Organizations can realize many benefi ts with KMSs.
• Best practices are readily available to a wide range of employees;
• Improved customer service;
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Chapter Glossary
• More effi cient product development;
• Improved employee morale and retention.
Challenges to implementing KMSs include:
• Employees must be willing to share their personal tacit knowledge;
• Organizations must create a knowledge management culture that rewards employees who add their expertise to the knowledge base;
• The knowledge base must be continually maintained and updated;
• Companies must be willing to invest in the resources needed to carry out these operations.
Organizations can use knowledge management to develop best practices, to establish the most effective and effi cient ways of doing things, and to make these practices readily available to a wide range of employees. Other benefi ts of knowledge management include improved customer service, more effi cient product development, and improved employee morale and retention.
A functioning KMS follows a cycle that consists of six steps: create knowledge, cap-ture knowledge, refi ne knowledge, store knowledge, manage knowledge, and disseminate knowledge.
[ Chapter Glossary ]
attribute Each characteristic or quality describing a particu-lar entity.
best practices The most effective and effi cient ways to do things.
Big Data Diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.
bit A binary digit—that is, a 0 or a 1.
byte A group of eight bits that represents a single character.
clickstream data Data collected about user behavior and browsing patterns by monitoring users’ activities when they visit a Web site.
data dictionary Collection of defi nitions of data elements;
data characteristics that use the data elements; and the indi-viduals, business functions, applications, and reports that use this data element.
data fi le A collection of logically related records (see table).
data governance An approach to managing information across an entire organization.
data mart A low-cost, scaled-down version of a data ware-house that is designed for the end-user needs in a strategic business unit (SBU) or a department.
data model Defi nition of the way data in a DBMS are con-ceptually structured.
data warehouse A repository of historical data that are orga-nized by subject to support decision makers in the organization.
database A group of logically related fi les that stores data and the associations among them.
database management system (DBMS) The software pro-gram (or group of propro-grams) that provides access to a database.
entity A person, place, thing, or event about which information is maintained in a record.
entity classes Groupings of entities of a given type.
entity-relationship (ER) diagram Document that shows data entities and attributes and relationships among them.
entity-relationship (ER) modeling The process of designing a database by organizing data entities to be used and identifying the relationships among them.
explicit knowledge The more objective, rational, and technical types of knowledge.
fi eld A grouping of logically related characters into a word, a small group of words, or a complete number.
fi le A grouping of logically related records.
identifi ers Attributes that are unique to an entity instance.
instance A particular entity within an entity class.
intellectual capital (or intellectual assets) Other terms for knowledge.
knowledge management (KM) A process that helps orga-nizations identify, select, organize, disseminate, transfer, and apply information and expertise that are part of the organiza-tion’s memory and that typically reside within the organization in an unstructured manner.
knowledge management systems (KMSs) Information tech-nologies used to systematize, enhance, and expedite intra- and interfi rm knowledge management.
master data A set of core data, such as customer, product, employee, vendor, geographic location, and so on, that spans an enterprise’s information systems.
master data management A process that provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely “single version of the truth”
for the company’s core master data.
multidimensional structure Storage of data in more than two dimensions; a common representation is the data cube.
168 CHAPTER 5 Data and Knowledge Management
normalization A method for analyzing and reducing a rela-tional database to its most streamlined form for minimum redundancy, maximum data integrity, and best processing performance.
NoSQL databases Databases that can manipulate structured as well as unstructured data and inconsistent or missing data; are useful when working with Big Data.
online transaction processing (OLTP) Processing of business transactions online as soon as they occur.
primary key The identifi er fi eld or attribute that uniquely identifi es a record.
query by example (QBE) Database language that enables the user to fi ll out a grid (form) to construct a sample or descrip-tion of the data wanted.
record A grouping of logically related fi elds; describes an entity.
relational database model Data model based on the simple concept of tables in order to capitalize on characteristics of rows and columns of data.
secondary key An identifi er fi eld or attribute that has some identifying information but typically does not identify the fi le with complete accuracy.
structured query language (SQL) Popular relational data-base language that enables users to perform complicated searches with relatively simple instructions.
table A grouping of logically related records (see data fi le).
tacit knowledge The cumulative store of subjective or experiential learning, which is highly personal and hard to formalize.
