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A Common Sence Methodology for Business Intelligence
Michael Hamsa
Carl Livingood, P.E.
Principal Consultant
GeoSpatial Innovations, Inc.
1311 Nightingale Drive
Cedar Park, TX 78613
Mobile: (512) 635-6004
Tel.: (512) 249-2766
Fax: (512) 996-9331
Email : cliving@gsiworks.com
Michael Hamsa
Carl Livingood, P.E.
Principal Consultant
GeoSpatial Innovations, Inc.
1311 Nightingale Drive
Cedar Park, TX 78613
Mobile: (512) 635-6004
Tel.: (512) 249-2766
Fax: (512) 996-9331
Email : cliving@gsiworks.com
Introduction
Utilities driven by the competition of deregulation are aggressively searching for new opportunities for increasing revenues while decreasing operating expenses. Many utility executives believe the key to competing in a deregulated environment is to leverage decades-long investments in technology and data maintenance to make better decisions. Some progressive utilities are using Business Intelligence technology to better manage assets including transmission and distribution facilities, company-owned buildings and properties, customer connections, and personnel.
Many utilities are still mired in GIS data collection and maintenance, but a few are starting to look beyond mapping, work management, and outage analysis applications to find new ways of using the tremendous investment in information technology and facility data. Utilities with a solid technology infrastructure are well-positioned to exploit the investment in facility data. Business intelligence and decision-making tools will be the next big technology to emerge in utilities. A good example of this forward-thinking asset management can be found at Allegheny Energy.
Mr. Robert L. Henry, Director, Asset Management for Allegheny Energy, has been intimately involved in AE’s re-engineering efforts of the past several years, and today has the responsibility for Asset Management. Mr. Henry describes his newly created job as trying to catch the revenue and expenses falling through the cracks of Transmission and Distribution System operations’ processes. Mr. Henry’s group has conducted a preliminary investigation of available business intelligence solutions, and is currently re-defining business processes based on a new company structure and focus. “We are actively studying business intelligence activities to become more competitive in T&D operations. If we can reduce our plant by just 3% of the three billion dollars, we will save ninety million dollars. We want to give our decision-makers and planners the tools to analyze facility data to form trends and make more informed decisions about where to invest, and just as importantly, where not to invest.” Mr. Henry and his team are researching data warehousing, data marts, and decision support software, collectively referred to as business intelligence, to meet these new corporate requirements.
Business intelligence is not about creating new data, but creating new tools for planners and decision-makers to leverage decades-long investments in legacy systems. The highest goals of business intelligence are:
It is easy to get that glazed look in your eyes when you start talking to consultants or the IT department about the benefits of Business Intelligence technologies. You are probably somewhat familiar with data warehousing because this technology has rolled out to financials in the accounting department. But the whole issue of Business Intelligence may seem like a “Black Hole” that is best left to the IT staff or a team of consultants dressed in expensive suits. But you don’t have to know all the buzz words to get a project started. Here are a few basic terms that are useful in researching and discussing business intelligence technologies.
This paper describes a development methodology for implementing Business Intelligence in the context of determining the value of retaining a group of customers in a remote portion of the service territory. The following example with be used throughout the paper to describe an implementation methodology for business intelligence:
All successful technology projects begin with a definitive project scope. Spending time up front helps to mitigate risks and set expectations. Business Intelligence projects should be started by defining the problem set in terms of a clear problem statement, schedule, budget, and set of deliverables. The project team should use the following guidelines while defining project scope.
Business Intelligence must be implemented to support re-engineered business processes and target specific business issues. Business issues may include managing construction and engineering crews, allocating material, siting substations and buildings, attracting and retaining profitable customers, managing company property, and billing jointly-owned facilities and foreign attachments. The IT department must engage key users and future owners of the application to accurately and completely define the business process to be targeted.
