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. 

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.

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.

The access management challenge no one talks about

Every Monday morning, IT teams worldwide face the same scenario: new starters need access to analytics, someone forgot their password, and that contractor from last quarter still has admin rights nobody remembered to revoke.

Consider this: The average employee has access to 11 different business applications. Each represents a potential security gap. Analytics platforms, containing sensitive business and user data, present particular risks when access isn’t properly controlled.

What exactly is SSO?

Single Sign-On (SSO) is an authentication method that allows users to access multiple applications with one set of credentials. Instead of managing separate usernames and passwords for each tool, employees authenticate once through a central identity provider.

Think of it this way: One password. One place to manage everything. This is the difference between carrying 20 different keys versus having one secure access card that opens all the doors you’re authorised to enter.

How SSO works in practice

1. User tries to access an application (like Matomo Analytics)
2. Application redirects to identity provider (such as Okta, Azure AD, or Google Workspace)
3. User authenticates once with their corporate credentials
4. Identity provider confirms authorisation and grants access
5. User accesses the application without additional passwords

This process happens in seconds and, once configured, becomes invisible to the end user.

The real cost of not having SSO

Security vulnerabilities

According to Verizon’s 2024 Data Breach Investigations Report, 68% of breaches involve the human element. Without SSO, organisations face multiple risks:

Without SSO, meeting these requirements becomes exponentially more difficult and expensive.

SSO isn’t just another IT checkbox

Single Sign-On might sound technical, but it’s actually about something simple: controlling WHO sees your data, when, and how.

With SSO, your team logs into Matomo using the same secure credentials they use for other business tools: One password. One place to manage everything.

Your team can signs in through your existing identity provider (Okta, Azure AD, Google Workspace, RSA, Ping Identity, ADFS, Shibboleth,…), meaning:

• Time saved – no more manual user management, or “forgot password” tickets
• Centralised access control – instantly grant or revoke access from one place
• Reduced breach risk – eliminate weak or forgotten passwords
• Audit-ready compliance – meet GDPR, ISO 27001 and internal security standards

Common SSO misconceptions debunked

“SSO is only for large Enterprises”

Reality: Organisations with as few as 30 employees benefit from SSO. The complexity of access management grows exponentially, not linearly.

“It’s too complex to implement”

Reality: Modern SSO integration typically takes 2-3 hours of technical configuration. Most identity providers offer step-by-step guides.

“It will slow down our workflow”

Reality: SSO actually accelerates workflows by eliminating password-related interruptions. Users save an average of 5 minutes daily.

“We can’t afford it”

Reality: Calculate your current costs: (hours spent on access management × hourly rate) + (risk of security incident × probability). SSO often pays for itself within 3-4 months.

Industry-specific considerations

Financial services and DORA

The EU’s Digital Operational Resilience Act requires “appropriate access controls” for all data systems. SSO satisfies this requirement comprehensively.

Public sector and sovereignty

Government agencies often require data sovereignty and strict access controls. SSO enables compliance whilst maintaining operational efficiency.

E-commerce and PCI DSS

Payment Card Industry standards mandate unique user IDs and regular access reviews. SSO automates both requirements.

Your SSO readiness checklist

Rate your organisation (1-5) on each criterion:

• We maintain accurate records of all system access
• We can revoke access within 15 minutes
• We enforce consistent password policies
• We conduct regular access audits
• We have documented offboarding procedures

Scoring:

• 20-25: Ready for Enterprise-grade SSO
• 15-19: SSO would significantly improve security
• 10-14: Critical gaps requiring immediate attention
• Below 10: High risk, prioritise access management immediately

If your score is low, don’t worry. You can already take steps to improve your security in under two hours.

Taking action: Your next steps

Implementing Single Sign-On (SSO) isn’t just an IT upgrade, it’s a step towards stronger data governance. Here’s how to prepare your team:

1. Start with an access audit: Review who currently has access to your analytics tools. You’ll often find inactive accounts or inconsistent permissions that increase security risks.
2. Pick the right identity provider: Match your setup to your existing tools:
   • Okta (for flexible, growing teams)
   • Azure Active Directory (for Microsoft-based environments)
   • OneLogin (for multi-cloud organisations)
   • Or others like Auth0, ADFS, Keycloak, Salesforce, AWS SSO, Forgerock, Oracle, SecureAuth,…
3. Define who sees what: Clarify access levels by role: who needs to view data, edit settings, or manage users. Make sure access can be revoked quickly when people leave the company.
4. Plan a smooth rollout: Start small – test with one department before rolling out company-wide. Provide short training sessions so everyone understands how SSO works and why it matters.
5. Review and improve: Once SSO is live, keep good habits: run quarterly access reviews, train new managers, and adjust policies as your team evolves.

