Toward a Goal-oriented, Business Intelligence Decision-Making Framework Alireza Pourshahid Gregory Richards Daniel Amyot [email protected]

Toward a Goal-oriented, Business Intelligence

Decision-Making Framework

Alireza PourshahidGregory Richards

Daniel Amyot

[email protected]

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 2011

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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


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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


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• 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


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• 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!


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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


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Reduce Cost

Increased profits

Principals

Reduce marketing

cost

Reduce staffing

cost

Have many work hours

Staff

Help

Hurt

Or

Soft Goal

Actor

Task

Contribution Link

Decomposition Link

Reduce Cost

Increased profits

25

Principals

Reduce marketing

cost

Reduce staffing

cost


-25

Staff

Help

Hurt

Or 100(*)0

100

Satisfaction level

Contribution level

InitializedEvaluation Level

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…)


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Staffing cost Increased profits

Make

4040(*)1300$

Date Location • Store 1• Store 2• Store 3• Online

Principals

Normalization function :|threshold-current| / |threshold-target|*100Example:

Target value: $1000Threshold value: $1,500Worst value: $2,500Current value: $1300

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


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Profit

45(*)39,000$

Revenue

50(*)250,000$

Costs

-6(*)210,000$

Stolen items

-25(*)20 items

METADATA:Formula = Revenue – Costs – Stolen * 50

Business Intelligence Decision-Making Framework


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• 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

• Does not require a high level of organization maturity• Does not necessitate up front large quantities of data

• 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

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


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First Step Models


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Increased profits

PrincipalsConsigners satisfied

Store managers

Revenue

100

ProfitMarket share

Market valueNumber of products sold

Costs

Staffing costs Marketing costs Store costs

Be the number one distributor

Higher store revenues

Staff satisfiedIncreased

number of items sold in store 100

75

100

Product category

Product type

Profit Revenue Staffing costsNumber of

products sold

DateLocation

Decision Model

Dimension Model

Second Step Models


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Change percentageof retail items vs.

consignment items

Increase retail items

Increase consignment

items

Xor

Increased profits Be the numberone distributor

Reduce cost

Reduce marketing

costs

Reduce staffing budget

OrReduce website

maintenance costs

Increase website

maintenance budget

Increase store staffing

budget

Or

Increase staffing budget

Increase number of items available for customers to buy

Increase numberof customers

Increase online

customers

Increase store

customers

Or

Increase advertising budget for

store

Increase advertising budget for

website

Increase outreachbudget

35

3535

25252575

75

Principals

Thir

d S

tep M

odels


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Number of staff per day

100(*)6,250,000$

Store managers

Revenue

100

Number of products sold

Staffing cost

Market value

75

100

Total number of staff

NewKPI

Number of consigners

10024,523

Marketing cost

100(*)90,000$

Store traffic

10018,000

Average turnover days

55(*)34

82(*)70,000

27(*)2,829,823$

Number of drop-offs

10043,160

100(*)19.5

Online businessinvestment

100(*)100,000$

Work hourper staff

82(*)1725

Number of productsavailable tocustomers

100(*)85,410

100683,280$

Marketing costs

100(*)90,000$

Staff total work hour

100(*)56,940 hrs

Costs

1002,756,173$

Profit

16(*)73,649.2$

Store costs

100(*)185,000$

Market share

56(*)45.27

100(*)33

Newrisk

Profit reduction risk due to

investment in online <<Risk>>

100(*)

56

Principals

Be the numberone distributor

Increased profits

16

Staff satisfied

45

Higher store revenues

27

Increasednumber of items

sold in store

61

Consigners satisfied

55

Earn cash for consignment as soon as possible

Consigners

55

Staff


61

100

75

100

100

75

-75

Note: Complex models are usually spread over many diagrams

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


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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


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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


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Documents

Toward a Goal-oriented, Business Intelligence Decision-Making Framework Alireza Pourshahid Gregory Richards Daniel Amyot [email protected]