Getting GDPR-compliant analytics for France used to mean working through a detailed checklist, tweaking buried settings, and hoping you hadn’t missed anything.

Matomo’s new 1-Click CNIL compliance feature handles that automatically, so you can focus on your data, not your configuration.

The new feature helps you assess your current setup against CNIL consent exemption conditions, apply supported settings in one click, and see clearly what still needs your attention.

Reminder: you need to comply with CNIL requirements as soon as your audience includes people in France, even if your organisation isn’t French.

Why this matters

For many teams, the hard part isn’t choosing a privacy-first analytics platform. The hard part is configuring it correctly, documenting it clearly, and reducing the back and forth between marketing, implementation, and compliance team.

That changes with today’s release. Instead of reviewing settings one by one across different parts of Matomo, the 1-Click CNIL compliance feature reduces that friction at every stage:

• Fewer back-and-forths between marketing, development and privacy teams during setup.
• Less risk of misconfiguration, because the platform enforces the required settings rather than relying on a checklist.
• Easier to review for stakeholders and DPOs, with a clear compliance status per site and a self-assessment document built in.
• Faster to deploy across multiple sites, without repeating the same manual process each time.

This is especially useful for teams that need a faster and clearer path to a CNIL-aligned setup, without relying on scattered documentation or repeated manual reviews.

It’s also relevant if you’re evaluating Matomo against alternatives. CNIL compliance has historically required external setup support or a specialist. It no longer does.

What 1-Click CNIL compliance does

The feature lives at Administration > Privacy > Compliance. Select a site from the dropdown and Matomo runs a full assessment of your current configuration against CNIL requirements.

Each setting is assigned one of three statuses:

• Compliant: your current configuration meets the requirement.
• Non-compliant: the setting needs to be changed, and Matomo can apply it automatically.
• Unknown: Matomo cannot verify this from within the platform. It requires a manual step on your end.

Once you’ve reviewed the results, enable “Enforce compliance where possible” and click Save. Matomo applies all supported settings in one go. The compliance page also links directly to the knowledge base and to the self-assessment document, which CNIL now requires analytics providers to make available to their customers.

What changes when you enable it

When CNIL mode is enforced, Matomo applies a restricted configuration for the selected site or app. That can include:

This is what makes the feature useful in practice. It does not just tell you what the requirements are. It helps you apply the supported settings in one place and makes the remaining gaps visible.

What still requires a manual step

This is worth reading before you enable the feature:

The opt-out mechanism is not configured automatically. CNIL requires that visitors can object to audience measurement, and this must be embedded in your privacy policy as an iframe or link. The compliance page flags this with an “Unknown” status. The configuration guide walks you through the setup.

Any settings marked Unknown in your assessment also need manual review. Matomo cannot verify them from within the platform, and CNIL compliance cannot be confirmed until they are addressed.

Custom goals and events you create must stay within the three categories of events permitted by CNIL: presence on a page, use of a feature, and page performance statistics. Anything outside that scope falls outside the exemption.

Finally, this feature supports the compliance process. It does not replace legal review. If you operate in a regulated sector or manage compliance across multiple jurisdictions, your legal or privacy team should validate your configuration.

Where to start

It’s already available for superusers in Privacy > Compliance. The feature is live now on Matomo Cloud and available on Matomo On-Premise with version 5.9.0.

If you want to use Matomo in a way that may qualify for CNIL consent exemption when properly configured, start here:

• go to Administration > Privacy > Compliance
• select the relevant site
• review the assessment results
• enable Enforce compliance where possible
• complete the remaining manual steps, especially opt-out setup
• review the detailed self-assessment and knowledge base guidance for the full scope and restrictions 

The full configuration guide and self-assessment document are available in our knowledge base:

• our English configuration guide
• our French configuration guide
• our technical documentation for 1-Click CNIL Compliance, with the CNIL self-assessment
• our opt-out setup guide

These resources explain the detailed conditions, scope limitations, and remaining manual actions required for your setup.

Analytics that are easier to review, easier to configure, and easier to trust

Privacy-conscious analytics should not require a maze of manual checks.

With 1-Click CNIL Compliance, Matomo gives your team a more direct way to assess its setup, apply supported CNIL-aligned settings, and document what still needs to be done.

It is a practical step toward analytics that are easier to configure, easier to review internally, and easier to operationalise across teams.

Learn more about this new feature here: How do I configure Matomo without tracking consent for French visitors (CNIL exemption)?

Data privacy management software comes in many different forms. There are consent managers, mapping tools, breach response systems, vendor risk platforms, and more. 

This guide explains the main categories of privacy management software, what each type does and when to use it. We’ll also show you how to map your organisation’s needs to the right type of tool and highlight five tools that showcase different approaches to data privacy management

What is data privacy management software? 

Data privacy management software helps businesses properly handle personal data, protect user privacy and comply with privacy laws such as the GDPR and CCPA, as well as other global regulations. These platforms range from simple consent tracking tools to comprehensive systems for ensuring compliance across an entire organisation. 

Here are some of the standard features:

• Consent management: Collecting and recording user consent for data collection and processing activities. 
• Data subject request handling: Automating and tracking requests from people who want to access, correct or delete their data. 
• Granular tracking and auditing: Monitoring data flows across systems, providing a detailed record of who accessed what and when. 
• Policy automation and compliance templates: Simplifying compliance with privacy policy templates and automatic updates as regulations change. 
• Third-party risk management: Verifying that external tools and partners follow the same privacy and compliance standards. 
• Customisable reporting and alerts: Automated reporting and custom notifications to identify compliance risks early. 

The primary objective of these tools is to enhance data privacy protections and support compliance with requirements such as the ePrivacy Directive implementing laws (e.g., PERC (the UK), TDDDG (Germany), LSSI (Spain), TKG (Austria), the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Different types of data privacy management software

Data privacy management software is an umbrella term for platforms that address specific parts of compliance and data protection. Below are some of the most common types of privacy management software, along with their primary use cases. 

Matching your needs with the right privacy solution

Before comparing vendors, make sure you know which type of privacy management platform you’re looking for. Use the guide below to match your needs with specific tool capabilities and use cases. 

If you need to…

• Collect personal information online and prove lawful consent:
  • Consider consent management software to:
    • Update cookie consent banners.
    • Manage user preferences and Consent Mode.
    • Document audit trails.
• Inventory and secure personal data across your organisation:
  • Consider data mapping and inventory software to:
    • Scan databases/clouds.
    • Visualise data flows.
    • Support compliance audits.
• Implement new data processing activities or technologies:
  • Consider privacy risk assessment software to:
    • Conduct DPIAs (Data Protection Impact Assessments).
    • Assign risk levels.
    • Document mitigation plans.
• Respond to frequent privacy rights requests:
  • Consider data subject rights (DSR) management software to:
    • Automate intake and identity verification.
    • Update privacy notices.
• Handle breaches or other privacy incidents:
  • Consider breach and incident management software to:
    • Detect, log and assess the severity of events.
    • Support internal audit and compliance efforts.
• Assess and manage vendor risks:
  • Consider third‑party risk management software to:
    • Perform vendor risk assessments.
    • Monitor third-party compliance.
    • Centralise contracts and certifications.
• Protect individual privacy while working with large datasets:
  • Consider data anonymisation & tokenisation software to:
    • Mask and anonymise personal identifiers.
    • Support data minimisation principles.

Consent management software

Consent management software collects, records and manages user consent for data processing. The platforms display cookie consent banners or pop-ups that inform users about how their data will be used. Users can then choose which types of data collection they accept. 

The software stores these preferences and updates records if someone changes their settings. For example, if a user wants to withdraw their contact information, the system updates to reflect this change. 

It logs every consent action in accordance with relevant privacy laws, such as the ePrivacy Directive, which requires opt-in for all trackers and non-essential cookies.

Privacy-centric analytics platforms, like Matomo, also support Consent Mode. This means tracking is adjusted based on user choices.

Note: Consent is one lawful basis under GDPR. Some data processing activities may use other bases, such as contractual requirements or legal obligations.

Best for: Businesses that collect personal data from users online and need to maintain transparent records for compliance. 

Data mapping and inventory software

Data mapping and inventory software identify where personal data is stored and how it flows across systems. The platforms automatically scan databases, servers and cloud tools to locate personal information and map its journey within the organisation. 

This visibility is crucial for data governance. It helps businesses understand:

• What data they have
• Where it resides 
• How/With whom it’s shared

The system monitors who’s accessing data and why, giving compliance teams a clear picture of data handling practices. This helps them spot potential risks early on.

Best for: Organisations that need visibility into where personal data is stored and how it’s used across systems. 

Privacy risk assessment software

Privacy risk assessment software lets businesses identify and mitigate potential data breaches. The technology assesses how personal data is collected, stored and shared, and assigns risk levels accordingly. 

The software also helps businesses conduct Data Protection Impact Assessments (DPIA), which are a key requirement under GDPR. Other privacy laws globally also require data controllers to carry out privacy impact assessments. The system:

• Documents the purpose of data processing
• Assesses potential privacy risks
• Evaluates the necessity and proportionality of the activity
• Records mitigate measures 

Best for: Companies performing privacy impact assessments for new data processing activities or third-party technologies. 

Data subject rights management (DSR) software

DSR software automates Data Subject Access Requests (DSARs), such as when individuals request access to, correct or delete their personal information. The platform speeds up request intake, verifies identities and tracks progress to ensure responses meet legal timeframes. 

Each request is logged and managed through a central dashboard, reducing manual effort and helping businesses meet their applicable privacy law or other compliance obligations.

Best for: Businesses that regularly receive data requests and need to manage them quickly and accurately. 