Cost Of Service Example
The cost and investment to provide service to a customer is comprised of many factors:
The project team must understand and document at a high level how data is stored and maintained in legacy systems. The purpose of this step is two-fold. First, the project team needs to recognize the value of investments in existing legacy systems. And second, the project team can define early in the project life cycle any data dependencies or prerequisites for the Business Intelligence application such as lack of connectivity, partial data conversion, or poor data accuracy. Remember, the goal for this step is to identify data sources in the legacy systems, not to correct deficiencies in the legacy systems or to implement a new computer systems to replace legacy systems. The project team must remain disciplined to stay at a high level, and not get bogged down in the problems of the legacy systems, or fail to find potential pitfalls that will inevitably surface later in the project.
Cost of Service Example
The legacy systems for our Cost Of Service applications include Geographic Information System (GIS), Customer Information System (CIS), Asset Management, Service Reliability Tracking, Engineering Analysis, and Facility Maintenance System. Here are some data descriptions maintained in these legacy systems required for a Cost Of Service application:
Step 4 Perform Reality Check
Consider that Information Technology projects in general have a 30 percent chance of not being delivered at all, and a 70 percent chance of being over budget, off specs, or significantly late, it is particularly important to consider risks early in the project and develop plans to mitigate these risks as much as possible. Although Business Intelligence projects have similarities to applications commonly developed at utilities, there are differences that must be taken into account.
Step 5 Define User Requirements
User requirements include common queries, sample data presentation on screens, anticipated user questions and answers, and reports. The user requirements also specify performance characteristics such as search times, limitations on ad hoc queries, and data timeliness.
A common mistake in implementing business intelligence systems is to wait until a technology is selected before defining user requirements. The project team should not allow technology to limit the design and flexibility of the business intelligence system, rather carefully specify the requirements and later apply appropriate technologies to the application.
Cost Of Service Example
The Cost Of Service application should be capable of determining the annualized cost of service values (max., min., mean, average) for a specific customer, customers within a geographic region, customers meeting a demographic or service criteria. The results can be output to the screen, a default printer, or saved to a file.
Step 6 Identify Business Events
Business events are the triggers that cause a change in source data for the Business Intelligence system. By identifying the business events relevant to the problem domain, the Business Intelligence will be constructed to refresh data from the source legacy systems in a timely manner that maintains to integrity of the application results.
Cost Of Service Example
The Cost of Service application is heavily dependent on the distribution network. If the distribution network changes in the GIS due to line switching or the construction of a new substation, the cost of service results based on energy losses, facility asset valuation, and maintenance data will correspondingly change.
Step 7 Map Data Movement and Transformations
Data from legacy systems must be periodically loaded to the Business Intelligence system using direct, transformation, or aggregation data loading. The direct load is where the field size and format in the legacy system are the same as the field size and format of the Business Intelligence system. Transformation means the data size or format is modified to provide consistency across disparate systems or to meet new requirements. Aggregation is a term referring to a mathematical calculation or consolidation of data into a higher level.
Cost Of Service Example
Our Cost Of Service application uses all three types of data manipulation, but for brevity we will illustrate only an aggregation: the energy loss calculations for a three-phase overhead primary span between two poles. The conductor data is stored in the GIS as separate instances for the three primary conductors and one neutral conductor making up the span. Each instance contains the conductor size, type, and length. The GIS may also record the primary span’s voltage, configuration, and spacing. Rather than loading all this conductor data into the Business Intelligence system for each span, the impedance calculation is performed prior to or during the data loading process. This data aggregation reduces data storage requirements and improves system performance by eliminating redundant calculations for a given distribution network.
Step 8 Determine Resource Requirements
Business Intelligence systems are often expensive and costly. Specialized skills and a complex hardware and software infrastructure are needed to implement the technology to meet user requirements. Prior to making this significant investment, the project team must determine the resource requirements in terms of in-house technical staff, contract and consulting resources, hardware, software, and network infrastructure.
