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Introduction

What is Tycho Data Osprey?

Tycho Data Osprey is a Data Quality software designed for Industrial IoT data. It automates data quality monitoring, tracks data lineage, and detects operational issues. The platform ensures that your data is reliable and actionable, enabling better decision-making and operational efficiency.

Why Data Quality Matters

In Industrial IoT environments, poor data quality can lead to incorrect decisions, operational inefficiencies, and even safety risks. Tycho Data Osprey addresses these challenges by providing tools to monitor, analyze, and improve data quality continuously.

Key Features

  • Automated Data Quality Checks: Automatically monitor data for issues like missing values, out-of-range readings, and stale data.
  • Interactive Dashboards: Visualize data quality metrics and trends in real-time.
  • Workflow Automation: Streamline issue detection and resolution with automated workflows.
  • PI Vision Trust Badge: Embed live data quality status into PI Vision dashboards for immediate insights.

Example Use Case

Imagine a manufacturing plant using the AVEVA PI System to monitor equipment performance. Tycho Data Osprey can:

  • Detect when sensor data is missing or outdated.
  • Alert operators to potential issues before they escalate.
  • Provide a clear view of data lineage to trace the root cause of problems.

Supported Data Systems

Currently, Tycho Data Osprey supports the AVEVA PI System, including:

  • PI Data Archive: For historical data storage and retrieval.
  • PI Asset Framework (AF): For organizing and contextualizing data.
  • PI Vision: For visualizing data and embedding quality metrics.

Future extensibility is planned for other systems via APIs, enabling broader integration with Industrial IoT ecosystems.

Quickstart Overview

Follow these 7 steps to get started quickly:

  1. Install and Configure: Download and install Tycho Data Osprey. Follow the installation guide to configure the software for your environment.
  2. Connect to PI System: Use the integration settings to connect to your PI Data Archive, AF, and Vision components.
  3. Set Up Scanners: Define scanners to monitor specific data sets and ensure they are active.
  4. Define Data Quality Checks: Create checks to monitor data freshness, availability, and other quality metrics.
  5. Explore Dashboards: Open the prebuilt dashboards to view data quality metrics and trends.
  6. Configure Notifications: Set up alerts to notify your team of critical issues via Slack, Teams, or email.
  7. Start Monitoring: Begin using the platform to monitor data quality and resolve issues as they arise.