Breach and incident management software

Breach and incident software detects, documents and responds to data breaches or security incidents. The platforms automatically log potential breaches, assess their severity and guide teams through the best way to address or mitigate the issue. 

Here are some of the common causes of data breaches: 

Data breach features allow organisations to respond quickly to these incidents, reducing damage and maintaining compliance. 

The software helps teams assess whether the incident requires regulatory reporting and prepares notifications for authorities and affected individuals. 

Best for: Organisations that need a reliable data breach and incident response process. 

Third-party risk management software

Third-party risk management systems evaluate and monitor the privacy practices of external vendors and partners. This means businesses can identify potential compliance gaps and reduce the risk of data breaches through their vendor networks. 

It uses automated questionnaires, risk scoring and continuous monitoring techniques to verify that third parties meet compliance standards. 

The platform also stores documentation, such as contracts, certifications, and audit reports to provide an up-to-date record of each vendor’s compliance status. Alerts immediately notify teams of changes or risks, so they can respond quickly. 

With Matomo’s OneTrust Tag Manager integration, teams can align tracking practices with their broader third‑party management processes and vendor risk workflows. 

Best for: Privacy operations that rely on external vendors and need to ensure they comply with data protection laws. 

Data anonymisation, pseudonymisation and tokenisation software

Data anonymisation software permanently and irreversibly removes or alters identifiers so that they cannot be linked back to an individual, making personal information unidentifiable. If effectively anonymised, datasets fall outside the scope of privacy laws, such as the GDPR. 

By removing or replacing identifiers with tokens and prioritising data minimisation, businesses protect personal information. 

Masking, encryption and tokenisation usually create pseudonymised data, which still counts as personal data under GDPR, even though it’s better protected

Best for: Organisations that analyse large datasets but must protect individuals’ identities and comply with privacy regulations. 

Top data privacy management software 

Here are five top data privacy solutions that help businesses collect, manage, and use data responsibly. 

1. Matomo: A privacy-first web and analytics system

Matomo is a privacy-first analytics platform that allows teams to capture and analyse 100% of user actions while respecting user privacy. Trusted by over one million websites across 190+ countries, it offers full data ownership, no third-party sharing and unsampled, accurate reporting.

Matomo captures traditional web metrics (like visits, traffic sources, and conversions) and can be configured to support compliance with strict global privacy laws, including GDPR, ePrivacy implementing laws, CCPA, PECR, HIPAA, and LGPD. 

Matomo On-Premise is one of the few analytics solutions that give teams full control over their data by allowing them to self-host their analytics data. And, it’s free.

Matomo’s web analytics dashboard

Many businesses use tools like Google Analytics without realising how much data they’re exposing to third parties. Unlike platforms that sample or externalise data, Matomo On-Premise provides complete data ownership and sovereignty. 

Best suited for: Businesses that need privacy-first analytics or open-source flexibility.

Key features:

• Built-in GDPR manager 
• Self-hosted or cloud-based deployment options with configurable compliance settings
• IP anonymisation and data masking features, other data minimisation and retention controls
• No data sampling
• No third-party data sharing
• Advanced segmentation, custom reporting, session recordings and heatmaps

Why it’s worth using:

• Integrates with cookie consent banners and most CMSs and CRMs
• Supports strict regulatory standards without sacrificing insight
• Complete data sovereignty, transparency and open-source flexibility

Try Matomo for free.

2. OneTrust: Privacy, risk and compliance management software 

OneTrust is a privacy management platform built for enterprises dealing with complex, global data protection requirements. The solution offers tools to manage privacy, risk, and governance at scale. 

OneTrust’s website details dashboard

Best for: Large organisations subject to strict compliance standards.

Key features:

• Comprehensive privacy, security and governance suite
• Consent management across multiple devices and jurisdictions
• Data mapping and third-party risk monitoring
• AI-driven data discovery and classification

Why it’s worth using:

• Enterprise scalability
• Strong support and integrations

3. Osano: Cookie compliance and consent management platform

Osano is a lightweight privacy solution focused on cookie compliance and consent management.

Osano’s privacy compliance dashboard

It offers automated consent banners, centralised tracking and real-time policy updates.

Best for: Small to mid-sized businesses that need a lightweight tool.

Key features:

• Easy-to-implement cookie banners and preference forms
• Real-time compliance status and policy change alerts
• Legal templates and pre-built settings for major laws (GDPR, CCPA)

4. TrustArc: Privacy and data governance platform 

TrustArc is a privacy solution that helps businesses map and monitor data flows and manage privacy risks. 

TrustArc’s data privacy laws dashboard (Image source: TrustArc)

It can also automate data inventories, risk assessments and compliance reporting. 

Best for: Mid- to large-sized businesses that require centralised oversight of data usage and privacy risk.

Key features:

• Inventory and flow visualisation
• Consent lifecycle management
• Templates for GDPR, CCPA and other frameworks

5. BigID: AI-powered data intelligence and sensitive data management platform

BigID is a data intelligence platform that uses machine learning to find and classify sensitive information across the organisation. It provides audit-ready DSAR reporting and automated DSAR workflows. 

BigID’s security dashboard (Image source: BigID)

Best for: Organisations that need to quickly locate and manage sensitive data at scale.

Key features:

• Automatic identification of PII, personal health information (or PHI, which is specific to US HIPAA law) and other regulated data
• Integrations with cloud platforms, SaaS apps and data lakes
• Custom privacy workflows for managing compliance and risk

What’s in store for data privacy in 2026? 

Data privacy is evolving rapidly, driven by stricter regulations, growing consumer expectations and the rise of AI. 

More countries are implementing privacy and AI laws, making global compliance far more complex. Here are a couple of examples:

• New EU and UK developments

Evolving privacy obligations in 2026 include the EU’s Digital Omnibus Act and the UK’s updated Privacy and Electronic Regulation Code (PERC). These frameworks are strengthening cookie consent rules, cross‑border enforcement, and AI accountability. 

• India’s Digital Personal Data Protection Act

Establishes a national framework for processing personal data, emphasising user consent, data minimisation and cross-border data transfer controls. 

• Expanding regulations

Several states in America have enacted their own privacy laws (like California’s CCPA and Virginia’s CDPA), each setting unique requirements for data collection, user rights and business obligations. Use the US State Privacy Legislation Tracker to keep up with changes. 

• AI accountability

The EU AI Act outlines regulations for AI systems. It entered into force in 2024 and is being implemented in phases, with initial provisions beginning in 2025 and the majority becoming enforceable in August 2026. Full compliance across all categories extends into 2027. 

Businesses should expect stricter disclosure requirements around:

• Communicating with customers regarding AI.
• Explaining how automated decisions are made.
• Documenting the data sources used to train AI models.

As a result of these tighter data regulations, we expect a continued increase in steep fines and public investigations into AI compliance. Regulators are already ramping up enforcement against major tech companies:

• Meta’s €1.2 billion fine as a result of an EDPB binding decision, which found violations in data transfers between the EU and the U.S.
• CNIL’s 2024 enforcement report shows how France’s data protection authority introduced a simplified sanctioning process to resolve minor cases quickly. It allows the CNIL to issue fines without a full committee review. 

Simplify data privacy compliance with Matomo

The right data privacy software will depend on your organisation’s specific needs, whether that’s consent tracking, data mapping, or incident response. This guide broke down the different categories of privacy management software to help you determine which one meets your business requirements.

Matomo supports compliance efforts by offering privacy-first analytics and integrations with platforms like OneTrust and Osano. 

Over a million websites choose Matomo because it delivers real insights — without compromising user privacy or data ownership 

Start your 21-day free Matomo trial today. No credit card required. 

This gap between what happened and why it happened is a common challenge in analytics. Standard dashboards surface general patterns but struggle to explain their context.

Custom dimensions offer a way to capture that missing context by attaching meaningful attributes to visits and actions. Details, such as user roles, content categories or subscription tiers, can transform raw activity into insight. 

This article explores what custom dimensions are, how they work in Matomo and how to set them up for clearer, more relevant reporting.

What are custom dimensions?

Custom dimensions are extra pieces of information attached to visits or actions in an analytics tool. Instead of relying only on default fields, such as page URL or traffic source, an analyst can store tailored attributes that matter to the business, then use them in reports for deeper insight.

Each custom dimension holds a name and a value. During tracking, the value is sent with the hit, and Matomo stores it alongside standard metrics. When reports run, Matomo groups and filters data by these values, which keeps the analysis accurate and consistent.

For example, a “subscription tier” custom dimension can record whether a visitor is on a Free, Pro or Enterprise plan. Another might capture “Content type,” such as article, video or product page. 

Custom dimensions can be set up to avoid personal data, which helps teams measure behaviour without tracking names or contact details. They also give analysts more say in how results are grouped in reports.

Common use cases

Custom dimensions are most useful when they add context that standard metrics miss. The examples below show how a few extra fields can turn log data into clear, practical findings.

Content performance tracking

Editors can tag visits with content author, category or content type. Reports then reveal which authors keep visitors engaged, which categories attract new audiences and whether articles, videos or product pages drive the most conversions.

User segmentation

Marketers often track subscription tier, user type or acquisition channel as custom dimensions. A tier such as Free, Pro or Enterprise can be followed through funnels to compare feature usage, upgrade rates and campaign performance with clear, transparent splits.

Ecommerce insights 

Stores can attach product attributes, such as brand or collection, along with the customer lifetime value band. That makes it easier to compare groups that spend more or stay longer, without storing personal data.

Technical tracking 

Teams can record a page load time band or an error type. Lining those values up against clicks and conversions shows where slow pages or repeating errors cause visitors to drop off.

