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• Error Opening RTMP Stream through FFmpeg command when executed through exec package [closed]

  3 octobre 2024, par Akhil

  I have been trying to transcode the live stream from RTMP server running on rtmp://localhost:1936/live/test with FFmpeg in a Go application using os/exec package, But seems to not work and gives the input/output error (I have attached below). The same exact ffmpeg command when I execute on terminal, works as its supposed to. Not Sure why that is, here is my code for reproducing and analyzing the mistakes.

  


  ffmpegCmd := fmt.Sprintf("ffmpeg -nostdin -i rtmp://localhost:1936/live/%s -c:v libx264 -s %s -f %s %s/stream.mpd",
        streamKey, resolution, sp.OutputFormat, outputPath)
    log.Printf("Executing FFmpeg command: %s", ffmpegCmd)

    // Prepare the command execution with a timeout context
    ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second) // Set a 60-second timeout
    defer cancel()

    cmd := exec.CommandContext(ctx, "bash", "-c", ffmpegCmd)


  


  the ffmpeg command looks like this :
  
ffmpeg -nostdin -i rtmp://localhost:1936/live/test -c:v libx264 -s 1920x1080 -f dash output/test/1080p/stream.mpd

  


  I get the following error :

  


  Error opening input: Input/output error

Error opening input file rtmp://localhost:1936/live/test.

Error opening input files: Input/output error

Exiting normally, received signal 2.

signal: interrupt


  


  I have already tried to break the command, and then execute it. Something like :

  


  cmd := exec.CommandContext(ctx,
        "ffmpeg",
        "-nostdin",
        "-i", "rtmp://localhost:1936/live/"+streamKey,
        "-c:v", "libx264",
        "-s", resolution,
        "-f", sp.OutputFormat,
        outputPath+"/stream.mpd")


  


  After running the ffmpeg command with -loglevel debug and -report :

  


  Here is the logs and errors I get :

  


  When I run it within the go application :

  


  ffmpeg started on 2024-10-02 at 12:00:06
Report written to "ffmpeg-20241002-120006.log"
Log level: 48
Command line:
ffmpeg -loglevel debug -report -i rtmp://localhost:1936/live/test -c:v libx264 -s 1920x1080 -f dash ./output/test/1080p/stream.mpd
ffmpeg version 7.0.2 Copyright (c) 2000-2024 the FFmpeg developers
  built with Apple clang version 15.0.0 (clang-1500.3.9.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.0.2_1 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags='-Wl,-ld_classic' --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libharfbuzz --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox --enable-neon
  libavutil      59.  8.100 / 59.  8.100
  libavcodec     61.  3.100 / 61.  3.100
  libavformat    61.  1.100 / 61.  1.100
  libavdevice    61.  1.100 / 61.  1.100
  libavfilter    10.  1.100 / 10.  1.100
  libswscale      8.  1.100 /  8.  1.100
  libswresample   5.  1.100 /  5.  1.100
  libpostproc    58.  1.100 / 58.  1.100
Splitting the commandline.
Reading option '-loglevel' ... matched as option 'loglevel' (set logging level) with argument 'debug'.
Reading option '-report' ... matched as option 'report' (generate a report) with argument '1'.
Reading option '-i' ... matched as input url with argument 'rtmp://localhost:1936/live/test'.
Reading option '-c:v' ... matched as option 'c' (select encoder/decoder ('copy' to copy stream without reencoding)) with argument 'libx264'.
Reading option '-s' ... matched as option 's' (set frame size (WxH or abbreviation)) with argument '1920x1080'.
Reading option '-f' ... matched as option 'f' (force container format (auto-detected otherwise)) with argument 'dash'.
Reading option './output/test/1080p/stream.mpd' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Applying option loglevel (set logging level) with argument debug.
Applying option report (generate a report) with argument 1.
Successfully parsed a group of options.
Parsing a group of options: input url rtmp://localhost:1936/live/test.
Successfully parsed a group of options.
Opening an input file: rtmp://localhost:1936/live/test.
[AVFormatContext @ 0x13f721f90] Opening 'rtmp://localhost:1936/live/test' for reading
[rtmp @ 0x13f6040e0] No default whitelist set
[tcp @ 0x13f7223d0] No default whitelist set
[tcp @ 0x13f7223d0] Original list of addresses:
[tcp @ 0x13f7223d0] Address ::1 port 1936
[tcp @ 0x13f7223d0] Address 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Interleaved list of addresses:
[tcp @ 0x13f7223d0] Address ::1 port 1936
[tcp @ 0x13f7223d0] Address 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Starting connection attempt to ::1 port 1936
[tcp @ 0x13f7223d0] Connection attempt to ::1 port 1936 failed: Connection refused
[tcp @ 0x13f7223d0] Starting connection attempt to 127.0.0.1 port 1936
[tcp @ 0x13f7223d0] Successfully connected to 127.0.0.1 port 1936
[rtmp @ 0x13f6040e0] Handshaking...
[rtmp @ 0x13f6040e0] Type answer 3
[rtmp @ 0x13f6040e0] Server version 13.14.10.13
[rtmp @ 0x13f6040e0] Proto = rtmp, path = /live/test, app = live, fname = test
[rtmp @ 0x13f6040e0] Window acknowledgement size = 5000000
[rtmp @ 0x13f6040e0] Max sent, unacked = 5000000
[rtmp @ 0x13f6040e0] New incoming chunk size = 4096
[rtmp @ 0x13f6040e0] Creating stream...
[rtmp @ 0x13f6040e0] Sending play command for 'test'
[rtmp @ 0x13f6040e0] Deleting stream...
[in#0 @ 0x13f721d40] Error opening input: Input/output error
Error opening input file rtmp://localhost:1936/live/test.
Error opening input files: Input/output error
Exiting normally, received signal 2.