Even for smaller teams, planning SSO early saves time later.

How Matomo supports Enterprise access management

For organisations requiring Enterprise-grade security, Matomo offers native SSO integration through our Enterprise plan. This includes:

• SAML 2.0 support for all major identity providers
• Group-based permissions mapping
• Comprehensive audit logging
• Dedicated implementation support

Most customers complete implementation within 48 hours, immediately eliminating password-related security risks whilst maintaining the privacy-first analytics Matomo is known for.

Security is not optional

In an era where data breaches make headlines daily and regulations grow stricter annually, proper access management isn’t a luxury, it’s a necessity. SSO represents the minimum viable security for any organisation serious about protecting their analytics data.

The question isn’t whether to implement SSO, but when. And given the rapid ROI and immediate security benefits, the answer for most organisations is: now. With Matomo’s privacy-first approach to enterprise analytics, you can simplify authentication, stay compliant, and keep your data truly yours.

Start your 21-day free trial today and experience analytics built for security and privacy — no credit card required.

Wellington, New Zealand – 24 September 2025 – Matomo, the leading open-source web analytics platform, today announced it has achieved ISO/IEC 27001:2022 certification with zero non-conformities following a rigorous independent audit.

This milestone confirms that Matomo’s practices, infrastructure, and controls meet the highest international benchmarks for information security — giving every Matomo customer, from our open-source community through to global enterprises, confidence that their analytics data is protected by the world’s most rigorous compliance and security standards.

Independent validation of security excellence

ISO/IEC 27001:2022 is the world’s most recognised standard for information security management systems (ISMS). Certification required a comprehensive review of Matomo’s processes across cloud hosting, software development, and internal operations. Passing with zero non-conformities demonstrates the strength, resilience, and maturity of Matomo’s security practices and its readiness to support even the most compliance-conscious organisations worldwide.

Reinforcing trust through ethical analytics

Since 2007, Matomo has been the trusted alternative to mainstream analytics platforms — focusing on 100% data accuracy, full user control, and zero data sampling. This certification is not a finish line but another step in Matomo’s mission to show that organisations can have ethical, privacy-first analytics and enterprise-grade security, without compromise.

“ISO/IEC 27001:2022 certification is part of our continuous journey to protect customer data while upholding our values of privacy, security, and independence,” added Adam Taylor. “It strengthens our vision of a future where every organisation owns and controls its data — compliant, secure, and free from lock-in.”

About Matomo

Matomo is the leading open-source, ethical, and privacy-first web analytics platform, trusted by over 1.4 million websites in 190+ countries and available in more than 50 languages. Driven by the belief that everyone has the right to data sovereignty and accuracy, Matomo empowers organisations to make better decisions through transparent, compliance-ready analytics — with 100% data ownership, no sampling, and no compromises.

For more information and start your free trial, visit our website.

Privacy-enhancing technologies can help achieve this by protecting sensitive information and enabling safe data sharing. 

This post explores privacy-enhancing technologies, including their types, benefits, and how our website analytics platform, Matomo, supports them by providing privacy-focused features.

What are privacy-enhancing technologies? 

Privacy Enhancing Technologies (PETs) are tools that protect personal data while allowing organisations to process information responsibly. 

In industries like healthcare, finance and marketing, businesses often need detailed analytics to improve operations and target audiences effectively. However, collecting and processing personal data can lead to privacy concerns, regulatory challenges, and reputational risks.

PETs minimise the collection of sensitive information, enhance security and allow users to control how companies use their data. 

