Building the Data WareHouse

1. Chapter 7: Executive Information Systems and the Data
Warehouse
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2. Agenda
1. Introduction
2. EIS – The Promise
3. A Simple Example
4. Drill-Down Analysis
5. Supporting the Drill-Down Process
6. The Data Warehouse as a Basic for EIS
7. Where to Turn
8. Event Mapping
9. Detailed Data and EIS
10. Keeping Only Summary Data in the EIS
11. Summary
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3. 7.1 Introduction
 Prior to data warehousing, there were Executive Information
Systems (EIS).
 EIS was a notion that computation should be available to everyone
in the corporation, not just the clerical community doing day-to-day
transactions.
 EIS presented the executive with a set of appealing screens.
 The entire idea behind EIS was presentation of information with no
real understanding of the infrastructure needed to create that
information in the first place.
 EIS has reappeared in many forms today—such as OLAP
processing and DSS applications like customer relationship
management (CRM).

4. 7.2 EIS — The Promise
 EIS is one of the most potent forms of computing.
 EIS processing is designed to help the executive
make decisions.
 EIS becomes the executive’s window into the corporation.
 Some of the typical uses of EIS are these :
 Trend analysis and detection
 Key ratio indicator measurement and tracking
 Drill-down analysis
 Problem monitoring
 Competitive analysis
 Key performance indicator monitoring
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5. 7.3 A Simple Example

6. 7.3 A Simple Example (Con’t)

7. 7.3 A Simple Example (Con’t)
The few approachs that
the manager can use EIS
effectively :
 Trend analysis and
comparison
 To do slicing and
dicing
Figure 7-3 shows a comparison that might be found in an EIS
analysis.

8. 7.4 Drill-Down Analysis
 Drilling
down refers to the ability to start at a
summary number and to break that summary into a
successively finer set of summarizations.

9. 7.4 Drill-Down Analysis
(Con’t)
 Another important aspect of EIS is the ability to
track key performance indicators.
 Although each corporation has its own set, typical
key performance indicators might be the following:
 Cash on hand
 Customer pipeline
 Length of sales cycle
 Collection time
 New product channel
 Competitive products

10. 7.4 Drill-Down Analysis
(Con’t)
 The difficult part of EIS is not in the graphical
presentation, but in discovering and preparing the
numbers – accurately, completely, and integrated—
that go into the graphics, as shown in Figure 7-5.

11. 7.5 Supporting the Drill-Down
Process
 Creatingthe basis of data on which to perform drill-
down analysis, then, is the major obstacle to
successfully implementing the drill-down
process, as shown in Figure 7-6.

12. 7.5 Supporting the Drill-Down
Process (Con’t)

13. 7.6 The Data WareHouse as a
Basic for EIS
 It is in the EIS environment that the data warehouse operates
in its most effective state.
 With a data warehouse, the EIS analyst does not have to
worry about the following:
 Searching for the definitive source of data
 Creating special extract programs from existing systems
 Dealing with unintegrated data
 Compiling and linking detailed and summary data and the
linkage between the two
 Finding an appropriate time basis of data (finding historical
data)
 Management constantly changing its mind about what
needs to be looked at next

14. 7.6 The Data WareHouse as a
Basic for EIS (con’t)

15. 7.7 Where to Turn
 TheEIS analyst can turn to various places in the
architecture to get data.

16. 7.7 Where to Turn (Con’t)
 There
is a very good reason for the order
shown, as indicated in Figure 7-10.

17. 7.7 Where to Turn (Con’t)
 Theways that EIS is supported by the data
warehouse are illustrated in Figure 7-11.

18. 7.7 Where to Turn (Con’t)
The EIS function uses the following :
 The data warehouse for a readily available supply of
summary data.
 The structure of the data warehouse to support the drill-
down process.
 Data warehouse metadata for the DSS analyst to plan how
the EIS system is built.
 The historical content of the data warehouse to support
the trend analysis that management wishes to see.
 The integrated data found throughout the data warehouse
to look at data across the corporation

19. 7.8 Event Mapping
 A useful technique in using the data warehouse for
EIS processing is event mapping.
 The simplest way to depict event mapping is to
start with a simple trend line.

20. 7.8 Event Mapping (con’t)
Figure 7-12 shows that corporate revenues have
varied by month, as expected.

21. 7.8 Event Mapping (con’t)

22. 7.8 Event Mapping (con’t)
 Misleading conclusions can be drawn, though, by
looking at correlative information. It often helps to
look at more than one set of trends that relate to
the events at hand.

23. 7.9 Detailed Data and EIS
 The following question must be answer :
 How much detailed data do you need to run your EIS/DSS
environment?
 What, then, is so wrong with keeping all the detail in the
world around when you are building an EIS/DSS
environment?
 Summary data is an integral part of the EIS/DDS
environment.

24. 7.10 Keeping Only Summary Data
in the EIS
 Some very real problems become evident with
keeping just summary data.
 First, summary data implies a process
 It may or may not be at the appropriate level of granularity
for the analytical purpose at hand.

25. 7.11 Summary
 There is a very strong affinity between the needs of the EIS analyst
and the data warehouse.
 The data warehouse explicitly supports all of the EIS analyst’s
needs. With a data warehouse in place, the EIS analyst can be in a
proactive rather than a reactive position.
 The data warehouse enables the EIS analyst to deal with the
following management needs:
 Accessing information quickly
 Changing their minds (that is, flexibility)
 Looking at integrated data
 Analyzing data over a spectrum of time
 Drilling down
 The data warehouse provides an infrastructure on which the EIS
analyst can build.
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