[ Discussion Questions ]
1. Is Big Data really a problem on its own, or are the use, control, and security of the data the true problem? Provide specifi c examples to support your answer.
2. What are the implications of having incorrect data points in your Big Data? What are the implications of incorrect or duplicated customer data? How valuable are decisions that are made based on faulty information derived from incorrect data?
3. Explain the diffi culties involved in managing data.
4. What are the problems associated with poor-quality data?
5. What is master data management? What does it have to do with high-quality data?
6. Explain why master data management is so important in companies that have multiple data sources.
7. Describe the advantages of relational databases.
8. Explain why it is important to capture and manage knowledge.
9. Compare and contrast tacit knowledge and explicit knowledge.
[ Problem-Solving Activities ]
1. Access various employment Web sites (e.g., www.monster .com and www.dice.com) and fi nd several job descriptions for a database administrator. Are the job descriptions simi-lar? What are the salaries offered in these positions?
2. Access the Web sites of several real estate companies. Find the sites that take you through a step-by-step process for buying a home, that provide virtual reality tours of homes in your price range and location, that provide mortgage and interest rate calculators, and that offer fi nancing for your home. Do the sites require that you register to access their services? Can you request that an e-mail be sent to you when properties in which you might be interested become available?
3. It is possible to fi nd many Web sites that provide demo-graphic information. Access several of these sites and see what they offer. Do the sites differ in the types of demo-graphic information they offer? If so, how? Do the sites require a fee for the information they offer? Would demo-graphic information be useful to you if you wanted to start a new business? If so, how and why?
4. The Internet contains many Web sites that provide infor-mation on fi nancial aid resources for students. Access sev-eral of these sites. Do you have to register to access the information? Can you apply for fi nancial aid on the sites, or do you have to request paper applications that you must complete and return?
5. Draw an entity-relationship diagram for a small retail store. You wish to keep track of the product name, descrip-tion, unit price, and number of items of that product sold to each customer. You also wish to record customer name, mailing address, and billing address. You must track each transaction (sale) as to date, product purchased, unit price, number of units, tax, and total amount of the sale.
6. Draw the entity-relationship diagram for the following patient appointment system. The business rules of this sys-tem are the following:
A doctor can be scheduled for many appointments but might not have any scheduled at all. Each appoint-ment is scheduled with exactly one doctor. A patient can schedule one or more appointments. One appointment
169
Closing Case Can Organizations Have Too Much Data is scheduled with exactly one patient. An appointment
must generate exactly one bill, and a bill is generated by only one appointment. One payment is applied to exactly one bill, and one bill can be paid off over time by several payments. A bill can be outstanding, having nothing yet paid on it at all. One patient can make many payments, but a single payment is made by only one patient. Some patients are insured by an insurance company. If they are insured, they can only carry insurance with one insurance company. An insurance company can have many patients carry their policies. For patients who carry insurance, the insurance company will make payments, with each single payment made by exactly one insurance company.
7. Access the Web sites of IBM (www.ibm.com), Sybase (www.sybase.com), and Oracle (www.oracle.com), and trace the capabilities of their latest data management products, including Web connections.
8. Enter the Web site of the Gartner Group (www.gartner .com). Examine the company’s research studies per-taining to data management. Prepare a report on the state of the art.
9. Calculate your personal digital footprint at http://www.emc .com/digital_universe/downloads/web/personal-ticker.htm.
10. Diagram a knowledge management system cycle for a fi c-tional company that sells customized T-shirts to students.
[ Closing Case Can Organizations Have Too Much Data? ]
The Problem
Organizations are hoarding (over-retaining) data that they no longer need. This massive accumulation of unnecessary data results from several technological and organizational fac-tors. From a technology standpoint, the growth of high-band-width Internet connections (discussed in Chapter 6) and the decrease in the price of hard drive storage (discussed in Tech-nology Guide 1) have made it relatively easy and inexpensive to move and store vast amounts of documents and fi les.
