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DataMarket

datamarket

Whether or not you realize or consent to it, big data can affect you and the way you live your life. Many find the buzzword as a pure blessing while others curse. Well, I personally feel that it is somewhere in between; it can contribute to both the insight and the fog of visibility. In the following post, I’m going to cut right to the chase and shed light on a few of the fantastic things that Big Data makes possible in the upcoming years. With the emergence of big data technology, businesses can make ground-breaking insights available in the real-time. And not only businesses but even . Whether you search on Google or social networking sites like Facebook, data can be derived from any source. With the advent of smartwatches, glasses and even smart clothes, we have started creating a world featuring high data collection mechanism. Unfortunately, many of you are found discouraging such potential of course because they fail to understand its capabilities. NGOs and several governments also seem to have benefited by leveraging insights offered by big data According to IDC, Big Data revenues are estimated to cross $187 billion in 2019. It is said that the amount of data in the world doubles every two years, and by 2020, the digital universe will reach 44 zettabytes or 4 trillion gigabytes in data. In fact, banking and manufacturing are the ones who invest heavily in big data these days. As a result, it is being touted as the “definitive source of competitive advantage” across industries. Where is Big Data Today? Speaking of the data universe, around 2.7 Zettabytes of data exists till date. Hard to believe, isn’t it? Approximately to conduct more and bigger data research projects. $200 million is being invested by the Obama Administration Facebook stores, accesses and analyzes 30+ Petabytes of user-generated data. By 2020 it is assumed that business transactions whether it is business to business or business to the consumer will reach 450 billion a day. If we seek globally, around 5 billion people are found calling, texting, tweeting and browsing on mobile phones. Walmart handles more than 1 million customer transactions every hour, and this is not it! This particular data is imported into databases where already you will find 2.5 petabytes of data. A few years ago, decoding the human genome took ten years, but now it can be achieved in a week- nothing more/ nothing less. 94% of Hadoop users perform analytics on large volumes of data. However it was not possible before; 88% analyze data in greater detail, and 82% can now retain more of their data. Every minute of the day a new video is uploaded on YouTube. Data production will be 44 times greater in 2020 than it was in 2009 What the future holds? Down below I would like to mention a few facts to take into account about big data market growth. Widespread adoption More and more companies are seen leveraging the and put those findings into “active use.” Apache Hadoop/ Hive, Apache Spark, and Presto, are some of the most popular engines used to accommodate data preparations, machine learning techniques, reporting and analysis workloads. big data technology Increasing volume of commands Again speaking about Apache Hadoop/Hive, Apache Spark, and Presto, the total usage across these engines has grown by 162%. Being known as one of the fastest growing engines, users are experiencing a 420 percent growth in compute hours and 365 percent expansion in total number of commands run. Get acquainted with new tools In addition to top-three engines, around 30 percent of organizations have started using these tools. For example- Apache Airflow, it is used to analyze sophisticated data preparation pipelines and operationalizing machine learning using Python code. In simple words, monitoring of jobs, handling failures and the list goes on. Apart from this, you can even think of considering tools like XGBoost (predictive machine learning tool), Pandas (Python-based data science tool used for statistical analysis) and MLLib (Apache Spark’s ML library) are also gaining in acceptance. Productivity has increased While usage and implementation grow, data-driven organizations are focused on optimizing many users running commands in each engine, such that costs reduce and the process is nearly automated. Increase in the requirement of Data scientists but do you think that you stand a chance? Organizations are found focusing on realizing business projects, leveraging the technologies and big data they have and need, to get the results they want, with a clear ROI. In order to drive more revenues, enhance customer experience, save costs, create new business models, find new sources of revenue, etc. It’s your fair chance to prove your value. So, go for it! The job market for data scientist seems to be a killer . _Author Bio:_ Charles Richard is a Business Analyst at Tatvasoft.co.uk . and A passionate writer who loves to write that matters and believes that writing is the best media to express what you want to share with the rest of the world. He can be found on Twitter at @charlesrich324

