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Chapter 17
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The great strength of computers is that they can reliably manipulate vast amounts of data very quickly. Their great weakness is that they don't have a clue as to what any of that data actually means.
Stephen Cass, IEEE Spectrum, Jan 2004
After reading this chapter you will be able to:
• Identify opportunities for using BI in your organization
• Explain examples of successful analytics applications.
In Chapter 16, you learned about presenting BI and analytics data. Scorecards, dashboards, reports, and BI portals are the formats that have proven to be effective. The Balanced Scorecard (BSC) provides a strategic view of an organization's progress toward high-level goals.
BI and analytics have many practical applications. Analytics are used in such diverse areas as sports, science, government, marketing, finance, and risk management:
• Sports – selecting players, finding competitor weaknesses, calling plays
• Science – finding extraterrestrial planets, genomics
• Healthcare – diagnosing illness, detecting drug interactions, proposing treatments
• Government – detecting tax fraud, dispatching services effectively
• Marketing – segmenting customers, improving customer retention, allocating resources to marketing campaigns, cross-selling
• Finance – planning and budgeting, recommending financial trades
• Risk Management – hedging risk, evaluating credit risks, scoring risks
• Fraud Detection – finding suspicious credit card and other transactions.
Financial BI Applications
Use of financial analytics can greatly assist in the management of an organization's finances. Data is provided to financial analytics from data sources such as general ledger, budgeting, accounts payable, accounts receivable, payroll, and fixed asset management systems. Trend analysis and alerts can help organizations manage to their budgets and detect issues. Table 17-01 shows example financial analytics design elements.
Examples of financial BI applications include:
• Asset management
• Budget management
• Collections effectiveness
• Cost management
• Overhead reduction analysis.
Table 17-01: Financial Analytics Design Elements
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Stars |
Dimensions |
Metrics |
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Asset Balance Fact Budget Line Fact GL Account Snapshot Fact GL Transaction Fact Payment Fact Revenue Transaction Fact |
Calendar Date Dimension Currency Dimension Facility Dimension Fund Dimension Geographic Area Dimension GL Account Dimension Organization Unit Dimension Product Dimension |
Asset Balance Amount Budget Amount Budget Variance Amount Equity Balance Amount Liability Balance Amount Payment Amount Stretch Goal Amount Transaction Amount |
Financial analytics make use of the BI operations of roll up, drill down, pivot, slice, and dice, as well as drill across. Hierarchies are supported in dimensions like the Organization Unit and Geographic Area Dimensions. This enables aggregated totals to be displayed for multiple levels of the organization. Storage of information at the financial transaction grain enables root cause analysis through drill down and drill across operations.
Financial performance is a key component of the Balanced Scorecard (BSC). Data managed in the financial data mart is a prime input to performance management scorecards and is critical to the management of successful organizations. Financial ratio KPIs can be calculated from the financial data.
Supply Chain and Manufacturing BI Applications
Supply chain and manufacturing analytics provide visibility to supply chain partners, including suppliers, manufacturers, transporters, wholesalers, and retailers. Data is provided to supply chain analytics from data sources such as procurement, manufacturing, inventory, and logistics systems. Table 17-02 shows example supply chain design elements.
Supply chain applications of BI include:
• Supplier performance analysis
• Plant and transportation hub location analysis
• Supply chain forecasting and planning
• Dynamic supply chain optimization
• Transportation efficiency analysis
• Inventory analysis
• Sales analysis.
Table 17-02: Supply Chain Analytics Design Elements
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Stars |
Dimensions |
Metrics |
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Shipment Fact Transport Fact Requisition Fact Supply Order Fact Billing Fact Procurement Fact Fulfillment Fact Inventory Snapshot Fact Demand Order Fact Inventory Transaction Fact |
Calendar Date Dimension Currency Dimension Facility Dimension Geographic Area Dimension Organization Unit Dimension Product Dimension Supply Chain Partner Dimension Commodity Dimension Unit of Measure Dimension Work Order Dimension Shipment Order Dimension |
Inventory Balance Amount Inventory Turn Count Procurement Lead Time Manufacturing Lead Time In Transit Unit Count Resource Capacity Unit Count Per Product Scrap Cost Maintenance Down Time Late Shipment Count Product Return Count Unit Production Cost |
Operations BI Applications
Operations management addresses the effective production of goods and services. Operations transform inputs (labor, material, facilities, machines) into outputs (goods and services) through a series of business processes and transformations. Table 17-03 depicts design elements that may be included in an operations data mart. Examples of operations application of BI include:
• Capacity planning
• Inventory analysis
• Operational performance and cost management
• Quality performance and safety analysis
• Scheduling of labor and facilities.
