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• CCPA vs GDPR : Understanding Their Impact on Data Analytics

  19 mars, par Alex Carmona

  With over 400 million internet users in Europe and 331 million in the US (11% of which reside in California alone), understanding the nuances of privacy laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is crucial for compliant and ethical consumer data collection.

  Navigating this compliance landscape can be challenging for businesses serving European and Californian markets.

  This guide explores the key differences between CCPA and GDPR, their impact on data analytics, and how to ensure your business meets these essential privacy requirements.

  What is the California Consumer Privacy Act (CCPA) ?

  The California Consumer Privacy Act (CCPA) is a data privacy law that gives California consumers control over their personal information. It applies to for-profit businesses operating in California that meet specific criteria related to revenue, data collection and sales.

  Origins and purpose

  The CCPA addresses growing concerns about data privacy and how businesses use personal information in California. The act passed in 2018 and went into effect on 1 January 2020.

  Key features

  • Grants consumers the right to know what personal information is collected
  • Provides the right to delete personal information
  • Allows consumers to opt out of the sale of their personal information
  • Prohibits discrimination against consumers who exercise their CCPA rights

  Key definitions under the CCPA framework

  • Business : A for-profit entity doing business in California and meeting one or more of these conditions :
    • Has annual gross revenues over $25 million ;
    • Buys, receives, sells or shares 50,000 or more consumers’ personal information ; or
    • Derives 50% or more of its annual revenues from selling consumers’ personal information
  • Consumer : A natural person who is a California resident
  • Personal Information : Information that could be linked to, related to or used to identify a consumer or household, such as online identifiers, IP addresses, email addresses, social security numbers, cookie identifiers and more

  What is the General Data Protection Regulation (GDPR) ?

  The General Data Protection Regulation (GDPR) is a data privacy and protection law passed by the European Union (EU). It’s one of the strongest and most influential data privacy laws worldwide and applies to all organisations that process the personal data of individuals in the EU.

  Origins and purpose

  The GDPR was passed in 2016 and went into effect on 25 May 2018. It aims to harmonise data privacy laws in Europe and give people in the European Economic Area (EEA) privacy rights and control over their data.

  Key features

  • Applies to all organisations that process the personal data of individuals in the EEA
  • Grants individuals a wide range of privacy rights over their data
  • Requires organisations to obtain explicit and informed consent for most data processing
  • Mandates appropriate security measures to protect personal data
  • Imposes significant fines and penalties for non-compliance

  Key definitions under the GDPR framework

  • Data Subject : An identified or identifiable person
  • Personal Data : Any information relating to a data subject
  • Data Controller : The entity or organisation that determines how personal data is processed and what for
  • Data Processor : The entity or organisation that processes the data on behalf of the controller

  CCPA vs. GDPR : Key similarities

  The CCPA and GDPR enhance consumer privacy rights and give individuals greater control over their data.

  Dimension	CCPA	GDPR
  Purpose	Protect consumer privacy	Protect individual data rights
  Key Rights	Right to access, delete and opt out of sale	Right to access, rectify, erase and restrict processing
  Transparency	Requires transparency around data collection and use	Requires transparency about data collection, processing and use

  CCPA vs. GDPR : Key differences

  While they have similar purposes, the CCPA and GDPR differ significantly in their scope, approach and specific requirements.

  Dimension	CCPA	GDPR
  Scope	For-profit businesses only	All organisations processing EU consumer data
  Territorial Reach	California-based natural persons	All data subjects within the EEA
  Consent	Opt-out system	Opt-in system
  Penalties	Per violation based on its intentional or negligent nature	Case-by-case based on comprehensive assessment
  Individual Rights	Narrower (relative to GDPR)	Broader (relative to CCPA)

  CCPA vs. GDPR : A multi-dimensional comparison

  The previous sections gave a broad overview of the similarities and differences between CCPA and GDPR. Let’s now examine nine key dimensions where these regulations converge or diverge and discuss their impact on data analytics.

  

  #1. Scope and territorial reach

  The GDPR has a much broader scope than the CCPA. It applies to all organisations that process the personal data of individuals in the EEA, regardless of their business model, purpose or physical location.

  The CCPA applies to medium and large for-profit businesses that derive a substantial portion of their earnings from selling Californian consumers’ personal information. It doesn’t apply to non-profits, government agencies or smaller for-profit companies.