It is especially important for these resource requirements to be clearly communicated to the executive and management teams. And again, since Business Intelligence can be a risky proposition, the project team should point out all relevant risk scenarios to management. The project team should proceed with the Business Intelligence project only if senior management is fully committed to the project.
Step 9 Apply Technology
Business Intelligence usually requires a mix of vendors, products, and technologies to meet the requirements defined in steps 1-8. Now is the time to bring in the specialists to actually implement the appropriate technologies. The management team will feel more comfortable paying for the consultants in the expensive suits after all the business issues are well-defined and documented. The overall project costs will be substantially reduced as well.
Step 10 Implement the Business Intelligence Application
The project team should conduct a pilot to test the usability and performance of the Business Intelligence application. Testing a Business Intelligence application can be very difficult because of integration to production legacy systems. The project team does not want an application error to jeopardize the reliability of a mission-critical system such as Customer Information System. The most effective method of testing the application is to develop a test plan that focuses on functionality first, and performance second. The IT staff and the users should develop the test plan together, but the users involved in design and development of the system requirements should take the lead in testing and give final approval for rollout.
The testers should validate that the application supports the existing business processes. Keep in mind that Business Intelligence applications are intended to be flexible and user-driven, so the project team will likely never really finish with the application. A well thought-out test procedure will provide structure and repeatability as new functionality is added through the months or years.
Most users are willing to accept less than perfect performance of the Business Intelligence application if it yields the proper results without a lot of manual calculations and reporting. However, users are less tolerant of screens and reports that are difficult to understand or organize. Both speed and usability problems can be solved in an iterative manner by prioritizing and tracking corrections using common project management tactics.
The Business Intelligence application will require support, maintenance, and upgrades after it is implemented. A formal maintenance procedure offers an effective transition from the development team to the support team. The procedures document the person assigned specific responsibilities for questions, user security changes, new functionality requests, upgrades to vendor products, testing, and modifications to the source legacy systems.
Business Intelligence applications are deployed and maintained just like any other technical application. There will be user training, infrastructure problems causing poor performance, and scaling issues to work through. But there is an upside: often Business Intelligence application have fewer users to train. And, Business Intelligence applications are generally requested by users to target specific tasks that are difficult or impractical to accomplish using manual approaches. Sometimes a Business Intelligence application is the only realistic solution to the problem domain. So enjoy the praise when the solution works.
Conclusions
Business Intelligence systems can be costly and risky to implement, but these systems may also provide the best opportunity for utilities to compete in an industry facing new challenges of de-regulation. Business Intelligence applies data warehousing, data mining, and decision support technology to leverage decades-long investments in computer systems and data maintenance. Projects teams can successfully implement Business Intelligence systems using a common-sense methodology that places emphasis on the development and documentation of business issues, rather than the acquisition of complex technologies. Delaying the acquisition of technology until later in the project lifecycle helps to mitigate the risks of technology obsolescence and costly failures. Also, putting the focus on business issues allows the project team to validate the legacy systems and infrastructure satisfy the problem domain. The project team should constantly assess the value of the investment in each system because of rapidly changing user requirements and business climate. Business Intelligence systems are never really complete, but if properly designed and implemented, can be valuable tools for planners and decision-makers to keep pace with a changing business landscape.
Utilities driven by the competition of deregulation are aggressively searching for new opportunities for increasing revenues while decreasing operating expenses. Many utility executives believe the key to competing in a deregulated environment is to leverage decades-long investments in technology and data maintenance to make better decisions. Some progressive utilities are using Business Intelligence technology to better manage assets including transmission and distribution facilities, company-owned buildings and properties, customer connections, and personnel.
Many utilities are still mired in GIS data collection and maintenance, but a few are starting to look beyond mapping, work management, and outage analysis applications to find new ways of using the tremendous investment in information technology and facility data. Utilities with a solid technology infrastructure are well-positioned to exploit the investment in facility data. Business intelligence and decision-making tools will be the next big technology to emerge in utilities. A good example of this forward-thinking asset management can be found at Allegheny Energy.
