Information is the lifeblood of every program. The ability to effectively Organize, Collect and Utilize that information is the key element of successful program execution.

Organize - Collect - Use

ORGANIZE – This phase leverages the expertise of knowledgeable business stakeholders to decompose critical business processes and to identify primary data elements that support these processes. Particular consideration is give to Key Performance Indicators (KPI) and other data points that will provide concise status on program milestones and deliverables. ProgramScope provides administrative and organizational tools that allow system administrators to incorporate this information into the application fabric.

COLLECT – This phase focuses on using ProgramScope’s role-based data collection screens to timely and accurately collect validated project information from the appropriate sources.  A primary benefit delivered by ProgramScope is immediate access to accurate information.   ProgramScope’s auditing and reporting features allow program management to easily monitor both the accuracy and timeliness of data being entered into the system.  Program managers can quickly identify employees or subcontractor who are not meeting project reporting requirements and take corrective action before it becomes an issue.

USE – This phase reflects the effective utilization of information contained within ProgramScope™ to drive more efficient business processes and improve competitive advantage.  ProgramScope’s intuitive user interface offers a comprehensive and easily customized "dashboard" view of data formatted in a familiar “spreadsheet-like” format.  A powerful reporting engine supports the creation of detailed project reports in multiple formats including the ability to export directly into an Excel spreadsheet or pivot table.

Data Integrity

The ultimate effectiveness of any management application and its acceptance by the user base hinges upon confidence in the accuracy and timeliness of information contained in that application.  In recognition of this principle, VQS has built extensive data integrity features into ProgramScope™.  ProgramScope’s logging and auditing features support a complete “chain of custody” that tracks the original source user and logs any changes to the data including when, by whom, the original data value and the modified data values.  Color coded reporting views allow management to easily identify changes and to check details related to those changes.  Our customers have noted that there is a significant behavioral influence on users when they become aware that this level of granularity is available to management to monitor accurate and timely data entry.

Process vs. Asset Oriented Data

Years of successful development and deployment experience has taught us that data can be classified into two general categories – process oriented and asset oriented.  The nature of this information influences how it is collected and from whom, how it is processed and stored, and, how it is reported and visualized.  Process oriented information deals with the execution of tasks and has it maximum value during the execution of those tasks.  Examples of process data include scheduling and forecasting, assignment of responsibilities, project status reporting, and, reference documents that may be relevant to these activities.  Process information continues to have value after completion of task execution to the extent that it can provide valuable insight into historical performance metrics and can be used to fine tune processes and establish benchmarks.  Asset data tends to be long lived and has ongoing value with regard to plant operation, configuration management and capital asset evaluation.  Examples of asset data include inventories, maintenance schedules and legal documents such as leases, permits and agreements. Optimal system performance and efficiency depends upon understanding the nature and use of each information type and building data collection, usage and reporting functionality that maximizes the value and usefulness of each data type.