  Impact on data analytics

  The difference in scope significantly impacts data analytics practices. Smaller businesses may not need to comply with either regulation, some may only need to follow the CCPA, while most global businesses must comply with both. This often requires different methods for collecting and processing data in California, Europe, and elsewhere.

  #2. Penalties and fines for non-compliance

  Both the CCPA and GDPR impose penalties for non-compliance, but the severity of fines differs significantly :

  CCPA	Maximum penalty
  	$2,500 per unintentional violation $7,500 per intentional violation

  “Per violation” means per violation per impacted consumer. For example, three intentional CCPA violations affecting 1,000 consumers would result in 3,000 total violations and a $22.5 million maximum penalty (3,000 × $7,500).

  

  The largest CCPA fine to date was Zoom’s $85 million settlement in 2021.

  

  In contrast, the GDPR has resulted in 2,248 fines totalling almost €6.6 billion since 2018 — €2.4 billion of which were for non-compliance.

  GDPR	Maximum penalty
  	€20 million or 4% of all revenue earned the previous year

  So far, the biggest fine imposed under the GDPR was Meta’s €1.2 billion fine in May 2023 — 15 times more than Zoom had to pay California.

  Impact on data analytics

  The significant difference in potential fines demonstrates the importance of regulatory compliance for data analytics professionals. Non-compliance can have severe financial consequences, directly affecting budget allocation and business operations.

  Businesses must ensure their data collection, storage and processing practices comply with regulations in both Europe and California.

  Choosing privacy-first, compliance-ready analytics platforms like Matomo is instrumental for mitigating non-compliance risks.

  #3. Data subject rights and consumer rights

  The CCPA and GDPR give people similar rights over their data, but their limitations and details differ.

  Rights common to the CCPA and GDPR

  • Right to Access/Know : People can access their personal information and learn what data is collected, its source, its purpose and how it’s shared
  • Right to Delete/Erasure : People can request the deletion of their personal information, with some exceptions
  • Right to Non-Discrimination : Businesses can’t discriminate against people who exercise their privacy rights

  Consumer rights unique to the CCPA

  • Right to Opt Out of Sale : Consumers can prohibit the sale of their personal information
  • Right to Notice : Businesses must inform consumers about data collection practices
  • Right to Disclosure : Consumers can request specific information collected about them

  Data subject rights unique to the GDPR

  • Right to be Informed : Broader transparency requirements encompass data retention, automated decision-making and international transfers
  • Right to Rectification : Data subjects may request the correction of inaccurate data
  • Right to Restrict Processing : Consumers may limit data use in certain situations
  • Right to Data Portability : Businesses must provide individual consumer data in a secure, portable format when requested
  • Right to Withdraw Consent : Consumers may withdraw previously granted consent to data processing

  	CCPA	GDPR
  Right to Access or Know	✓	✓
  Right to Delete or Erase	✓	✓
  Right to Non-Discrimination	✓	✓
  Right to Opt-Out	✓
  Right to Notice	✓
  Right to Disclosure	✓
  Right to be Informed		✓
  Right to Rectification		✓
  Right to Restrict Processing		✓
  Right to Data Portability		✓
  Right to Withdraw Consent		✓

  Impact on data analytics

  Data analysts must understand these rights and ensure compliance with both regulations, which could potentially require separate data handling processes for EU and California consumers.

  #4. Opt-out vs. opt-in

  The CCPA generally follows an opt-out model, while the GDPR requires explicit consent from individuals before processing their data.

  Impact on data analytics

  For CCPA compliance, businesses can collect data by default if they provide opt-out mechanisms. Failing to process opt-out requests can result in severe penalties, like Sephora’s $1.2 million fine.

  Under GDPR, organisations must obtain explicit consent before collecting any data, which can limit the amount of data available for analysis.

  #5. Parental consent

  The CCPA and GDPR have provisions regarding parental consent for processing children’s data. The CCPA requires parental consent for children under 13, while the GDPR sets the age at 16, though member states can lower it to 13.

  Impact on data analytics

  This requirement significantly impacts businesses targeting younger audiences. In Europe and the US, companies must implement different methods to verify users’ ages and obtain parental consent when necessary.

  The California Attorney General’s Office recently fined Tilting Point Media LLC $500,000 for sharing children’s data without parental consent.