A Few Facts to Take Into Account about Big Data Market Growth

A Visionary Approach to Financial Risk Management The world of financial risk management has always been unpredictable. Markets rise and fall based on a complex mix of macroeconomic factors, investor sentiment, and, sometimes, sheer speculation. Over the years, financial institutions have relied on traditional risk models—statistical frameworks, historical trends, and human judgment—to mitigate uncertainty. But as we’ve seen with major financial crises, these models are often reactive rather than proactive. Harshita, a recognized expert in financial analytics and AI-driven risk modeling, has spent years tackling this challenge. Her work in advanced data analytics focuses on making risk management smarter, faster, and more adaptive. Through her patented technologies, she’s introduced new ways to reduce bias in predictive models, optimize big data processing for financial transactions, and enhance cybersecurity in banking. “Financial risk models have long suffered from systemic flaws—bias in data, lagging insights, and over-reliance on human intervention. The real power of AI is its ability to continuously learn from changing market conditions, adapt, and make predictions that traditional models can’t.” — Harshita “Financial risk models have long suffered from systemic flaws—bias in data, lagging insights, and over-reliance on human intervention. The real power of AI is its ability to continuously learn from changing market conditions, adapt, and make predictions that traditional models can’t.” — Harshita The Uniqueness and Implications of Harshita’s Patents Each of Harshita’s patents addresses a fundamental issue in financial risk management: Reducing Bias in AI Prediction Models: Financial models can reflect human biases—whether in credit approvals, fraud detection, or loan underwriting. One of Harshita’s patents introduces an AI-powered system that continuously audits and corrects biases in predictive models, ensuring more equitable and accurate risk assessments. The impact? Financial institutions can improve lending fairness while reducing false positives and negatives in risk classification. Reducing Bias in AI Prediction Models: Financial models can reflect human biases—whether in credit approvals, fraud detection, or loan underwriting. One of Harshita’s patents introduces an AI-powered system that continuously audits and corrects biases in predictive models, ensuring more equitable and accurate risk assessments. The impact? Financial institutions can improve lending fairness while reducing false positives and negatives in risk classification. Reducing Bias in AI Prediction Models Big Data Processing for Financial Transactions: Real-time financial decision-making requires vast amounts of data to be processed in seconds. Harshita’s patented cloud-based analytics system enhances transaction speed, fraud detection, and compliance monitoring, enabling banks to operate with unmatched efficiency. Big Data Processing for Financial Transactions: Real-time financial decision-making requires vast amounts of data to be processed in seconds. Harshita’s patented cloud-based analytics system enhances transaction speed, fraud detection, and compliance monitoring, enabling banks to operate with unmatched efficiency. Big Data Processing for Financial Transactions AI-Driven Cybersecurity: With cyber threats increasing, financial institutions need better defenses. One of Harshita’s patents focuses on an AI-driven cybersecurity alert system that reduces false alarms while accurately identifying real threats. This is a game-changer for banks that struggle with fraud detection fatigue—where security teams are bombarded with alerts but lack precision in filtering actual risks. AI-Driven Cybersecurity: With cyber threats increasing, financial institutions need better defenses. One of Harshita’s patents focuses on an AI-driven cybersecurity alert system that reduces false alarms while accurately identifying real threats. This is a game-changer for banks that struggle with fraud detection fatigue—where security teams are bombarded with alerts but lack precision in filtering actual risks. AI-Driven Cybersecurity How AI and Machine Learning Are Redefining Risk Analytics How AI and Machine Learning Are Redefining Risk Analytics AI is not just another tool in risk management—it’s fundamentally changing how risk is measured, predicted, and mitigated. Unlike traditional models that depend on historical data, AI models evolve continuously, integrating real-time transaction data, market indicators, and even alternative data sources like social media sentiment. Financial institutions using AI-powered risk analytics have seen: More accurate risk predictions—by incorporating non-traditional data sources.
Faster fraud detection—through real-time anomaly detection in transactions.
