business intelligence for the modern utility pncwa... · challenges of data management for business...

Business Intelligence for the Modern Utility

Presented By:

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Presented By: Glenn Wolf, CISSP (Certified Information Systems Security Professional)

Senior ConsultantWestin Engineering, Inc.Boise, IDSeptember 15th, 2009 8:00-8:40am

Agenda

• Introduction• Performance Management• Challenges of Data Management for Decision Support• Transaction Processing vs. Business Intelligence Systems• Business Intelligence and Service Oriented Architectures• Benefits of Business Intelligence• Business Intelligence Applications• Dashboards vs. Scorecards• Business Intelligence Trends and Products• Secure Data Transfer from SCADA and Other Systems• Critical Success Factors• Next Steps

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Introduction

• Business Intelligence:The skills, technologies, applications and practices used to help a business (or organization) acquire a better understanding of its commercial context.

• Common Business Intelligence Applications• Spreadsheets, Ad-hoc Query Tools• Spreadsheets, Ad-hoc Query Tools• Reporting, Analysis (OLAP)• Dashboards / Scorecards• Data Mining / Predictive Analysis

• Business Intelligence Data Sources• Raw Source Systems (OLTP)• Data Warehouse / Data Mart / Operational Data Store (ODS)• Optimized BI Server Cache• Flat Files / Data Extracts

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Performance Management

DevelopAppropriateMeasures and

Targets


Targets

Routinely Monitor

the Results

Routinely Monitor

the Results

Analyze Results againstTargets


CommunicateResults to

Stakeholders


Stakeholders

ImplementAppropriateCorrectiveActions

DevelopBusinessStrategiesand Goals

Performance Reporting Process


Targets


Targets

Routinely Monitor

the Results

Routinely Monitor

the Results



Results toStakeholders


Stakeholders

ImplementAppropriateCorrectiveActions

DevelopBusinessStrategiesand Goals

Decision Support

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• Are we achieving our mission?

• Are we achieving our vision?

• What is our actual performance against desired targets?

• Are we continuously improving?

• Are we effectively communicating the results?

ActionsActions

Challenges of Data Management forBusiness Intelligence

• Many disparate systems (“silos”) – too much data, not enough information.

• Lack of integrated systems

• Proprietary database systems or data models.

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• Aggregation and reporting confined within each system.

• Reporting is cumbersome and slow, and often affects operational systems (slow-downs and crashes).

• Too many people using spreadsheets coming up with different answers to the same questions.

Transaction Processing Systems vs. Business Intelligence Systems

• Transaction Processing Systems• Highly optimized for specific business areas

• High frequency of quick updates and quick retrieval

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• Data is real-time

• Record-locking and query caching issues

• Servers need fast CPU, fast I/O (disk, network)

• Databases are highly normalized (broken down)

Transaction Processing Systems vs. Business Intelligence Systems

• Business Intelligence Systems• Highly optimized to aggregate data from many sources; data

aggregated into “cubes” built around subject matter interests

• Large bulk queries only; optimized to address sorting and grouping issues

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grouping issues

• Data is not real-time

• Servers need fast CPU, lots of memory, lots of disk space

• Metrics are pre-computed and management-approved

Business Intelligence and Service Oriented Architectures

• The Old Way• Self-contained, standalone BI applications• “Mish-mashed” infrastructure of acquired, force-

integrated platforms• Monolithic BI technologies, loosely integrated via

proprietary standards

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• Legacy architectures and proprietary licensing models

• The New Way• Componentized and modular• Service-implemented architecture

• Built “from the ground up” as a set of services• Exposed via AJAX and Web Services• Commitment to a standards-based architecture

and adoption-friendly licensing model

Business Intelligence:Benefits

• Support Performance Improvement Initiatives.

• Provide sustainable support for business processes through automation.

• Reduce staff time associated with report generation and production.

• Support data quality improvement initiatives and overall data

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• Support data quality improvement initiatives and overall data governance.

• Drive “one version of the truth.”

• Enable more timely (and accurate) decision making.

Business Intelligence Systems:A Different Kind of Data Management

Core UtilityApplications

Finance / HR

Budgeting

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Budgeting

Purchasing

Project Management

Maintenance Mgmt.