Implementing and managing custom dimensions

Implementing custom dimensions in Matomo follows two stages: define the dimension, then send values with each relevant hit. A little planning at this point protects accuracy, performance and privacy later.

Step 1: Plan and create the dimension

Before creating a new dimension, teams decide whether it should describe an entire visit (visit-scoped) or a single interaction, such as a page view or event (action-scoped). 

In Matomo, administrators click: 

• The Administration page (cog icon)
• Measurables or Websites (depending on setup) in the left-side menu
• Custom Dimensions

They can then add a name, choose the scope and set the dimension as active. 

Because each site has a limited number of slots per scope and dimensions usually can’t be deleted, only deactivated, most teams reserve them for stable concepts, such as subscription tier or content group, rather than volatile labels.

Step 2: Track values from the site or app

For sites that use the JavaScript tracker, custom dimensions are attached to hits through the _paq queue.

This simple example records a visitor’s plan:

This call runs before the relevant trackPageView or event, and Matomo stores the value alongside the standard metrics for that visit or action.

Matomo Tag Manager offers another route and keeps tracking logic in one place. A variable first captures the value, like a data layer field that holds userRole. In the Matomo Configuration Variable, the Custom Dimensions section maps a dimension index to that variable. When a tag that uses this configuration fires, it sends the custom dimension value with the hit. In preview mode, teams can check the container and see those values in the request before publishing any changes.

Server-side systems, background jobs or mobile apps that call the HTTP Tracking API add custom dimensions with dimension{id} parameters, such as dimension2=Enterprise. Separate ranges support visit and action scopes, which help keep imports structured and efficient.

Step 3: Maintain and validate

After tracking is live, teams should watch reports and logs for empty rows or odd values.

Action dimensions can also take values from URLs or page titles through extraction rules. That approach cuts down on code edits and makes it clear where each value comes from.

Periodic reviews of active dimensions, along with consent and data minimisation settings, help ensure the implementation remains accurate, privacy-friendly and easy to extend.

How custom dimensions affect analytics and reporting

Once custom dimensions begin collecting data, they become part of Matomo’s standard reporting flow. 

Each dimension appears in dedicated reports where metrics are grouped by the stored values. It keeps analysis consistent and makes it clear how attributes (like subscription tier or content type) relate to behaviour and results.

Matomo processes visit-scoped and action-scoped dimensions differently:

• Visit-level dimensions describe the whole session, so reports summarise complete visits and conversions by each value. 
• Action-level dimensions attach to individual events, page views or downloads. In these reports, a single visit can contribute multiple rows, which helps expose detailed patterns, like which content category generated the most downloads or form submissions.

Custom dimensions can also feed Custom Reports. Analysts can add a dimension as a row or column, then filter by action type to focus on events, downloads or other specific interactions. This level of control, combined with clear scopes, supports accurate reporting and efficient workflows without obscuring how Matomo stores and processes the underlying data.

Privacy and compliance considerations

Custom dimensions can touch personal data, depending on implementation, so they form an important part of privacy and compliance work.

Under GDPR and similar laws, any field that can identify or single out a person needs a lawful basis, a clear purpose and suitable safeguards. In practice, this means planning dimensions with legal and privacy teams, as well as analysts.

Data minimisation, careful consent management and anonymisation are at the core of a privacy-forward and compliant implementation.

For custom dimensions, that often means recording stable, non-identifying values, such as subscription tier or an internal segment label, instead of names or email addresses. It also means linking records with a pseudonymous ID.

Data minimisation keeps each dimension tied to a single purpose. Retention rules and deletion processes then clear out values once they are no longer needed. Anonymisation and aggregation features in Matomo, including IP masking and optional cookieless tracking, help reduce risk further when combined with explicit consent where required. 

Planned this way, custom dimensions support accurate analysis while maintaining transparency, user control and respect for local privacy requirements.

Advanced tips and best practices

Reserve slots for stable attributes

Custom dimension slots are limited and difficult to restructure later, so teams should stick to stable ideas and stay away from ultra-granular values that will bloat tables.

Planning ahead of time and consulting the Matomo Measurement Plan can prevent performance issues or dimension limit frustrations down the road.

Avoid high-cardinality values

High-cardinality dimensions, meaning those that have a large or infinite number of unique values, increase archive time and slow down reporting. Teams should avoid using dynamic values for their dimensions, like time stamps or full URLs with parameters.

Keep names simple and consistent

Naming matters. Simple labels such as “Subscription tier” or “Content category” make reports easier to scan and make future changes less painful. 

A shared naming convention for events, custom dimensions and variables helps everyone understand what each field stores and how it shows up in dashboards and exports.

Troubleshooting common issues

Data not appearing in reports

The most common cause of missing data is scope or timing. The dimension must be: 

• Active
• Attached to the correct site
• Sent before trackPageView or the relevant event

Reports only show data for the selected date range, so very recent hits may appear first in real-time or visit logs before they reach aggregated reports.

“undefined” or “Value not defined” dimension values

Reports may display “undefined” or “Value not defined” as a dimension value.

This has two causes:

• The tracker tried to use a variable that wasn’t defined when setCustomDimension was called, so it’s received as “undefined”
• The dimension was sent with an empty string, so it displays as “Value not defined”

To fix this, teams should set the dimension before the pageview or event is tracked and confirm that the variable returns a real value (unless intentionally left empty).

Inconsistent formatting

Inconsistent formats fragment results. For example, recording “pro”, “Pro” and “PRO” as separate values inflates the number of rows and makes comparisons harder. 

Shared naming conventions and validation on the data layer keep values accurate and readable.

Implementation validation

Tag Manager preview mode and browser Network tabs can confirm that dimension{id} is included in a tracking request. Teams can verify values in the Visitor Log before relying on aggregated reports.

Teams should also review dimension values to make sure no personal data is sent and the consent setup blocks tracking where required.

Using custom dimensions in Matomo

Custom dimensions fit neatly into Matomo’s privacy-first approach. The platform combines 100% data ownership with options such as IP anonymisation, cookieless tracking and no data sampling, so added context does not come at the expense of privacy or accuracy.

Matomo treats custom dimensions as first-class fields in many features. They appear in dedicated reports, can act as rows or columns in Custom Reports and can filter or group Goals, Funnels and E-commerce reports. A “Subscription tier” dimension, for example, can break down goal completions by Free, Pro and Enterprise across landing pages, events and revenue, which gives teams a clear view of how each tier behaves.

Business Matomo Cloud plans come with 15 visit-scope and 15 action-scope dimensions, but Enterprise’s total amount is customisable. On Matomo On-Premise, administrators can extend the default five per scope to around 50 per scope through a console command with SSH access.

Custom dimensions as a foundation for trusted analytics

Custom dimensions close the gap between raw metrics and meaningful insight by restoring context to every visit and action. 

Instead of isolated page views and bounce rates, teams gain a structured view of how real audiences behave across content, products and technical experiences. 

In Matomo, this richer picture rests on a trusted base: accurate data with no sampling, an open-source platform used by more than 1 million websites and features that can be configured for GDPR compliance. 

For organisations that value privacy and control, Matomo’s custom dimensions provide a practical path to clearer, more confident decisions.

Download Matomo and run it for free on your own server or start your free Matomo Cloud trial today — no credit card required.

FAQ

What is a custom dimension?

A custom dimension is a field that stores extra context for a visit or action, such as user role or content category. It appears in dedicated Matomo reports.

When should I use custom dimensions vs. custom variables?

Custom dimensions are the modern way to track extra metadata in Matomo. Custom variables are deprecated and mainly kept for legacy installations.

What’s the maximum number of custom dimensions allowed in Matomo?

Matomo Cloud Business Plan supports 15 custom dimensions per scope (visit and action), so 30 in total. The Enterprise plan has customisable limits. 

On-Premise starts at 5 per scope and can be extended to at least 50 per scope using console tools with SSH access. 

Can you add custom dimensions retroactively in Matomo?

Custom dimensions record values from the time tracking is implemented. Earlier visits without that value remain empty in reports.

How do custom dimensions differ from segments in Matomo?

A custom dimension adds a new field to the dataset, like a membership tier. A segment filters existing data, such as visits from a specific region.

Are Matomo custom dimensions GDPR-compliant?

Custom dimensions in Matomo can be made GDPR-compliant when configured and governed correctly, following consent, data minimisation, limited retention and anonymisation of personal data where possible. You can learn more in our handy GDPR guide.

Can I use custom dimensions in Matomo’s mobile app analytics?

Yes. Matomo’s mobile SDKs for Android and iOS support tracking custom dimensions alongside events, screens and ecommerce actions.

With privacy-centric methods like server‑side tagging and consent-based event measurement, marketing teams can still capture the contextual and behavioural signals they need to connect with target audiences and personalise content.

This guide explores first-party cookies and their use in marketing. We’ll discuss their benefits, how they differ from third-party cookies and their value in web analytics workflows, especially in marketing attribution. Finally, we’ll highlight potential risks to keep in mind and best practices to implement first-party cookies while promoting data minimisation, transparency and trust.

What are first-party cookies?

First-party cookies are a type of tracking code that helps a site remember visitor preferences. They keep people signed in, preserve baskets between pages, recall language and region choices and connect page views so analytics data can count user sessions and attribute conversions. 

They also give marketing teams direct customer behaviour signals without third-party intermediaries, which improves reporting accuracy and aligns with GDPR and other privacy requirements. 

Unlike Google Analytics and most legacy solutions that were initially designed around cross-site tracking, privacy-first tools are built around direct user interactions. These ethical analytics platforms focus on extracting insights while still respecting user privacy.

How do first-party cookies work?

When someone visits your website, your domain creates a small text file (the “cookie”) through your site’s script or web server and stores it in their browser to remember them.