  


  This is what i get when i run the same command on terminal :

  


  <same as="as" but="but" please="please" scroll="scroll" further="further">&#xA;&#xA;[rtmp @ 0x1437144c0] No default whitelist set&#xA;[tcp @ 0x143604f20] No default whitelist set&#xA;[tcp @ 0x143604f20] Original list of addresses:&#xA;[tcp @ 0x143604f20] Address ::1 port 1936&#xA;[tcp @ 0x143604f20] Address 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Interleaved list of addresses:&#xA;[tcp @ 0x143604f20] Address ::1 port 1936&#xA;[tcp @ 0x143604f20] Address 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Starting connection attempt to ::1 port 1936&#xA;[tcp @ 0x143604f20] Connection attempt to ::1 port 1936 failed: Connection refused&#xA;[tcp @ 0x143604f20] Starting connection attempt to 127.0.0.1 port 1936&#xA;[tcp @ 0x143604f20] Successfully connected to 127.0.0.1 port 1936&#xA;[rtmp @ 0x1437144c0] Handshaking...&#xA;[rtmp @ 0x1437144c0] Type answer 3&#xA;[rtmp @ 0x1437144c0] Server version 13.14.10.13&#xA;[rtmp @ 0x1437144c0] Proto = rtmp, path = /live/test, app = live, fname = test&#xA;[rtmp @ 0x1437144c0] Window acknowledgement size = 5000000&#xA;[rtmp @ 0x1437144c0] Max sent, unacked = 5000000&#xA;[rtmp @ 0x1437144c0] New incoming chunk size = 4096&#xA;[rtmp @ 0x1437144c0] Creating stream...&#xA;[rtmp @ 0x1437144c0] Sending play command for &#x27;test&#x27;&#xA;[flv @ 0x143604b30] Format flv probed with size=2048 and score=100&#xA;[flv @ 0x143604b30] Before avformat_find_stream_info() pos: 13 bytes read:2263 seeks:0 nb_streams:0&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 64, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 64, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 120, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft4_fwd_float_neon - type: fft_float, len: 4, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 128, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 64, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 480, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft16_ns_float_neon - type: fft_float, len: 16, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 512, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 256, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_pfa_15xM_inv_float_c - type: mdct_float, len: 960, factors[2]: [15, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft32_ns_float_neon - type: fft_float, len: 32, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: mdct_float, len: 1024, factors[2]: [2, any], flags: [unaligned, out_of_place, inv_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 512, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;Transform tree:&#xA;    mdct_fwd_float_c - type: mdct_float, len: 1024, factors[2]: [2, any], flags: [unaligned, out_of_place, fwd_only]&#xA;        fft_sr_ns_float_neon - type: fft_float, len: 512, factor: 2, flags: [aligned, inplace, out_of_place, preshuf]&#xA;[NULL @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[NULL @ 0x144124920] Decoding VUI&#xA;[NULL @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[NULL @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] nal_unit_type: 5(IDR), nal_ref_idc: 3&#xA;[h264 @ 0x144124920] Decoding VUI&#xA;[h264 @ 0x144124920] Format yuv420p chosen by get_format().