Table 17-03: Operations Analytics Design Elements
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Stars |
Dimensions |
Metrics |
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Production Fact Activity Fact Inspection Fact Inventory Fact Resource Fact Labor Usage Fact Component Usage Fact Process Measure Fact |
Business Process Dimension Calendar Date Dimension Commodity Dimension Currency Dimension Facility Dimension Geographic Area Dimension Organization Unit Dimension Product Dimension Resource Dimension Unit of Measure Dimension Work Order Dimension |
In Transit Unit Count Inventory Balance Amount Inventory Turn Count Late Shipment Count Maintenance Down Time Manufacturing Lead Time Per Product Scrap Cost Procurement Lead Time Product Return Count Resource Capacity Unit Count Unit Production Cost |
Performance Management BI Applications
Performance management applications of BI include:
• Balanced scorecard analysis
• Compensation analysis
• Resource and human capital management.
Risk Management BI Applications
Risk management includes numerous applications of analytics, such as hedging risk, evaluating credit risks, scoring risks, and detecting fraud. An example of flagging activity as potentially fraudulent is shown in Figure 17-01.
Figure 17-01: Fraud Detection Model
Risk management applications of BI include:
• Fraud detection
• Credit risk analysis
• Basel II and Solvency II analysis
• Hedging analysis.
Government BI Applications
Business intelligence and analytics have numerous applications in the government sector, where large volumes of data are gathered. Examples of government uses of BI include:
• Homeland security
• Crime prevention
• Tax fraud detection
• Military uniform sizing
• Education best practice analysis.
Case Study: Richmond, VA Policy Department
Business intelligence and analytics have helped the Richmond, Virginia Police Department (RPD) make dramatic steps in reducing crime. The city was able to drop its dangerous city ranking from 5th to 99th place through effective use of business intelligence.
To fight crime, the RPD decided to use analytics to find hidden patterns in its huge store of information obtained from 911 and other police systems. Analytics are used to predict the likelihood of crime and to effectively allocate police resources. Information is correlated from several data warehouses.
This was achieved by developing a statistical model using the IBM SPSS Modeler tool. Models are tied to color coded maps that are accessed by police officers in their patrol cars.
The use of analytics also enables proactive policing. Special units can be dispatched to where they can be most effectively utilized. In addition, certain property crimes are identified as having the potential to escalate to violent crimes. Proactive policing can head off more serious crime.
The results are impressive – from 2006 to 2007, crime rates fell; homicide declined 32%, rapes down 19%, robberies dropped 3%, and aggravated assaults fell 17%. These rates have continued to decline and the RPD continues to find new ways to use analytics to fight crime. (IBM SmartPlanet 2010)
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Key Points • BI and analytics can be used to produce results profitably in many areas. • Financial analysis and KPIs are prime inputs to performance management and the Balanced Scorecard. • Trend analysis and alerts support budget management and issue detection. • Supply chain and manufacturing analytics can help to optimize procurement and delivery of goods. • Manufacturing companies who use analytics can improve efficiency, inventory levels, and sales. • Analytics for operations can contribute toward the effective production of goods and services. • Performance management analytics such as the Balanced Scorecard (BSC) are great tools for improving the performance of the work force. • Risk management can make use of analytic applications such as hedging risk, evaluating credit risks, scoring risks, and detecting fraud. • Business intelligence can boost the effectiveness of law enforcement. Richmond Police Department used BI and analytics to improve law enforcement effectiveness and proactively reduce the crime rate. • BI and analytics have additional applications in government, from homeland security to military uniform sizing to education. |