Better credit assessments—by analyzing behavior patterns rather than just credit scores More accurate risk predictions—by incorporating non-traditional data sources. More accurate risk predictions Faster fraud detection—through real-time anomaly detection in transactions. Faster fraud detection Better credit assessments—by analyzing behavior patterns rather than just credit scores Better credit assessments “The biggest challenge isn’t just developing AI models; it’s making them trustworthy, explainable, and adaptive. Black-box AI isn’t enough—finance demands models that are both accurate and accountable.” — Harshita “The biggest challenge isn’t just developing AI models; it’s making them trustworthy, explainable, and adaptive. Black-box AI isn’t enough—finance demands models that are both accurate and accountable.” — Harshita The Shift from Static to Adaptive Risk Models The Shift from Static to Adaptive Risk Models Historically, financial institutions operated with static risk models—frameworks built on years of historical data, often updated quarterly or annually. But in today’s fast-moving financial landscape, static models don’t cut it. AI-powered risk models adjust dynamically, factoring in real-time economic trends, geopolitical events, and behavioral shifts in consumer spending. For instance, during the COVID-19 pandemic, traditional models failed to predict the massive shifts in credit risk. Meanwhile, AI-driven models that incorporated real-time spending patterns and sentiment analysis provided far more accurate risk forecasts. Harshita’s patented big data processing system plays a key role in this shift. It allows institutions to process enormous amounts of real-time financial data efficiently, reducing decision-making lag and improving market responsiveness. AI in High-Frequency Trading (HFT) and Market Stability AI in High-Frequency Trading (HFT) and Market Stability High-frequency trading (HFT) algorithms execute thousands of trades per second, maximizing speed and efficiency. But they also introduce market instability, contributing to flash crashes and liquidity crunches. AI-powered risk controls help stabilize these markets by: Detecting early warning signals in liquidity gaps.
Halting trading when markets exhibit extreme volatility (preventing flash crashes).
Dynamically adjusting trading algorithms based on live risk analysis. Detecting early warning signals in liquidity gaps. Halting trading when markets exhibit extreme volatility (preventing flash crashes). Dynamically adjusting trading algorithms based on live risk analysis. For traders and financial institutions using HFT, the key takeaway is clear: AI models need built-in risk safeguards to prevent catastrophic market swings. The Case for Explainable AI (XAI) in Risk Management The Case for Explainable AI (XAI) in Risk Management A major roadblock in AI adoption for financial risk management is the lack of transparency. Financial institutions can’t blindly trust black-box AI models when billions of dollars are at stake. Harshita advocates for Explainable AI (XAI)—a framework that makes AI decisions transparent and auditable. Techniques like SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are now being integrated into AI-driven risk models to provide greater clarity into how decisions are made. For financial institutions looking to implement AI-driven risk management, the golden rule is: **“**If your AI model makes a risk decision, you need to be able to explain why.” **“**If your AI model makes a risk decision, you need to be able to explain why.” The Future of AI in Financial Risk The Future of AI in Financial Risk AI-driven risk management is evolving rapidly, and several emerging trends will shape the industry: Quantum computing for ultra-fast risk assessments.
Decentralized finance (DeFi) risk modeling, leveraging AI to secure smart contracts.
Hybrid AI-human collaboration, ensuring that while AI enhances efficiency, human expertise remains central to decision-making. Quantum computing for ultra-fast risk assessments. Decentralized finance (DeFi) risk modeling, leveraging AI to secure smart contracts. Hybrid AI-human collaboration, ensuring that while AI enhances efficiency, human expertise remains central to decision-making. Harshita strongly believes that the most successful AI-driven risk models will be the ones that: Combine AI’s processing power with human intuition.
Ensure fairness and reduce bias in financial decision-making.
Prioritize transparency and regulatory compliance. Combine AI’s processing power with human intuition. Ensure fairness and reduce bias in financial decision-making. Prioritize transparency and regulatory compliance. Final Thoughts Final Thoughts AI isn’t just enhancing financial risk management—it’s transforming it. Through innovations in predictive analytics, real-time monitoring, and bias correction, AI is enabling financial institutions to shift from reactive risk assessment to proactive, data-driven decision-making. But for AI to truly revolutionize finance, it must be trusted. Transparency, fairness, and adaptability will define the next era of AI-driven risk management. “Technology isn’t the challenge—trust is. The institutions that get AI right will be the ones that build models people can rely on, not just for accuracy, but for fairness and accountability.” — Harshita “Technology isn’t the challenge—trust is. The institutions that get AI right will be the ones that build models people can rely on, not just for accuracy, but for fairness and accountability.” — Harshita About Harshita About Harshita About Harshita Harshita is an expert in financial data analytics, AI-driven risk modeling, and cybersecurity. With multiple patents in AI and big data processing, she specializes in developing cutting-edge risk management solutions that enhance transparency, fairness, and efficiency in the finance industry. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here

