All-in-one analytics vs connected analytics stack: How to choose

When your analytics stack becomes hard to manage 

Analytics stacks rarely start as architecture projects. A team adds web analytics, then a CRM, campaign platforms, experimentation, BI, a data warehouse and, increasingly, AI tools. Each addition solves a real problem. Over time, the setup can create a different one: overlapping capabilities, conflicting numbers, manual exports and integrations that somebody has to maintain. 

Analytics, marketing operations, data and IT leaders often face the following question during a major renewal, platform migration, warehouse project or push to reduce software spend: Should they consolidate more work into one platform or keep specialist tools and connect them? 

Matomo’s Future of Web Analytics report shows both pressures at once. 42.3% of the 300 web analytics experts surveyed describe their current setup as at least somewhat fragmented. At the same time, 49% expect hybrid approaches combining multiple systems to help define the future of web analytics, and 41% consider integration with existing tools when choosing an analytics solution. 

To determine the approach that suits them best, teams need to look at where consolidation removes unnecessary work, where specialist tools give teams something they genuinely need, and whether data can move between the systems without creating another reporting problem. 

All-in-one analytics vs a separate tools stack 

In practice, “all-in-one analytics” means consolidating more of the analytics workflow in one platform instead of using separate tools for each job. Depending on the platform, that might include web analytics, conversion analysis, funnels, behavioural analysis, experimentation or other optimisation capabilities. The aim is to reduce the number of separate analytics tools teams need to manage, while systems such as CRM, BI, advertising, ecommerce or data warehouses can still sit elsewhere. 

By contrast, a separate tools stack spreads more of the analytics workflow across specialist products. One tool might handle web analytics, another experimentation, another session replay and another product analytics.  

When those systems exchange data reliably with CRM, BI, warehouse or other systems, they form a connected stack. For example, a connected B2B stack might use web analytics for website behaviour, Salesforce or HubSpot for leads and accounts, Snowflake or BigQuery for central data, and Power BI or Tableau for cross-system reporting. Each system has its own job, while integrations, APIs or data pipelines move relevant data between them. 

Which criteria should you compare? 

The right level of consolidation depends on what your teams need from the stack and what they can realistically maintain. Compare the two approaches across the following areas. 

Area 1: Do you need specialist analytics capabilities? 

Start with the analytics workflows that matter most to you. If marketing needs acquisition and conversion reporting while product needs experimentation, replay and detailed behavioural analysis, check whether one platform covers all of these capabilities well enough. A broader platform reduces the number of tools only if its built-in capabilities are strong enough for the people who use them. If one team still needs an additional specialist product, the stack will remain mixed anyway. 

Practical check: List the analytics tasks each team relies on regularly. For an existing stack, note which tool currently handles each task, where capabilities overlap and where teams still need workarounds or specialist tools. If you’re choosing a stack from scratch, mark which tasks are essential and which need more advanced functionality. Then check whether one platform covers enough of those requirements to justify consolidation. 

Area 2: How much integration work can your team support? 

An all-in-one platform can reduce integration work when several analytics capabilities already use the same tracking setup, identifiers and data model. With a connected stack, more data has to move between systems, which means more APIs, connectors or pipelines to configure and maintain. 

Look beyond whether an integration exists. Check what data it actually sends, in which direction, how often it updates and whether important fields or historical data are missing. You may also need to align identifiers between systems, handle changes to schemas or APIs, monitor failed data transfers and decide who owns each connection. 

A connector that covers only part of the required workflow can still leave teams exporting data manually, reconciling records or maintaining custom integrations. 

Practical check: Map the systems that need to exchange analytics data. For each connection, note what data moves, in which direction, how often it updates, how records are matched, how failures are surfaced and who is responsible for maintaining it. 

Area 3: How will you manage governance across the stack? 

Fewer systems make it easier to manage permissions, retention settings and tracking configuration in one place. In a connected stack, those controls are spread across several tools, so teams need clear ownership of both the data and the rules around it. 

