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  • Matomo’s 2021 Year in Review

    13 décembre 2021, par erin — Community

    2021 has been an exciting year at Matomo !

    We’re grateful for all community members who reported feedback and suggestions, our awesome team of translators for their work, and our Premium features customers and Matomo Cloud hosting customers for their amazing support. 

    We wanted to share some quick highlights to remind you of the exciting things that happened in 2021.

    Matomo continues to develop

    In 2021 we released a number of new features including :

    The new SEO Web Vitals feature helps you track your critical website performance metrics, which are a core element of SEO best practice.

    SEO Web Vitals

    Measure the performance of your ads without giving up privacy.

    This exciting new feature supports privacy and compliance requirements by eliminating the need to put third-party advertising tracking codes on your site. Now marketers can easily import conversion data from Matomo into Google Ads, Microsoft Advertising or Yandex Ads.

    Say goodbye to spammers & bots making your data inaccurate and say hello to reliable data. 

    This powerful plugin provides our self-hosting users various options to prevent spammers and bots from making data inaccurate so you can rely on your data again.

    • In 2021 we moved from Matomo 4.1.0 to Matomo 4.6.0, with our new releases delivering over 600 updates to improve the stability and functionality of the product.

    Some of our team’s favourite updates in 2021 included :

      • Graphs now show a difference for data of “unfinished” and “complete” periods, with unfinished periods now indicated by a dashed line.
      • Improvements to Matomo Tag Manager’s debugger – now you can simply enter the URL in a form and click Debug.
      • Dashboards now show proportional evolution comparison for incomplete periods (rather than absolute values).
       
    • We also rolled out general bug fixes in Matomo Mobile 2.5 for iOS and Android.
    • Continuous improvements to Matomo for WordPress. 

    In other news

    If you haven’t explored our Marketplace yet, some of our most popular plugins include :

    Matomo Community working together

    MatomoCamp 2021 was a massive success thanks to our passionate community, sponsors and speakers. This virtual event was run by the Matomo Community, for the Matomo Community. 

    MatomoCamp is the first online event developed by and for the Matomo community.

    Some of the most popular sessions included :

    All MatomoCamp sessions are now available on-demand. Enjoy !

    More people are choosing ethical analytics 

    We surpassed the incredible milestone of 30K active Matomo for WordPress installations.

    How can you get involved in 2022 ?

    Our mission at Matomo is :

    “To create, as a community, the leading open digital analytics platform, that gives every user full control of their data”

    Join our mission by writing about Matomo on your blog, website, Twitter, talk at conferences or let your friends and colleagues know what is Matomo. 

    Use the Matomo forum if you have any questions or feedback (free support), or purchase a Support Plan to get professional support and guidance.

    To improve Matomo in your language, consider contributing to translations.

    You can also support our efforts by purchasing Premium Features for Matomo or try our Matomo Cloud solution.

    Thank you for being part of our Matomo community, we wish you all the best for 2022 !

  • Lean Analytics in a Privacy-First Environment – Bootcamp with Timo Dechau

    29 novembre 2024, par Daniel Crough — Banking and Financial Services, GDPR, Marketing, Privacy, Videos, Featured Banking Content

    In a recent bootcamp, Timo Dechau walked attendees through his approach to data and measurement in privacy-focused analytics environments. He demonstrates how to shift from a chaotic, ‘track-it-all’ mentality to a focused method that prioritizes quality over quantity. This post will summarize some of his key privacy-first analytics ideas, but be sure to check out the on-demand video for more detail.

    Watch the bootcamp on demand

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    the consequences of more data are missing and incomplete data that messes up attribution and measurement.

    Unrestrained data collection leads to data bloat

    Marketing and the business world are experiencing a data problem. Analysts and business intelligence teams grapple with large amounts of data that aren’t always useful and are often incomplete. The idea that “more data is better” became a guiding principle in the early 2000s, encouraging companies to gather everything possible using all available data collection methods. This unrestrained pursuit often led to an unexpected problem : data bloat. Too much data, too little clarity. Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.

    timeline diagram of the trend towards lean analytics

    Cutting through the noise, focusing on what matters

    The “more data is better” mindset emerged when digital marketers were beginning to understand data’s potential. It seemed logical : more data should mean more opportunities to optimise, personalise, and drive results. But in practice, gathering every possible piece of data often leads to a cluttered, confusing pile of metrics that can mislead more than guide.

