Why This Guide?

Choosing a data historian isn't just about picking software. It's about weighing what matters most for your plant, including reliability, scalability, and how well it fits into your existing systems. With so many options on the market, a clear way to compare them side by side makes the decision much easier. 

This guide will help you get started evaluating, comparing, and choosing the right data historian for your operations. Topics include:  

  • What a data historian does and why it matters for industrial operations. 
  • How to evaluate and compare different options. 
  • Questions worth asking vendors and internal teams. 
  • What to look for to set your plant up for long-term success. 

The right historian collects your data reliably and gives you the foundation to make smarter decisions from it. 

What Is a Data Historian and What a Good Data Historian Should Do

A data historian is a software system built to collect, store, and organize time-series data from your plant floor so that data can be searched, analyzed, and acted on later. In industrial environments, that data is constantly generated by machines, sensors, and control systems, often faster than teams can act on it in the moment. Without a system to capture and organize it, valuable information gets lost, making it harder to troubleshoot problems, spot trends, or plan ahead. A data historian gives that information a home, turning raw plant floor activity into something operators, engineers, and analysts can actually use to run the plant more effectively. 

A strong data historian should be more than a storage system. It should act as the backbone of your data-driven operations. At a minimum, a best-in-class historian will: 

  • Capture high-speed time-series data reliably. Even under heavy load, with minimal data loss.  
  • Store large volumes efficiently and accessibly. Retain years of data without bogging down performance. 
  • Pull data in from your plant floor systems. Connect securely with DCS, PLCs, SCADA, and other data sources.  
  • Push data out to the tools your teams rely on. Feed CMMS, ERP, MES, and BI platforms so insights reach the people who need them.  
  • Enable real-time and historical analysis. Help engineers, operators, and managers spot trends and anomalies quickly.  
  • Provide user-friendly search and visualization. Make insights accessible to technical and non-technical users alike.  
  • Support data integrity through role-based access and encryption. Help protect your data as part of a broader security strategy.  
  • Scale across sites and teams. Grow with your operations without major rework.  
  • Lay the groundwork for advanced analytics. Support machine learning, predictive maintenance, and optimization down the line. 

With these capabilities in mind, the next step is putting them to use. Below, we'll walk through how to evaluate and compare different data historian options against this list. 

How to Compare Data Historian Options

When evaluating potential solutions, it helps to break your assessment into clear categories. This makes it easier to compare options side by side and reduces the risk of overlooking a capability that matters to your plant. 

Key Data Historian Features 

Data Capabilities

  1. Tag resolution. What's the smallest interval it can reliably capture, and can it measure fast-changing values with the precision your processes require?  
  1. Tag volume limits. How many tags can it support before performance drops?  
  1. Data storage. How does it store large volumes of time-series data, and what does long-term retention look like as that data grows?  
  1. Data quality. What safeguards are in place to protect against data loss, including buffering, backups, and redundancy? 

Integration 

  1. Supported protocols. Does it handle OPC UA, Modbus, BACnet, and other standards for connecting directly to your system?  
  1. Third-party connections and exports. Does it support MQTT, APIs, and other standards for sending data out to third-party tools?  
  1. Pre-built system connectors. Beyond raw protocol support, does it offer ready-made integrations with other tools or will those need to be designed?  
  1. Architecture type. Does it offer flexibility across on-premises, cloud, and hybrid deployments, so your architecture can evolve as your infrastructure or business needs shift? 

Security & Compliance 

  1. Role-based access. Can permissions be customized by role or user group?  
  1. Audit trails. Are key actions and changes logged for compliance and traceability?  
  1. Encryption. How is data protected both at rest and in transit? 

Usability 

  1. Interface for engineers vs. data analysts. Is it designed for day-to-day plant users as well as advanced analysts?  
  1. Search and visualization tools. How easy is it to pull up the right data or create trend charts?  
  1. Compatibility with machine learning and analytics tools. Does it provide additional value to the platforms your team already uses (or plans to use) for anomaly detection or advanced analysis? 

Support & Flexibility 

  1. Customization options. Can the historian adapt to unique workflows?  
  1. Training availability. Does the vendor provide onboarding and ongoing training for staff?  
  1. Onboarding process. How structured and clear is their implementation plan? 

Critical Questions to Ask During Data Historian Evaluation

Before committing, press vendors on the details. A good partner should answer these clearly and confidently. But vendor answers only tell half the story. It's just as important to check in with your own internal teams to make sure the solution fits how your organization and various departments operate. 

Vendor Checklists: Questions & Why It Matters 

  • What does a typical implementation look like?
    Reveals timeline, resources needed, and potential disruptions. 
  • How long does onboarding usually take?
    Sets expectations for training and adoption. 
  • Can we migrate data from our existing historian?
    Ensures continuity of historical records. 
  • What's your pricing model (by tag, user, license)?
    Helps you forecast total cost of ownership. 
  • Can you support multi-site installations?
    Critical for scaling across plants or geographies. 
  • How do you support customers post-launch?
    Clarifies support hours, service levels, and escalation paths. 

Internal Checklists: Questions & Why It Matters 

  • Who will actually use this system day to day and what do they need from it?
    Ensures the solution fits operators, engineers, and analysts. 
  • What's our current tag count, and how much room do we need to grow?
    Prevents choosing a system that's undersized for future expansion. 
  • What systems does this need to connect to (SCADA, DCS, MES, ERP, BI)?
    Confirms integration requirements are mapped out before you commit. 
  • What's our internal bandwidth for implementation and training?
    Sets realistic expectations for rollout timeline and resourcing. 
  • What compliance or security requirements do we need to meet?
    Ensures the solution satisfies IT, security, and regulatory standards from day one. 
  • What's our budget and how does it align with the vendor's pricing model?
    Avoids surprises when comparing total cost of ownership against internal constraints. 

Setting Your Organization Up for Success

Choosing the right data historian means finding a system with the right features, plus a partner and process that set your team up to get real value from it quickly. Knowing what strong partners and solutions look like matters, and so does recognizing the warning signs early, before they become costly problems down the road. 

Warning Signs

Inflexible licensing. Watch for solutions that nickel-and-dime you, such as charging extra for basic features, or that force you into a specific structure (like subscription-only) with no flexibility for how you want to buy.  

Steep learning curve with no training. Watch for systems that require heavy upskilling with little vendor support, since a powerful tool is only useful if your team can actually use it.  

Limited integration options. Watch for systems that can't connect smoothly with your critical platforms, since a historian that can't integrate is more of a silo than a solution.  

Weak support or long response times. Watch for vendors without clear service level agreements or fast escalation paths, since downtime is costly and you need a partner who responds quickly.  

An overbuilt system. An overbuilt system can add cost and complexity without adding real value, so prioritize a solution that scales with you rather than overwhelms you today. 

HanAra is Here to Help

Choosing a data historian involves more than checking off a feature list. The best solution is the one that aligns with your specific use cases, scalability needs, integration requirements, and user adoption goals. 

Your next steps: 

  • Clarify your needs. Define how you'll use the historian today and in the future. 
  • Compare carefully. Use the questions and red flags in this guide to evaluate your options side by side. 
  • Think long term. A good historian should scale with your plant, not hold it back. 

Need help evaluating your current setup? Looking for a recommendation tailored to your operations? We're here to help. Reach out and we'll share an Evaluation Worksheet Template to help you map your priorities, along with our Vendor Checklist template and Buyer Personas & Use Case Examples showing how others in your industry are getting the most out of their historian investments.