Global privacy laws like the following are making PETs essential for compliance:

• General Data Protection Regulation (GDPR) in the European Union
• California Consumer Privacy Act (CCPA) in California
• Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada
• Lei Geral de Proteção de Dados (LGPD) in Brazil

Non-compliance can lead to severe penalties, including hefty fines and reputational damage. For example, under GDPR, organisations may face fines of up to €20 million or 4% of their global annual revenue for serious violations. 

Types of PETs 

What are the different types of technologies available for privacy protection? Let’s take a look at some of them. 

Homomorphic encryption

Homomorphic encryption is a cryptographic technique in which users can perform calculations on cipher text without decrypting it first. When the results are decrypted, they match those of the same calculation on plain text. 

This technique keeps data safe during processing, and users can analyse data without exposing private or personal data. It is most useful in financial services, where analysts need to protect sensitive customer data and secure transactions. 

Despite these advantages, homomorphic encryption can be complex to compute and take longer than other traditional methods. 

Secure Multi-Party Computation (SMPC)

SMPC enables joint computations on private data without revealing the raw data. 

In 2021, the European Data Protection Board (EDPB) issued technical guidance supporting SMPC as a technology that protects privacy requirements. This highlights the importance of SMPC in healthcare and cybersecurity, where data sharing is necessary but sensitive information must be kept safe. 

For example, several hospitals can collaborate on research without sharing patient records. They use SMPC to analyse combined data while keeping individual records confidential. 

Synthetic data

Synthetic data is artificially generated to mimic real datasets without revealing actual information. It is useful for training models without compromising privacy. 

Imagine a hospital wants to train an AI model to predict patient outcomes based on medical records. Sharing real patient data, however, poses privacy challenges, so that can be changed with synthetic data. 

Synthetic data may fail to capture subtle nuances or anomalies in real-world datasets, leading to inaccuracies in AI model predictions.

Pseudonymisation

Pseudonymisation replaces personal details with fake names or codes, making it hard to determine who the information belongs to. This helps keep people’s personal information safe. Even if someone gets hold of the data, it’s not easy to connect it back to real individuals. 

Pseudonymisation works differently from synthetic data, though both help protect individual privacy. 

When we pseudonymise, we take factual information and replace the bits that could identify someone with made-up labels. Synthetic data takes an entirely different approach. It creates new, artificial information that looks and behaves like real data but doesn’t contain any details about real people.

Differential privacy

Differential privacy adds random noise to datasets. This noise helps protect individual entries while still allowing for overall analysis of the data. 

It’s useful in statistical studies where trends need to be understood without accessing personal details.

For example, imagine a survey about how many hours people watch TV each week. 

Differential privacy would add random variation to each person’s answer, so users couldn’t tell exactly how long John or Jane watched TV. 

However, they could still see the average number of hours everyone in the group watched, which helps researchers understand viewing habits without invading anyone’s privacy.

Zero-Knowledge Proofs (ZKP)

Zero-knowledge proofs help verify the truth without exposing sensitive details. This cryptographic approach lets someone prove they know something or meet certain conditions without revealing the actual information behind that proof.

Take ZCash as a real-world example. While Bitcoin publicly displays every financial transaction detail, ZCash offers privacy through specialised proofs called Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). These mathematical proofs confirm that a transaction follows all the rules without broadcasting who sent money, who received it, or how much changed hands.

The technology comes with trade-offs, though. 

Creating and checking these proofs demands substantial computing power, which slows down transactions and drives up costs. Implementing these systems requires deep expertise in advanced cryptography, which keeps many organisations from adopting them despite their benefits.

Trusted Execution Environment (TEE)

TEEs create special protected zones inside computer processors where sensitive code runs safely. These secure areas process valuable data while keeping it away from anyone who shouldn’t see it.

TEEs are widely used in high-security applications, such as mobile payments, digital rights management (DRM), and cloud computing.

Consider how companies use TEEs in the cloud: A business can run encrypted datasets within a protected area on Microsoft Azure or AWS Nitro Enclaves. Due to this setup, even the cloud provider can’t access the private data or see how the business uses it. 

TEEs do face limitations. Their isolated design makes them struggle with large or spread-out computing tasks, so they don’t work well for complex calculations across multiple systems.

Different TEE implementations often lack standardisation, so there can be compatibility issues and dependence on specific vendors. If the vendor stops the product or someone discovers a security flaw, switching to a new solution often proves expensive and complicated.

