STATISTICA Extract, Transform, and Load
STATISTICA Extract, Transform, & Load (ETL) combines the capabilities of the STATISTICA system for efficient processing of data from standard databases (Microsoft SQL, Oracle) as well as specialized process databases using the optional PI Connector tool (e.g., OSI Pi), with the powerful STATISTICA data processing capabilities for data filtering, aggregation, and analyses.
If you need to manage and optimize complex processes, you need STATISTICA Extract, Transform, and Load (ETL).
Given your current databases and process monitoring methods, can you quickly determine how different process steps affected measured quality an hour ago, yesterday, last week?
Can you quickly determine whether or not changes in trends have occurred? Whether the relationships between certain process parameters are starting to drift?
STATISTICA ETL can be combined with the capabilities of STATISTICA Enterprise for a complete advanced statistical process monitoring solution. This solution can support highly specialized data warehouses that can integrate time-stamped parameter data for multiple process steps with quality, rework, and outcome data.
- STATISTICA ETL is the most advanced solution available today for creating data warehouses to support comprehensive views of your data, with tools to extract actionable information that will quickly create a significant return on investment from your existing data collection equipment, tools, and IT infrastructure.
- With STATISTICA ETL, deployed inside STATISTICA Enterprise, you can quickly:
- Set up standard control charting and process capability computations and monitoring
- Compute charts and process capability across multiple processes and from diverse data sources
- Apply to your whole process advanced process monitoring techniques such as neural-network based virtual sensors, advanced pattern recognition methods, sensitive change-point detection algorithms that will tell you that something "is about to go wrong" before it goes wrong, or the most advanced data mining algorithms and methods available today for efficient root cause detection in complex data
STATISTICA ETL - An Ideal Solution
- Building enterprise analysis platforms that will integrate process historians with quality control and advanced process monitoring systems
- Creating specialized data sets that will align and validate time-stamped (e.g., batch-time data, as they are commonly collected in various process industries) with outcome (e.g., assay) data
- Building data sets for ad-hoc and automated root cause analysis for complex manufacturing processes (e.g., chemical or pharmaceutical manufacturing, power generation, mining, etc.)
- Creating 21 CFR Part 11 compliant data sets for validated reporting, for complex processes
- Any analysis that requires specialized data validation, pre-processing, aggregation, standardization, or merging of unconventional data, and thus cannot be built with off-the-shelf standard database tools