  #6. Data security requirements

  Both regulations require businesses to implement adequate security measures to protect personal data. However, the GDPR has more prescriptive requirements, outlining specific security measures and emphasising a risk-based approach.

  Impact on data analytics

  Data analytics professionals must ensure that data is processed and stored securely to avoid breaches and potential fines.

  #7. International data transfers

  Both the CCPA and GDPR address international data transfers. Under the CCPA, businesses must only inform consumers about international transfers. The GDPR has stricter requirements, including ensuring adequate data protection safeguards for transfers outside the EEA.

  

  Other rules, like the Payment Services Directive 2 (PSD2), also affect international data transfers, especially in the financial industry.

  PSD2 requires strong customer authentication and secure communication channels for payment services. This adds complexity to cross-border data flows.

  Impact on data analytics

  The primary impact is on businesses serving European residents from outside Europe. Processing data within the European Union is typically advisable. Meta’s record-breaking €1.2 billion fine was specifically for transferring data from the EEA to the US without sufficient safeguards.

  Choosing the right analytics platform helps avoid these issues.

  For example, Matomo offers a free, open-source, self-hosted analytics platform you can deploy anywhere. You can also choose a managed, GDPR-compliant cloud analytics solution with all data storage and processing servers within the EU (in Germany), ensuring your data never leaves the EEA.

  #8. Enforcement mechanisms

  The California Attorney General is responsible for enforcing CCPA requirements, while in Europe, the Data Protection Authority (DPA) in each EU member state enforces GDPR requirements.

  Impact on data analytics

  Data analytics professionals should be familiar with their respective enforcement bodies and their powers to support compliance efforts and minimise the risk of fines and penalties.

  #9. Legal basis for personal data processing

  The GDPR outlines six legal grounds for processing personal data :

  • Consent
  • Contract
  • Legal obligation
  • Vital interests
  • Public task
  • Legitimate interests

  The CCPA doesn’t explicitly define lawful bases but focuses on consumer rights and transparency in general.

  Impact on data analytics

  Businesses subject to the GDPR must identify and document a valid lawful basis for each processing activity.

  Compliance rules under CCPA and GDPR

  Complying with the CCPA and GDPR requires a comprehensive approach to data privacy. Here’s a summary of the essential compliance rules for each framework :

  

  CCPA compliance rules

  • Create clear and concise privacy policies outlining data collection and use practices
  • Give consumers the right to opt-out
  • Respond to consumer requests to access, delete and correct their personal information
  • Implement reasonable security measures for consumers’ personal data protection
  • Never discriminate against consumers who exercise their CCPA rights

  GDPR compliance rules

  • Obtain explicit and informed consent for data processing activities
  • Implement technical and organisational controls to safeguard personal data
  • Designate a Data Protection Officer (DPO) if necessary
  • Perform data protection impact assessments (DPIAs) for high-risk processing activities
  • Maintain records of processing activities
  • Promptly report data breaches to supervisory authorities

  Navigating the CCPA and GDPR with confidence

  Understanding the nuances of the CCPA and GDPR is crucial for businesses operating in the US and Europe. These regulations significantly impact data collection and analytics practices.

  Implementing robust data security practices and prioritising privacy and compliance are essential to avoid severe penalties and build trust with today’s privacy-conscious consumers.

  Privacy-centric analytics platforms like Matomo enable businesses to collect, analyse and use data responsibly and transparently, extracting valuable insights while maintaining compliance with both CCPA and GDPR requirements.

  Start your 21-day free trial today !

  no credit card required

• Can't set seeker in GSTREAMER cv2, python

  29 avril, par Alperen Ölçer

  I want to skip n seconds forward and backward in gstreamer cv2 capture for recorded videos. But when I use cap_gstreamer.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second) it resets seeker to beginning of video. How can I solve it ? I wrote an example, used recorded clock video.