Obfuscation (Data masking)

Data masking involves replacing or obscuring sensitive data to prevent unauthorised access. 

It replaces sensitive data with fictitious but realistic values. For example, a customer’s credit card number might be masked as “1234-XXXX-XXXX-5678.” 

The original data is permanently altered or hidden, and the masked data can’t be reversed to reveal the original values.

Federated learning

Federated learning is a machine learning approach that trains algorithms across multiple devices without centralising the data. This method allows organisations to leverage insights from distributed data sources while maintaining user privacy.

For example, NVIDIA’s Clara platform uses federated learning to train AI models for medical imaging (e.g., detecting tumours in MRI scans). 

Hospitals worldwide contribute model updates from their local datasets to build a global model without sharing patient scans. This approach may be used to classify stroke types and improve cancer diagnosis accuracy.

Now that we have explored the various types of PETs, it’s essential to understand the principles that guide their development and use. 

Key principles of PET (+ How to enable them with Matomo) 

PETs are based on several core principles that aim to balance data utility with privacy protection. These principles include:

Data minimisation

Data minimisation is a core PET principle focusing on collecting and retaining only essential data.

Matomo, an open-source web analytics platform, helps organisations to gather insights about their website traffic and user behaviour while prioritising privacy and data protection. 

Recognising the importance of data minimisation, Matomo offers several features that actively support this principle:

• Cookieless tracking: Eliminates reliance on cookies, reducing unnecessary data collection.
• IP anonymisation: Automatically anonymises IP addresses, preventing identification of individual users.

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• Custom data retention policies: Allows organisations to define how long user data is stored before automatic deletion.

7Assets, a fintech company, was using Google Analytics and Plausible as their web analytics tools. 

However, with Google Analytics, they faced a problem of unnecessary data tracking, which created legal work overhead. Plausible didn’t have the features for the kind of analysis they wanted. 

They switched to Matomo to enjoy the balance of privacy yet detailed analytics. With Matomo, they had full control over their data collection while also aligning with privacy and compliance requirements.

Transparency and User Control

Transparency and user control are important for trust and compliance. 

Matomo enables these principles through:

• Consent management: Offers integration with Consent Mangers (CMPs), like Cookiebot and Osano, for collecting and managing user consent.
• Respect for DoNotTrack settings: Honours browser-based privacy preferences by default, empowering users with control over their data.

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• Opt-out mechanisms: These include iframe features that allow visitors to opt out of tracking. 

Security and Confidentiality

Security and confidentiality protect sensitive data against inappropriate access. 

Matomo achieves this through:

• On-premise hosting: Gives organisations the ability to host analytics data on-site for complete data control.
• Data security: Protects stored information through access controls, audit logs, two-factor authentication and SSL encryption.
• Open source code: Enables community reviews for better security and transparency.

Purpose Limitation

Purpose limitation means organisations use data solely for the intended purpose and don’t share or sell it to third parties. 

Matomo adheres to this principle by using first-party cookies by default, so there’s no third-party involvement. Matomo offers 100% data ownership, meaning all the data organisations get from our web analytics is of the organisation, and we don’t sell it to any external parties. 

Compliance with Privacy Regulations

Matomo aligns with global privacy laws such as GDPR, CCPA, HIPAA, LGPD and PECR. Its compliance features include:

• Configurable data protection: Matomo can be configured to avoid tracking personally identifiable information (PII).
• Data subject request tools: These provide mechanisms for handling requests like data deletion or access in accordance with legal frameworks.
• GDPR manager: Matomo provides a GDPR Manager that helps businesses manage compliance by offering features like visitor log deletion and audit trails to support accountability.

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Mandarine Academy is a French-based e-learning company. It found that complying with GDPR regulations was difficult with Google Analytics and thought GA4 was hard to use. Therefore, it was searching for a web analytics solution that could help it get detailed feedback on its site’s strengths and friction points while respecting privacy and GDPR compliance. With Matomo, it checked all the boxes.

Data collaboration: A key use case of PETs

One specific area where PETs are quite useful is data collaboration. Data collaboration is important for organisations for research and innovation. However, data privacy is at stake. 

This is where tools like data clean rooms and walled gardens play a significant role. These use one or more types of PETs (they aren’t PETs themselves) to allow for secure data analysis. 

