The data platform market has never been larger or more confusing. Every vendor promises a single source of truth, seamless integration, and actionable insights. This guide helps you evaluate what data platform is actually right for your organisation - whether you're a public sector supplier, investor, or an advisory team.

The data platform market has never been larger or more confusing. Every vendor promises a single source of truth, seamless integration, and actionable insights. This guide helps you evaluate what data platform is actually right for your organisation - whether you're a public sector supplier, investor, or an advisory team.

Choosing a data platform is one of the more consequential technology decisions an organisation makes, and one of the most commonly made badly. The consequences of a poor choice are not always visible immediately, but they compound over time: data that cannot be trusted, analytical workflows that are difficult to maintain, and a growing gap between what the platform promised in the sales process and what it delivers in practice.

Choosing a data platform is one of the more consequential technology decisions an organisation makes, and one of the most commonly made badly. The consequences of a poor choice are not always visible immediately, but they compound over time: data that cannot be trusted, analytical workflows that are difficult to maintain, and a growing gap between what the platform promised in the sales process and what it delivers in practice.

The market for data platforms has expanded significantly over the past decade. The combination of cloud infrastructure, declining storage costs, and growing organisational appetite for data-driven decision-making has produced a vendor landscape that is large, varied, and genuinely difficult to navigate. Capabilities that were once the exclusive domain of large enterprises are now available to organisations of almost any size. But the number of options has made evaluation harder, not easier.

The market for data platforms has expanded significantly over the past decade. The combination of cloud infrastructure, declining storage costs, and growing organisational appetite for data-driven decision-making has produced a vendor landscape that is large, varied, and genuinely difficult to navigate. Capabilities that were once the exclusive domain of large enterprises are now available to organisations of almost any size. But the number of options has made evaluation harder, not easier.

This guide sets out a practical framework for evaluating data platforms, covering the capabilities that genuinely differentiate useful tools from impressive-sounding ones, and the questions that cut through marketing claims to what matters in practice.

This guide sets out a practical framework for evaluating data platforms, covering the capabilities that genuinely differentiate useful tools from impressive-sounding ones, and the questions that cut through marketing claims to what matters in practice.

Start with use case, not features

The most common mistake in data platform evaluation is leading with a feature checklist rather than a clear statement of the use case the platform is meant to serve. This matters because different use cases have genuinely different requirements, a platform optimised for real-time operational analytics has a different architecture from one built for historical market intelligence, which is different again from one designed for self-service reporting.

The most common mistake in data platform evaluation is leading with a feature checklist rather than a clear statement of the use case the platform is meant to serve. This matters because different use cases have genuinely different requirements, a platform optimised for real-time operational analytics has a different architecture from one built for historical market intelligence, which is different again from one designed for self-service reporting.

Before evaluating any platform, an organisation should be able to answer three questions clearly:

Before evaluating any platform, an organisation should be able to answer three questions clearly:

1. What decisions will this platform inform, and on what timescale?
2. Who will use it, and what is their technical capability?
3. What does success look like in twelve months - what will people be doing differently because this platform exists?

These questions discipline the evaluation process by establishing criteria that are specific to the organisation's situation rather than generic to the category. A platform that scores well against generic criteria but poorly against specific ones is the wrong platform, regardless of how it performs in analyst rankings.

These questions discipline the evaluation process by establishing criteria that are specific to the organisation's situation rather than generic to the category. A platform that scores well against generic criteria but poorly against specific ones is the wrong platform, regardless of how it performs in analyst rankings.

Data coverage and quality

For platforms that aggregate external data — market intelligence, procurement records, financial data, sector information — data coverage and quality are the foundational evaluation criteria. Everything else depends on them.

For platforms that aggregate external data — market intelligence, procurement records, financial data, sector information — data coverage and quality are the foundational evaluation criteria. Everything else depends on them.