Define who can change tracking, create or edit conversion definitions, adjust retention settings and grant access. For important metrics, document which system is authoritative and what happens when another tool calculates the same metric differently. Changes to tracking or definitions should also have an owner and a clear approval process, so teams can trace why a number changed. 

Practical check: Pick one business-critical metric, such as a qualified lead or ecommerce conversion. Identify where it is defined, which system is authoritative, who can change it, who approves those changes and which other tools reuse or recalculate it. Then do the same for access and retention settings across the systems that hold that data. 

Area 4: Can you move your data when you need to? 

Data portability affects how easily you can use analytics data outside the platform, connect it to other systems and change vendors later. Check whether you can access the underlying data you need, not just export finished reports. That may include raw or event-level data, historical data, conversion records, custom dimensions and other fields used in downstream reporting. 

Also look at how that data leaves the platform. APIs and exports should provide the required fields in usable formats, at the volume and frequency your workflows need. Retention limits, API restrictions or aggregated-only exports can become a problem when you want to build warehouse reporting, run an audit or migrate years of analytics history. 

Consolidation raises the stakes because more analytics history and workflows may depend on one platform. In a connected stack, portability matters continuously because data already needs to move reliably between systems. 

Practical check: Choose three situations: sending analytics data to BI or a warehouse, retrieving historical data for an audit, and replacing the analytics platform. For each one, check what data you can export, at what level of detail, in which format, how far back it goes and whether API or volume limits would prevent you from using it as needed. 

Area 5: How dependent are you on individual vendors? 

Consolidating several analytics workflows with one vendor can simplify procurement, support and administration, but it also concentrates more of your setup in one place. Replacing that platform later may mean rebuilding tracking, dashboards, reports, integrations, user permissions, historical comparisons and internal processes at the same time. 

A connected stack can make individual components easier to replace, but that depends on how tightly they are coupled. Custom integrations, shared identifiers, warehouse models, downstream dashboards or workflows built around one tool’s data structure can create significant switching work even when the rest of the stack stays in place. 

Assess how much of your analytics setup depends on each vendor rather than counting vendors alone. 

Practical check: Pick one major platform in your current or planned stack and imagine replacing it next year. List the tracking setup, integrations, dashboards, reports, historical data and downstream processes that would need to change. This gives you a much clearer view of the actual dependency than the number of vendors alone. 

Area 6: What will the setup really cost to run? 

Compare the total cost of running the stack, not just software licences. A broader platform may reduce the number of contracts, integrations and support relationships your teams have to manage, but you may also pay for capabilities that only some teams use. 

A connected stack can give you more control over which specialist tools you pay for, but the software bill is only part of the cost. Add the engineering time needed to build and maintain integrations, connector fees, warehouse and data-processing costs, implementation work, training, support, administration and the time teams spend reconciling data between systems. 

Cost can also change as usage grows. Check whether pricing is based on visits, events, users, data volume, seats, integrations or feature tiers, and model what happens if traffic or data processing increases significantly. 

Practical check: Estimate the annual cost of both setups across software, implementation, integrations, data infrastructure, support and internal maintenance. Include the pricing metrics that scale with usage, then model at least one higher-growth scenario to see which costs rise and where. 

Area 7: How easily can the stack change later? 

Analytics requirements rarely stay fixed. Teams may add a warehouse, replace a CRM, introduce a specialist experimentation tool, change consent infrastructure or give authorised AI applications access to analytics data. Each of those changes can affect tracking, identifiers, integrations, reporting and governance. 

A flexible setup lets you add or replace one component without forcing changes across unrelated parts of the stack. Look at whether systems expose stable APIs and exports, whether data models and identifiers can be reused elsewhere, and whether integrations are loosely coupled enough to change independently. Proprietary dependencies or workflows built around one vendor’s specific data structure can make even a small change much more expensive. 

Flexibility also matters when requirements grow. A setup that works for one website or one team today may later need to support more properties, markets, data volumes or reporting use cases. 