Harnessing AI and Advanced Analytics to Navigate Market Volatility

Aprovechar la IA y el análisis avanzado para afrontar la volatilidad del mercado

AI eta analisi aurreratua aprobetxatzea merkatuaren hegazkortasuna nabigatzeko

Exploiter l'IA et l'analyse avancée pour gérer la volatilité du marché

A mesterséges intelligencia és az Advanced Analytics hasznosítása a piaci volatilitás szabályozására

Օգտագործելով AI և առաջադեմ վերլուծություն՝ շուկայի անկայունությունը նավարկելու համար

AIと高度な分析を活用して市場の変動を乗り切る

Нарық құбылмалылығын басқару үшін AI және кеңейтілген аналитиканы пайдалану

Kosalela AI mpe Analytiques avancées mpo na ko naviguer Volatilité ya marché

AI ir pažangios analizės panaudojimas rinkos nepastovumui valdyti

Përdorimi i AI dhe analitika e avancuar për të lundruar paqëndrueshmërinë e tregut

การใช้ประโยชน์จาก AI และการวิเคราะห์ขั้นสูงเพื่อนำทางความผันผวนของตลาด

Bazaryň üýtgemegine ýol açmak üçin AI we ösen analitikleri ulanmak

The consensus method plays a key role in data annotation when it is necessary to ensure high accuracy and reduce subjectivity in labeling. Based on Keymakr’s experience, implementing a consensus approach with multiple experts in specific cases can reduce annotation errors by 30–50%. Consensus minimizes mistakes, automates quality control, and helps create benchmark datasets — especially critical in high-responsibility areas such as medicine and autonomous driving. Tatiana Verbitskaya, a technical solution architect at Keymakr, talks about how this method works and the projects in which it has been successfully applied. Keymakr How It Works How It Works Consensus is achieved by gathering the opinions of multiple experts. When defining “ground truth” data, it is vital to establish an agreed-upon standard of accuracy. Consensus is critical when training a model on subjective data, such as color and shape, or when high accuracy is required. This method is actively used in the early stages when the model has not yet been trained on sufficient data or when additional training is needed, particularly for specific cases (e.g., subjective judgments). Additionally, consensus is critical in large-scale projects, such as annotating data for self-driving cars or monitoring transportation, as it enhances precision while reducing errors. Key Principles of Consensus: Key Principles of Consensus: Odd Number of Experts: To avoid deadlocks, consensus relies on an odd number of annotators, ensuring a definitive outcome even in cases of disagreement.
Disagreement Analysis: This method doesn’t just rely on the majority vote but also considers the frequency of disagreements. If discrepancies are too significant, the data may be flagged for additional review or not even used for the model training.
Error Detection Mechanisms: Even consensus-based data can contain errors if the cases are too subjective and not definitive. Odd Number of Experts: To avoid deadlocks, consensus relies on an odd number of annotators, ensuring a definitive outcome even in cases of disagreement. Disagreement Analysis: This method doesn’t just rely on the majority vote but also considers the frequency of disagreements. If discrepancies are too significant, the data may be flagged for additional review or not even used for the model training. Error Detection Mechanisms: Even consensus-based data can contain errors if the cases are too subjective and not definitive. Global technology leaders like Google, Tesla, Amazon, and Meta actively use consensus-based annotation to improve AI model performance. Google Health, for instance, applies multiple radiologist annotations to X-rays to enhance diagnostic accuracy. Tesla uses consensus to label data from autopilot cameras, reducing training errors in autonomous driving. Amazon SageMaker Ground Truth incorporates consensus annotation in NLP, computer vision, and satellite imagery analysis, while Meta employs it for facial and object recognition projects. Medical Consensus: An Annotation Council Medical Consensus: An Annotation Council One of the most critical applications of consensus is in medical image annotation for disease diagnosis. Experts say radiologists’ diagnoses can vary by as much as 20–30%, directly impacting patient outcomes. When a consensus-based approach is employed — where multiple radiologists independently annotate images and their inputs are aggregated based on expertise-weighted scoring — annotation accuracy can be improved by up to 40%. Keymakr actively applies this approach in complex medical projects. As a result, this helps to ensure precise image labeling for AI models trained to detect complex pathologies. Here, the process was built using the Keylabs platform — where you can compare the opinions of several experts, identify discrepancies, and form high-precision datasets. This approach significantly increases the reliability of algorithms used in automated diagnostics, minimizing the risk of wrong diagnosis. Keylabs Consensus in Copyright Content Usage Monitoring Consensus in Copyright Content Usage Monitoring Currently, Keymakr collaborates with SoundAware, a company that deploys automated music recognition technology to identify copyrighted music usage. The team reviews 10,000 URLs to assess the presence of copyrighted material. Keymakr SoundAware Video platforms are filled with content that can contain the author’s material, such as music, scenes from movies, or TV show fragments. Due to the vast amount of data and the subjective nature of copyright interpretation, manually analyzing each video is impractical. However, Keymakr identifies cases where copyrighted content is used or modified in ways automated systems cannot detect reliably yet. These include parodies, fan art, and homages. To eliminate subjectivity, Keymakr employs a consensus-based approach: each video is evaluated by multiple independent experts who answer the following questions: Does the video contain copyrighted music?
Does it feature scenes from a movie or TV show?
Has the content been modified, such as through editing or remixing? Does the video contain copyrighted music? Does it feature scenes from a movie or TV show? Has the content been modified, such as through editing or remixing? Based on the experts' responses, a final decision is made regarding potential copyright issues. Such projects are essential for enforcing copyright and ensuring rights holders receive fair compensation. Additionally, this process helps companies specializing in content monitoring refine their algorithms and accelerate the detection of copyrighted material. Consensus in Vehicle and Pedestrian Tracking Consensus in Vehicle and Pedestrian Tracking Consensus is also widely applied in AI training for autonomous vehicles, particularly in object recognition on roads (e.g., other vehicles, pedestrians, traffic signs). For instance, a camera might capture a pedestrian in motion, and human annotators might disagree on whether the object is a person or a shadow. Consensus ensures precise labeling in such scenarios. Keymakr team recently worked with analysis of video recorded on cameras to track vehicles. It was necessary to track the vehicle's movement through several cameras at a crossroads and ensure that the system correctly identified the same vehicle in different frames. Keymakr The cameras recorded one object (car) at several points. Several experts viewed the video from different cameras. They assessed whether this object is the same car because there could be differences in perception of appearance (for example, by color or brand). The information was used to train the model if five annotators confirmed the object’s identity. Otherwise, such data would have been excluded from the dataset. This has reduced the number of false alarms and increased the accuracy of car recognition systems, which is important for urban safety systems and automatic traffic control systems. The same approach can be applied to identify people in shopping malls or on the streets. Cameras capture movement by analyzing, for example, the color of clothes, height, or other characteristics. This method is used to: Enhanced security monitoring
Crime prevention
Retail visitor behavior analysis
Crowd flow assessment in public areas Enhanced security monitoring Crime prevention Retail visitor behavior analysis Crowd flow assessment in public areas The Future of Consensus in AI The Future of Consensus in AI The future of consensus-based data annotation is promising, particularly as AI models become more complex and data volume grows. The global Data Annotation and Labeling Market is projected to reach $3.6 billion by 2027, and many companies are adopting multi-layered annotation verification to enhance data quality. Studies show that models trained on datasets with consensus annotation demonstrate significantly higher accuracy than models trained on single-source labeling. is projected Despite the development of automatic annotation and generative AI, the human factor remains key: subjectivity and annotation disagreements necessitate multi-stage validation. Therefore, the consensus method will continue to be used, ensuring data reliability and reducing errors in critical areas such as autonomous systems, medicine, and financial analysis.

