1. Toward a Goal-oriented, Business Intelligence Decision-Making Framework
Alireza Pourshahid
Gregory Richards
Daniel Amyot

2. Motivation
Business Intelligence (BI) tools do not always help improving decision making
Difficulties in:
Integrating goals, indicators, and decisions into a single conceptual framework
Fitting with the cognitive decision models of managers
Adapting to organizational changes
Handling unavailability of some data when performance models are first put in place
Can we improve upon this situation?
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

3. Agenda Business Intelligence (BI) Based Decision Making
Goal-oriented Requirement Language (GRL)
GRL and KPI for Business Modeling
Formula-Based Evaluation Algorithm
Business Intelligence Decision-Making Framework
Real-Life Example: Retail Business
Lessons Learned
Conclusions
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

4. BI-Based Decision Making (1/3)
For 30 years, BI technology has helped managers make better decisions.
50% of BI implementations fail to influence decision makers in any meaningful way! (Ko and Abdullaev, 2007)
Issues (Hackathorn2002):
Cultural resistance
Lack of relevance
Lack of alignment with business strategy
Lack of actionable decision support technologies
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

5. BI-Based Decision Making (2/3)
Delivery schemes based on dimensional models of the data are technical sound, but not necessarily aligned with users’ decision models
Cognitive fit (Vessey, 1991)
When a good match exists between the problem representation (i.e., data presentation in BI tools) and the cognitive task (the way data is used) involved in making decisions
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

6. BI-Based Decision Making (3/3)
BI data and visualizations do not necessarily show the cause and effect relationships we need to make decision (Korhonen et al. 2008)
Key impact of a decision model is improving the probability of goal accomplishment.
The cause-effect nature of such decisions is often related to resource allocation.
Need to model goals and causal relationships visually to reduce the cognitive load!
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

7. Goal-oriented Requirement Language
GRL is part of ITU-T’s User Requirements Notation (URN)
GRL enables business analysts to model strategic goals, stakeholder concerns, and cause-effect relationships
Increased profits
Principals
Staff
Reduce Cost
Increased profits 25
Principals
Reduce marketing cost
Reduce staffing cost
Have many work
hours
-25
Staff
Help
Hurt Or
100(*)
100
Satisfaction level
Contribution level
Initialized
Evaluation Level
Actor
Have many work
hours
Soft Goal
Help
Contribution Link
Reduce Cost
Hurt
Task
Decomposition Link Or
Reduce marketing cost
Reduce staffing cost
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

8. GRL and KPI for Business Modeling
GRL was extended to support Key Performance Indicators (KPI)
KPI can be analyzed from various angles called dimensions
Worst, Threshold, Target, and Current values can be used to initialize a KPI
Current values are initialized either manually or using a BI tool (or sensors, or…)
Staffing cost
Increased profits
Make 40
40(*)
1300$
Date
Location
Store 1
Store 2
Store 3
Online
Principals
Normalization function :
|threshold-current| / |threshold-target|*100
Example:
Target value: $1000
Threshold value: $1,500
Worst value: $2,500
Current value: $1300
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

9. New Formula-Based Evaluation Algorithm
Beyond standard GRL evaluation algorithms, cause-effect analysis requires formula-based KPI aggregation
One KPI can drive the current value of another KPI
Brings flexibility in modeling relationships, without requiring changes to BI reports
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

10. Business Intelligence Decision-Making Framework
Create the initial organization goal model
Define the KPIs that support the goals
Identify the type of analysis
Step 1
Add/revise KPIs
Refine the cause-effect relationships
Create a decision options diagram
Make a decision
Step 2
Add risks
Add KPIs required to monitor the result
Evaluate and refine the model
Go to Step 2
Step 3
Built, e.g., based on interviews with executives and operational managers
Long term, short term, strategic and operational goals
Contribution and decomposition relationships between goals
KPIs with dimensions and contributions
Does not require a high level of organization maturity
Does not necessitate up front large quantities of data
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

