Data as a Resource: Properties, Implications, and Prescriptions

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In recent years, many studies have examined how leading corporations are better utilizing information and knowledge.1 Less noticed has been the management of data, “the sludge of the information age — stuff that no one has yet thought very much about.”2 Yet data are ubiquitous. Almost every activity in which an enterprise engages requires data.

Data are used in, and created by, all daily operations, from serving a customer, to manufacturing a product, to tracking inventory. Data support managerial and professional work. Data are the critical inputs into almost all decisions, at all levels of an enterprise. Through data, managers learn about an organization’s human and financial resources. Data may be combined in almost unlimited ways in the search for new opportunities, market niches, process improvements, and innovative products and services.

Because data implicitly define common terms like “customer,” they contribute to an organization’s culture. They “fill the white space” in the organization chart. Enterprises strive to convert tacit knowledge into data. For example, a salesperson may have a warm personal relationship with an important customer. But, for the enterprise as a whole to serve that customer, certain aspects of the relationship must be expressed in data.3

Not surprisingly, most companies readily admit that they should manage data as business resources, just as they manage human and financial resources. They just as readily admit that they do not do so. Few companies even know what data they have; people cannot gain access to needed data; the quality of data is low;4 and data are not used effectively (see the sidebar).

Data Issues Facing a Typical Company »



1. Collections of articles include:

D.A. Klein, The Strategic Management of Intellectual Capital (Boston: Butterworth-Heinemann, 1998);

P.S. Myers, Knowledge Management and Organizational Design (Boston: Butterworth-Heinemann, 1996);

D. Neef, The Knowledge Economy (Boston: Butterworth-Heinemann, 1998);

L. Prusak, Knowledge in Organizations (Boston: Butterworth-Heinemann, 1997); and

R.L. Ruggles III, Knowledge Management Tools (Boston: Butterworth-Heinemann, 1997). Recent books include:

T.H. Davenport with L. Prusak, Information Ecology: Mastering the Information and Knowledge Environment (New York: Oxford University Press, 1997);

T.H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know (Boston: Harvard Business School Press, 1998);

R.W. Lucky, Silicon Dreams: Information, Man, and Machine (New York: St. Martins Press, 1989);

I. Nonaka and H. Takeuchi, The Knowledge-Creating Company (New York: Oxford University Press, 1995);

A. Penzias, Ideas and Information: Managing in a High-Tech World (New York: W.W. Norton & Company, 1989); and

T. Stewart, Intellectual Capital (New York: Doubleday, 1997).

2. Lucky (1989).

3. Some authors have noted that “data” are the raw material for “information,” which is the raw material for “knowledge.” We think that the reverse direction is even more important. Knowledge created or developed by an individual or a group must eventually become structured data so others can apply that knowledge.

4. L.P. English, “The High Costs of Low Quality Data,” DM Review, volume 8, January 1998, pp. 38, 52, 54; and

T.C. Redman, “The Impact of Poor Data Quality on the Typical Enterprise,” Communications of the ACM, volume 41, number 2, 1998, pp. 79–82.

5. See: Davenport and Prusak (1998); and Stewart (1997).

6. Many authors either consider properties of information rather than data or treat the two concepts as synonyms. It is instructive to compare our summary of data properties in Table 1 with those of information provided, for example, by:

H. Cleveland, “Information as a Resource,” The Futurist, volume 16, December 1982, pp. 34–39; and

C.F. Burk and F.W. Horton, InfoMap: A Complete Guide to Discovering Corporate Information Resources (Englewood Cliffs, New Jersey: Prentice-Hall, 1988).

7. American Heritage Dictionary, second college edition (Boston: Houghton Mifflin, 1985).

8. Some authors use the term “assets” instead of “resources.” But “assets” is a narrower concept. According to the American Heritage Dictionary (1985), an asset is “anything owned that has exchange value” or “an entry on a balance sheet.” While almost all data clearly have value, most data are not for sale, and they do not appear on balance sheets, so they do not qualify as assets.

9. C. Fox, W. Frakes, and P. Gandel, “Foundational Issues in Knowledge-Based Information Systems,”The Canadian Journal for Information Science, volume 13, number 3, 1988, pp. 90–102;

F.N. Teskey, “User Models and World Models For Data, Information, and Knowledge,” Information Processing and Management, volume 25, number 1, 1989, pp. 7–14;

H. Theiss, “On Terminology,” in A. Debons and A.G. Larson, eds., Information Science in Action: System Design, volume 1 (Hague: Martinus Nijhoff Publishers, 1983), pp. 84–94; and

G. Wiederhold, “Knowledge Versus Data,” in M.L. Brodie and J. Mylopoulos, eds., On Knowledge Base Management Systems (New York: Springer-Verlag, 1986), pp. 77–82.

10. Fox, Frakes, and Gandel (1988).

11. American Heritage Dictionary (1985).

12. Ibid.

13. A.S. Loebl, “Accuracy and Relevance and the Quality of Data,” in G.E. Liepins and V.R.R. Uppuluri, eds., Data Quality Control: Theory and Pragmatics (New York: Marcel Dekker, 1990), pp. 105–144.

