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• Assembling frames into a video in Node.js using fluent-ffmpeg [closed]

  14 novembre 2024, par Andrei

  I have the original video and it's modified frames in a folder. Now I want to assemble the modified frames into a new video. I also want to take the audio from the original video and add it to the new video.

  


  My latest code is this using fluent-ffmpeg. But it's not working.

  


  await new Promise((resolve, reject) => {
  ffmpeg(path.join(processedFramesDir, "frame-%d.png"))
    .inputOptions(["-framerate 30", "-start_number 1"])
    .input(videoPath)
    .outputOptions([
      "-c:v",
      "libx264",
      "-c:a",
      "copy",
      "-shortest",
      "-r",
      "30",
      "-pix_fmt",
      "yuv420p",
    ])
    .outputOptions(["-map 0:v:0", "-map 1:a:0"])
    .save(outputVideoPath)
    .on("start", (commandLine) => {
      console.log("FFmpeg command: " + commandLine);
    })
    .on("stderr", (stderrLine) => {
      console.log("FFmpeg stderr: " + stderrLine);
    })
    .on("end", resolve)
    .on("error", (err, stdout, stderr) => {
      console.error("Error assembling video:", err);
      console.error("FFmpeg stderr:", stderr);
      reject(err);
    });
});


  


  I get this error even though the frames exist and they are valid pngs :
FFmpeg command: ffmpeg -framerate 30 -start_number 1 -i /tmp/video-processing-CyULOE/processedFrames/frame-%05d.png -i /tmp/video-processing-CyULOE/input.mp4 -y -c:v libx264 -c:a copy -shortest -r 30 -pix_fmt yuv420p -map 0:v:0 -map 1:a:0 /tmp/video-processing-CyULOE/output.mp4 2024-11-13 23:40:27 FFmpeg stderr: ffmpeg version 4.1.11-0&#x2B;deb10u1 Copyright (c) 2000-2023 the FFmpeg developers 2024-11-13 23:40:27 FFmpeg stderr:   built with gcc 8 (Debian 8.3.0-6) 2024-11-13 23:40:27 FFmpeg stderr:   configuration: --prefix=/usr --extra-version=0&#x2B;deb10u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared 2024-11-13 23:40:27 FFmpeg stderr:   libavutil      56. 22.100 / 56. 22.100 2024-11-13 23:40:27 FFmpeg stderr:   libavcodec     58. 35.100 / 58. 35.100 2024-11-13 23:40:27 FFmpeg stderr:   libavformat    58. 20.100 / 58. 20.100 2024-11-13 23:40:27 FFmpeg stderr:   libavdevice    58.  5.100 / 58.  5.100 2024-11-13 23:40:27 FFmpeg stderr:   libavfilter     7. 40.101 /  7. 40.101 2024-11-13 23:40:27 FFmpeg stderr:   libavresample   4.  0.  0 /  4.  0.  0 2024-11-13 23:40:27 FFmpeg stderr:   libswscale      5.  3.100 /  5.  3.100 2024-11-13 23:40:27 FFmpeg stderr:   libswresample   3.  3.100 /  3.  3.100 2024-11-13 23:40:27 FFmpeg stderr:   libpostproc    55.  3.100 / 55.  3.100 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646F8A20000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646C89F0000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x524946464EA10000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x5249464628A40000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646AAA30000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x524946467CA10000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646AAA30000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646BAA10000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646ACA30000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x5249464670A80000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646E8A60000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x524946469AA60000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x5249464672A90000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646B8A50000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646AAA80000. 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x52494646A0A70000. 2024-11-13 23:40:27 FFmpeg stderr:     Last message repeated 1 times 2024-11-13 23:40:27 FFmpeg stderr: [png @ 0x5630fb89a880] Invalid PNG signature 0x5249464646A80000. 2024-11-13 23:40:27 FFmpeg stderr: [image2 @ 0x5630fb898a40] decoding for stream 0 failed 2024-11-13 23:40:27 FFmpeg stderr: [image2 @ 0x5630fb898a40] Could not find codec parameters for stream 0 (Video: png, none(pc)): unspecified size 2024-11-13 23:40:27 FFmpeg stderr: Consider increasing the value for the &#x27;analyzeduration&#x27; and &#x27;probesize&#x27; options 2024-11-13 23:40:27 FFmpeg stderr: Input #0, image2, from &#x27;/tmp/video-processing-CyULOE/processedFrames/frame-%05d.png&#x27;: 2024-11-13 23:40:27 FFmpeg stderr:   Duration: 00:00:00.60, start: 0.000000, bitrate: N/A 2024-11-13 23:40:27 FFmpeg stderr:     Stream #0:0: Video: png, none(pc), 30 fps, 30 tbr, 30 tbn, 30 tbc 2024-11-13 23:40:28 FFmpeg stderr: Input #1, mov,mp4,m4a,3gp,3g2,mj2, from &#x27;/tmp/video-processing-CyULOE/input.mp4&#x27;: 