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• 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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• Overcoming Fintech and Finserv’s Biggest Data Analytics Challenges

  13 septembre 2024, par Daniel Crough — Banking and Financial Services, Marketing, Security

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

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

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

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

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

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

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

  1. Battling data silos

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

  

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

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

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

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

  

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

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

  2. Compliance with laws and regulations

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

  

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

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

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

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

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

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

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

  To avoid similar fates, companies should :

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

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

  3. Protecting against data security threats

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

  

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

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

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

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

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

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

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

  

  4. Protecting users’ privacy

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

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

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

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

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

  

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

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

  Read our guide to ethical web analytics for more information.

  5. Comparing customer trust across industries 

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

  

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

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

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

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

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

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

  6. Collecting quality data

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

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

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

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

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

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

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

  

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

  Unlock the full power of fintech data analytics with Matomo

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• Understanding The Dreamcast GD-ROM Layout

  24 mars 2022, par Multimedia Mike — Sega Dreamcast

  I’m finally completing something I set out to comprehend over a decade ago. I wanted to understand how data is actually laid out on a Sega Dreamcast GD-ROM drive. I’m trying to remember why I even still care. There was something about how I wanted to make sure the contents of a set of Dreamcast demo discs was archived for study.

  
  
  

  I eventually figured it out. Read on, if you are interested in the technical details. Or, if you would like to examine the fruits of this effort, check out the Dreamcast demo discs that I took apart and uploaded to the Internet Archive.

  If you care to read some geeky technical details of some of the artifacts on these sampler discs, check out this followup post on Dreamcast Finds.

  Motivation
  Why do I still care about this ? Well, see the original charter of this blog above. It’s mostly about studying multimedia formats, as well as the general operation of games and their non-multimedia data formats. It’s also something that has nagged at me ever since I extracted a bunch of Dreamcast discs years ago and tried to understand why the tracks were arranged the way they were, and how I could systematically split the files out of the filesystem. This turns out not to be as easy as it might sound, even if you can get past the obstacle of getting at the raw data.
  

  CD/CD-ROM Refresher
  As I laid out in my Grand Unified Theory of Compact Disc, every compact disc can be viewed conceptually as a string of sectors, where each sector is 2352 bytes long. The difference among the various CD types (audio CDs, various CD-ROM types) boils down to the format of contents of the 2352-byte sectors. For an audio CD, every sector’s 2352 bytes represents 1/75 of a second of CD-quality audio samples.

  Meanwhile, there are various sector layouts for different CD-ROM modes, useful for storing computer data. This post is most interested in “mode 1/form 1”, which uses 2048 of the 2352 bytes for data, while using the remaining bytes for error detection and correction codes. A filesystem (usually ISO-9660) is overlaid on these 2048-byte sectors in order to create data structures for organizing strings of sectors into files.

  A CD has between 1 and 99 tracks. A pure CD-ROM will have a single data track. Pure audio CDs tend to have numerous audio tracks, usually 1 per song. Mixed CDs are common. For software, this usually manifests as the first track being data and containing an ISO-9660 filesystem, followed by a series of audio tracks, sometimes for in-game music. For audio CDs, there is occasionally a data track at the end of the disc with some extra media types.

  GD-ROM Refresher
  The Dreamcast used optical discs called GD-ROMs, where the GD stands for “gigadisc”. These discs were designed to hold about 1 gigabyte of data, vs. the usual 650-700MB offered by standard CD solutions, while using the same laser unit as is used for CDs. I’m not sure how it achieved this exactly. I always assumed it was some sort of “double density” sector scheme. According to Wikipedia, the drive read the disc at a slower rate which allowed it to read more data (presumably the “pits” vs. “lands” which comprise the surface of an optical disc). This might be equivalent to my theory.

  The GD-ROM discs cannot be read in a standard optical drive. It is necessary to get custom software onto the Dreamcast which will ask the optical hardware to extract the sectors and exfiltrate them off of the unit somehow. There are numerous methods for this. Alternatively, just find rips that are increasingly plentiful around the internet. However, just because you might be able to find the data for a given disc does not mean that you can easily explore the contents.

  Typical Layout Patterns
  Going back to my study of the GD-ROM track layouts, 2 clear patterns emerge :

  All of the game data is packed into track 3 :

  
  
  

  Track 3 has data, the last track has data, and the tracks in between contain standard CD audio :

  
  
  

  Also, the disc is always, always 100% utilized.

  Track 1 always contains an ISO-9660 filesystem and can be read by any standard CD-ROM drive. And it usually has nothing interesting. Track 3 also contains what appears to be an ISO-9660 filesystem. However, if you have a rip of the track and try to mount the image with standard tools, it will not work. In the second layout, the data follows no obvious format.