Then on future visits or pageviews, the browser returns the same value to your domain, allowing you to link actions throughout a user session or over a short time frame.

First-party vs third-party 

First-party cookies are set and read by the site a person visits. Third-party cookies originate from embedded domains and are used for advertising purposes. Here’s a breakdown of their characteristics: 

While first-party cookies raise fewer ethical and privacy concerns, they still handle personal data and must be managed carefully. If responsibly implemented, with a clear purpose and transparency, they can provide significant benefits.

Benefits of first-party cookies

First-party cookies provide marketing teams with the necessary signals while keeping data within the bounds a visitor has chosen. The result is better measurement, clearer choices and a stronger foundation for privacy.

Clear ownership

Unlike tracking cookies used by advertisers and other third parties, first-party cookies are created and set by the website owner. Since tracking stays on your site and is limited to the purposes you declare, it’s much easier to explain to users. Visitors know exactly who is collecting their data and why, which builds trust.

Consistent data quality

Because first-party cookies travel between a browser and the site a person is on, they work consistently across your own pages. 

Teams get steadier session counts, cleaner attribution within a domain and fewer gaps caused by blocked third-party requests. 

You can also define sensible expiries to keep user data fresh, which improves the quality of conversion and cohort analysis.

Transparency and control

First-party setups are easier to explain and manage. You can show plain-language descriptions and provide a preference centre that lets people opt in or out later. 

It is straightforward to rotate identifiers, shorten lifetimes and minimise what you store. Clear naming and documentation create an audit trail that your legal and security teams can review.

Compliance support

Regulators emphasise transparency, purpose limitation and choice. Under the GDPR, CCPA and similar frameworks, data shouldn’t be kept any longer than necessary for the purpose it was collected. What’s considered a “reasonable” cookie expiry period varies by jurisdiction and industry.

First-party setups can be configured to support GDPR and similar rules by defining specific purposes, collecting only the minimum data, honouring consent, and setting sensible expiries. 

Teams should:

• Document expiry decisions and align them with local regulator guidance.
• Review expiries regularly as part of compliance checklists and audits.
• Adjust retention periods when business needs or regulatory expectations change.

Data privacy considerations with first-party cookies

First-party strategies avoid the broad cross-site profiling that made third-party cookies contentious. But they still involve personal data, so they require careful handling and safeguarding. Reusing identifiers or failing to obtain consent can increase data privacy risks.

Consent management issues

Under GDPR and similar laws, non-essential cookies need a lawful basis. So analytics and personalisation require consent. As an organisation using first-party cookies, make sure to stick to the following best practices: 

• Describe purposes in plain language.
• Honour preferences on every page load.
• Ensure settings sync across subdomains.
• Use a consent management platform.

Data storage and security considerations

Limit what a cookie stores. Keep values short, avoid storing sensitive data in the browser and set sensible expiration times. 

Secure attributes such as HttpOnly and SameSite help reduce exposure. In your systems, restrict access, log reads and changes and retain data only as long as needed for the declared purpose.

Cross-device tracking limitations

First-party cookies are browser-bound. They don’t link phones, tablets and laptops without an account or server-side logic. You can either accept these limits or consider explicit, consent-based methods such as signed-in measurement.

Balancing personalisation with privacy

Considering data privacy when using first-party cookies also means: Start with data minimisation. Use the least intrusive signal that achieves the goal. Prefer session-level metrics when possible. 

And always keep in mind to provide value in return for consent and make controls easy to find. The aim is to create more positive user experiences that respect data subjects’ choices and privacy.

Potential for misuse despite being “first-party”

Without proper implementation, first-party strategies can still have privacy risks. Watch out for common pitfalls to avoid. These include:

• Overly long lifetimes: Don’t keep identifiers longer than necessary, it can feel invasive and increase risk. Many tools default to 30‑day lifetimes, but privacy‑focused teams usually adopt shorter, purpose‑bound limits in the 7 to 14 day range.
• Fingerprint‑like IDs: Avoid using highly specific or persistent identifiers that resemble device fingerprinting
• Undisclosed reuse or repurposing: Be transparent if you reuse cookie data across contexts or for new purposes. 
• Sensitive data combinations: Be cautious when combining cookie data with sensitive information or using it for profiling or targeting.
• Rights handling: Users have the right to access or delete, or object to how their data is used. Make sure these options are easy for them to find and act on.

To avoid these pitfalls and make sure your first-party strategy is effective, start with the best practices below.

First-party cookie implementation best practices 

Done well, first-party cookies can support useful analytics and respectful personalisation. Follow the steps below to maintain a clear, auditable and user-centric setup.

Consent mechanisms

To meet the GDPR’s lawful basis, make sure to implement user-friendly consent mechanisms. Keep in mind to:

• Group cookies by purpose.
• Make it easy to change or withdraw consent.
• Obtain consent before setting non-essential cookies.

Value exchange

Help visitors understand how their choices shape their experience. You can add explanatory text to your cookie banners, for example:

• “Analytics cookies help us improve site performance and page loading times.”
• “Session cookies keep you signed in and save the items in your shopping cart.”
• “Preference cookies load the site with your preferred language and display settings.”
• “Personalisation cookies tailor content and product recommendations to your interests and region.”

Data minimisation 

To minimise privacy risk and support compliance, make data minimisation a top priority. Its core principles include the following:

• Store only what is necessary.
• Default to short randomised user IDs.
• Align expiries with purpose.
• Use session cookies where possible. 
• Scope strictly necessary cookies to the smallest path or subdomain that still works.

Audits & cookie lifecycle management

To encourage accountability and avoid unchecked cookie growth, conduct regular cookie audits and follow the following approaches:

• Maintain a cookie inventory that includes the name, purpose, domain, expiry date and owner.
• Regularly review inventory and remove legacy entries.
• Apply Secure, HttpOnly and SameSite attributes to strengthen browser protection.
• Enforce data retention limits
• Rotate identifiers regularly.

Privacy by design principles

To align internal privacy controls with regulator expectations, its crucial to understand privacy as a core principle of ethical marketing and embed it deep into your analytics approach:

• Conduct DPIAs for new feature releases or data uses.
• Opt for privacy-enhancing technology.
• Implement role-based access controls.
• Log all reads and changes, and document decisions for review and future reference.

When implemented with these safeguards, first‑party cookies can support ethical analytics and improve customer relationships.

From tracking to trust

First‑party cookies foster more respectful and transparent relationships with customers. When aligned with jurisdictional requirements and industry best practices, they’re effective and ethical analytics tools.

If your team needs a privacy-first approach to analytics, consider Matomo. It’s an open-source platform that lets you easily configure privacy settings to align with GDPR, CCPA and other privacy laws.

Whether you choose on-premises deployment or Matomo Cloud, you have full control over your customer data and everything you need to interpret user behaviour while still respecting their privacy.

Download Matomo On-Premise completely free, or start a 21-day free trial of Matomo Cloud.

First, it’s important to understand who’s bound by the GDPR. According to the regulations, any business established in the EEA must comply, regardless of whose data it processes. The GDPR also applies to organisations located outside of the EEA if they target or monitor individuals within the EEA.

It’s easy these days to lose track of what data you collect and why. But ignorance is no defence. At the heart of demonstrating compliance and managing this complexity lies a crucial, yet often misunderstood, requirement: the Record of Processing Activities (ROPA).

This article explains what a ROPA is, who needs to keep one, common challenges and why it’s a strategic asset and foundational document for GDPR compliance and ethical data handling.

What is a ROPA (Record of Processing Activities)?

A ROPA (Record of Processing Activities) is a GDPR-mandated inventory (under Article 30) detailing processing activities under an organisation’s responsibility. It includes information such as:

• Purposes of processing
• Categories of data subjects and personal data
• Categories of recipients
• Transfers to third countries
• Retention periods
• Security measures

Understanding ROPA roles and purpose

A ROPA is an internal, living document that demonstrates an organisation’s commitment to data protection. With proper attention and regular updates, it becomes a vital tool for accountability and data transparency with authorities and the public. 

There are two main parties responsible for its creation and maintenance:

• Data controllers: These are organisations that determine the purposes and means of processing personal data. They bear the ultimate responsibility for ensuring compliance with data protection regulations.
• Data processors: These are external organisations or entities that process personal data on behalf of a data controller, acting strictly on their instructions.

GDPR obligations of data controllers

Data controllers must maintain a record that includes specific information about the personal data their organisations handle. Unless there’s a valid reason not to, this record should detail:

• Contact details: For the controller, any joint controllers, representatives, or Data Protection Officers (DPO).
• Purposes of processing: The reasons for collecting and using the data.
• Categories of data: The types of individuals whose data are processed and the categories of personal data collected.
• Recipients of data: The types of organisations or individuals who receive the data, including those in other countries or international organisations.
• International transfers: Details of any data transfers outside the EU, specifying the country and documented protections.
• Retention periods: The envisaged time limits for data erasure.
• Security measures: A general description of the technical and organisational security measures used to protect the data, as required by GDPR Article 32(1).

GDPR obligations of data processors

Data processors are also required to maintain a record of their processing activities. This record must include:

• Contact details: For the processor and for each controller they work for, including any representatives or Data Protection Officers (DPO).
• Processing activities: The types of processing operations carried out on behalf of each controller.
• International transfers: Details of any data transfers to other countries or international organisations, and any protections in place for these transfers.
• Security measures: A general description of the technical and organisational security measures used to protect the data.

Why is ROPA important?