  


  import cv2

video_p = '/home/alperenlcr/Videos/clock.mp4'

cap_gstreamer = cv2.VideoCapture(video_p, cv2.CAP_GSTREAMER)
cap_ffmpeg = cv2.VideoCapture(video_p, cv2.CAP_FFMPEG)

fps = cap_gstreamer.get(cv2.CAP_PROP_FPS)
skip_second = 100

im1 = cv2.resize(cap_gstreamer.read()[1], (960, 540))
im1_ffmpeg = cv2.resize(cap_ffmpeg.read()[1], (960, 540))

cap_gstreamer.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second)
cap_ffmpeg.set(cv2.CAP_PROP_POS_FRAMES, fps*skip_second)

im2 = cv2.resize(cap_gstreamer.read()[1], (960, 540))
im2_ffmpeg = cv2.resize(cap_ffmpeg.read()[1], (960, 540))

merge_gstreamer = cv2.hconcat([im1, im2])
merge_ffmpeg = cv2.hconcat([im1_ffmpeg, im2_ffmpeg])

cv2.imshow(str(skip_second) + ' gstreamer', merge_gstreamer)
cv2.imshow(str(skip_second) + ' ffmpeg', merge_ffmpeg)
cv2.waitKey(0)
cv2.destroyAllWindows()

cap_gstreamer.release()
cap_ffmpeg.release()



  


  Result :


  


  My cv2 build is like :

  


  >>> print(cv2.getBuildInformation())

General configuration for OpenCV 4.8.1 =====================================
  Version control:               4.8.1-dirty

  Extra modules:
    Location (extra):            /home/alperenlcr/SourceInstalls/opencv_contrib/modules
    Version control (extra):     4.8.1

  Platform:
    Timestamp:                   2024-12-02T13:44:58Z
    Host:                        Linux 6.8.0-49-generic x86_64
    CMake:                       3.22.1
    CMake generator:             Unix Makefiles
    CMake build tool:            /usr/bin/gmake
    Configuration:               RELEASE

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (18 files):         + SSSE3 SSE4_1
      SSE4_2 (2 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (1 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (8 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (37 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
      AVX512_SKX (8 files):      + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                /usr/bin/c++  (ver 10.5.0)
    C++ flags (Release):         -fsigned-char -ffast-math -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
    C++ flags (Debug):           -fsigned-char -ffast-math -W -Wall -Wreturn-type -Wnon-virtual-dtor -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
    C Compiler:                  /usr/bin/cc
    C flags (Release):           -fsigned-char -ffast-math -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
    C flags (Debug):             -fsigned-char -ffast-math -W -Wall -Wreturn-type -Waddress -Wsequence-point -Wformat -Wformat-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
    Linker flags (Release):      -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed -Wl,--no-undefined  
    ccache:                      NO
    Precompiled headers:         NO
    Extra dependencies:          /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so /usr/lib/x86_64-linux-gnu/libz.so /usr/lib/x86_64-linux-gnu/libfreetype.so /usr/lib/x86_64-linux-gnu/libharfbuzz.so Iconv::Iconv m pthread cudart_static dl rt nppc nppial nppicc nppidei nppif nppig nppim nppist nppisu nppitc npps cublas cudnn cufft -L/usr/lib/x86_64-linux-gnu -L/usr/lib/cuda/lib64
    3rdparty dependencies:       libprotobuf ade ittnotify libwebp libopenjp2 IlmImf quirc ippiw ippicv

  OpenCV modules:
    To be built:                 aruco bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python3 quality rapid reg rgbd saliency shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
    Disabled:                    cudacodec world
    Disabled by dependency:      -
    Unavailable:                 alphamat cvv hdf java julia matlab ovis python2 sfm viz
    Applications:                tests perf_tests examples apps
    Documentation:               NO
    Non-free algorithms:         NO

  GUI:                           GTK2
    QT:                          NO
    GTK+:                        YES (ver 2.24.33)
      GThread :                  YES (ver 2.72.4)
      GtkGlExt:                  NO
    OpenGL support:              NO
    VTK support:                 NO

  Media I/O: 
    ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
    JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 80)
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
    TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.3.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES
      avcodec:                   YES (58.134.100)
      avformat:                  YES (58.76.100)
      avutil:                    YES (56.70.100)
      swscale:                   YES (5.9.100)
      swresample:                YES (3.9.100)
    GStreamer:                   YES (1.20.3)
    v4l/v4l2:                    YES (linux/videodev2.h)

  Parallel framework:            TBB (ver 2021.5 interface 12050)

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.8 [2021.8.0]
           at:                   /home/alperenlcr/SourceInstalls/opencv/build/3rdparty/ippicv/ippicv_lnx/icv
    Intel IPP IW:                sources (2021.8.0)
              at:                /home/alperenlcr/SourceInstalls/opencv/build/3rdparty/ippicv/ippicv_lnx/iw
    VA:                          NO
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  NVIDIA CUDA:                   YES (ver 11.5, CUFFT CUBLAS NVCUVID NVCUVENC FAST_MATH)
    NVIDIA GPU arch:             86
    NVIDIA PTX archs:

  cuDNN:                         YES (ver 8.6.0)

