Business Intelligence Solutions for the Workplace

APPA Business & Financial Conference 2006 April 21, 2023

Business Intelligence Solutionsfor the Workplace

Enhancing Analytical Reporting

Presented by: Jason Strain, Senior Project Manager, MEAG Power

Some content courtesy of John Hack, Director of Solution Development, OutlookSoft Corporation


Agenda

Evolution of Business Intelligence (BI) How we started with a BI/Business Performance

Management (BPM) software solution New business problem for 2006 Our chosen solution Sample reports Overview of the analysis website Live demo Lessons learned


Evolution of BI to true Performance Management

What happened? Reporting Performance

Why is it happening?»Understanding Performance

What to do about it?»Achieving Performance


From Business Intelligence…


…to Performance Management…


To Predictable Performance


Our Entry Into Business Performance Mgt.

Initial project was to replace a system of cost center budgeting spreadsheets.

Justified the implementation cost just by comparing it to products that only did budgeting as well as custom development.

Purchased OutlookSoft in the spring of 2005. Worked with a consultant to guide the implementation

that went live successfully in the summer of 2005. Used by 40+ cost center managers and their assistants

to budget the basics of salaries, travel, training, etc. Now distributing monthly variance reports online via the

OutlookSoft SharePoint Accelerator.


New Business Problem for 2006

Market volatility in natural gas supply and hourly market prices raised concerns over dependability of a once per year budget.

Decided to run the hourly budget model every month and reforecast the entire year.

The ideal solution would be able to report a combination of year to date actuals and remaining year forecasts to produce a rolling year end reprojection.


Solution Requirements

Needed a way to compile the rolling year end reprojection Use as many months of actual bills as possible Do not change the last forecast the board reviewed for the next

month Utilize as much of the latest forecast as possible for future

months. Needed to compare any combination of scenarios: Actual,

Budget, Forecast, and Reprojection. Needed quick turn around whenever the source system

data was updated. Needed the flexibility to create new reports and graphs

quickly. The numbers had to tie to the source systems. Analysis needed to include not just dollars, but also

energy and capacity.


Decision

Faced with 2 options Change the source transactional systems Use OutlookSoft

Built the Proof of Concept using OutlookSoft No consultants needed Quick implementation (4-6 week) Management saw the reports they wanted Bonus of a web-based, color coded site for quick analysis

Board of Directors is now getting this information each month


Solution Architecture

BillingDatabase

BudgetDatabase

MonthlyBill Results

MonthlyForecast

BoardApproved

Budget

CompileReprojection

Combine Actual Bill Results

with Future Forecast(s)

Excel BasedReports & Graphs

Insight Analysis

Slice and Dice by:Category: Actual, Budget, Each Forecast, ReprojectionParticipant City: All 49Project: All projects (funds)Account: Fuel, Generation, Margins, …Fixed or VariableUnit of Measure: Dollars, Energy, CapacityTime

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Sample Reports

These reports exceeded the initial requirements. Illustrate the basic reporting capabilities of any BI tool.

Note: Any correlation between the names on these reports and our actual participant names is purely coincidental. The names were replaced using state capitals.


Board Report


Variance from Budgeted c/kWh


Budget to Actual


Variable c/kWh


Graph of c/kWh


Project & Account by Unit of Measure


What is Predictive Analytics?

Discovery: Automated identification of relationships between KPIs and their

causes

Prediction: Early warning indicator of future performance deviation based on

discovered relationships

Delivery: Proactive alerting and delivery of structured and unstructured

data … in a personally relevant context


Discovery

Automated variance analysis : Reasons – base level results Root Causes – transactions Context – unstructured data


WHAT

Discovery

The KPI Value and Variance are highlighted


WHY

Discovery

REASONS are base level data elements that are automatically shown


WHY

Discovery

ROOT CAUSES (if any) are operational events

that are correlated to the KPI variance


CONTEXT

Discovery

Unstructured data that relates to the variance is automatically linked to

the KPI


Discovery

CONTEXT

One Click takes you to the unstructured data

linked to the KPI


Prediction

Based on Relationships in the Data This is NOT Statistical Forecasting!


Prediction – What Insight Tells You

The KPI at Risk is Identified



The predicted value is displayed



The correlated KPI drivers are listed


Executives

Business Analysts

Line of Business Managers

Suppliers/Partners

Delivery in a role-appropriate context


Demo OutlookSoftReports and Insight


Lessons Learned

Go with software that fits your existing environment. Know what you want to analyze. Make sure your source systems are in order. Have someone who understands the source data. Focus on creating a solid foundation for standard

reporting. Predictive analytics build on top of this foundation. You need to be able to know “what” is happening before you can

begin to look at “why” it is happening.

Start as simple and as small as possible Use iterations to build confidence over time.


Questions

?

Documents

Business Intelligence Solutions for the Workplace