A well-maintained Record of Processing Activities is a strategic asset for any organisation handling personal data. Beyond its legal mandate under Article 30 of the GDPR, here are a few more reasons why its importance is hard to overstate:

• It helps businesses understand their data: The record requires organisations to clearly document all personal data collected, the purpose of its collection, and its planned deletion and retention periods.
• It demonstrates accountability: Maintaining detailed records and strong documentation standards demonstrates an organisation’s commitment to data protection and GDPR compliance.
• It helps with risk management: Documenting data processing activities helps identify and resolve privacy risks, prevent breaches and ensure safer handling of personal data.
• It makes audits easier: A well-maintained ROPA simplifies data protection authority audits by demonstrating compliance with regulations.
• It builds trust: Responsible data handling and privacy practices help foster customer trust, brand loyalty, and a positive public image.

In short, a Record of Processing Activities helps businesses protect personal data, manage risks, and build trust with their customers. 

It also helps regulators assess compliance. GDPR’s emphasis on accountability through record-keeping set a global standard for privacy, not just EU compliance. 

Today, maintaining processing records is a baseline expectation in most modern privacy laws, even if the terminology differs. 

Who needs to keep a ROPA?

As mentioned before, the GDPR applies to any business in the EEA. It also applies to organisations outside the EEA that aim their services at or watch individuals within the EEA. 

There’s an exemption for firms with fewer than 250 employees. However, this exception only applies if their processing is: 

• not regular;
• unlikely to cause risk; and 
• does not involve special types of data or information related to criminal convictions.

These exceptions also don’t apply if the data being processed falls into the special categories listed in Article 9 of the GDPR. These categories include, for example, data that shows:

• Racial or ethnic origin
• Political opinions
• Religious or philosophical beliefs
• Trade union membership.

The GDPR also restricts the processing of genetic or biometric data if it is used to uniquely identify an individual. The same rule applies to health data or data about a person’s sex life or sexual orientation. Special category data requires a separate legal basis under Article 9(2) and enhanced safeguards.

In reality, most organisations process data regularly, so they usually need a ROPA. Even when exceptions apply, it’s generally considered best practice to keep one anyway.

How to create a ROPA

Creating and keeping a Record of Processing Activities is a structured process. Here are six steps that guide the process of documenting data processing operations:

• Step 1: Identify your role (controller or processor):
  • → First, determine if your organisation is a data controller, a data processor, or both.
  • • Controllers determine the nature and extent of data processing.
    • Processors execute the controller’s instructions.
  • → Your record needs different information based on your role, as per GDPR Article 30.
• Step 2: Map all processing activities:
  • → List every activity where your organisation handles personal data.
  • → This includes how data is collected, stored, used, shared and deleted across all departments and systems.
• Step 3: Document key ROPA elements (Article 30):
  • → For each activity, record the specific details required by GDPR Article 30.
  • → This covers:
  • • • Processing purposes
    • • Types of data subjects and personal data
    • • Data recipients (including international transfers)
    • • Data retention periods
    • • Security measures.
  • → Be precise and thorough.
• Step 4: Implement security measures:
  • → The ROPA requires a general description of your security measures.
  • → This means putting in place proper technical and organisational protections for personal data.
  • → Review and update these measures regularly to keep data secure.
• Step 5: Review and update regularly:
  • → Data processing changes frequently, so you must review and update your ROPA regularly.
  • → Update this regularly, ideally after major changes or at least annually, to keep it current.
• Step 6: Automate (where possible):
  • → Use privacy-first tools to help create and maintain your ROPA.
  • → Automation makes the process more efficient, reduces errors and keeps your ROPA current and visible.
  • → This is crucial for supervisory authority requests, which often require prompt responses.

Common challenges

Creating and maintaining a ROPA can present several challenges. Recognising them early can help prepare for and overcome them.

• Unclear data flows: Many organisations struggle to map how personal data moves through their systems and departments. Data is collected in various ways, processed by different teams, and shared with third parties, making it hard to see the full picture.
• Third-party risks: Sharing data with third parties and external processors requires verifying GDPR compliance, which can be complex. Documenting these transfers in the ROPA can also be challenging.
• Retention policies: Deciding how long to keep different types of personal data can be challenging due to conflicting legal, regulatory, and business priorities.
• Static documentation: A ROPA is a living document that requires regular updates due to frequent changes in data processing. Without these updates, the ROPA loses its value in terms of compliance and accountability.

Take a proactive approach to data protection 

Following privacy laws and strengthening data management practices helps mitigate the risks associated with data breaches and build trust with users.

Matomo can support your ROPA process by giving you clearer visibility into your analytics data processing activities. Matomo can make parts of your processing easier to document, like the analytics data you collect, how it’s processed, and where it’s stored.

To see how Matomo can support your compliance efforts, download Matomo On-Premise for free or start your 21-day free trial of Matomo Cloud today — no credit card required.

Consumers want relevant experiences. They expect personalization. 
But the moment it feels invasive, something breaks. Not the campaign. The relationship. 

People don’t dislike marketing. 
They resent feeling watched. 

They resent being followed from site to site. 
They resent ads that appear seconds after a private conversation. 
They resent the sense that their digital life is being monitored rather than understood. 

This growing discomfort has a name: surveillance marketing. And it’s one of the biggest trust challenges brands face today. 

What is Data Protection Day, and why should marketers care? 

Data Protection Day, also known as Data Privacy Day, takes place every year on January 28. Its aim is to raise awareness about how personal data is collected, used, and protected. 

While it’s often associated with regulation and compliance, its relevance goes far beyond legal frameworks. 

Because marketing sits at the intersection of data and people. Every campaign, conversion funnel, CRM strategy, and personalization engine relies on customer information. That puts marketers and data analysts in a unique position, not just to use data, but to define how responsibly it’s used. 

In other words: privacy isn’t an IT or legal issue anymore. It’s a brand issue. 

Trust is the foundation of modern marketing 

Every organisation wants the same thing: attention, loyalty, and long-term relationships with customers. 

But in a world driven by data, trust has become the most valuable currency of all. 

That’s why Data Protection Day matters, not just for legal teams or IT departments, but for professionals who care about building brands people actually believe in. Because responsible data practices are about respect, transparency and earning the right to be remembered. 

The real problem isn’t personalization. It’s control. Consumers aren’t against data-driven experiences. 

They’re against losing control. People are willing to share data when they understand how it’s used and feel respected in the process. What they reject is opacity.  When personalization happens without explanation, without consent, or without clear value, it stops feeling helpful, and starts feeling intrusive. Relevance without trust feels like surveillance. 

And once trust is lost, no amount of targeting can rebuild it. 

What trust looks like in practice 

Consumers today are more informed than ever. They know their data has value. They know when it’s being collected. And they know when something feels off. 

They are far more likely to engage with brands that: 

• Are clear about how data is used 

• Collect only what’s truly necessary 

• Give customers control over their information 

• Treat privacy as a value, not a checkbox or a necessary evil 

When customers believe a brand puts their privacy first, it changes the relationship entirely. They don’t feel like data points. They feel like participants. 

That belief fuels lasting business results. Customers stay longer, recommend more often, and engage more meaningfully. Loyalty grows. Churn drops. The relationship compounds over time. 

When trust is present, marketing works better. Personalization doesn’t feel creepy, it feels expected. Emails perform better. Campaigns convert better. Relationships last longer. 

Privacy isn’t the opposite of marketing. It’s what makes it work.

For years, privacy was framed as a limitation: More data meant better marketing. Less tracking meant fewer insights. Fewer insights meant weaker performance. 

But the reality marketers are now experiencing is different. 

Privacy-first marketing often results in better data, not less. 
First-party, consent-based information is typically more accurate than inferred or purchased data sets. When customers willingly share preferences, brands reduce guesswork, minimize bias, and deliver experiences that feel relevant rather than intrusive.  

In that sense, respecting privacy doesn’t limit insight, it improves its quality. 

Privacy-first marketing doesn’t just protect customer data. It improves its accuracy by relying on information people choose to share, rather than assumptions made about them. 

In crowded markets where products look similar and prices compete, values become the differentiator. Privacy is one of the clearest signals of those values. 

Where effectiveness really comes from 

Privacy doesn’t reduce effectiveness. It changes where effectiveness comes from: 

• From collecting everything to collecting what matters. 

• From tracking people to earning participation. 

• From inferred assumptions to accurate, first-party data customers choose to share. 

When customers trust a brand’s intentions, they don’t resist marketing. They engage with it. 

Data Protection Day is a reminder, not a checkbox 

This day isn’t about updating a policy page or publishing another compliance statement. 

It’s a moment to pause and ask harder questions: 

• Are we personalizing, or surveilling?  

• Are we collecting data because we can, or because it truly helps our customers? 

• Are we transparent, or just technically compliant? 

• Are we designing marketing for people, or just metrics? 

The brands that win long-term are not the ones with the most data. They’re the ones people feel safe engaging with. 

Because good marketing starts with trust. And trust doesn’t come from pixels, cookies, or dashboards. It comes from intention. Privacy protects that trust. Ethical marketing strengthens it. 

At Matomo, privacy-first and ethical marketing aren’t constraints, they’re commitments. Because sustainable growth isn’t built on extraction. It’s built on permission. 

And in an era where attention is fragile and skepticism is high, trust may be the most powerful growth strategy marketers have left. 

A good data analytics platform can be a lifeline. Data analytics platforms collect, organise and visualise business data. They help teams uncover hidden patterns and take action to improve the customer experience and the company’s bottom line. 

This article reviews five of the leading data analytics platforms in 2026 and walks through how to find the best solution for a specific use case. 

What is a data analytics platform?

A data analytics platform helps teams collect, process, analyse and visualise large volumes of data. They often extract and integrate a wide variety of source data before consolidating in a centralised interface.