Walled gardens restrict data access but allow analysis within their platforms. Data clean rooms provide a secure space for data analysis without sharing raw data, often using PETs like encryption. 

Tackling privacy issues with PETs 

Amidst data breaches and privacy concerns, organisations must find ways to protect sensitive information while still getting useful insights from their data. Using PETs is a key step in solving these problems as they help protect data and build customer trust. 

Tools like Matomo help organisations comply with privacy laws while keeping data secure. They also allow individuals to have more control over their personal information, which is why 1 million websites use Matomo.

In addition to all the nice features, switching to Matomo is easy:

To experience Matomo, sign up for our 21-day free trial, no credit card details needed. 

A well-structured SOC 2 compliance checklist serves as your roadmap to successful audits and effective security practices. In this post, we’ll walk through the essential steps to achieve SOC 2 compliance and explain how proper analytics practices play a crucial role in maintaining this important certification.

What is SOC 2 compliance?

SOC 2 compliance applies to service organisations that handle sensitive customer data. While not mandatory, this certification builds significant trust with customers and partners.

According to the AICPA, “SOC 2 reports are intended to meet the needs of a broad range of users that need detailed information and assurance about the controls at a service organisation relevant to security, availability, and processing integrity of the systems the service organisation uses to process users’ data and the confidentiality and privacy of the information processed by these systems.“

At its core, SOC 2 helps organisations protect customer data through five fundamental principles: security, availability, processing integrity, confidentiality, and privacy.

Think of it as a seal of approval that tells customers, “We take data protection seriously, and here’s the evidence.”

Companies undergo SOC 2 audits to evaluate their compliance with these standards. During these audits, independent auditors assess internal controls over data security, availability, processing integrity, confidentiality, and privacy.

What is a SOC 2 compliance checklist?

A SOC 2 compliance checklist is a comprehensive guide that outlines all the necessary steps and controls an organisation needs to implement to achieve SOC 2 certification. It covers essential areas including:

• Security policies and procedures
• Access control measures
• Risk assessment protocols
• Incident response plans
• Disaster recovery procedures
• Vendor management practices
• Data encryption standards
• Network security controls

SOC 2 compliance checklist benefits

A structured SOC 2 compliance checklist offers several significant advantages:

Preparedness

Preparing for a SOC 2 examination involves many complex elements. A checklist provides a clear, structured path, breaking the process into manageable tasks that ensure nothing is overlooked.

Resource optimisation

A comprehensive checklist reduces time spent identifying requirements, minimises costly mistakes and oversights, and enables more precise budget planning for the compliance process.

Better team alignment

A SOC 2 checklist establishes clear responsibilities for team members and maintains consistent understanding across all departments, helping align internal processes with industry standards.

Risk reduction

Following a SOC 2 compliance checklist significantly reduces the risk of compliance violations. Systematically reviewing internal controls provides opportunities to catch security gaps early, mitigating the risk of data breaches and unauthorised access.

Audit readiness

A well-maintained checklist simplifies audit preparation, reduces stress during the audit process, and accelerates the certification timeline.

Business growth

A successful SOC 2 audit demonstrates your organisation’s commitment to data security, which can be decisive in winning new business, especially with enterprise clients who require this certification from their vendors.

Challenges in implementing SOC 2

Implementing SOC 2 presents several significant challenges:

Time-intensive documentation

Maintaining accurate records throughout the SOC 2 compliance process requires diligence and attention to detail. Many organisations struggle to compile comprehensive documentation of all controls, policies and procedures, leading to delays and increased costs.

Incorrect scoping of the audit

Misjudging the scope can result in unnecessary expenses and extended timelines. Including too many systems complicates the process and diverts resources from critical areas.

Maintaining ongoing compliance

After achieving initial compliance, continuous monitoring becomes essential but is often neglected. Regular internal control audits can be overwhelming, especially for smaller organisations without dedicated compliance teams.

Resource constraints

Many organisations lack sufficient resources to dedicate to compliance efforts. This limitation can lead to staff burnout or reliance on expensive external consultants.

Employee resistance

Staff members may view new security protocols as unnecessary hurdles. Employees who aren’t adequately trained on SOC 2 requirements might inadvertently compromise compliance efforts through improper data handling.