  Cracking The Filesystem Code
  I figured out quite a few years ago that in the case of the consolidated data track 3, that’s simply a standard ISO-9660 filesystem that would work fine with standard ISO-9660 reading software… if the data track were located beginning at sector 45000. The filesystem data structures contain references to absolute sector numbers. Thus, if it were possible to modify some ISO-9660 software to assume the first sector is 45000, it ought to have no trouble interpreting the data.

  
  
  

  How about the split data track format ? Actually, it works the same way. If all the data were sitting on its original disc, track 3 would have data structures pointing to strings of contiguous sectors (extents) in the final track, and those are the files.

  To express more succinctly : track 3 contains the filesystem root structure and the directory structures, while the final track contains the actual file data. How is the filesystem always 100% full ? Track 3 gets padded out with 0-sectors until the beginning of any audio sectors.

  
  
  

  Why Lay Things Out Like This ?
  Why push the data as far out on the disc as possible ? A reasonable explanation for this would be for read performance. Compact discs operate on Constant Linear Velocity (CLV), vs. Constant Angular Velocity (CAV). The implication of this is that data on the outside of the disc is read faster than data on the inside. I once profiled this characteristic in order to prove it to myself, using both PC CD drives as well as a Dreamcast. By pushing the data to the outer sectors, graphical data gets loaded into RAM faster, and full motion videos, which require a certain minimum bitrate for a good experience, have a better guarantee that playback will be smooth.

  Implications For Repacking
  Once people figured out how to boot burned CDs in the Dreamcast, they had a new problem : Squeeze as much as 1 gigabyte down to around 650 megabytes at the most. It looks like the most straightforward strategy was to simply rework the filesystem to remove the often enormous amount of empty space in track 3.

  My understanding is that another major strategy is to re-encode certain large assets. Full motion video (FMV) assets are a good target here since the prevailing FMV middleware format used on Sega Dreamcast games was Sofdec, which is basically just MPEG-1 video. There is ample opportunity to transcode these files to lower bitrate settings to squeeze some bits (and a lot of visual quality) out of them.

  Further, if you don’t really care about the audio tracks, you could just replace them with brief spurts of silence.

  Making A Tool
  So I could make a tool that would process these collections of files representing a disc. I could also adapt it for various forms that a Dreamcast rip might take (I have found at least 3 so far). I could eventually expand it to handle lots of other disc formats (you know, something like Aaru does these days). And that would have been my modus operandi perhaps 10 or more years ago. And of course, the ambitious tool would have never seen daylight as I got distracted by other ideas.

  I wanted to get a solution up and running as quickly as possible this time. Here was my initial brainstorm : assemble all the tracks into a single, large disc while pretending the audio tracks consist of 2048-byte sectors. In doing so, I ought to be able to use fuseiso to mount the giant image, with a modification to look for the starting sector at a somewhat nonstandard location.

  To achieve the first part I wrote a quick Python script that processed the contents of a GDI file, which was stored alongside the ISO (data) and RAW (audio) track track rips from when I extracted the disc. The GDI is a very matter-of-fact listing of the tracks and their properties, e.g. :

  5
  1 0 4 2048 track01.iso 0
  2 721 0 2352 track02.raw 0
  3 45000 4 2048 track03.iso 0
  4 338449 0 2352 track04.raw 0
  5 349096 4 2048 track05.iso 0
  

  track number / starting sector / track type (4=data, 0=audio) / bytes per sector / filename / ??

  The script skips the first 2 filenames, instead writing 45000 zero sectors in order to simulate the CD-compatible area. Then, for each file, if it’s an ISO, append the data to the final data file ; if it’s audio, compute the number of sectors occupied, and then append that number of 2048-byte zero sectors to the final data file.

  Finally, to interpret the filesystem, I used an old tool that I’ve relied upon for a long time– fuseiso. This is a program that leverages Filesystem in Userspace (FUSE) to mount ISO-9660 filesystems as part of the local filesystem, without needing root privileges. The original source hasn’t been updated for 15 years, but I found a repo that attempts to modernize it slightly. I forked a version which fixes a few build issues.

  Anyway, I just had to update a table to ask it to start looking for the root ISO-9660 filesystem at a different location than normal. Suddenly, after so many years, I was able to freely browse a GD-ROM filesystem directly under Linux !

  Conclusion And Next Steps
  I had to hack the fuseiso3 tool a bit in order to make this work. I don’t think it’s especially valuable to make sure anyone can run with the same modifications since the tool assumes that a GD-ROM rip has been processed through the exact pipeline I described above.

  I have uploaded all of the North American Dreamcast demo discs to archive.org. See this post for a more granular breakdown of what this entails. In the course of this exercise, I also found some European demo discs that could use the same extraction.

  What else ? Should I perform the same extraction experiment for all known Dreamcast games ? Would anyone care ? Maybe if there’s a demand for it.

  Here is a followup on the interesting and weird things I have found on these discs so far.

  The post Understanding The Dreamcast GD-ROM Layout first appeared on Breaking Eggs And Making Omelettes.