Consensus in Data Annotation: How to Ensure Accuracy and Objectivity

AI-max Datos ukanakaparjamakiw ch’ikhi —Ukat jaqinakax wali sumaw etiquetañ tuqitxa

Vaša veštačka inteligencija je pametna samo onoliko koliko su pametni njeni podaci — a ljudi su još uvek najbolji u tome da ga označavaju

Su IA es tan inteligente como sus datos, y los humanos siguen siendo los mejores a la hora de etiquetarlos

هوش مصنوعی شما به اندازه داده هایش باهوش است و انسان ها هنوز در برچسب زدن آن بهترین هستند

ה-AI שלך חכם רק כמו הנתונים שלו - ובני אדם עדיין הכי טובים בתווית זה

AIの賢さはデータ次第。そして、AIにラベルを付けるのに最も優れているのは人間だ

Сиздин интеллектиңиз анын маалыматтарындай эле акылдуу жана адамдар дагы эле аны эн мыкты белгилешет

Jūsu mākslīgais intelekts ir tikpat gudrs kā tā dati — un cilvēki joprojām ir vislabākie to marķēšanā

Sua IA é tão inteligente quanto seus dados — e os humanos ainda são os melhores em rotulá-los

AI nisqaykiqa Datonkuna hinallam yachaysapa —Runakunapas Kunankamapas aswan allinmi Etiquetanankupaq

AI Yako Ni Mahiri Kama Data Yake Pekee—Na Wanadamu Bado Ndio Bora katika Kuiweka Lebo

AI ของคุณฉลาดเพียงเท่ากับข้อมูลของมันเท่านั้น และมนุษย์ก็ยังคงเป็นผู้ที่เก่งที่สุดในการติดป้ายกำกับมัน

Model Context Protocol (MCP): The USB-C of AI Data Connectivity

The Power of Data Visualization for Tech Companies. Is Your Strategy Up to Par?

Texnologiya şirkətləri üçün məlumatların vizuallaşdırılmasının gücü. Strategiyanın səviyyəsi yüksəkdirmi?

Styrken ved datavisualisering for teknologivirksomheder. Er din strategi på niveau?

El poder de la visualización de datos para las empresas tecnológicas. ¿Está su estrategia a la altura?

O poder da visualización de datos para as empresas tecnolóxicas. A túa estratexia está á altura?

Az adatvizualizáció ereje a műszaki vállalatok számára. Az Ön stratégiája megfelelő?

Il potere della visualizzazione dei dati per le aziende tecnologiche. La tua strategia è all'altezza?

テクノロジー企業のためのデータ視覚化の力。あなたの戦略は標準に達していますか?

Ny herin'ny fijerena angona ho an'ny orinasa teknolojia. Mifanaraka tsara ve ny paikadinao?

