We're hiring! https://angel.co/l/2wj7VX Cluey empowers consumers to become more informed and conscious of their impacts and influence on people, the planet, and politics. By making brand and corporate impacts transparent, Cluey helps identify which brands are aligned with each consumer’s unique set of personal values so they can buy accordingly. This collective result of daily individual consumer changes has the potential to be revolutionary in changing corporate practices at a macro-level. We’re currently building out our team and our product- Engage with us to learn more!

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Open-source is one of the major concepts in the cryptocurrency space as the whole industry basically grew up on it. Nowadays, building a crypto project without publishing at least a part of your code on GitHub is considered to be bad manners. Most of the developers involved in open-source projects are driven by the same goal: create a product that they will use themselves. This way, you get deeply involved and know about every single issue at stake. Could you think of better motivation? But there’s a flip side to this. Running a business based on an open-source model can be rather tricky. Firstly, you have to consider that it’s going to, at the very least, double the duration of the whole development process. And, since the issue of time can be especially critical for startup projects, this could be a massive game changer. Secondly, there is something delicate about exposing your flaws to strangers. It takes courage. It’s definitely not for everyone. Finally, there is this native fear that revealing the insides of your product will simply breed multiple copies. So you, basically, create your rivals yourself. But all these points certainly have their counter-arguments. Concentrating on a brighter future instead of getting immediate benefits is a great strategy and has proved itself many times for many companies and individuals. You just have to figure out if an open-source model will generate you real value in the future. Also, exhibiting your code might significantly improve it: the more number of un-blurred eyes watching, the higher are your chances are to find errors and perfect your work. And there’s no better impetus than healthy competition! Open-Source as a New Concept In all fairness, if you decide to share your code with the whole world to examine, it can really help with improving your technology, provided there are enough people who have the time and ability to review your code. Critics would have to stop withholding and take a good long look at your creation. The general public would feel a little more secure as you’ve actually reached out to them (simply by going open-source) and made a move to gain their trust. The open-source model allows for an easy entrance for absolutely anyone to launch a fintech startup. Start simple, write a line of code on top of Bitcoin or Ethereum’s blockchain and see where you can go from there. Monetizing your product won’t be an easy thing to do (unless you’re eager to gift the world yet another token) but it’s not impossible. And above all, building an open-source project is simply exciting! It becomes less of a job and more of a passion project. Going open-source will definitely help you realize that from now on, it’s not about the amount of hours you and your team spend in the office. Suddenly, it’s about learning and contributing to the community now. It’s about getting unbiased feedback, mastering your programming skills, and helping others. If you get stuck at some point, you don’t have to fight alone as you can ask the community to help you. However, it’s not all rainbows and butterflies, prepare yourself for harsh criticism from your fellow developers and get ready to toughen up. A Question of Trust and The Power of Community I recently came across by JP Aumasson, a cryptography researcher and engineer, in which he explores the topic “Open source crypto is no better than closed-source crypto”. He classifies four main groups of crypto bugs and his major point is that in order to find crypto errors one would require a very specific set of skills. The harder the bugs are, the less the odds are of finding them, which means that all these open-source measures won’t really benefit security on a higher level as there are very few people who can actually make a meaningful contribution. A few competent people, too much code, and too little free time. this article His other point on putting your code out there is also rather curious: “You can speculate that companies willing to share their source code for audits or other reasons do so because they’re more confident in their code’s quality (the opposite is also true, when companies ask for an audit because they know that their code sucks.)” And then let’s appeal to the philosophical side of the open/closed-source problem: can you really trust anyone? Let’s be realistic, consumers have no clue about code, bugs, spyware, or backdoors. Moreover, as I’ve mentioned earlier, not all developers have the right set of skills (or time) to review a certain code. This means we have to depend on the judgments of those who claim to be competent. But competent people are still people which means that they also make mistakes. Even the guys from Ethereum. Remember ? DAO So here’s the thing — we have no choice but to trust those reviewers. And how can you be one hundred percent sure they are not compromised or making a mistake? With closed-source, you choose to trust reviewers on the App Store or simply believe in the image that was created by the PR masterminds. At the end of the day, you still have to trust someone one way or another. And here’s where the power of community comes into play. In the open-source model, you build a strong community that you can trust and it’s crucial to invest a lot of effort in this task. Of course, it is hard. It’s hard to find and attract the right people and make them believe in your idea. have to But the open-source concept grants you access to a whole world of enthusiastic people, with no political or economic borders and no bureaucracy to hinder you. And then, for a second, think about how you’re going to raise your money via venture capitals and organize the whole routine from scratch for your ‘traditional’ startup, and tell me what’s harder. The Bottom Line and New Beginnings Open-source is a revolutionary way to design a product you would want to use yourself. A variety of approaches, freedom of development, general transparency, and collective work — those are the state-of-the-art principles for creating a meaningful product. I hardly doubt that it will ever replace a closed-source model, which works just fine for many companies and individuals who prefer to walk the beaten path and make a stable living with tough deadlines and a clear strategy. And there’s nothing wrong with that. As I said, open-source is not for everyone, and each company should really take their time to figure it out. It took us a while to go forward with the decision to make and just go for it. The amount of work that had to be done seemed overwhelming. But after we sat down together with our team and delved into the pros and cons, it became obvious that it simply . And in the final analysis, closed-source and crypto do not match very well. Lumi Wallet must be done Right now I’m happy to announce, that we are finally mentally prepared and have already begun to follow through on this concept. We’re doing our best, and are planning to have Lumi Wallet go open-source in the summer.