  OpenCL:                        YES (no extra features)
    Include path:                /home/alperenlcr/SourceInstalls/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  ONNX:                          NO

  Python 3:
    Interpreter:                 /usr/bin/python3 (ver 3.10.12)
    Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.10.so (ver 3.10.12)
    numpy:                       /usr/lib/python3/dist-packages/numpy/core/include (ver 1.21.5)
    install path:                lib/python3.10/dist-packages/cv2/python-3.10

  Python (for build):            /usr/bin/python3

  Java:                          
    ant:                         NO
    Java:                        NO
    JNI:                         NO
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    /usr/local
-----------------------------------------------------------------



  


• Choosing the best self-hosted open-source analytics platform

  16 juillet, par Joe

  Google Analytics (GA) is the most widely used analytics platform, with 50.3% of the top 1 million active websites using it today. You’re probably using it right now. 

  But despite being a free tool, Google Analytics is proprietary software, which means you’re handing over your browsing data, metadata and search history to a third party.

  Do you trust them ? We sure don’t.

  This lack of control can lead to potential privacy risks and compliance issues. These issues have so far resulted in fines under the EU’s General Data Protection Regulation (GDPR) of an average of €2.5 million each, for a total of almost €6.6 billion since 2018.

  Open-source analytics platforms offer a solution. They’re a safer and more transparent alternative that lets you retain full control over how you collect and store your customers’ data. But what are these tools ? Where do you find them ? And, most importantly, how do you choose the best one for your needs ?

  This guide explores the benefits and features of open-source analytics platforms and compares popular options, including Matomo, a leading self-hosted, open-source Google Analytics alternative.

  What is an open-source analytics platform ?

  An analytics platform is software that collects, processes and analyses data to gain insights, identify trends, and make informed decisions. It helps users understand past performance, monitor current activities and predict future outcomes.

  An open-source analytics platform is a type of analytics suite in which anyone can view, modify and distribute the underlying source code.

  In contrast to proprietary analytics platforms, where a single entity owns and controls the code, open-source analytics platforms adhere to the principles of free and open-source software (FOSS). This allows everyone to use, study, share, and customise the software to meet their needs, fostering collaboration and transparency.

  Open-source analytics and the Free Software Foundation

  The concept of FOSS is rooted in the idea of software freedom. According to the Free Software Foundation (FSF), this idea is defined by four fundamental freedoms granted to the user the freedom to :

  • Use or run the program as they wish, for any purpose.
  • Study how the program works and change it as they wish.
  • Redistribute copies to help others.
  • Improve the code and distribute copies of their improved versions to others.

  Open access to the source code is a precondition for guaranteeing these freedoms.

  The importance of FOSS licensing

  The FSF has been instrumental in the free software movement, which serves as the foundation for open-source analytics platforms. Among other things, it created the GNU General Public Licence (GPL), which guarantees that all software distributions include the source code and are distributed under the same licence.

  However, other licences, including several copyleft and permissive licences, have been developed to address certain legal issues and loopholes in the GPL. Analytics platforms distributed under any of these licences are considered open-source since they are FSF-compliant.

  Benefits and drawbacks of open-source analytics platforms

  Open-source analytics platforms offer a compelling alternative to their proprietary counterparts, but they also have a few challenges.

  

  Benefits of open-source analytics

  • Full data ownership : Many open-source solutions let you host the analytics platform yourself. This gives you complete control over your customers’ data, ensuring privacy and security.
  • Customisable solution : With access to the source code, you can tailor the platform to your specific needs.
  • Full transparency : You can inspect the code to see exactly how data is collected, processed and stored, helping you ensure compliance with privacy regulations.
  • Community-driven development : Open-source projects benefit from the contributions of a global community of developers. This leads to faster innovation, quicker bug fixes and, in some cases, a wider range of features.
  • No predefined limits : Self-hosted open-source analytics platforms don’t impose arbitrary limits on data storage or processing. You’re only limited by your own server resources.