Kuasa Visualisasi Data untuk Syarikat Teknologi. Adakah Strategi Anda Sesuai?

Siła wizualizacji danych dla firm technologicznych. Czy Twoja strategia jest na poziomie?

Моћ визуелизације података за технолошке компаније. Да ли је ваша стратегија на нивоу?

พลังของการแสดงภาพข้อมูลสำหรับบริษัทเทคโนโลยี กลยุทธ์ของคุณดีพอหรือยัง

GEORGETOWN, Cayman Islands, January 24th, 2025/Chainwire/--Silencio, the world’s first decentralized noise data engine, has officially launched, setting a new standard in environmental data utilization. Powered by community contributions, Silencio transforms smartphones into secure, anonymous sensors that crowdsource real-time, hyper-local noise data. At its launch, Silencio Network is operational with almost 800k members in over 180 countries, covering 43.86 billion square meters (+560 times the size of New York) and recording 8.1 million hours of data collection. $SLC Token Lists on Major Exchanges as Silencio Ecosystem Expands Central to the Silencio ecosystem is the $SLC token, powered by the Blocksound Foundation, which is used for rewarding contributors, transactions, staking, and governance across the network. As of its launch on January 24, 2025, $SLC is available on leading exchanges such as KuCoin, Gate.io, MEXC, Bitmart, and BingX, with more to be added soon. The $SLC token is designed to incentivize the collection of noise data, supporting the network's expansion with an initial issuance geared towards fostering sustainable growth. Silencio’s Vision: Transforming Noise Into Actionable Insights for a Quieter World Silencio’s mission is to harness real-time noise data to enable smarter decisions for individuals, businesses, and governments. With its cutting-edge approach, the network aims to address critical issues such as urban planning, public health, and environmental management, making it especially relevant for sectors aiming for decentralization and enhanced community engagement. Silencio is set to change the way people value real estate, select restaurants, and decide where to live. By providing actionable noise data, Silencio aims to impact the decision-making of millions of people worldwide, offering a new layer of insight that influences everyday choices and long-term planning. This approach ensures that noise levels, often overlooked, are now a central consideration in creating quieter, more enjoyable living and working environments. Leading the Web3 Revolution with Real-World, Decentralized Solutions Beyond its token utility, Silencio is also pioneering in governance and community engagement through its transparent, blockchain-based ecosystem. The network allows contributors to earn $SLC tokens in reward for their data contributions, ensuring that every user is a stakeholder in the network's success. Plans are underway to expand into the global personal data market, estimated to be worth more than $300 billion according to a McKinsey report. Silencio aims to commercialize any smartphone-collected data in a transparent and anonymized manner, providing full control and rewards to users. Looking ahead, Silencio plans to further expand its platform to include more diverse data applications, potentially integrating additional environmental variables and enhancing its impact on global data-driven decision-making. With a robust user base and a clear vision, Silencio is poised to lead the Web3 revolution, bringing blockchain solutions to the real-world challenges of noise pollution and beyond. Joining the Silencio Revolution Silencio paves the way for a quieter, more informed world, it invites individuals and industries to join its network, contribute data, and benefit from the insights generated. With its official launch, Silencio reaffirms its commitment to a sustainable and participatory approach to environmental data, welcoming all to be part of this revolutionary journey. About Silencio Network Silencio Network is revolutionizing the global approach to capturing, processing, and utilizing noise intelligence data. By transforming everyday smartphones into real-time noise sensors, Silencio provides hyper-local insights that drive impactful decisions across industries such as urban planning, real estate, and hospitality. Envisioning a future where noise-level data influences real estate pricing and guides daily choices in selecting hotels and restaurants, Silencio is paving the way for smarter, more informed decisions. With operations spanning over 180 countries and a network of almost 800k sensors, the platform generates more than 100,000 daily on-chain transactions, solidifying its position as the world’s largest noise intelligence platform. Contact Alex M. Blocksound Foundation marketing@blocksoundfoundation.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GEORGETOWN, Cayman Islands, January 24th, 2025/Chainwire/--Silencio, the world’s first decentralized noise data engine, has officially launched, setting a new standard in environmental data utilization. Powered by community contributions, Silencio transforms smartphones into secure, anonymous sensors that crowdsource real-time, hyper-local noise data. At its launch, Silencio Network is operational with almost 800k members in over 180 countries, covering 43.86 billion square meters (+560 times the size of New York) and recording 8.1 million hours of data collection. $SLC Token Lists on Major Exchanges as Silencio Ecosystem Expands Central to the Silencio ecosystem is the $SLC token, powered by the Blocksound Foundation , which is used for rewarding contributors, transactions, staking, and governance across the network. Blocksound Foundation Blocksound Foundation As of its launch on January 24, 2025, $SLC is available on leading exchanges such as KuCoin , Gate.io , MEXC , Bitmart , and BingX , with more to be added soon. The $SLC token is designed to incentivize the collection of noise data, supporting the network's expansion with an initial issuance geared towards fostering sustainable growth. KuCoin KuCoin Gate.io Gate.io MEXC MEXC Bitmart Bitmart BingX BingX Silencio’s Vision: Transforming Noise Into Actionable Insights for a Quieter World Silencio’s mission is to harness real-time noise data to enable smarter decisions for individuals, businesses, and governments. With its cutting-edge approach, the network aims to address critical issues such as urban planning, public health, and environmental management, making it especially relevant for sectors aiming for decentralization and enhanced community engagement. Silencio is set to change the way people value real estate, select restaurants, and decide where to live. By providing actionable noise data, Silencio aims to impact the decision-making of millions of people worldwide, offering a new layer of insight that influences everyday choices and long-term planning. This approach ensures that noise levels, often overlooked, are now a central consideration in creating quieter, more enjoyable living and working environments. Leading the Web3 Revolution with Real-World, Decentralized Solutions Beyond its token utility, Silencio is also pioneering in governance and community engagement through its transparent, blockchain-based ecosystem. The network allows contributors to earn $SLC tokens in reward for their data contributions, ensuring that every user is a stakeholder in the network's success. Plans are underway to expand into the global personal data market, estimated to be worth more than $300 billion according to a McKinsey report. Silencio aims to commercialize any smartphone-collected data in a transparent and anonymized manner, providing full control and rewards to users. Looking ahead, Silencio plans to further expand its platform to include more diverse data applications, potentially integrating additional environmental variables and enhancing its impact on global data-driven decision-making. With a robust user base and a clear vision, Silencio is poised to lead the Web3 revolution, bringing blockchain solutions to the real-world challenges of noise pollution and beyond. Joining the Silencio Revolution Silencio paves the way for a quieter, more informed world, it invites individuals and industries to join its network, contribute data, and benefit from the insights generated. With its official launch, Silencio reaffirms its commitment to a sustainable and participatory approach to environmental data, welcoming all to be part of this revolutionary journey. About Silencio Network Silencio Network is revolutionizing the global approach to capturing, processing, and utilizing noise intelligence data. By transforming everyday smartphones into real-time noise sensors, Silencio provides hyper-local insights that drive impactful decisions across industries such as urban planning, real estate, and hospitality. Silencio Network Silencio Network Envisioning a future where noise-level data influences real estate pricing and guides daily choices in selecting hotels and restaurants, Silencio is paving the way for smarter, more informed decisions. With operations spanning over 180 countries and a network of almost 800k sensors, the platform generates more than 100,000 daily on-chain transactions, solidifying its position as the world’s largest noise intelligence platform. Contact Alex M. Blocksound Foundation marketing@blocksoundfoundation.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Silencio Network Officially Launches, Revolutionizing Noise Data Collection Globally