Mr. Robert L. Henry, Director, Asset Management for Allegheny Energy, has been intimately involved in AE’s re-engineering efforts of the past several years, and today has the responsibility for Asset Management. Mr. Henry describes his newly created job as trying to catch the revenue and expenses falling through the cracks of Transmission and Distribution System operations’ processes. Mr. Henry’s group has conducted a preliminary investigation of available business intelligence solutions, and is currently re-defining business processes based on a new company structure and focus. “We are actively studying business intelligence activities to become more competitive in T&D operations. If we can reduce our plant by just 3% of the three billion dollars, we will save ninety million dollars. We want to give our decision-makers and planners the tools to analyze facility data to form trends and make more informed decisions about where to invest, and just as importantly, where not to invest.” Mr. Henry and his team are researching data warehousing, data marts, and decision support software, collectively referred to as business intelligence, to meet these new corporate requirements.
Business intelligence is not about creating new data, but creating new tools for planners and decision-makers to leverage decades-long investments in legacy systems. The highest goals of business intelligence are:
- Eliminate isolated islands of information within and between organizations.
- Move decision-making and information centrally located at the top, and distribute responsibility throughout the organization.
- Align the direction of IT more closely to organized business plans and practices.
- Create planning and analysis tools that can be easily refined and frequently adjusted for new business requirements.
It is easy to get that glazed look in your eyes when you start talking to consultants or the IT department about the benefits of Business Intelligence technologies. You are probably somewhat familiar with data warehousing because this technology has rolled out to financials in the accounting department. But the whole issue of Business Intelligence may seem like a “Black Hole” that is best left to the IT staff or a team of consultants dressed in expensive suits. But you don’t have to know all the buzz words to get a project started. Here are a few basic terms that are useful in researching and discussing business intelligence technologies.
- Business Intelligence – typically refers to the 3D’s: Data Warehousing, Data Mining, and Decision Support.
- Data Warehouse – repackaging of core business operations data into large-scale databases periodically populated with operations and customer data from production computer systems.
- Data Marts – Subsets of corporate data fashioned for a specific task.
- Decision Support – Includes OLAP, simulation, ad hoc queries, planning, modeling, what-if analysis, data
- Data Mining – refers to using tools to query huge raw data repositories called meta-data.
- On-line Analytical Processing (OLAP) – restructures data into simplified data schemas, pre-calculated aggregates and summaries, indexes data and aggregates between cubes to speed access, and links data in cubes to SQL access routines.
This paper describes a development methodology for implementing Business Intelligence in the context of determining the value of retaining a group of customers in a remote portion of the service territory. The following example with be used throughout the paper to describe an implementation methodology for business intelligence:
-
Our utility has been approached by another utility company interested in acquiring a portion of our service
territory in a cash deal. Executives have concluded that the remote nature of the territory is difficult to
support and maintain from an operational standpoint. The area is not contiguous to the remaining service
territory and it is far from any generation sources. There is a costly plan to install a natural gas turbine
generating station to support the remote area within a few years. However, there is conflicting opinions as
to whether the deal makes financial sense. The deal is to be closed within the next year.
The management team assigned to investigate and assess the value of the remote service territory has come to the accounting department for help in gathering and analyzing the data. The management team is soon frustrated that the accounting department is unable to determine the “real” value of the customers in the remote area, despite years and millions of dollars the utility has invested in building and maintaining computer systems. The Information Technology department is now on the spot to provide the data to the management team within the next nine months. The IT department decides to implement Business Intelligence to support the decision making process.
All successful technology projects begin with a definitive project scope. Spending time up front helps to mitigate risks and set expectations. Business Intelligence projects should be started by defining the problem set in terms of a clear problem statement, schedule, budget, and set of deliverables. The project team should use the following guidelines while defining project scope.
