Ch. 13A Data Assets Flashcards
How are (1) increasingly standardized data; (2) access to third-party datasets; (3) cheap, fast computing; and (4) easier-to-use software collectively enabling a new age of decision making? (5) Is Data a source of competitive advantange? Why?
- 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.
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? Name and discribe at least 4 hurdles and give examples
- Despite being awash in data, many organizations are data rich but information poor. The big culprit limiting business intelligence 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 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.
What are the issues to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts? Name and describe at least five issues and give examples.
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.
What is data mining? What are the key areas where businesses are leveraging data mining? Name and describe at least five. Give examples.
- 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.
Please provide a one-sentence definition for the following terms or acronyms in your own words (copy and paste earns you zero credit):
- CLV
- Neural network
- OLAP
- e-discovery
- Relational database
- CLV: Customer Lifetime Value is a reward incentive offered by firms to customers that generates future revenue streams to the firm when a customer makes a purchase.
Neural network: A type of artificial intelligence (AI) computing and data mining that discovers patterns and algorithms to better improve on current processes.
OLAP: Known as “online analytical processing”, OLAP is a type of query database that extracts relevant information from other resources and stores data into a special database.
e-discovery: The process of data mining within a organization’s own electronic records for information to support legal inquiries.
Relational database: A relational database organizes data by identifying “common keys” and maps its relation to other data points and tables.