Red Silencio ukax Oficialmente Uñstayatawa, Uraqpachan Ruido Datos apthapiñ Revolucionañataki

সাইলেন্সিও নেটওয়ার্ক আনুষ্ঠানিকভাবে চালু করেছে, বিশ্বব্যাপী নয়েজ ডেটা সংগ্রহের বিপ্লব ঘটাচ্ছে

Se lanza oficialmente Silencio Network, revolucionando la recopilación de datos de ruido a nivel mundial

Lanza oficialmente a Rede Silencio, que revoluciona a recollida de datos de ruído a nivel mundial

Silencio Network が正式に開始、世界規模で騒音データ収集に革命を起こす

បណ្តាញ Silencio បើកដំណើរការជាផ្លូវការ បដិវត្តការប្រមូលទិន្នន័យសំឡេងរំខានជាសកល

Silencio Network officieel gelanceerd, een revolutie in het verzamelen van geluidsgegevens wereldwijd

د سیلینسیو شبکه په رسمي ډول په نړیواله کچه د شور ډیټا راټولولو کې انقلاب رامینځته کوي

Официально запущена сеть Silencio, которая произвела революцию в сборе данных о шуме во всем мире

Mtandao wa Silencio Wazinduliwa Rasmi, Kubadilisha Ukusanyaji wa Data ya Kelele Ulimwenguni

Silencio Network เปิดตัวอย่างเป็นทางการ ปฏิวัติการรวบรวมข้อมูลเสียงทั่วโลก

Silencio Network 正式启动，彻底改变全球噪声数据收集方式

There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape. There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. (if not the most) Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Bright Data Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Bright Data Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. Bright Data How Bright Data Works How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape.