    This approach carries hidden costs. Excessive data collection burns resources, increases privacy concerns, and leaves teams unfocused. It’s easy to get lost trying to make sense of endless dashboards, metrics, and reports. More data doesn’t necessarily lead to better decisions ; it often just leads to more noise, hindering effective data management.

    Rethinking data management : From data overload to data mindfulness

    Data management has often prioritised comprehensive data gathering without considering the specific value of each data point. This approach has created more information, but not necessarily better insights.

    Data mindfulness is about taking a deliberate, focused approach to data collection and analysis. Instead of trying to collect everything, it emphasises gathering only what truly adds value. It’s about ensuring the data you collect serves a purpose and directly contributes to better insights and data-driven decision-making.

    Think of it like applying a “lean” methodology to data—trimming away the unnecessary and keeping only what is essential. Or consider embracing data minimalism to declutter your data warehouse, keeping only what truly sparks insight.

    Lean analytics marketing attribution framework

    Mindful data is ethical data

    Adopting a mindful approach to data can pay off in several ways :

    • Reduces overwhelm : When you reduce the clutter, you’re left with fewer, clearer metrics that lead to stronger decisions and actionable data insights.

    • Mitigates compliance risks : By collecting less, companies align better with privacy regulations and build trust with their customers. Privacy-first analytics and privacy-compliant analytics practices mean there’s no need for invasive tracking if it doesn’t add value—and customers will appreciate that.

    • Enhances data ethics : Focusing on the quality rather than the quantity of data collected ensures ethical data collection and management. Companies use data responsibly, respect user privacy, and minimise unnecessary data handling, strengthening customer relationships and brand integrity.

    • Improves data efficiency : Focused analytics means better use of resources. You’re spending less time managing meaningless metrics and more time working on meaningful insights. Many companies have found success by switching to a leaner, quality-first data approach, reporting sharper, more impactful results.

    Shifting towards simplicity and lean analytics

    If data mindfulness sounds appealing, here’s how you can get started :

    1. Ask the right questions. Before collecting any data, ask yourself : Why are we collecting this ? How will it drive value ? If you can’t answer these questions clearly, that data probably isn’t worth collecting. This is a key step in smart data management.

    2. Simplify metrics. Focus on the KPIs that truly matter for your business. Choose a handful of key metrics that reflect your goals rather than a sprawling list of nice-to-haves. Embracing data simplicity helps in targeting data collection effectively.

    3. Audit your current data. Review your existing data collection processes. Which metrics are you actively using to make decisions ? Eliminate any redundant or low-value metrics that create noise. Use ethical data management practices to ensure data efficiency and compliance. Understanding what is data management in this context is crucial.

    4. Implement lean analytics practices. Shift towards lean analytics by cutting down on unnecessary tracking. This can involve reducing reliance on multiple tracking scripts, simplifying your reporting, and setting up a streamlined dashboard focused on key outcomes. Embrace data reduction strategies to eliminate waste and boost effectiveness.

    Who should watch this bootcamp

    This bootcamp is perfect for data analysts, product managers, digital marketers and business leaders who are seeking a more streamlined approach to data measurement. If you’re interested in moving away from a chaotic “track-it-all” mentality and towards a focused, lean, and privacy-first analytics strategy, this workshop is for you.

    What you’ll discover

    • Practical steps : Learn actionable strategies to reduce data bloat and implement lean, privacy-first analytics in your organisation.

    • Real-life examples : Explore case studies of companies that have successfully adopted focused and privacy-first analytics.

    • Deep insights : Gain a deeper understanding of how to prioritise quality over quantity without sacrificing valuable insights.

    Watch the bootcamp on-demand

    For a comprehensive dive into these topics, watch the full workshop video or download the detailed transcript. Equip yourself with the knowledge and tools to transform your data management approach today.

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  • How HSBC and ING are transforming banking with AI

    9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

    We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

    Watch the on-demand webinar : Advancing analytics maturity.

    By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.





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    Meet the expert panel

    Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

     

    Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

     

    Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

     

    Key findings from the webinar

    The discussion highlighted four essential elements for advancing analytics capabilities :

    1. Strong data foundations matter most

    “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

    Johri explained. Banks need robust data governance that works across different regulatory environments.

