Differentia Consulting has spent over twenty years helping organisations understand one foundational truth about data: before you extract insight from it, you have to trust it.
Completeness. Accuracy. Consistency. These are the conditions that make data useful, whether it’s a sales pipeline in Qlik, a production ledger in SAP, or a logistics dataset flowing from JD Edwards. The discipline doesn’t change. The domain does.
There is now a domain that most organisations haven’t applied that discipline to; their own digital presence, websites, where data is king. And the cost of that gap is growing.
A new class of data consumer
AI agents, the systems behind ChatGPT, Claude, Perplexity and an expanding ecosystem of commercial AI tools, are becoming significant mediators of business discovery. They answer procurement questions. They surface service providers. They make product recommendations. They do so not by reading and interpreting copy, but by consuming structured, machine-readable entity data.
The standard for that data is Schema.org, being a shared vocabulary for describing organisations, products, people, services, locations, events and the relationships between them, expressed in JSON-LD and embedded in web properties. If an organisation’s Schema.org core data is incomplete, inaccurate or absent, the AI agent’s picture of that business is partial at best.
This is not a marketing problem. It is a data quality problem. And it sits in familiar territory for anyone who has managed enterprise data assets.
Applying BI discipline to digital structure
Through VISEON, Differentia Consulting has extended its data quality methodology into this domain. A VISEON assessment maps an organisation’s digital footprint against the Schema.org standard, and other frameworks, producing a structured view of what entity data exists, what is missing, and what is present but inaccurate or inconsistently expressed.
The output is presented as a visual footprint in Qlik: in an analytics dashboard that makes absence visible. Not just what the organisation has published, but the shape of what should be there; the entities, properties and relationships that would make the business fully legible to an AI agent.
For organisations familiar with data completeness reporting, the framing is immediately recognisable. The gap analysis is the same kind of exercise. The remediation logic is the same. The metrics for progress are the same. What’s different is that the asset being audited is the organisation’s public-facing structured data rather than an internal data warehouse.
The maturity progression
As with any data quality programme, the work follows a logical sequence.
Completeness is the entry point, establishing whether the required entity types and properties are present at all. Accuracy follows, verifying that what is declared correctly represents operational reality: correct addresses, accurate service descriptions, current personnel, valid credentials.
Once the hygiene baseline is established, the focus moves to utility, whether the data is expressed in a way that AI agents can act on in context, and then to diversity, ensuring coverage across the full range of entity types and relationship patterns relevant to the business.
This is a programme of work, not a one-time audit. Schema standards evolve. Agent capabilities evolve. Competitive data landscapes shift as more organisations recognise the imperative and act on it.
Why being AI discoverable matters now?
The urgency is commercial. AI-mediated discovery is an increasing share of how businesses are found, evaluated and selected, particularly in B2B contexts where the volume of available options makes AI-assisted shortlisting attractive to buyers.
The organisations that establish clean, complete, machine-readable entity data now are building a structural asset. The ones that don’t are, in effect, invisible, digitally obscure, to a growing class of decision-support system, regardless of how strong their traditional SEO position is or the investment in Ad spend.
For Differentia Consulting clients, the entry point is straightforward: the same data quality discipline that has governed twenty years of BI engagements, applied to a domain where most organisations are starting from a much lower baseline than they realise. We are here to help.
Investors and other organisations that need to know about your organisation, it’s capabilities and digital footprint, turn to AI for answers. These answers are surfaced by services provided by global data aggregators who get there information from your structured data. By omission they cannot find you. VISEON platform resolves that and with Qlik you can build out your digital presence to match your corporate presence and visibility needs.
see: our SMARTER.SEO VISEON.IO offering for more details.