Fear and Loathing and Open-Source

If you speak to anyone in marketing, branding, or communications within the space, they’re all likely to nod to at least one or more brands they’ve historically worked with that showed all the obvious signs of having an identity crisis. Web3 You see, this shouldn’t really come as a surprise. For any brand — from traditional firms to modern start-ups — having a kick-ass and tone of voice is pretty rare, but it’s the one thing that can change a product’s position from being simply useful, to immeasurably powerful. brand identity Now, this would be the cue for a Steve Jobs quote and a reference to ’s wildly successful branding — but we’re not going to do that, because I’m not actually here to talk about branding. Apple “Why the explicitly focused intro, then?” I hear you cry. And I’ll tell you. The thing is, while most traditional brands yearn to prosper with a strong and compelling brand, many in the Web3 space today are lagging behind their Web2 counterparts, and it’s having a really tough effect on the industry’s growth. Ledger’s fiasco last month was not down to a brand identity crisis, but, in order to understand what the issue was, this must be our starting point. Web3 Brands Haven’t a Clue Who They Are Disclaimer: Some do. Many don’t. One of the first rules of branding is to build the personified version of your product, , if you will. the soul of its being Web3 has seen immense growth over the past decade, with a lot of new projects hitting the scene, and many of them taking off to the moon. As an inherently technical industry, this immense and imminent growth resulted in a lot of projects launching to market with a predominantly technical team, focused simply on building a “good product.” This resulted in a lot of new products hitting the market with little thought as to how they look, feel, and connect with an audience. In the early days, though, this wasn’t really an issue — a tech-oriented team presenting products to a tech-oriented audience seemingly worked quite well, hence the number of unicorns — like Uniswap — that had a pretty poor user experience in their earlier successful versions. You see, the problem came later. As with any bull run, new adopters appear in the ecosystem, keen to try out the best applications available in the space. Great, right? Well, probably better if you’re ready for it. So as Web3 became more and more desirable to wider audiences who were a little less tech-focused and a little more used to their pocket-friendly Web2 apps, the industry seemingly hit a pretty big dilemma: Nobody could actually use their applications. As with any industry, of course what followed next was a to build the greatest Web3 app for mainstream adoption ahead of the next bull run, and that more or less brings us to where we are today. Darwinian battle But, brands don’t just have the issue of UX on their hands if they’re to meet the standards that wider audiences are used to — they need to up their branding game too. Much like the UX dilemma, when a lot of technically-oriented projects hit the market, it seems they may have skipped a session on branding, too. Touching back on our earlier point, a brand isn’t a way for projects to show off their grandiose ideas — it’s an emotive and powerful tool used to actually engage and create a connection with an audience. Having a lot of earlier projects built with a lower priority focus on this, what we’re faced with today is an overflowing ecosystem of really cool tech, but nobody to use it. A lot of projects today know exactly they’ve built, and exactly they’ve built it, yet, they have . what why absolutely no idea how they’re meant to sell it And that right there, is Web3’s brand identity crisis. The Industry’s Consumer Dilemma Picasso didn’t get his name by writing up the shade of paint and millimeter of the paintbrush he used for his work, so why do Web3 brands feel compelled to overshare the technicalities and underlying functions of their project over any emotive aspects? Well, actually, they’ve had pretty good reason to. You see, that heavy tech audience we keep circling back to pretty much made the industry what it is today, thanks to their loyalty and dedication to