Practical check: Choose two or three changes your organisation could realistically make over the next two years, such as adding a warehouse, replacing the CRM or introducing a specialist analytics tool. For each change, map which tracking, integrations, identifiers, dashboards and processes would need to change. The fewer unrelated components you have to rebuild, the more adaptable the architecture is. 

At a glance: Which setup fits your situation? 

More consolidation is likely to fit if: 

• Your team has limited engineering or data resources for maintaining integrations. 

• One platform covers the analytics capabilities your teams need in enough depth. 

• You want to reduce overlapping tools, contracts and administration. 

• Most analysis happens within the analytics platform. 

A connected stack is likely to fit if: 

• CRM, BI or a data warehouse already plays a central role in reporting. 

• Different teams rely on specialist capabilities that one platform doesn’t cover well. 

• You want to replace individual components without rebuilding the wider stack. 

• Data portability and reducing dependency on one vendor are important requirements. 

Evaluating analytics vendors? 

The Web Analytics Buyer’s Guide provides a wider evaluation framework for capabilities, privacy, deployment, data ownership, integrations, security, support and long-term fit. 

Download the guide 

When a connected stack becomes a fragmented stack 

A stack becomes fragmented when teams lose track of what each system is responsible for, how data moves between them or why the numbers don’t match. In practice, fragmentation often shows up in the following ways: 

• The same conversion has different definitions in web analytics, CRM and BI. 

• Teams copy data between systems manually because the integration doesn’t cover what they need. 

• Campaign, visitor, account or customer identifiers can’t be matched where a joined view is required. 

• Two tools collect overlapping events, but nobody knows which implementation is authoritative. 

• A dashboard changes and analysts can’t trace whether the cause was visitor behaviour, tracking, source classification or reporting logic. 

• Exports are too limited to rebuild reports outside the vendor platform. 

• An integration fails and there is no clear owner for fixing or validating the affected data. 

Tool consolidation is one way organisations may try to reduce fragmentation, especially when several products cover the same capabilities or require separate integrations. But reducing the number of tools does not solve every underlying issue. Teams still need clear metric definitions, ownership and documented reporting logic. 

Before replacing tools, identify which problems come from the number of systems and which come from how those systems are managed. Duplicated capabilities or unnecessary integrations may point towards consolidation. Conflicting definitions, unclear ownership or inconsistent reporting processes need governance fixes regardless of how many tools remain. 

Does a single source of truth require a single platform? 

A single source of truth is often treated as an argument for putting everything into one platform. In practice, different systems usually own different types of data. 

Web analytics may be the source for visits and on-site behaviour, the CRM for lead and opportunity status, ecommerce systems for orders and refunds, and finance for recognised revenue. A warehouse or BI layer can combine selected data from those systems for cross-functional reporting. 

The practical job is to define which system owns each measure and how other tools are allowed to use it. For a joined dashboard to be reliable, teams need to agree on metric definitions, use consistent identifiers and document how data is transformed between systems. 

A useful ownership exercise: 

• Choose three metrics that regularly appear in leadership or campaign reporting. 

• Name the system that is authoritative for each metric. 

• Document where the metric is copied, transformed or recalculated. 

• Record who owns the definition and who approves changes. 

• Check whether downstream dashboards use the same definition or create another version. 

What makes an analytics stack genuinely connected? 

A connected stack lets data move between systems in a form the next system can actually use. That requires more than an available connector. Teams need reliable APIs or exports, identifiers that can link records, agreed metric definitions, predictable update schedules and clear access controls once data moves into another system. 

Test each connection against a real workflow. Can analytics data reach your warehouse or BI environment with the fields and level of detail analysts need? Can records be matched reliably to CRM data? Can teams see when a transfer fails, a field disappears or a metric definition changes? 

