Evaluation Playbook — 2026 · Page 2 of 2

Evaluate, Benchmark,
and Build Your Case

Parts IV–VII: The scoring framework, Timebase benchmark, business case tools, and 30-day action plan.

← Back to Page 1: The 5 Failures & Warning Signs

Part Four

The Evaluation Framework

Eight criteria for selecting an industrial historian

Selection Framework

The Eight-Criteria Evaluation Framework

Not all historian features carry equal weight for every deployment. The framework below is designed for DX teams evaluating historians across both current operational requirements and future digital capabilities. Weight each criterion according to your organization's priorities, but do not ignore any! The ones that seem least relevant today are often the ones that block a project two years from now.

Score each vendor 1–5 per criterion, multiply by your weight, and total. Any vendor scoring below 3 on Criterion 6 (Licensing) or Criterion 7 (AI Readiness) should be viewed with significant caution regardless of total score.

Criterion Weight What to Evaluate & Ask Red Flags
Data Acquisition & Protocols 🔺 High OPC UA, MQTT, & SparkplugB native or via connector. Store-and-forward buffering for network outages. Collection rates and guarantees at scale. OPC DA still in use & OPC UA not native. No MQTT or SparkplugB support. Requires 3rd-party middleware for all connectivity.
Storage & Compression 🔺 High Lossless vs. lossy compression — understand the trade-off fully. Can full-resolution raw values be stored? At what cost? Lossy compression only, no raw-value storage option. Proprietary binary without an open export path.
Reliability & Redundancy 🔺 High Active-active vs. active-passive architecture. Data loss during failover: quantify RPO exactly. Store-and-forward at the collector level. Active-passive only, data gap on failover with potential synched file corruption. No collector-level store-and-forward. Vendor cannot quantify RPO/RTO.
Data Access & Integration 🔺 High REST API: documented, versioned, concurrent-query tested. Real-time push via WebSocket or MQTT. Polling only, no event-driven push. Proprietary SDK required for all access. No documented public REST API.
Trending & Analytics Medium Real time data trending with solid period over period comparison capabilities. Adhoc trending usable by OT engineers without coding or writing scripts. Proprietary client only for trending. No export to open formats. No real time trending or 'live mode'.
Licensing & Total Cost of Ownership 🔺 Critical Per-tag vs. unlimited. Model the 5-year scenario at 3× current tag count. Annual maintenance as % of license. Cost structure at scale: 50K, 100K, 500K+ tags. Per-tag cost above $2/tag/year at scale. "Free tier" with hidden caps. Annual maintenance above 20% of license cost.
AI & Next-Gen Readiness Medium (growing) Native MCP server for AI agent / LLM connectivity. REST + WebSocket for real-time AI workflow triggers. MQTT/SparkplugB for UNS participation. No MCP server or AI agent roadmap. No MQTT, cannot participate in a UNS.
Deployment Flexibility Medium-High Docker container support. Windows AND Linux compatibility. Cloud-connected edge deployment. Windows-only. No container support. Requires dedicated on-premises server hardware.

Part Five

The Modern Benchmark

Timebase Historian against the eight criteria

Reference Architecture

Timebase Historian: The Modern Benchmark

Every evaluation needs a benchmark. For this guide, we use Timebase Historian (not because it is the only modern option) but because it addresses all five systemic failures documented in Part I and meets all eight evaluation criteria in Part IV, while eliminating the cost barrier that has historically made historian decisions difficult.

Data Acquisition and Protocol Coverage

Timebase supports OPC UA natively, with MQTT and SparkplugB built into the core platform. This positions it directly in the Unified Namespace architecture where a central MQTT broker serves as the integration backbone and historians subscribe to relevant topics. For brownfield environments, OPC connectivity is available or Timebase provides connection to the Ignition driver utilities via an Ignition by Inductive Automation data collector. Store-and-forward buffering is included at the collector level, ensuring network interruptions do not produce data gaps in the archive.

Licensing and Total Cost of Ownership

This is where Timebase makes its clearest statement. Timebase Historian is completely free. Not free with a tag cap. Not free for a trial period. Not free for a "starter" feature set. Free permanently, unconditionally, for any number of tags, any number of datasets, any number of users, in any deployment environment.