2024-11-13 23:40:28 FFmpeg stderr:   Metadata: 2024-11-13 23:40:28 FFmpeg stderr:     major_brand     : isom 2024-11-13 23:40:28 FFmpeg stderr:     minor_version   : 512 2024-11-13 23:40:28 FFmpeg stderr:     compatible_brands: isomiso2avc1mp41 2024-11-13 23:40:28 FFmpeg stderr:     encoder         : Lavf60.16.100 2024-11-13 23:40:28 FFmpeg stderr:   Duration: 00:00:00.62, start: 0.000000, bitrate: 4289 kb/s 2024-11-13 23:40:28 FFmpeg stderr:     Stream #1:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1920x1080 [SAR 1:1 DAR 16:9], 4395 kb/s, 30 fps, 30 tbr, 15360 tbn, 60 tbc (default) 2024-11-13 23:40:28 FFmpeg stderr:     Metadata: 2024-11-13 23:40:28 FFmpeg stderr:       handler_name    : VideoHandler 2024-11-13 23:40:28 FFmpeg stderr:     Stream #1:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 2 kb/s (default) 2024-11-13 23:40:28 FFmpeg stderr:     Metadata: 2024-11-13 23:40:28 FFmpeg stderr:       handler_name    : SoundHandler 2024-11-13 23:40:28 FFmpeg stderr: Stream mapping: 2024-11-13 23:40:28 FFmpeg stderr:   Stream #0:0 -> #0:0 (png (native) -> h264 (libx264)) 2024-11-13 23:40:28 FFmpeg stderr:   Stream #1:1 -> #0:1 (copy) 2024-11-13 23:40:28 FFmpeg stderr: Press [q] to stop, [?] for help 2024-11-13 23:40:28 FFmpeg stderr: [image2 @ 0x5630fb898a40] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8) 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb9b7700] Invalid PNG signature 0x52494646F8A20000. 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb988dc0] Invalid PNG signature 0x52494646C89F0000. 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba3edc0] Invalid PNG signature 0x524946464EA10000. 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba40600] Invalid PNG signature 0x5249464628A40000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb8c7900] Invalid PNG signature 0x52494646AAA30000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb9b7700] Invalid PNG signature 0x524946467CA10000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb988dc0] Invalid PNG signature 0x52494646AAA30000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba3edc0] Invalid PNG signature 0x52494646BAA10000. 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba40600] Invalid PNG signature 0x52494646ACA30000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr:     Last message repeated 1 times 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb8c7900] Invalid PNG signature 0x5249464670A80000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb9b7700] Invalid PNG signature 0x52494646E8A60000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb988dc0] Invalid PNG signature 0x524946469AA60000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba3edc0] Invalid PNG signature 0x5249464672A90000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba40600] Invalid PNG signature 0x52494646B8A50000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb8c7900] Invalid PNG signature 0x52494646AAA80000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb9b7700] Invalid PNG signature 0x52494646A0A70000. 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fb988dc0] Invalid PNG signature 0x52494646A0A70000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr: [png @ 0x5630fba3edc0] Invalid PNG signature 0x5249464646A80000. 2024-11-13 23:40:28 FFmpeg stderr: Error while decoding stream #0:0: Invalid data found when processing input 2024-11-13 23:40:28 FFmpeg stderr:     Last message repeated 4 times 2024-11-13 23:40:28 FFmpeg stderr: Cannot determine format of input stream 0:0 after EOF 2024-11-13 23:40:28 FFmpeg stderr: Error marking filters as finished 2024-11-13 23:40:28 FFmpeg stderr: Conversion failed! 2024-11-13 23:40:28 FFmpeg stderr:  2024-11-13 23:40:28 Error assembling video: Error: ffmpeg exited with code 1: Cannot determine format of input stream 0:0 after EOF 2024-11-13 23:40:28 Error marking filters as finished 2024-11-13 23:40:28 Conversion failed! 2024-11-13 23:40:28  2024-11-13 23:40:28     at ChildProcess.<anonymous> (/usr/src/app/node_modules/fluent-ffmpeg/lib/processor.js:180:22) 2024-11-13 23:40:28     at ChildProcess.emit (node:events:519:28) 2024-11-13 23:40:28     at ChildProcess._handle.onexit (node:internal/child_process:294:12)</anonymous>

  


  The code used to

  


  ffmpeg(videoPath) .screenshots({ folder: framesDir, filename: "frame-%i.png", size: "?x1080", count: 10, }) .on("end", resolve) .on("error", reject);


  


  The pngs are valid and displaying in windows photos viewer

  


• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.

• 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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