  Cons of open-source analytics

  • Technical expertise required : Setting up and maintaining a self-hosted open-source platform often requires technical knowledge.
  • No live/dedicated support team : While many projects have active communities, dedicated support might be limited compared to commercial offerings.
  • Integration challenges : Integrating with other tools in your stack might require custom development, especially if pre-built integrations aren’t available.
  • Feature gaps : Depending on the specific platform, there might be gaps in functionality compared to mature proprietary solutions.

  Why open-source is better than proprietary analytics

  Proprietary analytics platforms, like Google Analytics, have long been the go-to choice for many businesses. However, growing concerns around data privacy, vendor lock-in and limited customisation are driving a shift towards open-source alternatives.

  No vendor lock-in

  Proprietary platforms lock you into their ecosystem, controlling terms, pricing and future development. Migrating data can be costly, and you’re dependent on the vendor for updates. 

  Open-source platforms allow users to switch providers, modify software and contribute to development. Contributors can also create dedicated migration tools to import data from GA and other proprietary platforms.

  Data privacy concerns

  Proprietary analytics platforms can heighten the risk of data privacy violations and subsequent fines under regulations like the GDPR and the California Consumer Privacy Act (CCPA). This is because their opaque ‘black box’ design often obscures how they collect, process and use data. 

  Businesses often have limited visibility and even less control over a vendor’s data handling. They don’t know whether these vendors are using it for their own benefit or sharing it more widely, which can lead to privacy breaches and other data protection violations.

  These fines can reach into the millions and even billions. For example, Zoom was fined $85 million in 2021 for CCPA violations, while the largest fine in history has been the €1.2 billion fine imposed on Meta by the Irish Data Protection Act (DPA) under the EU GDPR.

  Customisation

  Proprietary platforms often offer a one-size-fits-all approach. While they might have some customisation options, you’re ultimately limited by what the vendor provides. Open-source platforms, on the other hand, offer unparalleled flexibility.

  Unlimited data processing

  Proprietary analytics platforms often restrict the amount of data you can collect and process, especially on free plans. Going over these limits usually requires upgrading to a paid plan, which can be a problem for high-traffic websites or businesses with large datasets. 

  Self-hosted tools only limit data processing based on your server resources, allowing you to collect and analyse as much data as you need at no extra cost.

  No black box effect

  Since proprietary tools are closed-source, they often lack transparency in their data processing methods. It’s difficult to understand and validate how their algorithms work or how they calculate specific metrics. This “black box” effect can lead to trust issues and make it challenging to validate your data’s accuracy.

  11 Key features to look for in an open-source analytics platform

  Choosing the right open-source analytics platform is crucial for unlocking actionable insights from your customers’ data. Here are 11 key features to consider :

  

  #1. Extensive support documentation and resource libraries

  Even with technical expertise, you might encounter challenges or have questions about the platform. A strong support system is essential. Look for platforms with comprehensive documentation, active community forums and the option for professional support for mission-critical deployments.

  #2. Live analytics

  Having access to live data and reports is crucial for making timely and informed decisions. A live analytics feature allows you to :

  • Monitor website traffic as it happens.
  • Optimise campaign performance tracking.
  • Identify and respond to issues like traffic spikes, drops or errors quickly, allowing for rapid troubleshooting.

  For example, Matomo updates tracking data every 10 seconds, which is more than enough to give you a live view of your website performance.

  #3. Personal data tracking

  Understanding user behaviour is at the heart of effective analytics. Look for a platform that allows you to track personal data while respecting privacy. This might include features like :

  • Creating detailed profiles of individual users and tracking their interactions across multiple sessions.
  • Track user-specific attributes like demographics, interests or purchase history.
  • Track user ID across different devices and platforms to understand user experience.

  #4. Conversion tracking

  Ultimately, you want to measure how effective your website is in achieving your business goals. Conversion tracking allows you to :

  • Define and track key performance indicators (KPIs) like purchases, sign-ups or downloads.
  • Identify bottlenecks in the user journey that prevent conversions.
  • Measure the ROI of your marketing campaigns.

  #5. Session recordings

  Session recordings give your development team a qualitative understanding of user behaviour by letting you watch replays of individual user sessions. This can help you :

  • Identify usability issues.
  • Understand how users navigate your site and interact with different elements.
  • Uncover bugs or errors.

  #6. A/B testing

  Experimentation is key to optimising your website and improving conversion rates. Look for an integrated A/B testing feature that allows you to :

  • Test different variations of your website in terms of headlines, images, calls to action or page layouts.
  • Measure the impact on key metrics.
  • Implement changes based on statistically significant differences in user behaviour patterns, rather than guesswork.