Analytics and SOC 2 compliance: A critical relationship

One often overlooked aspect of SOC 2 compliance is the handling of analytics data. User behaviour data collection directly impacts multiple Trust Service Criteria, particularly privacy and confidentiality.

Why analytics matters for SOC 2

Standard analytics platforms often collect significant amounts of personal data, creating potential compliance risks:

1. Privacy concerns: Many analytics tools collect personal information without proper consent mechanisms
2. Data ownership issues: When analytics data is processed on third-party servers, maintaining control becomes challenging
3. Confidentiality risks: Analytics data might be shared with advertising networks or other third parties
4. Processing integrity questions: When data is transformed or aggregated by third parties, verification becomes difficult

How Matomo supports SOC 2 compliance

Matomo’s privacy-first analytics approach directly addresses these concerns:

1. Complete data ownership: With Matomo, all analytics data remains under your control, either on your own servers or in a dedicated cloud instance
2. Consent management: Built-in tools for managing user consent align with privacy requirements
3. Data minimisation: Configurable anonymisation features help reduce collection of sensitive personal data
4. Transparency: Clear documentation of data flows supports audit requirements
5. Configurable data retention: Set automated data deletion schedules to comply with your policies

By implementing Matomo as part of your SOC 2 compliance strategy, you address key requirements while maintaining the valuable insights your organisation needs for growth.

Conclusion

A SOC 2 compliance checklist helps organisations meet critical security and privacy standards. By taking a methodical approach to compliance and implementing privacy-respecting analytics, you can build trust with customers while protecting sensitive data.

Start your 21-day free trial — no credit card needed.

Data powers innovation in financial technology (fintech), from personalized banking services to advanced fraud detection systems. Industry leaders recognize the value of strong security measures and customer privacy. A recent survey highlights this focus, with 72% of finance Chief Risk Officers identifying cybersecurity as their primary concern.

Beyond cybersecurity, fintech and financial services (finserv) companies are bogged down with massive amounts of data spread throughout disconnected systems. Between this, a complex regulatory landscape and an increasingly tech-savvy and sceptical consumer base, fintech and finserv companies have a lot on their plates.

How can marketing teams get the information they need while staying focused on compliance and providing customer value? 

This article will examine strategies to address common challenges in the finserv and fintech industries. We’ll focus on using appropriate tools, following effective data management practices, and learning from traditional banks’ approaches to similar issues.

What are the biggest fintech data analytics challenges, and how do they intersect with traditional banking?

Recent years have been tough for the fintech industry, especially after the pandemic. This period has brought new hurdles in data analysis and made existing ones more complex. As the market stabilises, both fintech and finserve companies must tackle these evolving data issues.

Let’s examine some of the most significant data analytics challenges facing the fintech industry, starting with an issue that’s prevalent across the financial sector:

1. Battling data silos

In a recent survey by InterSystems, 54% of financial institution leaders said data silos are their biggest barrier to innovation, while 62% said removing silos is their priority data strategy for the next year.

Data silos segregate data repositories across departments, products and other divisions. This is a major issue in traditional banking and something fintech companies should avoid inheriting at all costs.

Siloed data makes it harder for decision-makers to view business performance with 360-degree clarity. It’s also expensive to maintain and operationalise and can evolve into privacy and data compliance issues if left unchecked.

To avoid or remove data silos, develop a data governance framework and centralise your data repositories. Next, simplify your analytics stack into as few integrated tools as possible because complex tech stacks are one of the leading causes of data silos.

Use an analytics system like Matomo that incorporates web analytics, marketing attribution and CRO testing into one toolkit.

Matomo’s support plans help you implement a data system to meet the unique needs of your business and avoid issues like data silos. We also offer data warehouse exporting as a feature to bring all of your web analytics, customer data, support data, etc., into one centralised location.

Try Matomo for free today, or contact our sales team to discuss support plans.

2. Compliance with laws and regulations

A survey by Alloy reveals that 93% of fintech companies find it difficult to meet compliance regulations. The cost of staying compliant tops their list of worries (23%), outranking even the financial hit from fraud (21%) – and this in a year marked by cyber threats.

