Data Optimizer is an asset data verification, creation, and cleansing solution designed to give asset-intensive companies the mobility, accuracy, and control they need to properly develop, analyze, and structure the critical information supporting their maintenance, reliability, and production strategies.

The sustainability of any system, process, or program depends on the integrity of the data supporting it. Data Optimizer provides the visibility and speed needed to quickly analyze taxonomy, nomenclature, conventions, hierarchies, systems, conditions, completeness, relationships, and other vital components of your asset performance management data.

User Configurable

Tablet Ready

EAM / CMMS Integration 

Multi-user Access & Permissions

Sync Function 

Customer Support


See it live by scheduling a personal demo.



Preparing for improvement success

  • Identify duplicates, incompletes, and target other gaps in data quality
  • Configurable governance and controls satisfy any enterprise compliance requirements
  • Identify and target critical data for improvement strategies (i.e. criticality analysis, pm optimization, reliability analysis, etc.)
  • Enables rapid data build on new, acquired, or design assets
  • Mobile functionality facilitates accurate collection, completeness, and revision
  • Instant updates and progress performance
  • Controlled revisions through EAM / CMMS connections
  • Historical records are stored and accessible for future reference
  • Provides data requirements to contractors and engineers in a EAM / CMMS ready interface


Data Quality design

Smart, self-learning logic provides assurance that naming and structures are developed according to predetermined criteria.


Define every detail of a data review or build, including field requirements, priority, and approving authority. Track progress at a cumulative level, by individual user, or during approvals; ensuring projects are on the right trajectory.

component classes

A library of built-in class structures provides users with a quick start in industry standard alignment. Flexibility allows users to create robust component classes specifically for their industry or operating context.

Data metrics

Organizations can assess their data against industry standards or define site standards to continuously measure, benchmark, and improve data completeness and quality.

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Asset Health Evaluation Criteria (HEC)
Visualize the current and historical condition of assets, assess management risks, forecast maintenance and replacement costs and determine the effectiveness of the planning and scheduling functions.
Data Mgmt Tip #1

Asset Taxonomy must include hierarchical equipment structure along with asset classification for ease of cost analysis.

Data Mgmt Tip #2

Less than 30% of organizations surveyed have confidence their asset hierarchy is accurate.

Asset taxonomy combined with nomenclature data (metadata) and performance data along with asset health information are some of the important inputs Read More

Data Mgmt Tip #3

Typical hierarchy should be 4-7 levels, starting from corporate headquarters down to a maintainable asset for cost tracking.

Data Mgmt Tip #4

Do not build your Asset Hierarchy down to a nut and bolt level as this is not cost effective.

Data Mgmt Tip #5

Multi-site companies should adopt a common taxonomy structure across plants, this will allow benchmarking with greater transparency.

Data Mgmt Tip #6

Don't include replaceable components or spare parts in the asset taxonomy; your cost/failure tracking should be on asset level.