  #7. Custom reporting and dashboards

  Every business has unique reporting needs. Look for a flexible platform that allows you to :

  • Build custom reports that focus on the metrics that matter most to you.
  • Create personalised dashboards that provide a quick overview of those KPIs.
  • Automate report generation to save your team valuable time.

  #8. No data sampling

  Data sampling can save time and processing power, but it can also lead to inaccurate insights if the sample isn’t representative of the entire dataset. The solution is to avoid data sampling entirely.

  Processing 100% of your customers’ data ensures that your reports are accurate and unbiased, providing a true picture of customer behaviour.

  #9. Google Analytics migration tools

  If you’re migrating from Google Analytics, a data export/import tool can save you time and effort. Some open-source analytics projects offer dedicated data importers to transfer historical data from GA into the new platform, preserving valuable insights. These tools help maintain data continuity and simplify the transition, reducing the manual effort involved in setting up a new analytics platform.

  #10 A broad customer base

  The breadth and diversity of an analytics platform’s customer base can be a strong indicator of its trustworthiness and capabilities. Consider the following :

  • Verticals served
  • The size of the companies that use it
  • Whether it’s trusted in highly-regulated industries

  If a platform is trusted by a large entity with stringent security and privacy requirements, such as governments or military branches, it speaks volumes about its security and data protection capabilities.

  #11 Self-hosting

  Self-hosting offers unparalleled control over your customers’ data and infrastructure.

  Unlike cloud-based solutions, where your customers’ data resides on third-party servers, self-hosting means you manage your own servers and databases. This approach ensures that your customers’ data remains within your own infrastructure, enhancing privacy and security.

  There are other features, like analytics for mobile apps, but these 11 will help shortlist your options to find the ideal tool.

  Choosing your self-hosted open-source analytics platform : A step-by-step guide

  The right self-hosted open-source analytics platform can significantly impact your data strategy. Follow these steps to make the best choice :

  

  Step #1. Define your needs and objectives

  Begin by clearly outlining what you want to achieve with your analytics platform :

  • Identify relevant KPIs.
  • Determine what type of reports to generate, their frequency and distribution.
  • Consider your privacy and compliance needs, like GDPR and CCPA.

  Step #2. Define your budget

  While self-hosted open-source platforms are usually free to use, there are still costs associated with self-hosting, including :

  • Server hardware and infrastructure.
  • Ongoing maintenance, updates and potential support fees.
  • Development resources if you plan to customise the platform.

  Step #3. Consider scalability and performance

  Scaling your analytics can be an issue with self-hosted platforms since it means scaling your server infrastructure as well. Before choosing a platform, you must think about :

  • Current traffic volume and projected growth.
  • Your current capacity to handle traffic.
  • The platform’s scalability options.

  Step #4. Research and evaluate potential solutions

  Shortlist a few different open-source analytics platforms that align with your requirements. In addition to the features outlined above, also consider factors like :

  • Ease of use.
  • Community and support.
  • Comprehensive documentation.
  • The platform’s security track record.

  Step #5. Sign up for a free trial and conduct thorough testing

  Many platforms offer free trials or demos. Take advantage of these opportunities to test the platform’s features, evaluate the user interface and more.

  You can embed multiple independent tracking codes on your website, which means you can test multiple analytics platforms simultaneously. Doing so helps you compare and validate results based on the same data, making comparisons more objective and reliable.

  Step #6. Plan for implementation and ongoing management

  After choosing a platform, follow the documentation to install and configure the software. Plan how you’ll migrate existing data if you’re switching from another platform.

  Ensure your team is trained on the platform, and establish a plan for updates, security patches and backups. Then, you’ll be ready to migrate to the new platform while minimising downtime.

  Top self-hosted open-source analytics tools

  Let’s examine three prominent self-hosted open-source analytics tools.

  Matomo

  Main Features	Analytics updated every 10 seconds, custom reports, dashboards, user segmentation, goal tracking, e-commerce tracking, funnels, heatmaps, session recordings, A/B testing, SEO tools and more advanced features.
  Best for	Businesses of all sizes and from all verticals. Advanced users
  Licencing	GPLv3 (core platform).Various commercial licences for plugins.
  Pricing	Self-hosted : Free (excluding paid plugins).Cloud version : Starts at $21.67/mo for 50K website hits when paid annually.

