Successful Data Governance Structure

View Estimating Software

Our estimating software is based on reference data that include: Labor, Equipment, Crews, Materials, Activity Codebook, Bid Item Codebook, & Assemblies.

AACE’s Total Cost Management Frame Work recommends:
The reference data needs to be consistent, reliable, and competitive with a well defined basis (e.g., assumptions, conditions, etc.) such that any project team can determine how its requirements and basis conditions differ from the reference and adjust accordingly.

We recommend that during your initial data setup you normalize the data into standard activities or units and assign the associated labor, equipment and materials to each activity/unit. We have two sets of reference data: Estimate and System.

Estimate reference data consists of: Labor, Equipment, Crews and Materials

System reference data consists of: Activities/Units, Bid Items, and Assemblies

We control the access to both sets of data in our master estimates. Users can limit access to both our Estimate and System data in user administration. This allows you to control who access the data. We also have an audit trail to document who and what changes anyone makes to the system.

When you create a new estimate it will pull all the reference data from that associated master estimate. Depending on user permissions estimate reference data can then be changed by the estimator in each individual estimate and adjust according to that projects unique conditions without changing any of the reference data.

System Reference data can only be changed in a master estimate and access is limited by both passwords, and user administration.

AACE Total Cost Management Framework

We believe that data governance should follow AACE Total Cost Management Framework that may include:

  • Roles and responsibilities
  • Allocated resources
  • Collection methods (during the project and at closeout)
  • Data structure and format (i.e., work breakdown or cost code structure)
  • Level of detail and comprehensiveness of records
  • Data and record quality
  • Storage and maintenance (tools and systems)
  • Access and retrieval (methods and access rights/security)
  • Analysis methods (where applicable)
  • Information product quality (data validation)
  • Legal issues (retention, claims issues, etc.)

By identifying the topics above including in terms of where the data is coming from, who will be updating it, the team’s roles and responsibilities, and developing a cadence of when the data is to be updated you will ensure that your projects have the proper reference data.

In addition we believe in governing the way our data is stored and controlled to ensure data integrity. We have standardized our tables in our data warehouse to ensure that can be extracted into our client’s enterprise reporting systems. This allows for the creation of a data dictionary in an enterprise data model with standard business terminology which allows for mapping to fields in other systems like P6, and Unifier.