Coverage refers to the breadth of sources a platform draws from. Gaps in coverage are gaps in the analytical picture, and they are often not visible until you try to answer a specific question and find that the relevant data is not there. Evaluating coverage requires testing the platform against the specific questions it is meant to answer, not just checking a list of connected sources.

Coverage refers to the breadth of sources a platform draws from. Gaps in coverage are gaps in the analytical picture, and they are often not visible until you try to answer a specific question and find that the relevant data is not there. Evaluating coverage requires testing the platform against the specific questions it is meant to answer, not just checking a list of connected sources.

Data quality is more nuanced than coverage and harder to assess in a demonstration. It encompasses several distinct dimensions:

Data quality is more nuanced than coverage and harder to assess in a demonstration. It encompasses several distinct dimensions:

• freshness (how current is the data?)

  freshness (how current is the data?)

• completeness (are there systematic gaps in certain fields or time periods?)

  completeness (are there systematic gaps in certain fields or time periods?)

• accuracy (does the data reflect reality?)

  accuracy (does the data reflect reality?)

• consistency (is the same entity represented the same way across all records?)

  consistency (is the same entity represented the same way across all records?)

The last of these, consistency, is particularly important for platforms aggregating data from multiple sources, where entity resolution and normalisation are prerequisites for reliable analysis.

The last of these, consistency, is particularly important for platforms aggregating data from multiple sources, where entity resolution and normalisation are prerequisites for reliable analysis.

The most reliable way to assess data quality is to bring a dataset you already know well to the evaluation and compare what the platform shows against what you know to be true. Discrepancies are informative about where the platform's quality investment has and has not been made.

The most reliable way to assess data quality is to bring a dataset you already know well to the evaluation and compare what the platform shows against what you know to be true. Discrepancies are informative about where the platform's quality investment has and has not been made.

Analytical depth vs data access

There is a fundamental distinction between platforms that provide access to data and platforms that provide intelligence built on data. Both are legitimate, but they serve different purposes and should be evaluated against different criteria.

There is a fundamental distinction between platforms that provide access to data and platforms that provide intelligence built on data. Both are legitimate, but they serve different purposes and should be evaluated against different criteria.

Data access platforms — portals, aggregators, and data lakes — surface records in searchable or queryable form. Their value is in breadth and freshness of coverage. The analytical work is done by the user, not the platform.

Data access platforms — portals, aggregators, and data lakes — surface records in searchable or queryable form. Their value is in breadth and freshness of coverage. The analytical work is done by the user, not the platform.

Intelligence platforms go further: they apply analytical logic to the underlying data to produce insights that answer specific commercial questions directly. Rather than showing a user a list of procurement notices, an intelligence platform might show them which contracts in their category are approaching renewal, ranked by value and earliest engagement opportunity. The underlying data is the same; the analytical layer transforms it into something directly actionable.

Intelligence platforms go further: they apply analytical logic to the underlying data to produce insights that answer specific commercial questions directly. Rather than showing a user a list of procurement notices, an intelligence platform might show them which contracts in their category are approaching renewal, ranked by value and earliest engagement opportunity. The underlying data is the same; the analytical layer transforms it into something directly actionable.

For organisations with strong in-house analytical capability, data access platforms can be appropriate, the analytical layer is built internally on top of the data. For organisations where the goal is to put commercial intelligence in the hands of people who are not data specialists, an intelligence platform that does more of the analytical work within the tool produces higher adoption and better commercial return.

For organisations with strong in-house analytical capability, data access platforms can be appropriate, the analytical layer is built internally on top of the data. For organisations where the goal is to put commercial intelligence in the hands of people who are not data specialists, an intelligence platform that does more of the analytical work within the tool produces higher adoption and better commercial return.

Integration and workflow fit

A platform that exists as an analytical silo, valuable in isolation but disconnected from the workflows where decisions actually happen, will see lower adoption and less commercial return than one that fits naturally into existing processes.