11. Retail Business Real-Life Example
Ontario-based (small) retail business, 15 years old
4 local stores, and plans expansion nationally
Scorecard that tracked key operational indicators, but some data unavailable (e.g., flows of customers)
Most revenues earned through consignment sales
Started selling new items as well, and planning to invest in an online business (might be risky)
Considering different marketing approaches
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

12. Be the number one distributor Increased number of items sold in store
First Step Models
Increased profits
Principals
Consigners
satisfied
Store managers
Revenue
100
Profit
Market share
Market value
Number of
products sold
Costs
Staffing costs
Marketing costs
Store costs
Be the number one distributor
Higher store
revenues
Staff satisfied
Increased number of items sold in store 75
Decision Model
Product category
Product type
Profit
Revenue
Staffing costs
Number of
products sold
Date
Location
Dimension Model
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

13. Second Step Models
Change percentage
of retail items vs.
consignment items
Increase
retail items
consignment
items
Xor
Increased profits
Be the number
one distributor
Reduce cost
Reduce marketing costs
Reduce staffing budget Or
Reduce website maintenance costs
website maintenance budget
store staffing
budget
staffing budget
Increase number of
items available for
customers to buy
Increase number of customers
Increase online customers
store
customers
Increase advertising budget for store
Increase advertising budget for website
outreach 35 25 75
Principals
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

14. Number of staff per day
100(*)
6,250,000$
Store managers
Revenue
100
Number of
products sold
Staffing cost
Market value 75
Total number of staff
New
KPI
consigners
24,523
Marketing cost
90,000$
Store traffic
18,000
Average
turnover days
55(*) 34
82(*)
70,000
27(*)
2,829,823$
drop-offs
43,160
19.5
Online business
investment
100,000$
Work hour per staff
1725
Number of products
available to
customers
85,410
683,280$
Marketing costs
Staff total
work hour
56,940 hrs
Costs
2,756,173$
Profit
16(*)
73,649.2$
Store costs
185,000$
Market share
56(*)
45.27 33
risk
Profit reduction risk due to investment in online <<Risk>> 56
Principals
Be the number one distributor
Increased profits 16
Staff satisfied 45
Higher store
revenues 27
Increased number of items sold in store 61
Consigners
satisfied 55
Earn cash for consignment as soon as possible
Staff
Have many work hours
-75
Third Step Models
Note: Complex models are usually spread over many diagrams
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

15. Lessons Learned: Business Management Perspective
Modeling not only helps with documentation of the known aspects of the business but also helps clarify the unknown or uncertain aspects (e.g., relationships)
Decision snapshots can be taken and compared (decision trails to document rationale and adjust models)
When no historical data is available, use industry standard or “best guesses” to define cause-effect relationships (improved in later iterations)
Still not sure of how much information we have to show in the model and how much to keep in DBs or BI reports
The ability to adjust the range of acceptable values for a KPI is useful for registering risk
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

16. Lessons Learned: Technical Perspective
Our new extensions to GRL and the new formula-based algorithm provide a great deal of flexibility for model evaluation
New topic for study in ITU-T’s URN standard
The new algorithm still has room for improvement, especially when it comes to using goals as contributors to KPIs (e.g., for risks)
Creating different versions of a model in different iterations and keeping them consistent for comparison purposes can be painful with current tool support
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech

17. Conclusions
Conventional BI systems show a cognitive gap between technical data models and managers’ decision models
By integrating the decision framework into the BI system, we attempt to improve cognitive fit
View complementary to BI tools, not a substitute
We extended GRL to better display cause-effects relationships between KPIs and objectives, enable formula-based evaluations, and integrate risk
We introduced an iterative framework to create, refine, and analyze models
We used a retail business example (and the jUCMNav tool) to illustrate the framework in a real context
Toward a Goal-oriented, Business Intelligence Decision-Making Framework, MCeTech