decentralization. Fundamental as their contributions were, their overwhelming presence in the evolution of the industry is the very thing that has placed it in a critical consumer dilemma as we move toward the next phase of growth. The early adopters of Web3 have been through it all, yet, they’re still with us today. They’re here because they truly believe in the opportunity the industry presents, and they believe in decentralization. So much so, for many old-timers in the space, the more decentralized a product is the better — no matter how much things like usability are compromised. This desire for more decentralization was a pretty huge factor as to why so many loyal Ledger users were left feeling unhappy with the project’s announcement of a new recovery feature that would enable users to regain their private keys last month. But it wasn’t just that. You see, even maximalists know that if we’re ever to go mainstream, we must improve adaptability across the board — and storing seed phrases and maintaining private keys is a far cry from consumer friendly. The thing that really kicked the can wasn’t building an “off-brand” feature only really fit for an entirely new market, it was their product positioning, consumer consciousness, and communication surrounding that feature. By pushing to market the way they did, they presented long-term, loyal users with these questions: What actually Ledger? is What do they care about? actually Who are they building for? actually In doing so, they uprooted many of the trusted foundations they had managed to set over the years, and established a colossal wedge between themselves and the users who helped them become the brand they are today. In addition, they barely seemed to touch the wider market that the product have been most appealing to. should Here’s the thing — Ledger is fighting the good fight. Now, they’re ramping up their efforts to help drive the next wave of adoption. But, in positioning this the way that they did, they failed to recognize their market and effectively engage their consumers. Their consumer consciousness in the decision to push this to market felt overwhelmingly negligent. Why Ledger Shouldn’t Be The Scapegoat Though the attempted launch was considered a pretty big failure, Ledger’s faux pas was not entirely their fault. Yes, they should have thought about the product positioning. They should have thought about the emotive response their existing users would have, and they should have thought about how to properly market the product to the intended audience, but we’ll leave that one to them. The thing is, Ledger’s gross misdirection of messaging and lack of consumer consciousness boils back down to the same issue we’re seeing surface time and time again in the space. The industry is growing. More diverse consumers with wider-spanning needs are adopting our technology, and while this is fantastic — — too many brands have no idea how to grow with it while maintaining their existing position within the ecosystem. it’s exactly what we hoped would happen The goal is to enable a globally adoptable Web3. Maximalists will never have the same requirements as my neighbor Bill, but, the industry needs to cater to them both — and it’s up to brands to make that happen. In order to do that successfully, brands need to reconnect with their identity and adopt their own consumer consciousness, otherwise, we’re going to be seeing a lot more fiascos and a lot less impactful growth. While this may mean compromising some aspects of technology to improve on others, the idea of creating a decentralized ecosystem was not to decentralization, but to dismantle centralization, was it not? enforce forced There’s no reason Ledger cannot have a branch of the brand that speaks to wider, less advanced audiences while maintaining the same core mission and keeping their promises to their earlier loyal users, but chucking it all in the mix together and hoping for the best was going to be a recipe for disaster. always In order for us to move forward, brands have got to realize this cautionary tale is much closer to home, and therefore, must take their next steps with a true consideration towards the value of consumer consciousness.