Marketing teams, for example, can use web analytics to better understand customer journeys. For example, multi-channel conversion attribution reports show how different touchpoints (like paid ads, email marketing and social media) contribute to an eventual conversion.

They also help marketers analyse engagement, attribute conversions, and identify areas for improvement. 

Matomo heatmap showing visitor scroll depth.

For instance, imagine running a campaign and the paid ads are generating plenty of traffic, but no one is converting. 

Advanced analytics features, such as heatmaps and session recordings, can help troubleshoot the issue by showing teams what visitors see, or what they may not see. With those insights, it’s much easier to determine the problem, develop and implement a solution and monitor the result. 

This example is just one of many use cases for a data analytics platform. Specific capabilities and functionalities vary by platform, as you’ll see in the next section. 

The top data analytics platforms in 2026

Below, you’ll find detailed reviews of five of the leading data analytics platforms that highlight their capabilities, benefits, drawbacks and pricing. 

1. Matomo

Best for: Privacy-centric web analytics

Matomo is an open-source analytics platform that takes a privacy-first approach to website data collection, analysis and reporting.

Main dashboard in Matomo

It has cookieless tracking, IP anonymisation and other data minimisation tools that teams can easily configure to align with the GDPR, CCPA, and other data privacy laws.

The platform also offers automated reporting capabilities and advanced analytics tools to dig deeper into user behaviour, such as heatmaps, custom event tracking and session recordings. Unlike Google Analytics and other solutions that sample data, with Matomo, you have 100% of your data, and you know the numbers in your reports always reflect reality. 

Standout features include:

• No data sampling: Unlike competitors that use data sampling, Matomo gives you 100% of your data. 
• Advanced analytics tools: Use heatmaps, session recordings, A/B testing and multi-channel attribution to understand why customers behave the way they do.
• Customer segmentation tools: Split your audience into cohorts to analyse how different groups interact with your site.

Matomo’s self-hosted deployment option, combined with its free and open-source nature, makes it particularly attractive for businesses that require data sovereignty and control.

Pricing starts from €23 per month for cloud hosting. On-premise hosting is free.

2. Amplitude Analytics 

Best for: Product analytics

Amplitude Analytics is an analytics platform for product teams. It provides tools to create announcements, guides and surveys to improve user outcomes and encourage them to reach milestones. 

Source: Amplitude

Behaviour-based op-ups, microsurveys and other product announcements can request user feedback at the most opportune times. To prevent too many pop-ups from annoying users, teams can apply prioritisation logic to create built-in guardrails.

Standout features include:

• Self-service analytics: Improves operational efficiency with a no-code/low-code setup that makes insights more accessible and actionable.
• AI-powered assistants: Get immediate answers to product questions.
• Best-practice templates: Choose from a library of pre-built templates for a variety of forms, guides, surveys and checklists. 

Pricing starts from $49 per user per month, billed annually. A limited free version is available.

3. Microsoft Power BI 

Best for: Enterprise business intelligence

Power BI is an enterprise business intelligence and data visualisation platform.

Source: Microsoft

Power BI supports advanced data science and big data workflows. It also offers data mining, data preparation and data warehousing capabilities. 

It helps teams consolidate data from different operating units and pull it into a unified interactive dashboard. Its data visualisation tools identify trends in performance and user behaviour that feed future decision-making and product improvements.

Standout features include:

• Near-real time business intelligence: The platform’s AI-powered chatbot lets you ask questions about your data using natural language processing.
• Reporting and visualisation features: Create data visualisations and interpret key trends.
• Strong ecosystem: Integrates naturally with other Microsoft tools like Azure and Excel.

Pricing starts from $14 per user per month, billed annually. A limited free version is available.

4. Tableau

Best for: Data visualisation

Tableau helps teams turn large datasets into interactive visuals to support storytelling and decision making.

Source: Tableau

It has over 30 pre-built visualisation types that users can easily customise and embed. 

Standout features include:

• Drag-and-drop interface: Makes it easy for less technical users to customise and embed reports and visualisations.
• AI suggestions: The platform uses artificial intelligence to recommend the most appropriate visualisation for different types of data.
• Extensive integration library: Connects with most spreadsheets, databases and third-party platforms. Advanced analytics capabilities. 

Tableau can also run forecasts and perform other statistical analyses.

Pricing ranges from around $15 to $75 per user, per month, billed annually.

5. Alteryx

Best for: Data preparation and automation

Alteryx is an advanced data analytics, preparation and blending platform. It helps teams clean and integrate data from multiple sources with minimal coding.

Source: Alteryx

Alteryx uses built-in machine learning and predictive analytics to help teams streamline data ingestion, data preparation, and data transformation processes. Its drag-and-drop interface allows non-technical users to build workflows without the need for a developer.

Standout features include:

• Available integrations: Connects with platforms like Databricks, Google Cloud, Snowflake and Salesforce.
• Low/No-code: Its drag-and-drop interface makes the tool accessible and user-friendly.
• Advanced analytics: Includes predictive, spatial, and text analytics capabilities.

Alteryx is ideal for organisations that need to democratise data access for a wide range of technical and non-technical users. However, small businesses may find the platform too complicated for their needs. 

Pricing starts at $250 per user, per month, when billed annually. 

How do data analytics platforms work?

While no two data analytics platforms are the same, most use a similar architecture.

• Ingestion layer: This layer automates the collection of data from internal and external sources, including websites, CRMs, apps, and marketing tools.
• Processing layer: Turns all that data into a standardised format for storage and analytics. 
• Storage layer: Stores raw and transformed data in the cloud or on an on-premise server.
• Analytics and visualisation layers: Tools for advanced reporting, statistical analysis and intuitive visualisation, like interactive dashboards, heatmaps, charts and predictive analytics models.
• Security and governance layer: Manages access rights, privacy controls and compliance with industry regulations like the GDPR or CCPA.

With the basics covered, let’s discuss how to choose the right one.

How to find the right data analytics tool for you

To create a shortlist of potential analytics tools, start by carefully evaluating your requirements. What do you need the tool to do?

Once you have a complete list of the specific features and capabilities that are critical for your business needs, you can begin to assess each platform’s compatibility. 

Here are some key criteria to help guide your assessment.

Data privacy and governance

Data privacy should be a significant concern for any organisation that deals with customer data. IBM’s 2025 Cost of a Data Breach report found that personally identifiable information (PII) is targeted more than any other data category. 

It’s important to select a tool that can be easily configured to comply with any applicable privacy laws or standards, such as the GDPR, HIPAA, CCPA, LGPD and PECR. 

Look for platforms with data minimisation and anonymisation features that can help teams avoid collecting unnecessary data by anonymising IP addresses and making it easy for visitors to opt out of tracking.

Integration capabilities

Look for integration with your data sources, tools and third-party applications to ensure you can import all the data you need.

Your analytics are only as good as your data sources, after all, so it’s important to connect as many as possible. 

For example, marketers will likely need tools that can connect to the following places:

• CMS
• CRMs
• Consent managers 
• Ecommerce platforms
• Advertising platforms
• Email marketing tools

Matomo, for example, natively integrates with a host of CMS, ecommerce, CRMs, and data platforms, including WordPress, Magento, Shopify, and Power BI. 

It helps even non-technical users quickly connect with third-party sources and speed up time to insight.

Security and compliance

Opting for a tool with strong security features to keep all of the data you ingest secure and compliant. 

Look out for the following security features:

• Data encryption
• User access controls
• Audit logs

For organisations in jurisdictions with strict data residency requirements, such as the EU, Canada, or Australia, look for solutions with on-premises deployment and regional hosting options that align with local data sovereignty laws.

Cost

For many small and medium-sized businesses, the right analytics platform will come down to cost. 

When considering a platform, it’s important to examine both upfront license costs and ongoing operational expenses. 

Depending on their needs, SMBs may be better off with a smaller, dedicated tool than a big enterprise platform subscription and dozens of features they won’t need or use. 

Conclusion

There is no universal “best” solution. It always depends on the organisation’s needs and priorities.

For teams that need privacy-first analytics, Matomo is trusted by over one million websites in 190 countries. Unlike other platforms that sample your data and show you metrics and reports based on estimates, Matomo gives you 100% of your data and more reliable, accurate insights.

To see for yourself, start your 21-day free trial. No credit card required.

• Respect: We respect people’s rights by collecting data lawfully and treating people’s information with care. Fairness: We avoid biases in how we collect and analyse data that could lead to unfair or discriminatory results. 
• Transparency: We’re open and honest. This helps build trust between people and the organisations that collect their data.
• Control: We make it easy for them to control their own information.

This is very important because our world relies on decisions made using data. Organisations must remember that collected data is essentially borrowed from users and must be returned when requested. Using ethical data practices builds trust with users and encourages them to provide consent.

How did we get here?

Problems with the misuse of personal data emerged early in the digital age, prompting governments to consider implementing laws that protect data privacy. The process gradually accelerated in the 2010s. The European Union (EU) took a big step in 2016 by passing the General Data Protection Regulation (GDPR).

Because the EU, one of the world’s largest markets, took this so seriously, it got the attention of many other governments. What drew the attention of the general public and really sped things up was the rise in data breaches and privacy scandals around the same time GDPR became law.

Facebook and Cambridge Analytica scandal

The most significant of these was the 2018 scandal involving Facebook and Cambridge Analytica. It was revealed that Cambridge Analytica had improperly accessed Facebook user data and used it for political advertising without their knowledge or informed consent.

The news caused people to become much more concerned about how their personal information was being handled and who had access to it. It also led to more and stronger calls on governments to create and enforce stricter data rules. The scandal highlighted the importance of prioritising privacy and ethical analytics that align with GDPR requirements. It also showed how quickly people can turn against companies that fail to respect user privacy.

