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Energy · head to head

Cognite Data Fusion vs Wood Mackenzie Lens

Cognite Data Fusion logo

Cognite Data Fusion

Energy

Industrial data platform that contextualises OT, IT and engineering data into an asset-centric knowledge graph

From
On request
Rated
-
Full reviewWebsite
Wood Mackenzie Lens logo

Wood Mackenzie Lens

Energy

Subscription research data and analytics covering oil, gas, power, renewables and mining assets

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Cognite Data Fusion the platform is only as good as the contextualisation work, and that mapping effort is a consulting project that regularly costs more than the first-year subscription.; Wood Mackenzie Lens licensing is per named seat and per dataset, so sharing a login across an analyst team breaches the contract and licensing the team properly multiplies the fee rather than adding to it.
  • They diverge on capability: Cognite Data Fusion covers Asset-centric data model, Wood Mackenzie Lens covers Upstream asset data.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Cognite Data Fusion and Wood Mackenzie Lens actually diverge.

Attributes where Cognite Data Fusion and Wood Mackenzie Lens differ
AttributeCognite Data FusionWood Mackenzie Lens
PlatformsWeb, CloudWeb, API

Identical on both: starting price (On request), pricing model (quote), free tier (No), user rating (Not yet rated), category (Energy).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Cognite Data Fusion

  • Asset-centric data model
  • Entity matching
  • P&ID parsing
  • 3D contextualisation
  • Cognite Atlas AI
  • Data workflows
  • Open SDKs
  • Extractors

Only in Wood Mackenzie Lens

  • Upstream asset data
  • Power and renewables
  • Metals and mining
  • Gas and LNG
  • Energy transition scenarios
  • Emissions data
  • Lens Direct
  • Scenario and portfolio tools

What people use each for

The jobs each tool is most often brought in to do.

Cognite Data Fusion

  • An operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one searchnot Wood Mackenzie Lens
  • A company standardising asset data across sites so an analytics team can build once and deploy to many plantsnot Wood Mackenzie Lens
  • An upstream operator building a production-optimisation model that needs sensor data joined to equipment metadatanot Wood Mackenzie Lens
  • A team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meantnot Wood Mackenzie Lens

Wood Mackenzie Lens

  • A corporate development team screening acquisition targets against independent asset economicsnot Cognite Data Fusion
  • A lender or investor testing a project sponsor case against third-party cost and production estimatesnot Cognite Data Fusion
  • A strategy team building long-term scenarios that need consistent cross-commodity assumptionsnot Cognite Data Fusion
  • A trading or origination desk that needs asset level supply data pulled into its own models through the APInot Cognite Data Fusion

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Cognite Data Fusion

  • The platform is only as good as the contextualisation work, and that mapping effort is a consulting project that regularly costs more than the first-year subscription.
  • Pricing is consumption-based and unpublished, so costs move with data volume and usage patterns you cannot forecast well until a year in.
  • It does not replace your historian, your ERP or your maintenance system, so Cognite is an additional recurring cost layered on systems you still pay for.
  • The reference base and data model lean heavily towards Norwegian and wider oil, gas and process industries; discrete manufacturing fit is weaker and the local partner network thinner outside energy.
  • Getting value out requires in-house Python and data engineering skill; organisations without a data team end up dependent on Cognite professional services for every new use case.

Wood Mackenzie Lens

  • Licensing is per named seat and per dataset, so sharing a login across an analyst team breaches the contract and licensing the team properly multiplies the fee rather than adding to it.
  • The content is analyst-modelled rather than operator-reported, so asset economics and reserve figures are estimates and can differ materially from what the owner of the asset publishes.
  • Modules are split by commodity and by region, and a question that spans gas and power, or Americas and EMEA, can require two or three separate subscriptions to answer.
  • Content refresh follows the research publication calendar, so individual asset records can lag a transaction or a project decision by a quarter or more, which matters on live deals.
  • Ownership passed from Verisk to Veritas Capital in 2023, so buyers signing multi-year terms are contracting with a private equity holding whose plans for pricing and packaging are not public.

Pricing, plan by plan

Cognite Data Fusion

On request
  • Cognite Data Fusion$undefined/year
    • Consumption-based pricing on data volume, compute and users
    • Available through cloud marketplaces with private offers
    • Contextualisation and onboarding quoted as a separate engagement

Wood Mackenzie Lens

On request
  • Lens subscription$undefined/year
    • Quoted per dataset and per named user
    • Regional variants of a module are priced as separate subscriptions
    • Lens Direct API access licensed on top of platform access

Which should you pick?

Choose Cognite Data Fusion if

  • You need asset-centric data model.
  • You work on Web, Cloud.
  • You also want entity matching.
Cognite Data Fusion review

Choose Wood Mackenzie Lens if

  • You need upstream asset data.
  • You work on Web, API.
  • You also want power and renewables.
Wood Mackenzie Lens review

Questions people ask

Is Cognite Data Fusion or Wood Mackenzie Lens better?
Neither clearly leads. Cognite Data Fusion starts at On request and Wood Mackenzie Lens at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cognite Data Fusion or Wood Mackenzie Lens?
Cognite Data Fusion starts at On request and Wood Mackenzie Lens at On request.
Does Cognite Data Fusion or Wood Mackenzie Lens run on more platforms?
Cognite Data Fusion runs on Web, Cloud. Wood Mackenzie Lens runs on Web, API.
What is Cognite Data Fusion best used for?
Cognite Data Fusion is most often used for an operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one search, a company standardising asset data across sites so an analytics team can build once and deploy to many plants, an upstream operator building a production-optimisation model that needs sensor data joined to equipment metadata, a team replacing a stalled internal data-lake project where nobody could reconstruct what the tag names meant. Of those, an operator that wants engineers to find the drawing, the sensor trend and the last work order for a valve from one search and a company standardising asset data across sites so an analytics team can build once and deploy to many plants are not what Wood Mackenzie Lens is typically brought in for.
What can Cognite Data Fusion do that Wood Mackenzie Lens cannot?
Cognite Data Fusion covers Asset-centric data model, Entity matching, P&ID parsing, 3D contextualisation. Wood Mackenzie Lens covers Upstream asset data, Power and renewables, Metals and mining, Gas and LNG.

Answered from the vendors’ own pages

Cognite Data Fusion: Is Cognite a historian?

No. It reads from historians such as PI System and adds context. You still need the historian underneath.

Wood Mackenzie Lens: Am I buying software or research?

Research. Lens is the delivery platform for Wood Mackenzie analysis, and the analysis is what the price reflects.

Cognite Data Fusion: How is it priced?

Consumption-based on data, compute and users, quoted per customer. Nothing is published.

Wood Mackenzie Lens: Who owns Wood Mackenzie?

Veritas Capital, which acquired it from Verisk in 2023.

Cognite Data Fusion: How long does a deployment take?

First useful graph in a few months is realistic; full plant contextualisation across a site is typically a year or more.

Wood Mackenzie Lens: Can a team share one licence?

No. Licences are per named user, and sharing breaches the subscription terms.

Cognite Data Fusion: Can we do the contextualisation ourselves?

Technically yes, the SDKs and matching tools are open, but most customers use Cognite or a partner for the first site.

Wood Mackenzie Lens: Can I get the data into my own models?

Yes, through Lens Direct, which is licensed separately from platform access.

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