Toward a Goal-oriented, Business Intelligence Decision-Making Framework Alireza Pourshahid Gregory Richards Daniel Amyot damyot@site.uottawa.ca

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 2

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 2011 3

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 2011 4

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 2011 5

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 2011 6

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 2011 7

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 2011 8

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 2011 9

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 2011 10

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 2011 11

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 2011 12

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 2011 13

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 2011 14

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 2011 15

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 2011 16

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 2011 17