The Ledger Recovery Storm: Highlighting Web3's Consumer Consciousness Problem

The outbreak of COVID-19 has shaken every industry thereby influenced the preferences and priorities of consumers. During the pandemic, those business models who adapted to the change survived while others got lost in the thin air. Gradually, as the post-pandemic phase arrived, one thing that helped marketers predict their consumer behavior was Data Science. The Role of Big Data In Branding The only way to market your products and services is by knowing the customers and understanding the market demographics. That’s where data science becomes effective. It provides comprehensive insights to create a robust marketing plan backed with a result-oriented strategy. With the help of Machine Learning and Artificial Intelligence, marketers can now delve deeper into finding the right demographics to initiate the campaigns. Giving an effective funnel to marketers, data science makes it easier to stay focused on activation, acquisition, and spreading awareness. Here are some amazing statistics of Big Data about its impact on consumer behavior and : sales growth How Big Data Is Shaping The Post Pandemic Marketing Strategies Unpredictable consumer behavior and shaky market condition were the two prominent aftereffects of the pandemic. To strategist branding in the post-pandemic world, Data Science targeted the two grey areas and gave an effective plan to generate profitable outcomes. Learn about the prominent areas of marketing, professionals get the chance to understand and evaluate through Data Science. It Helps To Prove Relevancy During the pandemic, consumers began to prioritize things differently. Where some began concerned about their health and fitness, others were fearful about their economic instabilities. With the help of Data Science Analytics, marketers get to understand the . Discounts, offers, and all the other marketing tactics were designed to facilitate the target audience. insights about consumer behavior Data Science helps to declutter the data. Using a series of the algorithm, data is collected about the consumers who stay active and are more connected to the brand activities. What companies are doing is they are utilizing this data to offer personalized services and packages. In this way, their clientele remains intact and stable. Back in 2019, a Gartner survey found that over are likely to connect with a brand that offers valuable assistance. 20% of buyers It Reveals The Buying Pattern Currently, marketers are experiencing two different phases of a single market- the pandemic and the post-pandemic phase. During both of these two phases, consumer behavior differs. Things that consumers used to buy rarely got in demand more than ever and similarly, those items that were the best sellers began to lose their position. So how will marketers identify a customer’s next move? This is where data science comes in. It helps businesses examine comprehensive consumer analytics. The link between the sale of each grocery item in a supermarket can be mapped in the following manner. When you have such a detailed analysis through data, mapping you can easily come up with different marketing plans. It Strategies As Per The Current Consumer's Economic Condition It's important to value the sentiments of your target audience. You must stay connected to what your audience is going through and how to bring things in their favor. You need to create a marketing plan by segmenting your consumers. The key benefits of segmentation through Data Science involves the following: It helps in identifying profitable marketing opportunities It creates tailor-made, personalized marketing initiatives It helps in deciding the pricing models It helps to create an effective product development strategy Since the outbreak, consumers are focused on buying. They prefer not to spend money on anything, which is not essential. This is affected by the market demographics. Now if you understand the financial priorities of your consumers you can efficiently navigate their attention to your brand. You can find ways to encourage them to get connected to your offerings. Segment your target audience into different groups starting from high-paying consumers to inconsistent shoppers. It Helps Brands To Build A Result-Driven Customer Journey Many brands initiated new avenues in their business model during the COVID-19 crises. They shifted their attention towards the more selling goods. During this time, what helped them stay on track is the initiative to build a progressive consumer journey. With the help of Machine learning, they predicted the churn rates and created a strategy to target the right group of the target audience. Where the major selling products during the pandemic were healthcare and fitness, other industries that benefited include gaming and clothing. AI offered quantities analysis to let brands identify targeted groups of the audience to market their products. Through a systematic procedure, each customer was engaged and gained brand awareness. It Helps Predict Future Strategies From Past Downtimes The best part of data science is that you can predict future marketing strategies by pondering over the past downtimes. Researchers once studied the economic crisis periods from 2001 to 2008 to find clues of the future. The report surfaced that over 80% of Americans changed their buying habits after getting a minor income raise. With the survey, the researchers get to know the reinvestment rates as well. It is one of the tactics that help brands to gain the leading edge in the market. It helps companies in keeping an eye on the future and effectively change their key products as per the market demographics. Such surveys help in determining how the target audience responds to the marketing campaigns and what measures can be taken to make benefitting outcomes. It Provides Real-Time Analytics One of the most effective uses of data science is real-time analytics. It helped marketers identify the right time to market their products and services. As a major part of the workforce, in almost every industry, is shifted to remote working, consumers can be online at an unusual time. Therefore, through Data Science it becomes easier to fetch information and track the activities of consumers to find out how to engage and indulge them. You can keep a regular track as to when they get online and indulge in browsing other stuff, what are the most used platforms, and the best possible areas in the search engine where you can post your promotional advertisements. Wrap Up In the world of digital marketing, data scientists are the real storytellers. They let a brand mark its position in the search engine. They assist brands to take the right initiative to reach out and engage the target audience. Whether it's about handling marketing during the crises or even after that, you can identify the appropriate set of strategies to make your position consistent and progressive. So, if you want to drive out the best possibilities of success and want to make your brand stand out from the clutters then incorporate state-of-the-art tools to gather data that can garner the attention of your target audience efficiently.