14. See, for example, H. Brinberg, “Information Economics: Valuing Information,” Information Management Review, volume 4, number 3, 1989, pp. 59–63; and

A. Repo, “Economics of Information,” in M.E.Williams, ed., Annual Review of Information Science and Technology, volume 22 (Amsterdam: Elsevier Science Publishers, 1987), pp. 3–36.

15. Brinberg (1989).

16. J.L. King and K.L. Kraemer, “Information Resource Management: Is It Sensible and Can It Work?,” Information & Management, volume 15, number 1, 1988, pp. 7–14.

17. American Heritage Dictionary (1985).

18. These observations do not contradict the observations regarding the difficulties of valuing data: to ascertain the qualitative fact of diminishing value, one does not have to know how to measure it precisely.

19. Process Quality Management & Improvement Guidelines, Issue 1.1 (AT&T, 1988).

20. It could be argued that the “ability to store data” is a fundamental property of computers, not the other way around. This may be true. But the practical consequences to managers are the same.

21. B. Ronen and I. Spiegler, “Information as Inventory,” Information and Management, volume 21, number 4, 1991, pp. 239–247.

22. Process Quality Management & Improvement Guidelines (1988);

T.C. Redman, Data Quality for the Information Age (Norwood, Massachusettts: Artech House, 1996).

23. W.H. Inmon, C. Imhoff, and R. Sousa, Corporate Information Factory (New York: Wiley, 1998).

24. M.H. Brackett, Data Sharing Using a Common Data Architecture (New York: Wiley, 1994).

25. T.H. Davenport, R.G. Eccles, and L. Prusak, “Information Politics,” Sloan Management Review, volume 34, Fall 1993, pp. 53–65.

26. B. Violino, “Tempting Fate,” InformationWeek, 4 October 1993, pp. 42–52.

27. A. Branscomb, Who Owns Information? From Privacy to Public Access (New York: HarperCollins, 1994); and

H.J. Smith, “Privacy Policies and Practices: Inside the Organizational Maze,” Communications of the ACM, volume 36, December 1993, pp. 105–122.

28. W.M. Bulkeley, “Databases Are Plagued by Reign of Error,” Wall Street Journal, 26 May 1992, p. B6.

29. R. Knight, “The Data Pollution,” Computerworld, 28 September 1992, pp. 81–84; and

L. Wilson, “Devil in Your Data,” InformationWeek, 31 August 1992, pp. 48–54.

30. For an in-depth discussion, see:

Redman (1996); and

R.Y. Wang and D.M. Strong, “Beyond Accuracy: What Data Quality Means to Data Consumers,” Journal of Management Information Systems, volume 14, number 4, pp. 5–34.

31. K. Orr, “Data Quality and Systems Theory,” Communications of the ACM, volume 41, number 2, 1998, pp. 66–71.

32. For methods of solution and case studies, see: L.P. English, “Data Quality: Definition and Principles,” DM Review, volume 6, November 1996, pp. 46–51;

R. Kovac, Y.W. Lee, and L.L. Pipino, “Total Data Quality Management: The Case of IRI,” in D.M. Strong and B.K. Kahn, eds., The 1997 Conference on Information Quality (Cambridge, Massachusetts: MIT, 1997), pp. 63–79;

T.C. Redman, “Improve Data Quality for Competitive Advantage,” Sloan Management Review, volume 36, Winter 1995, pp. 99–107;

Redman (1996);

G.K. Tayi and D.P. Ballou, “Introduction” (Special Section: “Examining Data Quality”), Communications of the ACM, volume 41, number 2, 1998, pp. 54–57; and

R.Y. Wang, “A Product Perspective on Total Data Quality Management,” Communications of the ACM, volume 41, number 2, 1998, pp. 58–65.

33. Redman (1996).

34. “Business Is Turning Data into a Potent Strategic Weapon,” Business Week, 22 August 1983, p. 92.

35. D.L. Goodhue, J.A. Quillard, and J.F. Rockart, “Managing the Data Resource: A Contingency Perspective,” MIS Quarterly, volume 12, June 1988, pp. 373–392;

R. Sabherwal and W.R. King, “Toward a Theory of Strategic Use of Information Resources,” Information & Management, volume 20, number 3, 1991, pp. 191–212; and

E.G. Vesely, Strategic Data Management: The Key to Corporate Competitiveness (Englewood Cliffs, New Jersey: Yourdon Press, 1990).

36. See: Davenport, Eccles, and Prusak (1993); and

J.L. Weldon, “Who Owns Data?” Journal of Information Systems Management, volume 3, Winter 1986, pp. 54–57.

37. See, also: P. Strassman, The Politics of Information Management (New Canaan, Connecticut: Information Economics Press, 1994).

38. See, also: B.K. Kahn, “Some Realities of Data Administration,” Communications of the ACM, volume 26, October 1983, pp. 794–799.

39. See: T.K. Landauer, The Trouble with Computers: Usefulness, Usability, and Productivity (Cambridge, Massachusetts: MIT Press, 1995); and

P. Strassman, The Squandered Computer: Evaluating the Business Alignment of Information Technologies (New Canaan, Connecticut: Information Economics Press, 1997).

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