  

  Matomo Analytics dashboard

  Matomo is a powerful web analytics platform that prioritises data privacy and user control. It offers a comprehensive suite of features, including live analytics updated every 10 seconds, custom reporting, e-commerce tracking and more. You can choose between a full-featured open-source, self-hosted platform free of charge or a cloud-based, fully managed paid analytics service.

  Matomo also offers 100% data ownership and has a user base of over 1 million websites, including heavyweights like NASA, the European Commission, ahrefs and the United Nations.

  Plausible Analytics

  Main Features	Basic website analytics (page views, visitors, referrers, etc.), custom events, goal tracking and some campaign tracking features.
  Best for	Website owners, bloggers and small businesses.Non-technical users.
  Licencing	AGPLv3.
  Pricing	Self-hosted : FreeCloud version : Starts at $7.50/mo for 10K website hits when paid annually.

  

  Plausible Analytics 
  (Image source)

  Plausible Analytics is a lightweight, privacy-focused analytics tool designed to be simple and easy to use. It provides essential website traffic data without complex configurations or intrusive tracking.

  Fathom Lite & Fathom Analytics

  Main features	Basic website analytics (page views, visitors, referrers, etc.), custom events and goal tracking.
  Best for	Website owners and small businesses.Non-technical users.
  Licencing	Fathom Lite : MIT Licence (self-hosted).Fathom Analytics : Proprietary.
  Pricing	Fathom Lite : Free but currently unsupported.Cloud version : Starts at $12.50/month for up to 50 sites when paid annually.

  

  Fathom Analytics 
  (Image source)

  Fathom started as an open-source platform in 2018. But after the founders released V1.0.1, they switched to a closed-source, paid, proprietary model called Fathom Analytics. Since then, it has always been closed-source.

  However, the open-source version, Fathom Lite, is still available. It has very limited functionality, uses cookies and is currently unsupported by the company. No new features are under development and uptime isn’t guaranteed.

  Matomo vs. Plausible vs. Fathom

  Matomo, Plausible, and Fathom are all open-source, privacy-focused alternatives to Google Analytics. They offer features like no data sampling, data ownership, and EU-based cloud hosting.

  Here’s a head-to-head comparison of the three :

  	Matomo	Plausible	Fathom
  Focus	Comprehensive, feature-rich, customizable	Simple, lightweight, beginner-friendly	Simple, lightweight, privacy-focused
  Target User	Businesses, marketers and analysts seeking depth	Beginners, bloggers, and small businesses	Website owners and users prioritising simplicity
  Open Source	Fully open-source	Fully open-source	Limited open-source version
  Advanced analytics	Extensive	Very limited	Very limited
  Integrations	100+	Limited	Fewer than 15
  Customisation	High	Low	Low
  Data management	Granular control, raw data access, complex queries	Simplified, no raw data access	Simplified, no raw data access
  GDPR features	Compliant by design, plus GDPR Manager	Guides only	Compliant by design
  Pricing	Generally higher	Generally lower	Intermediate
  Learning curve	Steeper	Gentle	Gentle

  The open-core dilemma

  Open-source platforms are beneficial and trustworthy, leading some companies to falsely market themselves as such.

  Some were once open-source but later became commercial, criticised as “bait-and-switch.” Others offer a limited open-source “core” with proprietary features, called the “open core” model. While this dual licensing can be ethical and sustainable, some abuse it by offering a low-value open-source version and hiding valuable features behind a paywall.

  However, other companies have embraced the dual-licensing model in a more ethical way, providing a valuable solution with a wide range of tools under the open-source license and only leaving premium, non-essential add-ons as paid features.

  Matomo is a prime example of this practice, championing the principles of open-source analytics while developing a sustainable business model for its users’ benefit.

  Choose Matomo as your open-source data analytics tool

  Open-source analytics platforms offer compelling advantages over proprietary solutions like Google Analytics. They provide greater transparency, data ownership and customisation. Choosing an open-source analytics platform over a proprietary one gives you more control over your customers’ data and supports compliance with user privacy regulations.

  With its comprehensive features, powerful tools, commitment to privacy and active community, Matomo stands out as a leading choice. Make the switch to Matomo for ethical, user-focused analytics.

  Try Matomo for free.