The Importance of Data Quality in Industrial Data Analysis
In the era of Industrial IoT, data volume increases, yet its quality is crucial for valuable insights. Poor data results in misdiagnoses and inefficiencies. Key issues include storage inconsistencies and human errors. Solutions involve standardizing data practices, auditing pipelines, monitoring sensor accuracy, and integrating systems to enhance analytics and insights effectively.
Unlocking Hidden Patterns: A Complete Guide to Matrix Profile Time Series Analysis
Time series data is everywhere around us—operation data, sensor data, environment data. But buried within these seemingly chaotic sequences lie valuable patterns and anomalies that can provide critical insights for businesses, operators, and analysts. The challenge? Traditional methods of pattern discovery are either too slow, too complex, or require extensive domain knowledge. In this post,…
End-to-end ML/Analytics Projects
Here are the 8 step process for any ML/analytics project for your industrial use case Step 1: Understand the real objectives and big pictureModel building may not be the end goal!Who are the customers/users ?How are these models gonna be used ?Current approach/solution (if any), limitations, efforts, manual/automated ? Above points need to be clarified…
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