Project Nightingale and Google

Google also faced ethical scrutiny due to its collaboration on “Project Nightingale” with a national healthcare provider. The goal of this project was to gather health data from millions of patients.

But there were two glaring problems with this. First, the data included highly sensitive personal information, such as lab results, diagnoses, and hospital records. And second, it was being collected without the direct knowledge or consent of the patients themselves.

Prompted by significant public backlash, regulators took a closer look at the collaboration and implemented changes. Project Nightingale continued, but with guardrails put in place to promote transparency, privacy, and personal data security. These rules include the Health Insurance Portability and Accountability Act (HIPAA).

Toronto’s Sidewalk Labs

Another year, another country and another scandal involving Google’s parent company, Alphabet. In 2020, Alphabet brought ethical data practices and privacy-first analytics into the spotlight again with Sidewalk Labs, a controversial smart city project in Toronto. The initiative aimed to build a high-tech neighbourhood, but it faced massive public backlash. 

The main concerns were about the quantity and nature of data to be collected from residents and visitors. They also didn’t have clear answers about how this data would be used, stored, and protected. There were concerns about constant surveillance and the potential for private information to be exploited.

The project eventually scaled back its ambitions significantly but ultimately failed to gain public support. However, this and the other two examples are reminders that innovation and progress must go hand in hand with strong ethical data practices and transparency.

Sidewalk Labs’ proposed design for Parliament Slip, south-east of downtown Toronto © Sidewalk Labs

A different world

These events helped create the world we know today. Three-quarters of the world’s governments have passed data privacy laws or data protection regulations, many of which are based on the EU’s GDPR. They also heightened awareness of data privacy issues and made people realise why they should demand responsible data collection practices and privacy-first web analytics.

What does ethical data collection look like?

For over a decade, we’ve discussed data privacy extensively. This has given us a good idea of what ethical data collection should be. It begins with six fundamental principles:

1. Transparency
2. Choice and control
3. Privacy and security
4. Fairness and equity
5. Data minimisation and purpose limitation
6. Accountability and responsibility

1. Transparency

Transparency requires being upfront and clear about the personal data you collect and how you use it. Transparent practices make sure that people fully understand what happens and how you’re using their data from the moment it’s collected. A GDPR-compliant privacy notice is a good start.

Ethical data collection also involves clear privacy policies that are easy for visitors to find, read and understand. People feel more comfortable sharing their information when they know exactly how it’ll be used and for what reasons.

2. Consent and control

Here, the focus is on ensuring that people have genuine choice and power over their data. Depending on your region and the type of data:

• Some activities require consent.
• Others may rely on legitimate interests or other lawful bases.

Ethical analytics also aligns with national ePrivacy rules, which regulate tracking technologies independently from GDPR. In most EU countries, ePrivacy laws require prior consent before tracking.

When consent is required, organisations must obtain valid, informed consent before collecting any personal information. This fulfils the “informed” requirement in informed consent by clearly explaining what data will be collected and for what intended purpose.

It also requires companies to provide simple and accessible ways for people to withdraw their consent at any time. Data owners should also be able to update their consent preferences, access their data, and request its deletion at any time. This promotes a culture of respect, trust and empowerment.

To simplify the process, you can integrate your analytics platform directly with a consent manager platform (CMP) to automatically collect and manage user consent.

→ Explore our Consent User Guide to learn more about consent and privacy in Matomo.

3. Privacy and security

Safeguarding private data with strong security features, such as secure hosting, encryption, firewalls and access controls, prevents data breaches and builds customer trust. Regular security updates are vital to stay ahead of threats.

To protect customer privacy and strengthen data security measures, there are two main techniques to mention:

• Anonymisation: Removes all personal details, creating anonymised data, ensuring that no individual can be re-identified using reasonably likely methods. 
• Pseudonymisation: This replaces direct identifiers with codes, allowing you to link data without it pointing directly to individuals.

Both methods help organisations use data responsibly while protecting privacy. Companies should also restrict internal access and train employees on proper data handling.

4. Fairness and equity

Organisations need to make it a point to understand how their data practices impact different groups, then work to prevent negative outcomes.

Fairness involves using data in a way that respects the rights of users and promotes privacy. This involves regularly reviewing systems and processes for bias and implementing the necessary controls and safeguards.

5. Data minimisation and purpose limitation

Organisations should have a clear and specific purpose for all the data they collect. Avoid collecting more personal information or data than necessary. For instance, on a newsletter subscription signup page, if you only need an email address, don’t ask for a home address or phone number.

Also, if you collect data for a specific reason, don’t use it for a different purpose later without the owner’s consent, unless you can rely on another legal basis. This ensures that data is used responsibly, as people expect. 

→ Learn how to disable the visits log and visitor profiles in Matomo to enhance privacy.

6. Accountability and responsibility

Under an ethical data collection mandate, organisations must take care of their users’ personal data, follow data protection rules and have systems in place to ensure that they do. This goes beyond just obeying laws. It means actively taking steps to protect data privacy and showing that your internal controls and privacy policies are effective.

It’s vital to clearly define who’s responsible for data practices within an organisation. Everyone, from top management to individual employees, should understand their role in protecting data. This helps create a culture where data privacy is a key part of how an organisation works, not just something added on later.

The business case for ethical data collection

All of that is seen from the consumer’s perspective, but what’s the business case for organisations to prioritise privacy and data ethics? 

Embracing a strong code of ethics around privacy and data sharing builds trust with customers. When people know their data is handled responsibly through responsible, minimalist analytics, they’re more likely to engage and become loyal customers. 

Ethical principles and strong data governance can be a competitive advantage. Companies known for respecting privacy and implementing ethical marketing practices stand out in the market. This can attract new business and strengthen existing relationships. 

Thirdly, ethical data practices help with long-term success. By considering ethical impacts, following data protection rules, and being transparent with users, organisations can avoid costly fines and legal problems. This proactive approach enables them to stay ahead of changing laws and keep operations running smoothly. Ultimately, it’s about smart business that benefits everyone.

The other side of the coin

The potential risks and negative impact of a major data breach underscore the importance of ethical data collection. 

For example, the 2017 Equifax breach exposed the personal information of millions of people. The company faced substantial financial penalties, including a multi-million-dollar settlement agreement with the U.S. Federal Trade Commission (FTC), the Consumer Financial Protection Bureau (CFPB), and various U.S. states. But the real damage was in the market: news of the breach caused Equifax’s stock price to drop by nearly a third.

A year later, the Marriott group disclosed a similar data breach affecting hundreds of millions of guests. In addition to the regulatory settlement of $52 million with the FTC and various states, the company’s share price also suffered as a result.

These incidents show that when personal data is mishandled, consumers often lose trust, stop using the company’s services, and share their negative experiences with others. This can devastate a business and make it very hard to win back customers.

A call to action

So there you have it. Adopting ethical data collection practices and GDPR-compliant analytics isn’t only the right thing to do, but also essential for maintaining trust and credibility. And not doing so may very well turn out to be an existential threat.

Respecting user privacy through privacy-first analytics and cookie-free tracking helps businesses build trust with customers and gain a competitive advantage. Luckily, ethical analytics solutions make this much easier.

Download our Ethical Marketing Guide for a deeper dive into privacy-first practices and more actionable strategies.

Or if you’re ready to try a privacy-first, ethical analytics solution, you can start your 21-day free trial today — no credit card required.

For analytics teams, compliance leaders, and digital decision-makers, the next 12–18 months will bring concrete changes to how audience measurement can be configured, justified, and audited.

If your organisation relies on web analytics without consent, or is actively trying to reduce consent friction while staying compliant, these updates matter.

From France’s new CNIL self-assessment framework to the EU’s Digital Omnibus initiative and the UK’s updated PECR rules, significant changes are on the horizon. Some of these could make privacy-first analytics easier to use without consent. Others raise important questions about the future of data protection.

Here’s what you need to know, what is changing, and how Matomo is preparing ahead of time. 

France: CNIL moves to a self-assessment framework for consent exemption

The CNIL (France’s data protection authority) is introducing a new self-assessment framework for analytics tools seeking to rely on consent exemption.

Previously, the CNIL provided a list of pre-approved analytics solutions. Under the updated approach, all analytics providers must now evaluate their own compliance against standardised criteria outlined in the CNIL’s revised guide: “Cookies : solutions pour les outils de mesure d’audience”. Self-assessment is not a CNIL certification and does not prevent the CNIL from reaching a different conclusion in an investigation or audit.

Instead of informal interpretations, analytics providers will be expected to demonstrate compliance against clearly defined criteria.

What this means in practice:

• Compliance will be evaluated against explicit, published criteria
• Responsibility shifts more clearly to the analytics controller and the analytics solution provider
• Documentation, transparency, and configuration clarity become critical

What this means for Matomo users in France

Matomo has long been recognised by the CNIL as a privacy-compliant analytics solution. Under the new framework, we’re preparing detailed self-assessment documentation for early 2026 to help you demonstrate compliance.

The goal is simple: make compliance verifiable, auditable, and understandable, not interpretive.

EU: the Digital Omnibus initiative could reshape analytics rules

At the European Union (EU) level, the European Commission adopted the Digital Omnibus initiative last month.

If passed into law, it would bring substantial amendments to the GDPR, the ePrivacy Directive, and other data privacy regulations across Europe, potentially taking effect in 2026.

Proposed changes worth watching

Some amendments include:

• A narrower definition of “personal data” in the GDPR
• Allowing certain uses of personal data, including AI training, without explicit consent

But one proposed amendment stands out and is particularly relevant for analytics teams.