From an organizational perspective, few managers are con-cerned about what is being stored when it seems on the surface to be so cheap to simply keep everything. In fact, in most orga-nizations, no one is responsible for limiting the amount of data that is being stored. Business unit managers typically do not see a budget line item for all of the costs associated with unused or unneeded data, so they do not make it a management priority—
at least, not until huge amounts of corporate data are involved in a legal matter or a government investigation.
Although storing vast amounts of hoarded data seems to be cheap, this is not really the case. Hoarding data actually involves signifi cant costs. These costs fall into three broad cat-egories: infrastructure costs; hidden costs; and legal, compli-ance, and regulatory costs.
Infrastructure Costs. When companies closely analyze their data, they typically fi nd that 80 percent of their ostensi-bly “active” fi les and folders have not been accessed for three to fi ve years. This situation results in unnecessary IT expen-ditures for electronic data storage, disaster recovery, and data migration as old servers and systems are retired. Some organi-zations also have tens of thousands of backup tapes in storage, many of which are essentially useless. Nevertheless, they are generating storage fees and excess costs if they are included in the discovery process for litigation (discussed below).
Hidden Costs. Other costs associated with unnecessary data hoarding are hidden—out of sight, and out of mind. One example of hidden costs is lost productivity when employees
have to search through volumes of unused and unwanted materials to fi nd the information they need.
Legal, Compliance, and Regulatory Costs. The largest costs of over-retained data frequently arise when a company becomes entangled in legal actions. When a legal matter arises, the court issues a legal hold for all data pertaining to the matter; that is, a company cannot dispose of any relevant data after the hold is issued. In essence, the legal hold super-sedes the company’s right to dispose of information that is not required for any specifi c operational or regulatory require-ments. The process of examining a company’s data to fi nd if they are pertinent to the case is called discovery.
The discovery process can be extraordinarily expensive.
In many cases, companies must hire attorneys to examine data fi les to determine whether they are pertinent to discov-ery requests or subpoenas. Even if companies use electronic discovery software, the costs are still substantial. For example, although an e-discovery company, Blackstone Discovery (www.blackstonediscovery.com), helped one company analyze 1.5 million documents for less than $100,000, that fi rm still incurred those costs. The key point here is that if the company had disposed of the data before the legal matter arose, these costs would have been substantially lower. As this example illustrates, companies should proactively and appropriately dispose of unnecessarily hoarded data.
In addition, companies increasingly must adhere to state privacy legislation that requires them to notify state offi cials and implicated state citizens if private information such as Social Security numbers or credit card numbers is breached or disclosed. For example, Belmont Bank in Massachusetts discovered that a backup tape had been left on a table and disposed of by the cleaning crew. It appeared that the tape had been incinerated and therefore was not actually dis-closed to any third parties. Nevertheless, the bank had to pay a $7,500 civil penalty.
Companies also incur costs when legal problem arise from incidents involving the actual loss of credit card
MIS
170 CHAPTER 5 Data and Knowledge Management information. Security experts estimate the cost of the TJX breach to be $256 million or more. At the risk of stating the obvious, hackers and thieves cannot take what organizations no longer possess. One excellent protection against a breach is to dispose of data as soon as they are no longer needed for business purposes or legal matters.
The Solution
So, how can a company reduce the risks and costs associated with data hoarding? First, companies must understand that the key to avoiding legal diffi culties is to make good-faith, reason-able efforts to meet record-keeping obligations and to document those efforts. Perfection is not required for legal purposes. Fur-thermore, companies normally are obligated to keep only “a”
copy of relevant information, not “all” copies. That is, there is little or no need to keep all backups. Companies that recognize this simple fact can sometimes dispose of tens of thousands of unneeded backup fi les, thereby generating enormous savings.