What to check	What it means in practice	Questions to ask
APIs and structured exports	Analysts and downstream systems need a reliable way to retrieve analytics data without relying on manual downloads. The available API or export should provide the fields, level of detail and formats required by the systems that consume the data.	What data can you retrieve? Is it available at report or event level? Which formats are supported? Are there rate, volume or historical-data limits? Does the API expose the information teams actually use in the interface?
Shared identifiers	Data from different systems can only be joined reliably when there is a consistent way to match the relevant records. That might involve user, account, order or other identifiers, depending on the workflow.	Which records need to be matched across systems? Where is each identifier created? Is it passed consistently between systems? Which systems are allowed to receive it? What happens when an identifier is missing or changes?
Warehouse and BI pipelines	If analytics data feeds a warehouse or BI environment, that connection becomes part of the organisation’s reporting infrastructure. Teams need to know whether the data arrived completely and on time, and whether transformations changed it along the way.	How often does the pipeline run? How are failed or incomplete transfers detected? Who monitors it? How are schema changes handled? Which transformations are applied before the data reaches dashboards?
Shared metric definitions	Moving data between systems does not guarantee that those systems calculate metrics in the same way. Definitions for conversions, active users, qualified leads and other important measures need a clear owner and documented calculation rules.	Which system is authoritative for each metric? Is the definition reused downstream or recalculated? Who can change it? How are definition changes communicated to teams using the data?
Access controls and governance	Once analytics data reaches another system, access to that copy is governed by the receiving system as well. Permissions therefore need to be reviewed across the whole data flow, especially when data can be linked to customers or accounts.	Who can view, query, export or combine the data in each system? Are permissions role-based? Who reviews access? Does moving the data create broader access than it had in the analytics platform?
MCP for AI-assisted workflows	MCP gives compatible AI applications a structured way to access data and tools exposed through an MCP server. It can sit alongside APIs, exports and warehouse pipelines when teams want authorised AI applications to query analytics data.	What data and tools does the MCP server expose? How is access authenticated? Which permissions apply? Which AI applications are allowed to connect? Does the workflow depend on definitions or context held in other systems?

For each connection, check what data moves, in which direction, how often it updates, how failures are surfaced and who owns it.  

How Matomo fits into a connected analytics stack 

Matomo covers a broad part of the web analytics workflow itself, including acquisition reporting, Goals, Ecommerce, Funnels, Heatmaps, Session Recordings and A/B Testing. Organisations can keep those workflows in Matomo to reduce unnecessary tool switching, while still using other systems for CRM, BI, warehousing or specialist workflows. 

For connected stacks, Matomo supports integrations with more than 100 technologies, including CMS, ecommerce platforms, tag managers and other tools. This includes integrations with platforms such as WordPress, Drupal, WooCommerce, Shopify and Google Tag Manager. Teams can also move Matomo data into other systems through the Reporting API, which returns reports in formats such as CSV, JSON and XML, or export reports and raw data to BI and warehouse tools through APIs and, for Matomo Cloud, the Data Warehouse Connector.  

The Matomo MCP Server adds another route for AI-assisted work. Authorised tools such as ChatGPT, Codex and Claude can connect to Matomo through a structured interface and query analytics data in natural language. Access still follows the permissions and authentication configured for the connection. 

For example, a B2B team could analyse website acquisition and behaviour in Matomo, export analytics data to its BI or warehouse environment, keep lead and opportunity data in its CRM, and use the Matomo MCP Server when an authorised AI application needs to query Matomo data. 

Choose the architecture around the work your teams need to do 

Consolidating more analytics workflows in one platform can reduce operational overhead. A connected stack gives organisations more freedom to keep different systems responsible for different jobs and change individual components, but it requires more discipline around integrations, definitions and governance. 

The decision should come down to the work behind the architecture. Check which capabilities teams use, where data needs to go, who maintains the connections, which system owns each metric and how difficult it would be to change the setup later. 

The Future of Web Analytics research suggests that hybrid setups will remain common. For many organisations, the practical aim is to keep the systems they need while making sure data moves reliably and supports decisions without creating another reconciliation exercise. 

See how Matomo fits into your analytics stack. Start your free trial and test which analytics workflows you can bring into one platform while keeping the tools and integrations your teams already rely on.