    2. Transform rather than tweak

    Li Mandri emphasised the need to reconsider entire processes :

    “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

    3. Bridge technical and business understanding

    Both leaders stressed the value of analytics translators who understand both technology and business needs.

    “We’re investing in this layer we call product leads,”

    Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

    4. Consider production costs early

    Moving from proof-of-concept to production requires careful planning. As Johri noted :

    “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

    This includes :

    • Ongoing monitoring requirements
    • Maintenance needs
    • Regulatory compliance checks
    • Regular model updates

    Real-world applications

    ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

    Customer experience enhancement

    The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

    “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

    Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

    The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

    The bank also uses AI to personalise customer communications.

    “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

    noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

    Operational efficiency transformation

    ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

    “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

    Li Mandri explained. The bank developed an AI-powered system that :

    • Automates document verification
    • Flags potential compliance issues for human review
    • Maintains consistent standards across jurisdictions
    • Reduces processing time while improving accuracy

    This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

    In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

    Sustainable finance initiatives

    ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

    “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

    The bank developed AI models that :

    • Analyse company sustainability metrics
    • Compare environmental performance against industry benchmarks
    • Assess transition plans for high-emission industries
    • Monitor ongoing compliance with sustainability commitments

    This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

    “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

    Li Mandri noted.

    HSBC’s innovative approach

    Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

    Environmental monitoring through advanced technology

    HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

    “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

    Johri explained.

    The system provides several key capabilities :

    • Analysis of forest coverage and deforestation rates
    • Assessment of biodiversity impact in specific regions
    • Monitoring of environmental changes over time
    • Measurement of environmental risk in lending portfolios

    “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

    Johri noted. This technology enables HSBC to :

    • Make informed lending decisions
    • Monitor environmental commitments of borrowers
    • Support sustainability-linked lending programmes
    • Provide accurate environmental impact reporting

    Transforming document analysis

    HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

    “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

    Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

    The bank’s approach includes :

    • Natural language processing to understand complex documents
    • Machine learning models to extract relevant information
    • Validation systems to ensure accuracy
    • Integration with existing compliance frameworks

    “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

    This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

    Building future-ready analytics capabilities

    Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

    Developing clear governance frameworks

    “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

    Johri emphasised. Banks need to establish governance structures that :

    • Define acceptable uses for AI
    • Establish monitoring and control mechanisms
    • Ensure compliance with evolving regulations
    • Maintain transparency in AI decision-making

    Creating solutions that scale

    Li Mandri stressed the importance of building systems that grow with the organisation :

    “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

    Successful scaling requires :

    • Standard approaches to model development
    • Clear evaluation frameworks
    • Simple processes for model updates
    • Strong monitoring systems
    • Regular performance reviews

    Investing in people and skills

    Both leaders highlighted how important skilled people are to analytics success.

    “Having a good hiring strategy as well as creating that data literacy is really important,”

    Johri noted. Banks need to :

    • Develop comprehensive training programmes
    • Create clear career paths for analytics professionals
    • Foster collaboration between technical and business teams
    • Build internal expertise in emerging technologies

    Planning for the future

    Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

    • Adapting to new privacy regulations
    • Making AI decisions more explainable
    • Improving data quality and governance
    • Strengthening cybersecurity measures

    Practical steps for financial institutions

    The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

    Key steps for success

    Financial institutions looking to enhance their analytics capabilities should :

    1. Start with strong foundations
      • Invest in clear data governance frameworks
      • Set data quality standards
      • Build thorough documentation processes
      • Create transparent data tracking
    2. Think strategically about AI implementation
      • Focus on transformative rather than small changes
      • Consider the full costs of AI projects
      • Build solutions that can grow
      • Balance innovation with risk management
    3. Invest in people and processes
      • Develop internal analytics expertise
      • Create clear paths for career growth
      • Foster collaboration between technical and business teams
      • Build a culture of data literacy
    4. Plan for scale
      • Establish monitoring systems
      • Create governance frameworks
      • Develop standard approaches to model development
      • Stay flexible for future regulatory changes

    Learn more

    Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

    • Detailed technical insights from both banks
    • Extended Q&A with the speakers
    • Additional case studies and examples
    • Practical implementation advice
     
     

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    Watch the on-demand webinar : Advancing analytics maturity.

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