Second, companies should hire a properly insured external consultant or expert who will go on record as authorizing the fi nal disposition of data fi les. Organizational employees are not typically comfortable saying “throw out those data,” and many employees are too busy to devote the time that an elec-tronic data housecleaning (e-housecleaning) project requires.
Going further, employees may not be familiar with the legal standards governing the disposition of information. As a result, organizations are more comfortable delegating the responsi-bility for directing data disposal to external experts. In fact, these experts are frequently the persons whose deposition is ultimately taken if anyone questions the disposition decision.
Third, companies should launch an e-housecleaning project.
The fi rst step is to review company policies regulating records retention and legal holds to confi rm that the company is operat-ing in a reasonable and defensible manner. The basic inquiry here is whether the company appears to be placing the proper documents and information on hold when litigation arises.
The next step is to inventory physical data containers, such as hard drives, servers, tapes, and other media, and make reasonable efforts to determine their source. If the data are required for business, regulatory or legal hold purposes, then they should be placed on retention schedules. If not, then companies should dispose them off.
Finally, for maximum protection, the external expert should draft an opinion letter explaining the process and directing the fi nal disposition of unneeded data. This step ensures that if the data disposition is ever challenged, the company can point to this process and its associated docu-mentation as evidence of their good-faith effort to comply with their record-keeping obligations.
The Results
Properly performed e-housecleaning efforts offer a large return on investment. Some companies have been able to remove thousands of backup fi les that have been determined to be irrelevant to legal hold, and others have freed up signifi cant amounts of storage space—all of this in addition to avoiding discovery and data breach costs.
In some fi rms, an executive or business unit insists on hold-ing onto unused data, claimhold-ing that they may someday need to access those data. The most effective strategy for dealing with
“just-in-case hoarders” is to allow them keep their data, but with the understanding that they are now the “owner” of the data, meaning they must accept all of the responsibilities that ownership entails. Therefore, they will assume all ownership costs, including data storage, backup, and data breach respon-sibility, as well as all legal costs associated with reviewing and producing the data if the data are ever swept into litigation dis-covery or governmental investigations. Once these individuals understand the full costs associated with ownership, they usu-ally opt to dispose of the data instead.
Sources: Compiled from S. Mathieson, “Civil Servants Are Not to Blame for Gov-ernment Data Hoarding,” The Guardian, April 10, 2013; J. Clark, “Big Data or Big Data Hoarding?” The Datacenter Journal, March 14, 2013; “Security Implications of Improper Data Disposal?” InfoShield Security, March 11, 2013; A. Kidman, “Data Disposal 101: Don’t Use Rubbish Bins,” lifehacker.com.au, February 21, 2013;
J. Jaeger, “Changing Your Data-Hoarding Ways,” Compliance Week, February 5, 2013; A. Samuel, “E-Discovery Trends for 2013,” CMS Wire, January 17, 2013;
J. Dvorak, “Stop Your Data Hoarding!” PC Magazine, December 11, 2012; L. Luellig,
“A Modern Governance Strategy for Data Disposal,” CIO Insight, December 5, 2012; A. Kershaw, “Hoarding Data Wastes Money,” Baseline Magazine, April 16, 2012; T. Claburn, “Google Apps Vault Promises Easy E-Discovery,” Information-Week, March 29, 2012; T. Harbert, “E-Discovery in the Cloud? Not So Easy,”
Computerworld, March 6, 2012; B. Kerschberg, “E-Discovery and the Rise of Pre-dictive Coding,” Forbes, March 23, 2011; M. Miller, “Data Theft: Top 5 Most Expensive Data Breaches,” The Christian Science Monitor, May 4, 2011; E. Savitz,
“The Problem with Packrats: The High Cost of Digital Hoarding,” Forbes, March 25, 2011; J. Markoff, “Armies of Expensive Lawyers, Replaced by Cheaper Software,” The New York Times, March 4, 2011.
Questions
1. Compare and contrast this case with the material in this chapter on Big Data. That is, given the disadvantages of over-retained data, how should an organization manage
1. Compare and contrast this case with the material in this chapter on Big Data. That is, given the disadvantages of over-retained data, how should an organization manage