Future of Marketing: How Data Science Predicts Consumer Behavior

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

What's Inside Play to Earn

ইনসাইড কি আছে উপার্জন করতে খেলুন

Was ist drin? Spielen Sie, um zu verdienen

¿Qué hay dentro de Play para ganar?

Qu'y a-t-il à l'intérieur du jeu pour gagner

अंदर क्या है खेलो और कमाओ

プレイして稼ぐ内容

무엇이 들어있나요? 플레이해서 수익을 얻으세요

O que há dentro Jogue para ganhar

На чем можно заработать в Inside Play

Kazanmak İçin Oynamanın İçinde Neler Var?

Có gì bên trong Chơi để kiếm tiền

玩赚游戏有什么用

On-Chain Data Product Insights: The Data Analysis Revolution in the Web3 Era

For too long, the siren song of data mesh has lured companies into a false sense of security, promising a utopian future of seamless data management. But the harsh reality is sinking in: data mesh isn't the panacea it's cracked up to be. As costs soar, trust in data erodes, and decision-making stalls, it's time to shatter the illusion and explore a new path forward. For many organizations, the allure of data mesh, a decentralized approach to data management, has turned into a frustrating chase. While it promised data democratization and agility, the reality often involves siloed data, soaring costs, and eroded trust in data-driven decision-making. A recent implementing data mesh experienced significant data quality issues. Surveys paint a concerning picture: survey by Unisphere Research, a division of Information Today, Inc., in partnership with ChaosSearch, found that 68% of IT managers Another survey conducted by revealed that struggled with increased complexity and lack of collaboration after adopting data mesh. PwC in 2023 72% of businesses "Data mesh, in theory, sounds great," says Experts are also raising concerns: Zhamak Dehghani, a renowned data management consultant and author of the book 'Data Mesh: Delivering Data-Driven Value in the Age of Big Data.' "However, the reality is that many organizations lack the cultural and organizational maturity to successfully implement it, leading to fragmented data landscapes and inconsistent data practices." The Data Marketplace Data Marketplace vs. Data Mesh: A Comparative Look Feature Data Mesh Data Marketplace Data Ownership Decentralized (domain teams) Decentralized (domain teams own data, but data products are managed by data experts) Data Storage Distributed across domain-specific data products Centralized data lake with isolated access zones Data Governance Loosely coupled, relies on domain teams Robust and centralized, with standardized processes Data Access Open access to all data for domain teams Controlled access based on skills and expertise Data Products Developed and maintained by domain teams Created and managed by data experts based on defined needs Focus Agility and data ownership Focus, collaboration, and data quality Here's how the data marketplace overcomes the challenges of data mesh: By focusing on relevant data and eliminating duplication, the data marketplace minimizes storage and processing expenses. Reduced Costs: Robust governance and centralized data management ensure consistent data standards and quality across the organization. Improved Data Quality: The marketplace fosters communication and collaboration between data producers and consumers, leading to better insights and decision-making. Enhanced Collaboration: While data ownership remains with domain teams, the data marketplace ensures accountability for data products through designated data experts. Clearer Ownership: Data Marketplace Principals Here are my 8 principles for using the data marketplace concept that will ensure your organization leverages the right data at the right time to make the right decisions: - In the marketplace, we keep the data producers owners of the data they produce, but they do not own the data products built on top of it, which I think is a fear thing. Decentralized Data Ownership One of my biggest criticisms about data mesh is the decentralized ownership which requires now a front-end engineer and a product manager to own a data product based on their data, they are required to design and maintain it, and they have zero clue why they do it, or how it’s being used, and if I will be honest, none of the tools they used is actually native to their work. Instead, it just lets them set up the events or APIs calls, sets them with the tools to document what they send, to ingest the data without them touching anything, and from there, lets the experts work with it. This system makes more sense as they only need to worry that the data is correct and reliable and are not required to fix data products gone wrong. -  Yes, please stop storing all the data warehouses! Even if it’s S3 and “cheap,” there is no reason to store so much and process so much data; the reality is that most of this data is just sitting there getting dust. Transactional-based data By decentralizing the data, we allow the owners to decide what they store. With the marketplace, I wish to take a different approach: the analysts, the middle people between the data consumers and producers can see all the data produced in the company, but they can decide what data to buy and sign a “contract” with the producer which describe basically what they will use the data, what are their expectations and what happens in case of a data incident. By doing so, we ensure that the producers understand why they produce the data and what impact it has on the organization, and allow us to clean unused data either into deep storage or into the abyss by deleting it, and the analysts on the other side need to create a value case to why this data is important for them to use. It’s creating a win-win situation that also addresses the missing link between the data producers and analysts on who using the data in the data lake structure, these transactions are logged and stored. - Basic rules must be in the