Information Filtering

1. Information Filtering Converting Data into Relevant Information for Decision Making

2. Information as Decision Support • Accountants add value by helping turn data into useful information • Students asked to analyze quantitative data often have trouble extracting and presenting information

3. The Problem • Masses of data and minimal structure • Non routine task

4. The “Solution” • Exposure to examples of how data can be turned into information • Practice in analyzing or filtering data to provide decision relevant information

5. Creating Information Using Data • Ways information can be turned into information: • Examining the data for relationships (data mining or analysis) • Categorizing the data in ways that clarify alternatives (summarizing using various criteria)

6. A Class on Information Filtering • Introduction: examples of information filtering in businesses • An example from sales • An example from production • Conclusions

7. National Highway Safety Administration • Ascential Software works with large data sets (an average of 10 million to 20 million data records) to extract information for customers • The firm has worked on projects for Lockheed Martin, the U.S. Navy, and the National Highway Safety Administration. • The NHSA project involved matching police records with ambulance reports to justify the need for seat belts. Signal (February 1999)

8. Decision Relevant Information Through Data Filtering • One grocerychain segmented customers by recency, frequency and spending and found that 30% of their customers produced 70% of their sales. And now they know which ones.

9. Decision Relevant Information Through Data Filtering • Wal-Mart uses data to decide where to display items (they put bananas near the cereal because people typically buy them together, tissues near cold remedies, and measuring spoons near baking items).

10. Decision Relevant Information Through Data Filtering • The Veterans Health Administration used a new computerized system to determine the relationship between pneumonia vaccination and death or serious illness • The results were used to support a decision to push vaccination, resulting in rates of about 84% versus national rates of about 50%

11. Decision Relevant Information Through Data Filtering • Blue Cross used an analysis of heart patient outcomes by hospital to determine which hospitals to include as reimbursable under their programBurton, T. M. 1999. Bed Check: HMO Rates Hospitals. Wall Street Journal April 22: A1.

12. Creating Decision Relevant Information Through Data Filtering • One telephone order business has a computer feed the order taker information about related products the customer might also buy given what they just entered on the orderPublic Utilities Fortnightly Winter Supplement (1999)

13. Consider an Order Processing Group • Look the following slide representing information on orders processed • This is the raw data for a small work group (imagine the data for a large one) • Is it currently "decision relevant information" or does it need further processing?

15. Order Processing Information • Consider the next slide. • It presents information for the same group that has been summarized. • What can you tell about the processing group from this slide?

17. Applying an Information Filter • How can we filter the information to remove “noise”?S

18. Applying an Information Filter • Consider the following slide. • This is filtered information. • It represents number of order processing workers who were were below their 20 day average number of orders processed by day. • What can you tell about the process from this?

20. Applying an Information Filter • The underlying “reality” that produced the preceding numbers: • One person quit (Day 25) and was replaced by a more effective employee (Day 26). • Two people were sick, one on Day 30 and one on Day 33. • The system went down on Days 15 and 21. • ALL the rest of the variation was due to random “noise.”

21. What Information Is Needed on Processes? • Now consider a production process with 6 products and 4 production departments. • The firm wants to reduce cycle time to better meet customer needs. • The following slide represents firm current performance and performance by a firm with similar production processes. • What information does this provide?

22. Our firm Benchmark firm

23. Now that they know improvement is possible, where should they start?

24. What Information Is Needed? • Assume all you have on the benchmark is what is shown. • What “slicing” would you like next on your own processes to help you decide where to start the improvement process?

25. Where Should They Start? S

26. Where Should They Start? The areas that are the largest and the most amenable to change: which areas qualify?

27. Where Should They Start?

28. Where Should They Start? Is there a “baseline” for what you could reasonably expect to achieve in the “problem” areas? Outliers

29. What Changes Might Be Reasonable? • You can simulate results. • What would the results be if we could reduce allwait times to two days and all setups to oneday? NS

31. FilteringInformation • Look for relationships: • Accidents and seat belt use • Sales of products that move together (Kleenex and cold remedies, catalog sales) • Medical treatments and outcomes • Sales orders processed versus “normal” • Categorize: • Customers with high sales levels • Various types of costs (production activities)