Data Conditioning & Data Analysis

Transforming Sensor Data into Digital Assets

Digital transformation is now the main driver for changes and improvement in the process and manufacturing industries and has become the strategic innovation challenge for engineers and scientists, as well as OT and IT professionals.  Paying attention to the lifecycle of data, from sensors to its use as a digital asset is a journey involving many challenges and creating new opportunities for continous improvements.


Reduce the margin of error

The goal of data conditioning is to reduce the margin of error to enable better outcome for people, business processes, other applications, digital transformation initiatives and any data dependent activity

Lower marginal cost of curiosity

A system's approach to data conditioning and data analysis creates a context where datasets can be repurposed, modified, expanded faster and with less effort.

Enhance Discovery Capability

Data Analysis tools, methods and systems greatly augment our discovery capability and become a prime driver for Innovation and Continous Improvement.

Shorten time to decision

In the process and manufacturing industries, decision are often dependent on multiple sources of data combined into a dataset.  Improving the overall accuracy of these datasets and generating them faster with minimal human intervention can signficantly accelerate decision making and analysis.

Optimal Data Quality

Implementing Data Conditioning as infrastructure service creates a business environmetn where datat quality is continously optimized relative to the desired business and operational outcome and the losses due to inherent data degradation are minimized.


Deall with the inherent precision limitations of sensors

The perfect sensor does not exist. Sensors and measurement systems have inherent precision limitations which impacts the confidence level we can have in process and manufacuting data

Catch data defects in a timely manner

Data defects occur as a result of caliabration and configuration errors, performance degradation, wear and tear, data communications error, etc. The detection of defects in a timely manner is beyond the capability of human vigilance.

Infer missing data with confidence

Finding missing data after the fact poses a unique challenge of inference.  We must then rely on the available information redundancy to infer critical missing data.

Value hidden in the data

Machines  and systems are generating very large quantities of streaming data and events and find the hidden value in this data can be like searching a needle in haystack.

Assess data quality and improve over time

Assessment of data quality in process and manufacturing can show the path to continious improvement. Developing a holistic approach to data quality to drive continous improvement requires  methodical and sustainable approach.

Roadmap to success

The roadmap to a successfull solution involves understanding of the requirements, assessing the degree of fitness of commercial off the shelf solutions vs custom built solution, gap analysis, design, implementation and validation of benefits.  Omicron Development is the trusted partner that will accompany you in this journey.

Get started today!

We are ready to assist you in transforming streaming OSIsoft PI data & events into tangible business and operational outcomes.