game, what is the metadata required, how and where to document the data ingested, produced, and visualized, what monitors should be in place to ensure quality, how the data contracts are created, and how data anomalies, incidents, and conflicts are handled; by doing, so we ensure we standardized the process. Robust Governance & Standards I see a big need to have 2-3 types of data contracts but enforce that every data created has a schema validation process to ensure quality, and in case of an issue, it’s rejected, and notifications are sent to the owner of it to be aware “Huston we have a problem.” Governance is important, and also, the enforcement of the regulation keeps this thing running with strong work-frames that address the use cases of data and serve as a system for future-proofing the data products. - Let’s talk architecture to make the data marketplace successful by creating three “isolated” levels of data on the data warehouse system; each level is accessible and usable based on the skills and expertise of the individuals and is meant to act as a safe net for data consumption: Isolated Architecture Environments serves as a secure storage for all data, regardless of quality, to ensure sensitive information protection. Think of it as a vault for safeguarding all types of data. Data then undergoes preparation steps like defining sessions, identifying bots, and ensuring compliance before being transformed into a usable format for analysis. This process can be compared to transforming raw ingredients into a ready-to-use meal for a chef. The Raw Data Zone empowers analysts by providing access to the prepared data through aggregated tables. Analysts can utilize exploration tools to query, analyze, and gain insights from the data. This zone, along with the tools it provides, plays a crucial role in enabling analysts to create valuable decision-making dashboards. The Marketplace Data Zone serves as a single source of truth for the entire company. This zone contains rigorously tested, secured, and verified data presented through user-friendly visualizations. Anyone in the company can access and explore this data in a controlled environment, either directly via the provided tools or by extracting it for use in tools like Excel. The Decision-making Zone The data here is deemed the "official" version - any conflicting information from other sources should be considered inaccurate. Data that enters the data lake has one access point where the data is validated, is it what needs to be here, the data is checked for anomalies, and only if it passes the inspection, it will arrive in a raw data landing zone; then from there, the data will have small components that will run and clean for bot traffic, add attribution to the session, or even enrich missing metadata to ensure the data can be used. When it arrives to the data consumers, they will only see the data that was certified pre-calculated, and built, to enable the look and feel of a self-exploration environment without the fear of losing control over the numbers because “Self-serve analytics” is a nice dream but will never become reality due to the level of data literacy in the organizations. - Based on the above and the transaction-based, why in hell should we have a push system where anyone interested can just push data into the lake or a dashboard? Pull System We need to bring back control over the data and have a purpose and a clear why; otherwise, we’re just wasting money on moving data around. I am sure your cloud provider is happy and also convincing you this is cheap if you buy the right saving plans, but in reality, you have nothing to do with the data sitting there collecting dust. Some industry standards estimate that around 87% of the data ingested into the data lake have no clear purpose or use case; think about the amount of money you are spending right now on having it stored. The pull system ensures that only those with purpose arrive at the common areas and are used. Otherwise, it will just become history in a short period saving you a lot of money on wasted accomilated data. - The data domain subject matter experts are the ones who can build data products from the raw data and move it into the marketplace zone, or approve a new data product from the marketplace zone into the decision-making zone. Data Parliaments In the data mesh, one of the biggest issues is that anyone can pull data and build a data product with it, but this sometimes causes cases of data not matching or worse decisions to be made over wrong data. The parliament members can be, for example, responsible only for the marketplace revenue tables; they are the only ones who know how to define the true source of the revenue, how to present it, and also how to handle issues with its quality when things go wrong. On the other side, a parliament can handle the company KPIs table and will know which data in the marketplace is the one true source. Contrary to data mesh, we create a function of data controllers who validate that the marketplace has one true source of data, so each analyst using the marketplace zone knows they are using the right data, and they are the ones that ensure the correct data is carried across the company. This also answers the governance being overwhelmed with so many topics they need to manage and helps them focus on the work-frames and the parliaments on the data products. - This is a vital part if we wish to ensure people in the organization consume the right data and make decisions based on the best available data to them. Data Certification We need to certify the data; the certification process allows us to, on the one side, ensure that we have one, revenue numbers used in the decision-making zone, and also ensure it has the right process behind it from data monitoring to error handling and up to how it should be documented. The certification of the