It would exempt consent for accessing or storing data on terminal equipment when it is strictly necessary for creating aggregated audience measurement, provided that:

• The website controller carries out the analytics for itself: you, as the site owner, collect the data to understand your audience, not a third party.
• The data is used solely for your own purposes.
• The data isn’t combined with other datasets.
• The analytics provider does not reuse the data for its own purposes: your analytics tool doesn’t siphon off your data for its own commercial interests.

This distinction is critical. It would explicitly favour first-party, privacy-focused analytics models like Matomo Analytics, and exclude solutions like Google Analytics that monetise, enrich, or repurpose analytics data across multiple clients.

Where the analytics line is drawn

The exemption would not apply to solutions where:

• Analytics data is combined with other datasets
• The analytics provider reuses the data for its own commercial or secondary purposes

In other words, platforms that monetise, enrich, or repurpose analytics data across multiple clients, like Google Analytics, would fall outside the scope of this exemption.

This would effectively favour privacy-focused analytics tools and exclude surveillance-based platforms that monetise user data.

Why this matters for Matomo users

If adopted, this amendment would be a significant win for privacy-first analytics. Matomo is designed precisely for this use case.

Matomo On-Premise gives you full control: your data stays on your own infrastructure, with no third-party involvement whatsoever. Your tracking remains uninterrupted and fully under your control.

Matomo Cloud, while hosted on Matomo’s infrastructure, preserves controller ownership and full control:

• Data is collected, processed, and stored independently for each customer
• Tracking is completely isolated, no data is shared or combined across clients
• Analytics data is never reused by Matomo for its own purposes

This means Matomo Cloud aligns with the core requirement of the proposal: analytics data remains under the exclusive control of the website owner (you) and is used only to measure their own audience.

We’ll continue monitoring the legislative process and provide updated guidance as the final text is clarified.

Timeline: The Digital Omnibus is currently under review with the European Parliament. If passed, changes could take effect in 2026.

United Kingdom: PECR updates to simplify consent-free analytics

In the UK, the Data (Use and Access) Act 2025 introduces updates to the Privacy and Electronic Communications Regulations (PECR). These changes are expected to make it easier to use privacy-friendly analytics without requiring consent, as long as certain safeguards are met.

When consent-free analytics will be allowed under the updated PECR

In practical terms, analytics may be used without consent where:

1. The use is strictly statistical, to improve the website or service
2. Data is not shared or reused for any other purpose
3. Users receive clear and comprehensive information about the tracking
4. Users have a simple way to opt out, and have not done so

Current status

These PECR-related changes aren’t yet in force. They’re expected to apply as part of a later rollout of Part 5 of the Act, likely in early 2026.

The ICO (UK’s data protection regulator) is also expected to publish updated Direct Marketing and Privacy and Electronic Communications Guidance, which will clarify the limits of this exemption. Initial publication is anticipated in winter 2025/2026.

What we’ll do: Once the ICO guidance is released, we’ll confirm the best ways to configure Matomo to comply with the new UK consent-exemption criteria, ensuring teams can confidently align with UK-specific requirements.

What this means for your analytics strategy

Across France, the EU, and the UK, one trend is consistent. These regulatory shifts share a common thread: privacy-first analytics is becoming the standard, not the exception.

If you’re using an analytics tool that:

• Shares data with third parties
• Combines analytics with advertising profiles
• Operates outside your control

You may face increasing compliance challenges, and lose access to valuable insights when users decline consent.

How Matomo is preparing for those privacy changes

As a privacy-first platform, regulatory change is not something Matomo reacts to after the fact. Our teams are already analysing and preparing for what’s coming.

With Matomo, you’re already positioned for any upcoming privacy changes:

• 100% data ownership: Your data stays yours, whether self-hosted or on our EU cloud
• No third-party data sharing: We never access, sell, or monetise your analytics
• Configurable for consent exemption: Matomo can be set up to meet CNIL, GDPR, and PECR requirements for cookieless, consent-free tracking
• Transparent compliance documentation: We provide clear guidance for every regulatory framework

The objective isn’t only to keep Matomo compliant, but to help your team stay compliant with confidence. You shouldn’t have to guess whether your analytics setup will stand up to scrutiny.

Our team is actively monitoring these developments and working through every requirement. Here’s what’s in progress:

Staying compliant without compromising insight

The next wave of regulation reinforces a principle Matomo has held from the beginning.

We will continue to:

• share regulatory updates
• publish clear, actionable configuration guidance
• support you through upcoming changes

Privacy regulation will keep evolving. Your analytics should be built to evolve with it. As a privacy-first platform, helping you navigate these changes is part of what we do.

We’ll continue to share updates, provide clear configuration guidance, and support you through whatever comes next.

You’re in good hands. And that is where compliance becomes a strategic advantage.

Start your 21-day free trial to take control of your data. No credit card required.

The breach in brief (and its business impact)

In September, Adobe Analytics made headlines when an upgrade error caused proprietary analytics data to appear in unrelated customer dashboards. For a brief period, user accounts and personal information were essentially floating around beyond the control of the organisations to which they belonged.

According to a report by Mi3, the leaked information included “search terms, domain data and navigation structures”, many of which these businesses were legally obligated to protect under data privacy laws.

Adobe was able to revert the change and resolve the issue within 24 hours, as reported by BleepingComputer. While that did address the immediate problem, there are ongoing regulatory, governance, and operational impacts for those organisations affected. 

Adobe’s misrouted data shows the risk of shared infrastructure, and the advantage of on-premise control.

Compliance consequences

Analytics platforms collect demographic and behavioural data that can re-identify people when combined, which is why it’s protected under the GDPR. 

In incidents where such personal data, personally identifiable information, or sensitive datasets are exposed, it doesn’t matter whether the exposure is intentional or accidental. The organisation that owns the data is always responsible for it, even when management or security is outsourced to a third party.

Any exposure, breach or other security incident involving these types of data automatically triggers mandatory reporting, legal, and disclosure requirements. 

There’s also the financial cost: remediation, forensics, fines, penalties, stalled sales, unfulfilled contract obligations and other opportunity costs. You’ll also pay for employees to fix the vendor’s mistake instead of working on something that actually brings in revenue.

Shared infrastructure = shared risk 

Cybersecurity incidents and data breaches aren’t always the result of threat actors or security issues.

In shared environments, system‑level errors can cross organisational boundaries. This can expose proprietary information, campaign insights and customer attributes to competitors or cause them to be lost altogether.

When dealing with shared infrastructure and personal details are involved, a glitch with one tenant can have governance and compliance consequences for thousands of others. Even when incidents are resolved quickly and exposure periods are brief, the operational hit can be significant. 

Data integrity and contamination

In security incidents where unknown data injects itself into organisational networks or systems, things can spread quickly. 

When contaminated data enters a platform as interconnected as Adobe’s, the level of exposure and potential damage multiplies. Reporting becomes skewed, dashboards are distorted, and organisations are left to fix problems they didn’t cause.

And for global organisations with multiple connectors, stakeholders and regional requirements, even minor breaches can quickly escalate into serious compliance issues.

Maintaining direct control over your analytics environment is the most effective safeguard against unwanted data spreading across divisions and jurisdictional boundaries.

Governance and accountability

Every digital system carries some level of risk, and in the worst-case scenario, mistakes can expose sensitive data and trigger specific compliance obligations. 

Vendors handle data on your behalf, but they aren’t ultimately responsible for it. Organisations are always accountable for protecting their data, even when its management, handling, or security is outsourced to a third party.

On-premise systems are the most effective safeguards. By keeping critical data flows in-house, organisations can minimise data exposure risk. With on-premise solutions, you aren’t at the mercy of vendor mishaps and can implement privacy and compliance frameworks on your terms.

Without on-premise control, organisations risk fines, penalties, lawsuits, and reputational damage due to events out of their control.

Data sovereignty: 90-day action plan 

The Adobe incident is a prompt for executives to reassess governance and prioritise visibility, control and accountability.

• How quickly could you contain a similar vendor failure? 
• How much visibility do you have into your data right now?
• How dependent are you on external vendors for managing and storing your data?

The 90-day action plan below will help your organisation take proactive steps to strengthen data sovereignty and build resilience.

Day 1-30: Alignment

• Map where your data resides and who has access to it.
• Review vendor contracts and processing agreements for residency and tenant separation terms.
• Perform vendor risk assessments.

Day 31-60: Reinforcement

• Request vendor documentation on tenant segregation and incident response processes.
• Create a sovereignty map showing storage locations, flows and jurisdictions.
• Update contracts and procurement documentation to include explicit provisions regarding residency and liability.

Day 61–90: Resilience

• Create a sovereignty dashboard to track outsourced functions and associated risks.
• Develop a roadmap to bring high-risk categories in-house.
• Perform periodic reviews to monitor and communicate progress.

By day 90, sovereignty and accountability will begin to be embedded, but sustaining them requires ongoing effort.

Prioritising privacy and sovereignty from the start

The Adobe Analytics data breach had nothing to do with the quality of Adobe products. The reality is that there will always be inherent risks in cloud security. Even the most trusted vendors can suffer failures that push sensitive customer data or other legally protected information beyond anyone’s control.

Moving toward sovereign, on-premise systems is the clearest path toward data sovereignty. By bringing analytics flows and keeping critical data on-site, organisations can strengthen governance and avoid third-party risks. 

Matomo is the #1 open-source web analytics platform, and one of the few globally that offers a true on-premise option. With Matomo On-Premise, you can build privacy protection and accountability directly into your operations.

The next step is simple: bring your highest-risk data flows in-house and make privacy and sovereignty a built-in function of your organisation. That way, you don’t have to put your faith in someone else’s cloud, keeping your information safe.