that they can 'announce their presence' as they make their way along an organization's value chain.
RFID
4. _____ are the most common standard for expressing databases, whereby tables (files) are related based on common keys.
Rational databases
. Why would a firm choose Hadoop for a big data project instead of using conventional relationial databases?
Roughly 80 percent of corporate data is messy and unstructured, and it is not stored in conventional, relational formats. Conventional tools often choke when trying to sift through the massive amounts of data collected by many of today's firms. The open-source project known as Hadoop was created to analyze massive amounts of raw information better than traditional, highly structured databases. Hadoop advantages include: • Flexibility: Hadoop can absorb any type of data, structured or not, from any type of source (geeks would say such a system is schema-less). But this disparate data can still be aggregated and analyzed. • Scalability: Hadoop systems can start on a single PC, but thousands of machines can eventually be combined to work together for storage and analysis. • Cost effectiveness: Since the system is open source and can be started with low-end hardware, the technology is cheap by data-warehousing standards. Many vendors also offer Hadoop as a cloud service, allowing firms to avoid hardware costs altogether. • Fault tolerance: One of the servers running your Hadoop cluster just crashed? No big deal. Hadoop is designed in such a way so that there will be no single point of failure. The system will continue to work, relying on the remaining hardware.
2. _____ is a language used to create and manipulate databases.
Structured Query Language (SQL)
2. The San Francisco Giants leverage business analytics to price game tickets. What factors are considered? What are the risks associated with allowing prices to fluctuate?
The Giants analyze data to gauge demand associated with ticket supply and price accordingly. Factors include the appeal of the game (is it a rival, are teams doing well), weather, as well as the date and time of the game. Dynamic pricing can be tricky. In some cases, it can leave consumers feeling taken advantage of (it is especially tricky in situations where consumers have alternative choices like grocery or department store shopping).
2. _____ is the term used to describe some form of business exchange.
Transaction
1. Systems that record a transaction (some form of business-related exchange), such as a cash register sale, ATM withdrawal, or product return are referred to as _____.
Transaction processing systems
1. Systems that can absorb any type of data, structured or not, from any type of source are often called schema-less.
True
2. All SQL databases are relational databases.
True
2. 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
2. Data obtained from outside sources, when combined with a firm's proprietary internal data assets, can give the firm a competitive edge.
True
2. 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
2. In many organizations, the majority of available data is not exploited to advantage.
True
3. Any data-centric effort should involve input not only from business and technical staff, but from the firm's legal team, as well.
True
3. Firms that base decisions on hunches are said to be gambling, not managing.
True
3. 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
4. The data a firm can leverage is a true strategic asset when it is valuable, rare, imperfectly imitable, and non-substitutable.
True
5. 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
5. 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
2. Having too much inventory or insufficient inventory is known as a retailer's "twin nightmares."
Twin nightmares
2. The three Vs of "Big Data" are _____,_____, and ______; characteristics that distinguish it from conventional data analysis problems and require a new breed of technology.
Volume, variety, velocity
1. How has Wal-Mart leveraged information technology to become the world's largest retailer?
Wal-Mart demonstrates how a physical product retailer can create and leverage a data asset to achieve world-class supply chain efficiencies targeted primarily at driving down costs. Technology tightly coordinates the Wal-Mart value chain from tip to tail, while these systems also deliver a mineable data asset that's unmatched in U.S. retail. Data-driven value chain: Each time an item is scanned by a Wal-Mart cash register, Wal-Mart's proprietary Retail Link system not only records the sale, it also automatically triggers inventory reordering, scheduling, and delivery. This process keeps shelves stocked, while keeping inventories at a minimum. The firm's annual inventory turnover ratio of 8.5 means that Wal-Mart sells the equivalent of its entire inventory roughly every six weeks. Data mining prowess: Wal-Mart also mines its mother lode of data to get its product mix right under all sorts of varying environmental conditions, protecting the firm from "a retailer's twin nightmares: too much inventory, or not enough." Data mining also helps the firm tighten operational forecasts, helping to predict things like how many cashiers are needed at a given store at various times of day throughout the year. Data drives the organization, with mined reports forming the basis of weekly sales meetings, as well as executive strategy sessions. Data sharing: While Wal-Mart is demanding of its suppliers, it also shares data with them, too. Data can help firms become more efficient so that Wal-Mart can keep dropping prices, and data can help firms uncover patterns that help suppliers sell more. Even though Wal-Mart shares sales data with relevant suppliers, the firm otherwise fiercely guards this asset. Wal-Mart stopped sharing data with information brokers like Information Resources and ACNielsen years ago. The firm's scale is so big, the additional data provided by brokers was not adding much value, and it no longer made sense to allow competitors access to what was happening in its own huge chunk of retail sales.
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
2. _____ 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
3. 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
6. 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
6. In database systems, a _____ refers to a list of data. a. file b. column c. field d. row e. record
a
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
2. 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
2. 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
3. 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
3. _____ 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
4. 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
8. 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. _____ 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
2. 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
2. 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
4. 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
7. In database systems, a table is also known as a _____. a. field b. record c. file d. row e. key
c
7. 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
9. 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
3. _____ systems are often used to empower employees to track and record data at nearly every point of customer contact.
customer relationship management/CRM
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 the term used to describe raw facts and figures. a. information b. knowledge c. analytics d. data e. intelligence
d
10. In database systems, a row is also known as a _____. a. table b. column c. key d. record e. field
d
3. _____ 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
4. _____ refers to software for creating, maintaining, and manipulating data. a. Extranet b. ROM c. RAM d. DBMS e. Internet 2
d
9. 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. 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
10. _____ 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
2. 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
2. 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
4. 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
5. _____ is by far the most popular language for creating and manipulating databases. a. XML b. HTML c. PHP d. Java e. SQL
e
5. _____ 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
8. In database systems, a _____ defines the data that a table can hold. a. row b. key c. record d. file e. field
e
1. The open-source project known as ____________ was created to analyze massive amounts of raw information better than traditional, highly structured databases.
hadoop
1. database technology known by the term __________________ is especially popular with Internet firms that rely on massive, unwieldy, and disparately structured data; and this technology is often at the heart of what are often characterized as "big data" efforts.
noSQL
3. An integrated shopping experience and unified customer view across channels is sometimes referred to as _______________.
omnichannel
Systems that can absorb any type of data, structured or not, from any type of source are often called ____________.
schema-less
1. Data are raw facts that must be turned into information in order to be useful and valuable.
true