Data privacy laws are constantly changing, and the landscape varies across global regions, making adherence even more challenging for fintechs and traditional banks operating in multiple markets. 

In the US market, companies grapple with regulations at both federal and state levels. Here are some of the state-level legislation coming into effect for 2024-2026:

• Oregon – Oregon Consumer Privacy Act (July 1, 2024)
• Florida – Florida Digital Bill of Rights (July 1, 2024)
• Texas – Texas Data Privacy and Security Act (July 1, 2024)
• Montana – Montana Consumer Data Privacy Act (October 1, 2024)
• Delaware – Delaware Personal Privacy Act (January 1, 2025)
• Iowa – Iowa Consumer Data Protection Act (January 1, 2025)
• New Jersey – SB 332 (January 15, 2025)
• Tennessee – Tennessee Information Protection Act (July 1, 2025)
• Indiana – Indiana Consumer Data Protection Act ( January 1, 2026)

Other countries are also ramping up regional regulations. For instance, Canada has Quebec’s Act Respecting the Protection of Personal Information in the Private Sector and British Columbia’s Personal Information Protection Act (BC PIPA).

Ignorance of country- or region-specific laws will not stop companies from suffering the consequences of violating them.

The only answer is to invest in adherence and manage business growth accordingly. Ultimately, compliance is more affordable than non-compliance – not only in terms of the potential fines but also the potential risks to reputation, consumer trust and customer loyalty.

This is an expensive lesson that fintech and traditional financial companies have had to learn together. GDPR regulators hit CaixaBank S.A, one of Spain’s largest banks, with multiple multi-million Euro fines, and Klarna Bank AB, a popular Swedish fintech company, for €720,000.

To avoid similar fates, companies should:

1. Build solid data systems
2. Hire compliance experts
3. Train their teams thoroughly
4. Choose data analytics tools carefully

Remember, even popular tools like Google Analytics aren’t automatically safe. Find out how Matomo helps you gather useful insights while sticking to rules like GDPR.

3. Protecting against data security threats

Cyber threats are increasing in volume and sophistication, with the financial sector becoming the most breached in 2023.

The cybersecurity risks will only worsen, with WEF estimating annual cybercrime expenses of up to USD $10.5 trillion globally by 2025, up from USD $3 trillion in 2015.

While technology brings new security solutions, it also amplifies existing risks and creates new ones. A 2024 McKinsey report warns that the risk of data breaches will continue to increase as the financial industry increasingly relies on third-party data tools and cloud computing services unless they simultaneously improve their security posture.

The reality is that adopting a third-party data system without taking the proper precautions means adopting its security vulnerabilities.

In 2023, the MOVEit data breach affected companies worldwide, including financial institutions using its file transfer system. One hack created a global data crisis, potentially affecting the customer data of every company using this one software product.

The McKinsey report emphasises choosing tools wisely. Why? Because when customer data is compromised, it’s your company that takes the heat, not the tool provider. As the report states:

“Companies need reliable, insightful metrics and reporting (such as security compliance, risk metrics and vulnerability tracking) to prove to regulators the health of their security capabilities and to manage those capabilities.”

Don’t put user or customer data in the hands of companies you can’t trust. Work with providers that care about security as much as you do. With Matomo, you own all of your data, ensuring it’s never used for unknown purposes.

4. Protecting users’ privacy

With security threats increasing, fintech companies and traditional banks must prioritise user privacy protection. Users are also increasingly aware of privacy threats and ready to walk away from companies that lose their trust.

Cisco’s 2023 Data Privacy Benchmark Study reveals some eye-opening statistics:

• 94% of companies said their customers wouldn’t buy from them if their data wasn’t protected, and 
• 95% see privacy as a business necessity, not just a legal requirement.

Modern financial companies must balance data collection and management with increasing privacy demands. This may sound contradictory for companies reliant on dated practices like third-party cookies, but they need to learn to thrive in a cookieless web as customers move to banks and service providers that have strong data ethics.

This privacy protection journey starts with implementing web analytics ethically from the very first session.

The most important elements of ethically-sound web analytics in fintech are:

1. 100% data ownership: Make sure your data isn’t used in other ways by the tools that collect it.
2. Respecting user privacy: Only collect the data you absolutely need to do your job and avoid personally identifiable information.
3. Regulatory compliance: Stick with solutions built for compliance to stay out of legal trouble.
4. Data transparency: Know how your tools use your data and let your customers know how you use it.