- Manage the size of the project. Don’t let the scope get so large that the project team is spending more time working with users to understand the problem set than to implement the technology.
- Choose a business problem that is well understood and will provide significant improvements to an existing well-defined process or business issue.
- Focus on a specific business issue. Narrow the list of possible projects and prioritize the available applications based on estimated payback.
- Problem Statement: Determine the cost and investment components of providing service to customers in the remote area, and present this information to utility executives in a common-sense way.
- Schedule: The management team must develop a plan within 30 days, with the value of the remote area due within nine months.
- Budget: The management team is skeptical of the business intelligence technology, and approves only enough resources for the initial planning and design of the system. The business intelligence team must seek additional resources in increments.
- Deliverables: The management team expects the Cost Of Service application to show the value of the customers in the remote area compared to other service areas, and the application must be re-useable for future evaluations.
Business Intelligence must be implemented to support re-engineered business processes and target specific business issues. Business issues may include managing construction and engineering crews, allocating material, siting substations and buildings, attracting and retaining profitable customers, managing company property, and billing jointly-owned facilities and foreign attachments. The IT department must engage key users and future owners of the application to accurately and completely define the business process to be targeted.
Cost Of Service Example
The cost and investment to provide service to a customer is comprised of many factors:
- Cost of Energy is the cost the utility incurs to produce or purchase the electricity. Although this factor may be readily available on the customer bill or in public bulletins, the real cost of energy depends on time of usage, demand, and load characteristics.
- Energy Losses are associated with the transmission and distribution of a commodity. These losses are calculated using load flow algorithms and are dependent on conductor size, type, configuration, voltage, and load for electric transmission and distribution systems.
- Facility Asset Value is the customer’s share of all the assets required to serve the customer from the generating station to the meter. These assets include conductors, structures, devices, and even not so obvious costs such as purchased Rights-of-Way. Determining the customer’s share may be further complicated by asset depreciation, multiple circuit and network configuration, and incomplete data.
- Facility Maintenance has many meanings: painting steel structures and pad-mounted equipment; controlling vegetation along Rights-of-Way; and inspecting facilities for clearance obstructions and safety.
- Service Reliability is a measure of power outages and interruptions. Service Reliability can be interpreted as a cost for making repairs and loss of revenue when deciding investment in facility improvement and rehabilitation.
- Taxes and Franchise Fees add costs to doing business in certain political boundaries.
- Human and Support Investment are investments in the utility’s local and central offices to support energy delivery. Trucks, buildings, people, and technologies all contribute to the cost of providing service to a customer. Usually, utilities spread these “soft” costs uniformly across the customer base. But it may not cost the same to support rural and congested areas.
The project team must understand and document at a high level how data is stored and maintained in legacy systems. The purpose of this step is two-fold. First, the project team needs to recognize the value of investments in existing legacy systems. And second, the project team can define early in the project life cycle any data dependencies or prerequisites for the Business Intelligence application such as lack of connectivity, partial data conversion, or poor data accuracy. Remember, the goal for this step is to identify data sources in the legacy systems, not to correct deficiencies in the legacy systems or to implement a new computer systems to replace legacy systems. The project team must remain disciplined to stay at a high level, and not get bogged down in the problems of the legacy systems, or fail to find potential pitfalls that will inevitably surface later in the project.
Cost of Service Example
The legacy systems for our Cost Of Service applications include Geographic Information System (GIS), Customer Information System (CIS), Asset Management, Service Reliability Tracking, Engineering Analysis, and Facility Maintenance System. Here are some data descriptions maintained in these legacy systems required for a Cost Of Service application:
- Geographical Information System (GIS) contains a Distribution Network that details all of the facilities serving a customer, from the transmission system or substation to the customer meter. The distribution network includes major equipment characteristics including type and kind of facility, quantity, compatible unit, installation date, and electrical connectivity.