&#xA;[h264 @ 0x144124920] Reinit context to 1280x720, pix_fmt: yuv420p&#xA;[h264 @ 0x144124920] no picture &#xA;[flv @ 0x143604b30] All info found&#xA;[flv @ 0x143604b30] rfps: 29.666667 0.016552&#xA;[flv @ 0x143604b30] rfps: 29.750000 0.009347&#xA;[flv @ 0x143604b30] rfps: 29.750000 0.009347&#xA;[flv @ 0x143604b30] rfps: 29.833333 0.004197&#xA;[flv @ 0x143604b30] rfps: 29.916667 0.001104&#xA;[flv @ 0x143604b30] rfps: 29.916667 0.001104&#xA;[flv @ 0x143604b30] rfps: 30.000000 0.000067&#xA;[flv @ 0x143604b30] rfps: 30.000000 0.000067&#xA;[flv @ 0x143604b30] rfps: 60.000000 0.000270&#xA;[flv @ 0x143604b30] rfps: 60.000000 0.000270&#xA;[flv @ 0x143604b30] rfps: 120.000000 0.001079&#xA;[flv @ 0x143604b30] rfps: 120.000000 0.001079&#xA;[flv @ 0x143604b30] rfps: 240.000000 0.004316&#xA;[flv @ 0x143604b30] rfps: 240.000000 0.004316&#xA;[flv @ 0x143604b30] rfps: 29.970030 0.000204&#xA;[flv @ 0x143604b30] rfps: 29.970030 0.000204&#xA;[flv @ 0x143604b30] rfps: 59.940060 0.000814&#xA;[flv @ 0x143604b30] rfps: 59.940060 0.000814&#xA;[flv @ 0x143604b30] After avformat_find_stream_info() pos: 496783 bytes read:496783 seeks:0 frames:179&#xA;Input #0, flv, from &#x27;rtmp://localhost:1936/live/test&#x27;:&#xA;  Metadata:&#xA;    |RtmpSampleAccess: true&#xA;    Server          : NGINX RTMP (github.com/arut/nginx-rtmp-module)&#xA;    displayWidth    : 1280&#xA;    displayHeight   : 720&#xA;    fps             : 30&#xA;    profile         : &#xA;    level           : &#xA;  Duration: 00:00:00.00, start: 6.742000, bitrate: N/A&#xA;  Stream #0:0, 138, 1/1000: Audio: aac (LC), 48000 Hz, stereo, fltp, 163 kb/s&#xA;  Stream #0:1, 41, 1/1000: Video: h264 (High), 1 reference frame, yuv420p(tv, bt709, progressive, left), 1280x720 [SAR 1:1 DAR 16:9], 0/1, 2560 kb/s, 30 fps, 30 tbr, 1k tbn&#xA;Successfully opened the file.&#xA;Parsing a group of options: output url ./output/test/1080p/stream.mpd.&#xA;Applying option c:v (select encoder/decoder (&#x27;copy&#x27; to copy stream without reencoding)) with argument libx264.&#xA;Applying option s (set frame size (WxH or abbreviation)) with argument 1920x1080.&#xA;Applying option f (force container format (auto-detected otherwise)) with argument dash.&#xA;Successfully parsed a group of options.&#xA;Opening an output file: ./output/test/1080p/stream.mpd.&#xA;[out#0/dash @ 0x123707480] No explicit maps, mapping streams automatically...&#xA;[vost#0:0/libx264 @ 0x123707d60] Created video stream from input stream 0:1&#xA;detected 10 logical cores&#xA;[h264 @ 0x123607b70] nal_unit_type: 7(SPS), nal_ref_idc: 3&#xA;[h264 @ 0x123607b70] Decoding VUI&#xA;[h264 @ 0x123607b70] nal_unit_type: 8(PPS), nal_ref_idc: 3&#xA;[aost#0:1/aac @ 0x144028080] Created audio stream from input stream 0:0&#xA;Transform tree:&#xA;    mdct_inv_float_c - type: md&#xA;&#xA;<it simply="simply" starts="starts" working="working">&#xA;</it></same>

  


  I am not sure if there is something to do with Permissions.