Read our guide to ethical web analytics for more information.

5. Comparing customer trust across industries 

While fintech companies are making waves in the financial world, they’re still playing catch-up when it comes to earning customer trust. According to RFI Global, fintech has a consumer trust score of 5.8/10 in 2024, while traditional banking scores 7.6/10.

This trust gap isn’t just about perception – it’s rooted in real issues:

• Security breaches are making headlines more often.
• Privacy regulations like GDPR are making consumers more aware of their rights.
• Some fintech companies are struggling to handle fraud effectively.

According to the UK’s Payment Systems Regulator, digital banking brands Monzo and Starling had some of the highest fraudulent activity rates in 2022. Yet, Monzo only reimbursed 6% of customers who reported suspicious transactions, compared to 70% for NatWest and 91% for Nationwide.

So, what can fintech firms do to close this trust gap?

• Start with privacy-centric analytics from day one. This shows customers you value their privacy from the get-go.
• Build and maintain a long-term reputation free of data leaks and privacy issues. One major breach can undo years of trust-building.
• Learn from traditional banks when it comes to handling issues like fraudulent transactions, identity theft, and data breaches. Prompt, customer-friendly resolutions go a long way.
• Remember: cutting-edge financial technology doesn’t make up for poor customer care. If your digital bank won’t refund customers who’ve fallen victim to credit card fraud, they’ll likely switch to a traditional bank that will.

The fintech sector has made strides in innovation, but there’s still work to do in establishing trustworthiness. By focusing on robust security, transparent practices, and excellent customer service, fintech companies can bridge the trust gap and compete more effectively with traditional banks.

6. Collecting quality data

Adhering to data privacy regulations, protecting user data and implementing ethical analytics raises another challenge. How can companies do all of these things and still collect reliable, quality data?

Google’s answer is using predictive models, but this replaces real data with calculations and guesswork. The worst part is that Google Analytics doesn’t even let you use all of the data you collect in the first place. Instead, it uses something called data sampling once you pass certain thresholds.

In practice, this means that Google Analytics uses a limited set of your data to calculate reports. We’ve discussed GA4 data sampling at length before, but there are two key problems for companies here:

1. A sample size that’s too small won’t give you a full representation of your data.
2. The more visitors that come to your site, the less accurate your reports will become.

For high-growth companies, data sampling simply can’t keep up. Financial marketers widely recognise the shortcomings of big tech analytics providers. In fact, 80% of them say they’re concerned about data bias from major providers like Google and Meta affecting valuable insights.

This is precisely why CRO:NYX Digital approached us after discovering Google Analytics wasn’t providing accurate campaign data. We set up an analytics system to suit the company’s needs and tested it alongside Google Analytics for multiple campaigns. In one instance, Google Analytics failed to register 6,837 users in a single day, approximately 9.8% of the total tracked by Matomo.

In another instance, Google Analytics only tracked 600 visitors over 24 hours, while Matomo recorded nearly 71,000 visitors – an 11,700% discrepancy.

Financial companies need a more reliable, privacy-centric alternative to Google Analytics that captures quality data without putting users at potential risk. This is why we built Matomo and why our customers love having total control and visibility of their data.

Unlock the full power of fintech data analytics with Matomo

Fintech companies face many data-related challenges, so compliant web analytics shouldn’t be one of them. 

With Matomo, you get:

• An all-in-one solution that handles traditional web analytics, behavioural analytics and more with strong integrations to minimise the likelihood of data siloing
• Full compliance with GDPR, CCPA, PIPL and more
• Complete ownership of your data to minimise cybersecurity risks caused by negligent third parties
• An abundance of ways to protect customer privacy, like IP address anonymisation and respect for DoNotTrack settings
• The ability to import data from Google Analytics and distance yourself from big tech
• High-quality data that doesn’t rely on sampling
• A tool built with financial analytics in mind

Don’t let big tech companies limit the power of your data with sketchy privacy policies and counterintuitive systems like data sampling. 

Start your Matomo free trial or request a demo to unlock the full power of fintech data analytics without putting your customers’ personal information at unnecessary risk.