- Asset Management System (AMS) is usually maintained by accounting personnel to track the dollar value for facilities. The facility asset value for the individual facility in GIS will be mapped to a class of facility in the Asset Management System using the type and kind, and installation date of the facility. The accounting personnel must be involved to define “rules” for this facility mapping to assets.
- Customer Information System (CIS) or Customer Billing System generally store and maintain average customer loads, peak usage, kWh usage, or other available load information used in the calculation of customer share and energy losses.
- Vegetation Control, Rights-Of-Way, Equipment Maintenance systems are usually legacy mainframe systems operated by specific groups within the utility. This type of data is usually stored by the “pole number” that can be related to the distribution network.
Step 4 Perform Reality Check
Consider that Information Technology projects in general have a 30 percent chance of not being delivered at all, and a 70 percent chance of being over budget, off specs, or significantly late, it is particularly important to consider risks early in the project and develop plans to mitigate these risks as much as possible. Although Business Intelligence projects have similarities to applications commonly developed at utilities, there are differences that must be taken into account.
| Risk | Mitigation |
| •Some Business Intelligence applications have only 1-2 users. | •Prioritize the applications based on largest estimated
payback. •Involve key users from the start. |
| •Business Intelligence is infrastructure intensive, demanding expensive hardware, software, developer skills. | •Perform most planning and design development activity prior to selecting and purchasing any technology. |
| •Using the wrong development methodology can yield dramatic failure. | •Use iterative, diagnostic development approaches that depend on the situation and planning problem being resolved, rather than a traditional waterfall approach. |
| •Storage structures, use patterns, and return on investment used in business intelligence projects are very different from traditional operational systems legacy. | •Develop Business Intelligence applications to map onto, then supplement and support, existing planning, budgeting, and control systems and processes. |
| •User requirements constantly shift as organizations respond to deregulation and ever-increasing competition. | •Engage active, on-going senior management so that
the Business Intelligence team continues to
understand the business impact to application
development. •Prepare the Business Intelligence team to be flexible with application development. |
Step 5 Define User Requirements
User requirements include common queries, sample data presentation on screens, anticipated user questions and answers, and reports. The user requirements also specify performance characteristics such as search times, limitations on ad hoc queries, and data timeliness.
A common mistake in implementing business intelligence systems is to wait until a technology is selected before defining user requirements. The project team should not allow technology to limit the design and flexibility of the business intelligence system, rather carefully specify the requirements and later apply appropriate technologies to the application.
Cost Of Service Example
The Cost Of Service application should be capable of determining the annualized cost of service values (max., min., mean, average) for a specific customer, customers within a geographic region, customers meeting a demographic or service criteria. The results can be output to the screen, a default printer, or saved to a file.
Step 6 Identify Business Events
Business events are the triggers that cause a change in source data for the Business Intelligence system. By identifying the business events relevant to the problem domain, the Business Intelligence will be constructed to refresh data from the source legacy systems in a timely manner that maintains to integrity of the application results.
Cost Of Service Example
The Cost of Service application is heavily dependent on the distribution network. If the distribution network changes in the GIS due to line switching or the construction of a new substation, the cost of service results based on energy losses, facility asset valuation, and maintenance data will correspondingly change.
Step 7 Map Data Movement and Transformations
Data from legacy systems must be periodically loaded to the Business Intelligence system using direct, transformation, or aggregation data loading. The direct load is where the field size and format in the legacy system are the same as the field size and format of the Business Intelligence system. Transformation means the data size or format is modified to provide consistency across disparate systems or to meet new requirements. Aggregation is a term referring to a mathematical calculation or consolidation of data into a higher level.