Free. Forever. No Exceptions.
No Tag Limits. No Feature Tiers. No Maintenance Fees.

The implications for a DX program are significant. Licensing cost is eliminated as a factor in data collection decisions. Tag counts can grow to match operational needs without budget conversations. Every smart instrument diagnostic tag, every IIoT data stream, every digital twin feed can be collected as a matter of operational design, not procurement negotiation.

AI and Next-Gen Readiness

Timebase ships with a native MCP (Model Context Protocol) server, one of the only historians in the market to do so. MCP is the open standard that enables AI agents and large language models to connect to tools and data sources through a standardized protocol. With an MCP server in the historian, AI agents can query operational data directly, without any custom middleware.

An AI agent with access to the Timebase MCP server can answer questions like:

  • "What was the average temperature on Line 3 during last week's second shift?"
  • "How does my tag coverage compare across all pump skids? Highlight the gaps."
  • "Show me the five most volatile tags in the past 24 hours."
  • "Flag any tags that went out of their configured operating range between midnight and 6 AM."
  • "Where are my stale data or poor data quality issues?"

These are production capabilities available in the current release. No competitor in the industrial historian market offers more robust or out-of-the-box ready native MCP server support.

For DX Leaders

Every AI initiative in your organization that touches operational data will eventually need to access the historian. A historian with native MCP support lowers the barrier for every subsequent AI use case — it is a force multiplier across your entire AI roadmap.

Capability Timebase Historian Typical Legacy Historian
OPC UANative, no additional license or middlewareNative in modern versions; older deployments OPC DA-primary
MQTT / SparkplugBNative, UNS-ready out of the boxRequires add-on module or third-party middleware
Store-and-forwardIncluded at collector level, zero-gap replayVaries; often an add-on or absent
CompressionLossless by default; full-resolution archiveLossy/exception-based by default; configurable but storage-sensitive
Active-active redundancyIncluded; both nodes collect simultaneouslyActive-passive typical; data gap on failover
REST APIFully documented, concurrent-query capableVaries; commonly proprietary SDK required
WebSocket pushNative, sub-second from collection to consumerPolling typically required; WebSocket uncommon
MCP server (AI agents)Native, ships with the productNot available in any incumbent product
Docker / Linux supportWindows + Linux; Docker-nativeWindows-only in most legacy products
LicensingCompletely free; unlimited tags; no tiersPer-tag; count-based tiers; annual maintenance

Part Six

Building the Business Case

Organizational buy-in, TCO modeling, and risk mitigation

Decision Framing

Framing the Replacement Decision

The technical case for a modern historian is usually straightforward. The organizational case, that is getting leadership alignment and budget approval for a migration, requires a different framing.

Frame It as Infrastructure Modernization

Historian replacements framed as "ripping out the old system" face the highest resistance. Reframe the initiative as infrastructure modernization; the same category of investment as network upgrades, cloud migration, or MES upgrades. The question is not "should we replace the historian?" but "is our data infrastructure ready to support our DX roadmap?" Make a conscious decision to start adding new tags to the new solution, even if complete replacement will take time. Best to start with momentum to the new architecture than continue to sink capital into legacy solutions.

Quantify the Current Historian's True Cost

Build a five-year total cost of ownership model. Include:

  • Annual licensing and maintenance fees: current and projected as tag count grows at your DX roadmap pace
  • Internal labor: IT/OT staff time for administration, upgrades, and troubleshooting
  • External services: vendor professional services, upgrade projects, custom integrations
  • Shadow IT: estimated cost of workaround infrastructure and the engineering labor to maintain it
  • Opportunity cost: projects delayed or descoped because of historian limitations (quantify at least two)

This number is almost always larger than organizations expect. The licensing line item is visible. The labor, services, and opportunity costs are not — but they are real, and they compound year over year.

Use a Side-by-Side Pilot

The lowest-risk migration path is a parallel pilot: deploy the candidate historian alongside the existing system for 60–90 days, collecting the same data from the same sources. This approach:

  • Validates the new historian's performance and reliability without production risk
  • Gives engineers and operations staff time to evaluate the new interface
  • Produces side-by-side data for quality and completeness comparison
  • Creates organizational confidence before the cutover decision

With Timebase, the pilot has zero licensing cost. The evaluation is of the technology, not a commitment to pay for access to it.