  


• Banking Data Strategies – A Primer to Zero-party, First-party, Second-party and Third-party data

  25 octobre 2024, par Daniel Crough — Banking and Financial Services, Privacy

  Banks hold some of our most sensitive information. Every transaction, loan application, and account balance tells a story about their customers’ lives. Under GDPR and banking regulations, protecting this information isn’t optional – it’s essential.

  Yet banks also need to understand how customers use their services to serve them better. The solution lies in understanding different types of banking data and how to handle each responsibly. From direct customer interactions to market research, each data source serves a specific purpose and requires its own privacy controls.

  Before diving into how banks can use each type of data effectively, let’s look into the key differences between them :

  Data Type	What It Is	Banking Example	Legal Considerations
  First-party	Data from direct customer interactions with your services	Transaction records, service usage patterns	Different legal bases apply (contract, legal obligation, legitimate interests)
  Zero-party	Information customers actively provide	Stated preferences, financial goals	Requires specific legal basis despite being voluntary ; may involve profiling
  Second-party	Data shared through formal partnerships	Insurance history from partners	Must comply with PSD2 and specific data sharing regulations
  Third-party	Data from external providers	Market analysis, demographic data	Requires due diligence on sources and specific transparency measures

  What is first-party data ?

  

  First-party data reveals how customers actually use your banking services. When someone logs into online banking, withdraws money from an ATM, or speaks with customer service, they create valuable information about real banking habits.

  This direct interaction data proves more reliable than assumptions or market research because it shows genuine customer behaviour. Banks need specific legal grounds to process this information. Basic banking services fall under contractual necessity, while fraud detection is required by law. Marketing activities need explicit customer consent. The key is being transparent with customers about what information you process and why.

  Start by collecting only what you need for each specific purpose. Store information securely and give customers clear control through privacy settings. This approach builds trust while helping meet privacy requirements under the GDPR’s data minimisation principle.

  What is zero-party data ?

  

  Zero-party data emerges when customers actively share information about their financial goals and preferences. Unlike first-party data, which comes from observing customer behaviour, zero-party data comes through direct communication. Customers might share their retirement plans, communication preferences, or feedback about services.

  Interactive tools create natural opportunities for this exchange. A retirement calculator helps customers plan their future while revealing their financial goals. Budget planners offer immediate value through personalised advice. When customers see clear benefits, they’re more likely to share their preferences.

  However, voluntary sharing doesn’t mean unrestricted use. The ICO’s guidance on purpose limitation applies even to freely shared information. Tell customers exactly how you’ll use their data, document specific reasons for collecting each piece of information, and make it simple to update or remove personal data.

  Regular reviews help ensure you still need the information customers have shared. This aligns with both GDPR requirements and customer expectations about data management. By treating voluntary information with the same care as other customer data, banks build lasting trust.

  What is second-party data ?

  

  Second-party data comes from formal partnerships between banks and trusted companies. For example, a bank might work with an insurance provider to better understand shared customers’ financial needs.

  These partnerships need careful planning to protect customer privacy. The ICO’s Data Sharing Code provides clear guidelines : both organisations must agree on what data they’ll share, how they’ll protect it, and how long they’ll keep it before any sharing begins.

  Transparency builds trust in these arrangements. Tell customers about planned data sharing before it happens. Explain what information you’ll share and how it helps provide better services.

  Regular audits help ensure both partners maintain high privacy standards. Review shared data regularly to confirm it’s still necessary and properly protected. Be ready to adjust or end partnerships if privacy standards slip. Remember that your responsibility to protect customer data extends to information shared with partners.

  Successful partnerships balance improved service with diligent privacy protection. When done right, they help banks understand customer needs better while maintaining the trust that makes banking relationships work.