Cost Of Service Example
Our Cost Of Service application uses all three types of data manipulation, but for brevity we will illustrate only an aggregation: the energy loss calculations for a three-phase overhead primary span between two poles. The conductor data is stored in the GIS as separate instances for the three primary conductors and one neutral conductor making up the span. Each instance contains the conductor size, type, and length. The GIS may also record the primary span’s voltage, configuration, and spacing. Rather than loading all this conductor data into the Business Intelligence system for each span, the impedance calculation is performed prior to or during the data loading process. This data aggregation reduces data storage requirements and improves system performance by eliminating redundant calculations for a given distribution network.
Step 8 Determine Resource Requirements
Business Intelligence systems are often expensive and costly. Specialized skills and a complex hardware and software infrastructure are needed to implement the technology to meet user requirements. Prior to making this significant investment, the project team must determine the resource requirements in terms of in-house technical staff, contract and consulting resources, hardware, software, and network infrastructure.
It is especially important for these resource requirements to be clearly communicated to the executive and management teams. And again, since Business Intelligence can be a risky proposition, the project team should point out all relevant risk scenarios to management. The project team should proceed with the Business Intelligence project only if senior management is fully committed to the project.
Step 9 Apply Technology
Business Intelligence usually requires a mix of vendors, products, and technologies to meet the requirements defined in steps 1-8. Now is the time to bring in the specialists to actually implement the appropriate technologies. The management team will feel more comfortable paying for the consultants in the expensive suits after all the business issues are well-defined and documented. The overall project costs will be substantially reduced as well.
Step 10 Implement the Business Intelligence Application
The project team should conduct a pilot to test the usability and performance of the Business Intelligence application. Testing a Business Intelligence application can be very difficult because of integration to production legacy systems. The project team does not want an application error to jeopardize the reliability of a mission-critical system such as Customer Information System. The most effective method of testing the application is to develop a test plan that focuses on functionality first, and performance second. The IT staff and the users should develop the test plan together, but the users involved in design and development of the system requirements should take the lead in testing and give final approval for rollout.
The testers should validate that the application supports the existing business processes. Keep in mind that Business Intelligence applications are intended to be flexible and user-driven, so the project team will likely never really finish with the application. A well thought-out test procedure will provide structure and repeatability as new functionality is added through the months or years.
Most users are willing to accept less than perfect performance of the Business Intelligence application if it yields the proper results without a lot of manual calculations and reporting. However, users are less tolerant of screens and reports that are difficult to understand or organize. Both speed and usability problems can be solved in an iterative manner by prioritizing and tracking corrections using common project management tactics.
The Business Intelligence application will require support, maintenance, and upgrades after it is implemented. A formal maintenance procedure offers an effective transition from the development team to the support team. The procedures document the person assigned specific responsibilities for questions, user security changes, new functionality requests, upgrades to vendor products, testing, and modifications to the source legacy systems.
Business Intelligence applications are deployed and maintained just like any other technical application. There will be user training, infrastructure problems causing poor performance, and scaling issues to work through. But there is an upside: often Business Intelligence application have fewer users to train. And, Business Intelligence applications are generally requested by users to target specific tasks that are difficult or impractical to accomplish using manual approaches. Sometimes a Business Intelligence application is the only realistic solution to the problem domain. So enjoy the praise when the solution works.
Conclusions
Business Intelligence systems can be costly and risky to implement, but these systems may also provide the best opportunity for utilities to compete in an industry facing new challenges of de-regulation. Business Intelligence applies data warehousing, data mining, and decision support technology to leverage decades-long investments in computer systems and data maintenance. Projects teams can successfully implement Business Intelligence systems using a common-sense methodology that places emphasis on the development and documentation of business issues, rather than the acquisition of complex technologies. Delaying the acquisition of technology until later in the project lifecycle helps to mitigate the risks of technology obsolescence and costly failures. Also, putting the focus on business issues allows the project team to validate the legacy systems and infrastructure satisfy the problem domain. The project team should constantly assess the value of the investment in each system because of rapidly changing user requirements and business climate. Business Intelligence systems are never really complete, but if properly designed and implemented, can be valuable tools for planners and decision-makers to keep pace with a changing business landscape.
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