Address the Migration Risk Directly

Leadership resistance to historian replacement is most often rooted in risk aversion. Address it with a structured risk mitigation plan:

  1. Parallel operation: both historians run simultaneously, eliminating single-system risk during migration.
  2. Data validation protocol: documented comparison confirming parity before cutover.
  3. Data conversion: tools already exist (thanks to integrators like Corso Systems) to migrate AVEVA PI, Wonderware, and Canary data archives to Timebase Historian.
  4. Phased site rollout: start with a non-critical site or line to validate the migration process.
  5. Rollback plan: documented criteria and procedure for reverting if needed.
  6. Training and knowledge transfer: operations and IT staff trained before cutover.

Key Message for Leadership: This is not a question of 'if', this is a question of 'how much longer'

The risk of replacing a functional historian is manageable and time-bounded. The risk of not replacing an aging historian is structural and compounding. Every year the old system remains, the technical debt grows, the integration gap widens, and the AI readiness gap becomes harder to close. Digital transformation initiatives built on outdated data infrastructure are not transforming anything. They are building a digital facade over an analog foundation.

Part Seven

Your 30-Day Evaluation Plan

From diagnosis to deployment decision

Action Plan

Move from Decision to Deployment in 30 Days

Use this timeline to run a structured, low-risk evaluation that produces a defensible recommendation.

Week 1 Internal Alignment
  • Document the current historian's costs using the TCO framework. (Part VI)
  • Identify the top three DX initiatives currently blocked or constrained by historian limitations.
  • Define your evaluation criteria weights using the framework from Part IV.
  • Identify the pilot environment: a non-critical line, site, or application.
Week 2 Vendor Evaluation & Pilot Setup
  • Download and install Timebase Historian in your pilot environment.
  • Connect Timebase to an OPC UA data source (or MQTT broker) and verify collection.
  • Test the REST API and WebSocket push against a sample downstream consumer (dashboard, BI tool, or script).
  • Evaluate the MCP server with an AI agent or LLM tool of your choice.
Week 3 Parallel Operation
  • Run Timebase alongside the existing historian, collecting the same data.
  • Compare data quality, completeness, and latency between systems — specifically examine any periods where the legacy historian would have triggered compression.
  • Benchmark API and WebSocket performance against your integration requirements.
  • Evaluate trending capabilities with your process engineering team.
Week 4 Decision & Planning
  • Score all evaluated vendors against the eight criteria.
  • Build the five-year TCO comparison using the cost model from Part VI.
  • Develop the migration phasing plan (lean into consultants and integrators that have shown their expertise with Timebase) — which sites or lines migrate first and when.
  • Present findings and recommendation to leadership with the risk mitigation plan.
Conclusion

The Historian Decision Is a DX Foundational Choice

Digital transformation in manufacturing does not fail because the strategy is wrong. It fails because the data infrastructure cannot support it. The historian, the system at the center of your operational data architecture, is either an accelerator or a brake on everything your team is trying to build.

The five systemic failures documented in this guide are not hypothetical.

  • They are the lived reality of Process Controls Engineers waiting 15 minutes for a trend during a live process upset, and Application Engineers maintaining 2 AM batch extracts because the API cannot handle concurrent queries.
  • They are root causes that stay invisible because the historian silently discarded the 200 ms pressure spike before it was ever written to disk.
  • They are DX roadmaps whose ROI calculations were quietly destroyed by per-tag licensing fees that were never on the original procurement spreadsheet.
  • They are choosing what NOT to historize due to unnecessarily expensive tag licensing models.

The open source alternative is not a free option. Open source is the beginning of a product you must build, fund, and maintain internally, indefinitely. The decision looks different when the full scope of the build is on the table.

Timebase Historian was built to eliminate each of these failure modes: lossless data, native OPC UA and MQTT, active-active redundancy, open REST and WebSocket APIs, native MCP server for AI agents, Docker deployment, and a licensing model that ends the tag tax conversation permanently.

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