data ensures that everyone knows what the correct number is and has the trust that the data has all the required steps to ensure its reliability and trustworthiness; one of the things I met the most is the missing trust of leaders in the data they consume to make decisions, and this mistrust causes them to doubt the data and ask analysts to check it constantly whenever it doesn’t fit their expectation, wasting a lot of time of the analysts on debugging. But by creating a data certification, we can ensure that, in case of a data incident, the user will know it before it’s being used, and the analysts will know where they need to look to solve it. Basically, creates better trust between the data consumers to the analysts, and in case the data incident is not fixed where the problem is and who is blocking it from being resolved, creating full transparency and accountability over the data. Return on investment! We spoke earlier on transactions and explained the use case. Well, with that, we need to have some system that evaluates the effort invested in creating a data product, the storage, the processing, the analyst time invested, the cost of maintaining it on the one side, and on the other, the possible profits by having the data. ROI - I agree here that not every data source will have it, but I will argue it’s possible :-). By having, for example, the campaign performance on Facebook, we can optimize the campaigns and invest more money in campaigns that drive more profit or cut campaigns that waste money. By doing so, we save and increase the revenue, and it can go into the calculation. Another example is by finance having the AWS costs available, they can find areas to improve savings either by buying saving plans or investigating high costs and possibly reducing them. Another method to evaluate the revenue of the data is by incrementally testing; having this data means you can save or generate X amount of extra money; if not, this is the amount of money lost. The ROI gives everyone in the chain of the data a know what the value of their data is and also the ability to evaluate how to prioritize a data incident with a clear or estimated monetary value next to it. More data does not equal better insights; it just means more time lost on making decisions, more time wasted on bad data, and the bad thing, loss of focus; here is what I am trying to correct with my data marketplace concept. - Focus on the data that make sense for the organization to make decisions. Focus Not all events and all data sources are relevant, and most definitely, having more data does not mean you will be able to make better decisions. Focusing on a small amount of highly relevant KPIs will help the business oriented around what is working or not, will ensure higher focus on the quality of the data, and ensure everyone knows how their work impacted or not the success of the organization. - If you produce data, you need to know WHY you produce it and what is the added value of having it. Without having it in place, you waste money. WHY I don’t know how many times in my life as an analyst I was required to run some super expensive and complicated query that requested me to give some number or shares, but after asking what they were used for or what decisions they drove, I discovered that my three days work was “Just for good to know” and had no impact what so ever on the decision making or actions taken. I am thinking a lot about the wasted time of analysts answering questions that do not serve a real purpose and the amount of time gone on checking the data quality and compatibility, understanding it, and then trying to run the most efficient query, and it all goes with action. - People should be able to self-serve themselves, and no, when I talk about self-serve, I am not talking about this drag and drop that we all fall in love with from Google Analytics. Data usability What I mean is that if you are an analyst or data consumer, you should have the right tools and access to data based on your skills and expertise. I do not wish to see data consumers playing with raw data and trying to drive learning without knowing what can be the results of them using the numbers they encounter; at the same time, I do wish to see it. Even if you create more work-frames around the data mesh, the reality is that when you ask the wrong people to do the right things without the expertise, you increase their anxiety levels to handle situations they are not ready for, and worse, you cause them to lose focus on what their unit purpose is, and for this, you are wasting more growth opportunities. Hiring more data engineers and data product managers won’t solve your problem; training unrelated people to own data products will fail. I call you to try something new, reduce the noise from data increase your focus, and if you want, after you master the data marketplace and the delicate balance between centralized to decentralized, you can go in the direction of data mesh. But all the time you don’t, you risk accumulating more costs in lost revenues and overspending on data the decision is yours! Embracing the data marketplace isn't just about efficiency; it's about empowering all stakeholders in the data ecosystem to harness the true potential of data. By focusing on collaboration, clarity, and purpose, we can unlock the power of data and navigate a path toward sustainable success. Share your thoughts on data management and explore whether the data marketplace could be a viable solution for your organization. Join the conversation!

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CLUEY CONSUMER

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CLUEY CONSUMER

clueyconsumer.com

1-10 employees

Since 2020

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CLUEY CONSUMER

EVERGREEN INDEX #2911

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