Benefits of using a DataOps platform with Tulip

What is a DataOps platform?

DataOps refers to integrated software solutions that streamline the collection, transformation, contextualization, and delivery of industrial data across operational technology (OT) and information technology (IT) systems.

Common DataOps platforms used by Tulip customers include:

• Litmus
• HighByte

Core Value Proposition

DataOps platforms serve as an industrial data infrastructure layer between shop floor assets and Tulip applications, handling data collection, transformation, and contextualization at scale.

Key benefits

1. Simplified machine connectivity

• Pre-built protocol libraries enable connection to diverse machines without custom development
• Tulip apps consume standardized data streams rather than managing individual machine integrations
• Benefit: Faster deployment and reduced technical complexity for app builders

2. Data harmonization and contextualization

• Transforms raw machine signals into meaningful, standardized business data before reaching Tulip
• Adds context at the edge by linking machine states to production context
• Benefit: Tulip apps receive analytics-ready data, not raw sensor streams

3. Enhanced root cause analysis

• Merges machine data and operator inputs from Tulip into unified datasets
• Enables correlation between equipment behavior and human actions or quality events
• Benefit: Deeper insights, such as scrap root cause analysis that links machine parameters to operator observations

4. Enterprise-scale analytics

• Combines Tulip operational data with business systems like ERP, SCADA, and QMS
• Delivers unified datasets to analytics platforms (e.g. Power BI, Tableau) and cloud data platforms (e.g. Snowflake, Databricks, Amazon Redshift)
• Benefit: Executive dashboards that connect shop floor performance to business outcomes

5. Edge processing and data efficiency

• Performs edge analytics to reduce data volume sent to cloud or Tulip
• Filters, aggregates, and contextualizes data locally before transmission
• Benefit: Lower bandwidth costs, faster response times, and improved real-time decision-making

6. Real-time machine health monitoring

• Streams live machine status to Tulip for immediate operator feedback
• Enables predictive maintenance workflows within Tulip apps
• Benefit: Operators receive actionable alerts on machine performance without leaving their workflow

7. Scalability across enterprise

• Creates reusable data pipelines that work across sites, lines, and machine types
• Standardized data models enable Tulip app rollout without site-by-site reconfiguration
• Benefit: Multi-site manufacturers achieve consistency and accelerate digital transformation

Architecture pattern

DataOps handles: Collection, transformation, storage, protocol translation
Tulip handles: Operator guidance, workflow execution, quality capture, frontline analytics

Considerations and trade-offs

When DataOps adds significant value

✅ Large-scale deployments (multiple sites or facilities)
✅ Heterogeneous equipment landscape
✅ Multiple consuming applications beyond Tulip
✅ Complex data transformation requirements
✅ Need for advanced edge computing
✅ Strict data governance requirements

When direct integration suffices

⚠️ Single facility with homogeneous equipment
⚠️ Simple data requirements (basic I/O)
⚠️ Tulip is primary or only consumer of machine data
⚠️ Limited budget for infrastructure
⚠️ Rapid proof of concept or pilot phase (you can add DataOps later)

Cost considerations

Additional investment required:

• DataOps platform licensing
• Edge hardware to support DataOps platform (gateways, servers)
• Implementation and configuration services
• Ongoing maintenance

ROI drivers:

• Reduces integration time for additional applications
• Eliminates redundant integrations (integrate once, consume many times)
• Scales faster across sites
• Lowers citizen developer skill requirements
• Improves data quality and consistency

Summary

Using a DataOps platform with Tulip creates a separation of concerns that enables:

• IT/OT teams to manage data infrastructure centrally
• Manufacturing engineers to build Tulip apps without data integration expertise
• Operations to access real-time, contextualized insights at the point of work
• Executives to gain enterprise visibility by connecting shop floor to boardroom

This architecture accelerates time-to-value while maintaining scalability and governance.

Related resources

Use the links below to find relevant resources for your machine monitoring solutions.

Knowledge Base

• Machine Monitoring - Foundational concepts and setup guide
• Machine Monitoring Solution Architecture - solution architecture fundamentals
• Machine Monitoring Architecture - Network topology and IT infrastructure