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Data Management & Visualization

You Can't Fix
What You Can't See

The practice of Data Management is essential to virtually all organizations in all industries that desire to reduce operating costs and increase revenue. Data Management methodologies and processing techniques are used extensively in virtually all aspects of today’s enterprise to satisfy organizational information and knowledge needs ranging from customer service requests to forecasting sales and production needs.

There are six primary considerations in effective data management strategies: data integrity; accessibility, backup, archive, disaster recovery and data availability. In our Data Management consulting and implementation efforts, we carefully analyze and consider each of these factors in designing the best overall solution with the lowest TOTAL cost of ownership for our clients.

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WHAT WE IMPROVE

Comprehensive
Data Visualization

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Integrating Diverse Data Streams

Presenting Information To The User

Understanding The Complete Picture

Reporting Results To Upper Management

In today’s business environment, an organization’s information processing needs not only continuously grow larger, but become more complex as business processes and software systems evolve. Multiple geographical locations, data sources, data formats and applications present unique challenges and contribute to the difficulty of data storage, maintenance and utilization. Data Science Automation extends enterprise data management to other organizational layers from the production floor to the boardroom.

What We Perfect

Graphical System Design

Data Integrity

Data Accessibility

Data Archive

Data Analytics

Database Tools

Real-Time
Data Processing

Data Governance & Compliance

Interactive Charts & Graphs

Traceability

Custom Data
Dashboards

Automated Report Generation

LabVIEW

MES/MOM/SCADA

Access/SQL/Oracle

Remote Diagnostics

Business Intelligence

Augmented Reality

Key Performance Indicators

Live Website Data Streaming

Get Started Today

Robust, modular test system architectures can result in short test development times and higher test capacities; and with more products getting tested sooner, more products will be sold and shipped. High throughput testing for pass/fail results is a worthy goal, but so much more can be accomplished. Data Science Automation extends the test strategy to automated text, web and database reporting, and ultimately to process improvements focused on defect prevention.

Would you like one of our Automated Test experts to contact you to discuss your application?