A platform that exists as an analytical silo, valuable in isolation but disconnected from the workflows where decisions actually happen, will see lower adoption and less commercial return than one that fits naturally into existing processes.

Integration capability matters at two levels. Technical integration covers the platform's ability to connect with other systems in the organisation's stack, CRM platforms, ERP systems, communication tools, and reporting infrastructure. The more readily data can flow from the analytics platform into the tools people already use, the more likely insights are to influence decisions rather than sit in a report nobody reads.

Integration capability matters at two levels. Technical integration covers the platform's ability to connect with other systems in the organisation's stack, CRM platforms, ERP systems, communication tools, and reporting infrastructure. The more readily data can flow from the analytics platform into the tools people already use, the more likely insights are to influence decisions rather than sit in a report nobody reads.

Workflow integration is a broader concept that covers how the platform fits into the day-to-day habits of the people who will use it. Alerting features that push relevant information to users rather than requiring them to log in and search; export formats that match downstream consumption; and interface design that makes the most common tasks efficient rather than requiring navigation — these factors often determine adoption rates more reliably than the sophistication of underlying analytical capabilities.

Workflow integration is a broader concept that covers how the platform fits into the day-to-day habits of the people who will use it. Alerting features that push relevant information to users rather than requiring them to log in and search; export formats that match downstream consumption; and interface design that makes the most common tasks efficient rather than requiring navigation — these factors often determine adoption rates more reliably than the sophistication of underlying analytical capabilities.

Scalability and total cost of ownership

Platforms that are affordable and performant for today's data volumes and user numbers may become expensive or slow as the organisation's requirements grow. Understanding how pricing and performance scale, and what the total cost of ownership looks like at two or three times current usage, is an important context for any platform decision.

Platforms that are affordable and performant for today's data volumes and user numbers may become expensive or slow as the organisation's requirements grow. Understanding how pricing and performance scale, and what the total cost of ownership looks like at two or three times current usage, is an important context for any platform decision.

Total cost of ownership is broader than the subscription fee. Implementation costs, training requirements, ongoing maintenance overhead, and the internal resource required to keep the platform current all contribute to the real cost of a platform investment. Platforms that are cheaper to subscribe to but more expensive to run and maintain are often more costly in practice than nominally more expensive alternatives with lower operational overhead.

Total cost of ownership is broader than the subscription fee. Implementation costs, training requirements, ongoing maintenance overhead, and the internal resource required to keep the platform current all contribute to the real cost of a platform investment. Platforms that are cheaper to subscribe to but more expensive to run and maintain are often more costly in practice than nominally more expensive alternatives with lower operational overhead.

Questions that cut through vendor claims

Platform evaluation processes are often dominated by demonstrations that showcase the best-case scenario with pre-configured data. The following questions are more likely to reveal real-world performance.

Platform evaluation processes are often dominated by demonstrations that showcase the best-case scenario with pre-configured data. The following questions are more likely to reveal real-world performance.

Question to ask:

Question to ask:

Can you show me this with our own data, not your demo data?

Can you show me this with our own data, not your demo data?

One of the easiest ways to assess a platform is to see how it performs using your actual data rather than a polished demonstration dataset. Demo environments are often curated to showcase the best possible outcomes, but real-world data is rarely that clean. Asking for a proof of concept using your own information helps reveal how the platform handles inconsistencies, gaps, and complexity in practice.

One of the easiest ways to assess a platform is to see how it performs using your actual data rather than a polished demonstration dataset. Demo environments are often curated to showcase the best possible outcomes, but real-world data is rarely that clean. Asking for a proof of concept using your own information helps reveal how the platform handles inconsistencies, gaps, and complexity in practice.

How is data quality maintained, and what happens when a source changes format?

How is data quality maintained, and what happens when a source changes format?

Data sources change constantly, whether through website redesigns, API updates, or changes to reporting standards. Understanding how a vendor maintains data quality and responds to source changes provides insight into the maturity of its data engineering processes. Strong answers should demonstrate robust monitoring, validation, and remediation workflows rather than relying on manual fixes after issues are discovered.