LIMS

SCADA

PIMS

CAD


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Utility Intelligence Applications: Providing Information for Decision Making

PerformancePerformanceDashboards & ScorecardsDashboards & Scorecards

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SupervisorsSupervisors DepartmentDepartmentHeadsHeads

AnalystsAnalystsGeneral General ManagersManagers

Utility Intelligence Systems:Comparing Technologies

Dashboard Scorecard

Purpose Monitors and measures operational processes

Monitors progress toward established objectives

Data Events, Transactional Aggregates, Consolidation

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Refresh Rate

Near real-time (“latest and greatest”)

Periodic (“snapshots”)

Measures Exceptions linked to operations

Targets linked to strategic plans

Utility Intelligence Systems:Not Mutually Exclusive

Manage

Measure

Measures Progression

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Measure

Monitor

Measures Anything


Dashboard Scorecard




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Refresh Rate





Performance Dashboards:Monitoring Operational Processes

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Dashboard Scorecard




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Refresh Rate





The Balanced Scorecard:A Framework for Monitoring Progress

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Objectives Measures Targets Initiatives

Performance Scorecards:Drilling Down

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Business Intelligence Products:Software Tools with Different Capabilities

Complex

Develo

pment

Tools

Simple

Develo

pment

Tools

Web

Access Tools

Business Objects SAS Web Report StudioPentaho BI

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• Rulers

• Relative Object Placement

• Programmatic control of layout and formatting

• Multiple grids & graphs

• Highlight exceptions

• Easily position objects on page

• Mixed grid / graph

Crystal ReportsHyperion Production Reports (SQR)Microsoft Reporting Services

Cognos Report StudioMicroStrategy Report Services

IBI Web Focus

Business ObjectsWeb Intelligence

HyperionInteractive Reports

SAS Web Report Studio

MicroStrategy Web

Cognos Query Studio

Pentaho BIJaspersoft

Business Intelligence:Key Trends

• Wide Variety of Software Tools – addressing the needs of different user groups

• Software Vendor Consolidation – only a few vendors remaining, including the big ones (Microsoft, IBM, SAP)

• BI Software Suites – buyers want integrated toolsets

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• BI Software Suites – buyers want integrated toolsets

• Open Source BI – solid market presence due to ease of integration

• Continuous Improvement - Strong links to Performance Management

The Benefits of BI Suites

• Business flexibility• Extensive features and functionality built in• Improved cost-benefit ratio

• Reduced complexity• Pre-Integrated Components• Vendor commitments to enhancements

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• Vendor commitments to enhancements• Streamlined design and implementation

• Additional benefits of open source BI• Lower costs• Lower upfront costs – pilot projects more feasible• Extensive community of experts• Pre-integrated components from the ground up• Future plans fully communicated

Secure Data Transfer from SCADA and Other Systems

• SCADA should be on its own network• Create DMZ (DeMilitarized Zone) between SCADA and

IT networks• SCADA network “pushes” data to copy of servers in the DMZ• IT network users can access server copies in DMZ

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• IT network users can access server copies in DMZ• No access from IT network to SCADA network• If a DMZ server is compromised, intruder is stuck there

• Monitor interconnections and access points with Network-based Intrusion Detection System (NIDS)

Business Intelligence Implementation:Critical Success Factors

• Provide the technical basis for the BI environment• Servers, network connectivity, data source accessibility• Scalability, reliability

• Identify and define the data• Data sources – databases and/or manual inputs• Data quality and data quality assurance processes

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• Data quality and data quality assurance processes• Data mapping, derivation, transformation, and aggregation according to

requirements and business rules• Data terms defined in business terms (metadata)

• Utilize proven practices for application design and implementation

• Assemble business requirements• Define the applications for queries, reports, and charts• Design the information model

Business Intelligence: Next Steps

• Business Intelligence Needs Assessment

• Start with Pilot Project• Identify the Top Questions that Need Answering• Automate the Most Useful / Time Consuming Reports

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• Phased Rollout• Keep Stakeholders Involved• End-User Training on Tools• Maintain Realistic Performance Targets

Questions

Glenn Wolf, CISSP

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Glenn Wolf, CISSPSenior Consultant, Westin Engineering, Inc.

[email protected]

[email protected] or westinsolutions.com

Use for Slides Containing Large Graphical Diagrams

Overview

Overview

Overview

Overview

Overview

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Extra pieces for color reference etc.

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