  What is third-party data ?

  

  Third-party data comes from external sources outside your bank and its partners. Market research firms, data analytics companies, and economic research organizations gather and sell this information to help banks understand broader market trends.

  This data helps fill knowledge gaps about the wider financial landscape. For example, third-party data might reveal shifts in consumer spending patterns across different age groups or regions. It can show how customers interact with different financial services or highlight emerging banking preferences in specific demographics.

  But third-party data needs careful evaluation before use. Since your bank didn’t collect this information directly, you must verify both its quality and compliance with privacy laws. Start by checking how providers collected their data and whether they had proper consent. Look for providers who clearly document their data sources and collection methods.

  Quality varies significantly among third-party data providers. Some key questions to consider before purchasing :

  • How recent is the data ?
  • How was it collected ?
  • What privacy protections are in place ?
  • How often is it updated ?
  • Which specific market segments does it cover ?

  Consider whether third-party data will truly add value beyond your existing information. Many banks find they can gain similar insights by analysing their first-party data more effectively. If you do use third-party data, document your reasons for using it and be transparent about your data sources.

  Creating your banking data strategy

  

  A clear data strategy helps your bank collect and use information effectively while protecting customer privacy. This matters most with first-party data – the information that comes directly from your customers’ banking activities.

  Start by understanding what data you already have. Many banks collect valuable information through everyday transactions, website visits, and customer service interactions. Review these existing data sources before adding new ones. Often, you already have the insights you need – they just need better organization.

  Map each type of data to a specific purpose. For example, transaction data might help detect fraud and improve service recommendations. Website analytics could reveal which banking features customers use most. Each data point should serve a clear business purpose while respecting customer privacy.

  Strong data quality standards support better decisions. Create processes to update customer information regularly and remove outdated records. Check data accuracy often and maintain consistent formats across your systems. These practices help ensure your insights reflect reality.

  Remember that strategy means choosing what not to do. You don’t need to collect every piece of data possible. Focus on information that helps you serve customers better while maintaining their privacy.

  Managing multiple data sources

  

  Banks work with many types of data – from direct customer interactions to market research. Each source serves a specific purpose, but combining them effectively requires careful planning and precise attention to regulations like GDPR and ePrivacy.

  First-party data forms your foundation. It shows how your customers actually use your services and what they need from their bank. This direct interaction data proves most valuable because it reflects real behaviour rather than assumptions. When customers check their balances, transfer money, or apply for loans, they show you exactly how they use banking services.

  Zero-party data adds context to these interactions. When customers share their financial goals or preferences directly, they help you understand the “why” behind their actions. This insight helps shape better services. For example, knowing a customer plans to buy a house helps you offer relevant savings tools or mortgage information at the right time.

  Second-party partnerships can fill specific knowledge gaps. Working with trusted partners might reveal how customers manage their broader financial lives. But only pursue partnerships when they offer clear value to customers. Always explain these relationships clearly and protect shared information carefully.

  Third-party data helps provide market context, but use it selectively. External market research can highlight broader trends or opportunities. However, this data often proves less reliable than information from direct customer interactions. Consider it a supplement to, not a replacement for, your own customer insights.

  Keep these principles in mind when combining data sources :

  • Prioritize direct customer interactions
  • Focus on information that improves services
  • Maintain consistent privacy standards across sources
  • Document where each insight comes from
  • Review regularly whether each source adds value
  • Work with privacy and data experts to ensure customer information is handled properly

  Enhance your web analytics strategy with Matomo

  

  The financial sector finds powerful and compliant web analytics increasingly valuable as it navigates data management and privacy regulations. Matomo provides a configurable privacy-centric solution that meets the requirements of banks and financial institutions.

  Matomo empowers your organisation to :

  • Collect accurate, GDPR-compliant web data
  • Integrate web analytics with your existing tools and platforms
  • Maintain full control over your analytics data
  • Gain insights without compromising user privacy

  Matomo is trusted by some of the world’s biggest banks and financial institutions. Try Matomo for free for 30 days to see how privacy-focused analytics can get you the insights you need while maintaining compliance and user trust.

• 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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  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.

  

  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.

  

  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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