Industrial Data Analytics and Predictive Maintenance: Prevent Unplanned Downtime
Predict equipment failures before they happen with big data analytics and machine learning.
Industrial Analytics & AI
Consistent metrics, interactive analytics and AI on trustworthy, contextual data: GoodData Analytics with HighByte’s MCP services.
Challenges we meet most often
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Inconsistent metrics
Metrics such as OEE or unplanned downtime are calculated differently from dashboard to dashboard, so reports contradict each other.
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Data preparation load
Analytics teams spend a large share of their time finding, cleaning and mapping data.
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Projects stuck at pilot
Models built on contextless, inconsistent data may work on one line but do not scale.
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AI agents and governance
If it is unclear which data and actions agents can reach, and with what traceability, the production environment is put at risk.
Our approach
Four steps that start small, get validated and scale with templates.
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A trustworthy data layer
Data is connected, modeled and conditioned at the source; analytics and AI consume that ready data.
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Semantic layer
Business metrics and dimensions are defined once so everyone works from the same definitions.
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Analytics and dashboards
Interactive dashboards and embedded analytics are delivered on a multi-tenant architecture.
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AI use cases
Use cases such as predictive maintenance and quality, and curated data access for agents through MCP.
Products behind this solution
GoodData Analytics
AI-assisted analytics: semantic layer, interactive dashboards, embedded and multi-tenant analytics.
HighByte Intelligence Hub
Analytics- and AI-ready data; MCP services expose pipelines to agents as “tools”.
Free tool
OEE Calculator
Turn shift data into availability, performance and quality insights.
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Key concepts behind this solution
With short, plain definitions in our glossary.
Frequently asked questions
Why do industrial AI projects stall at pilot?
The common cause is the data rather than the model: contextless tags, inconsistent naming and stale data keep a model from being reused on other lines and plants. Modeling data at the source reduces that barrier.
Why is a semantic layer needed?
It makes metric logic central and reusable, so the same metric is calculated the same way in every dashboard and reports stay consistent.
What is MCP and why does it matter?
MCP (Model Context Protocol) is the open protocol that lets AI applications connect to external tools and data sources in a standard way. In industry, what matters is which data agents can reach and under what governance.
Which use case should we start with?
Pick a scenario whose data is already collected and whose impact is measurable, such as analyzing downtime reasons and OEE losses. Starting small and replicating with templates is more sustainable.
Let’s plan your project together
Share your goals and current systems and we will shape the right approach together.