Data sources change constantly, whether through website redesigns, API updates, or changes to reporting standards. Understanding how a vendor maintains data quality and responds to source changes provides insight into the maturity of its data engineering processes. Strong answers should demonstrate robust monitoring, validation, and remediation workflows rather than relying on manual fixes after issues are discovered.

Who are your three longest-tenured customers in our sector, and can we speak to them?

Who are your three longest-tenured customers in our sector, and can we speak to them?

Customer references are valuable, but recent customers may only reflect a successful onboarding experience. Speaking with long-term customers helps determine whether the platform continues to deliver value over several years. It can also provide a more realistic picture of adoption, support quality, and the platform's ability to evolve alongside customer needs.

Customer references are valuable, but recent customers may only reflect a successful onboarding experience. Speaking with long-term customers helps determine whether the platform continues to deliver value over several years. It can also provide a more realistic picture of adoption, support quality, and the platform's ability to evolve alongside customer needs.

What does the implementation timeline and internal resource requirement look like?

What does the implementation timeline and internal resource requirement look like?

The subscription cost is only one part of the investment. Organisations should also understand the time, effort, and internal resources required to achieve value from the platform. Asking about implementation timelines, training requirements, and stakeholder involvement helps uncover the true cost and potential disruption associated with deployment.

The subscription cost is only one part of the investment. Organisations should also understand the time, effort, and internal resources required to achieve value from the platform. Asking about implementation timelines, training requirements, and stakeholder involvement helps uncover the true cost and potential disruption associated with deployment.

What does your data coverage look like for [specific source or category]?

What does your data coverage look like for [specific source or category]?

Broad coverage claims can be misleading if they do not apply to the areas most relevant to your organisation. Instead of evaluating coverage in aggregate, ask vendors to demonstrate their depth and accuracy within the specific sources, sectors, geographies, or datasets that matter most to your use case.

Broad coverage claims can be misleading if they do not apply to the areas most relevant to your organisation. Instead of evaluating coverage in aggregate, ask vendors to demonstrate their depth and accuracy within the specific sources, sectors, geographies, or datasets that matter most to your use case.

How does pricing change if our data volume or user count doubles?

How does pricing change if our data volume or user count doubles?

A platform may appear cost-effective today but become significantly more expensive as usage grows. Understanding how pricing scales with data volumes, user numbers, or additional functionality helps identify whether the commercial model supports long-term growth or introduces unexpected cost increases over time.

A platform may appear cost-effective today but become significantly more expensive as usage grows. Understanding how pricing scales with data volumes, user numbers, or additional functionality helps identify whether the commercial model supports long-term growth or introduces unexpected cost increases over time.

Choosing the right data platform

The right data platform is not the one with the most features or the most impressive demonstration. It is the one that most reliably answers the specific questions your organisation needs to answer, in the hands of the people who need to answer them, at a cost that reflects the value it delivers.

The right data platform is not the one with the most features or the most impressive demonstration. It is the one that most reliably answers the specific questions your organisation needs to answer, in the hands of the people who need to answer them, at a cost that reflects the value it delivers.

That evaluation requires clarity about use case before vendor selection, rigorous testing against real data and real questions, and honest assessment of total cost of ownership rather than headline pricing. Organisations that run this process well end up with platforms they actually use. Those that lead with features and demos end up with expensive subscriptions and adoption problems.

That evaluation requires clarity about use case before vendor selection, rigorous testing against real data and real questions, and honest assessment of total cost of ownership rather than headline pricing. Organisations that run this process well end up with platforms they actually use. Those that lead with features and demos end up with expensive subscriptions and adoption problems.

Apply this framework to Arcamus — bring your own data to a working session and we'll show you what coverage looks like for your category.

Apply this framework to Arcamus — bring your own data to a working session and we'll show you what coverage looks like for your category.