How Bright Data Simplifies Web Scraping/Data Collection for AI Training

Як AI Bright Data зрабіў скрабаванне/збор вэб-даных простым: праблемы перад рашэннямі AI Bright Data

Com Bright Data AI va fer que el raspat/recollida de dades web sigui sense esforç: els reptes abans de les solucions Bright Data AI

Cómo Bright Data AI hizo que la recopilación y el raspado de datos web fueran sencillos: los desafíos que enfrentaban las soluciones de Bright Data AI

Kako je Bright Data AI skrapiranje/prikupljanje web podataka učinio jednostavnim: Izazovi pred Bright Data AI rješenjima

Bright Data AI が Web データのスクレイピング/収集をいかに簡単にしたか: Bright Data AI ソリューションの課題

Cara AI Data Cerah Menjadikan Pengikisan/Pengumpulan Data Web Mudah: Cabaran Sebelum Penyelesaian AI Data Cerah

څنګه روښانه ډیټا AI د ویب ډیټا سکریپینګ/راټولول بې ځایه کړي: د روښانه ډیټا AI حلونو دمخه ننګونې

Bright Data AI විසින් Web Data Scraping/එකතු කිරීම වෙහෙසකින් තොරව සිදු කළේ කෙසේද: Bright Data AI විසඳුම් ඉදිරියේ ඇති අභියෝග

Jinsi Data Angavu AI Ilivyofanya Uchakachuaji/Ukusanyaji wa Data ya Wavuti Bila Jitihada: Changamoto Kabla ya Suluhisho Mkali za Data AI

எப்படி பிரைட் டேட்டா AI இணையத் தரவை ஸ்கிராப்பிங்/சேகரிப்பதை சிரமமின்றி உருவாக்கியது: பிரைட் டேட்டா AI தீர்வுகளுக்கு முன் உள்ள சவால்கள்

کس طرح برائٹ ڈیٹا اے آئی نے ویب ڈیٹا کو اسکریپنگ/جمع کرنے کو آسان بنایا: برائٹ ڈیٹا اے آئی کے حل کے سامنے چیلنجز

Bright Data AI đã giúp việc thu thập/sao chép dữ liệu web trở nên dễ dàng như thế nào: Những thách thức trước khi có giải pháp Bright Data AI

The Power of Market Disruption: How to Detect Fraud With Graph Data

Startups of The Year: Meet the Web3 Industry

شركات ناشئة لهذا العام: تعرف على صناعة Web3

বছরের স্টার্টআপস: Web3 শিল্পের সাথে দেখা করুন

Startups del año: conozca la industria de la Web3

Startups de l'année : découvrez l'industrie du Web3

वर्ष के स्टार्टअप: वेब3 उद्योग से मिलिए

Startup Tahun Ini: Temui Industri Web3

今年のスタートアップ: Web3 業界を知る

Startups of the Year: Hihaona amin'ny indostrian'ny Web3

Startupy roku: poznaj branżę Web3

د کال پیل: د ویب 3 صنعت سره لیدنه وکړئ

වසරේ ආරම්භක: Web3 කර්මාන්තය හමුවන්න

Yılın Girişimleri: Web3 Endüstrisiyle Tanışın

Small Businesses Are Turning to AI for Content Marketing, New Semrush Report Shows

ትናንሽ ንግዶች ለይዘት ግብይት፣ አዲስ የሴምሩሽ ሪፖርት ትርኢቶች ወደ AI እየተቀየሩ ነው።

Малыя прадпрыемствы звяртаюцца да AI для маркетынгу кантэнту, паказвае новая справаздача Semrush

Mala preduzeća se okreću veštačkoj inteligenciji za marketing sadržaja, pokazuje novi Semrush izveštaj

Malé firmy se obracejí na AI pro obsahový marketing, ukazuje nová zpráva Semrush

Las pequeñas empresas recurren a la IA para el marketing de contenidos, según un nuevo informe de Semrush

Semrush の新しいレポートによると、中小企業はコンテンツ マーケティングに AI を活用し始めている

کوچني سوداګرۍ د مینځپانګې بازارموندنې لپاره AI ته مخه کوي ، د نوي سیمرش راپور ښیې

Bizneset e vogla po i drejtohen AI për marketing të përmbajtjes, tregon raporti i ri i Semrush

Kiçi kärhanalar Mazmun marketingi üçin täze AI-e ýüz tutýarlar, Täze Semrush hasabat görkezişleri

Küçük İşletmeler İçerik Pazarlaması İçin Yapay Zeka'ya Yöneliyor, Yeni Semrush Raporu Gösteriyor

Các doanh nghiệp nhỏ đang chuyển sang AI cho tiếp thị nội dung, báo cáo mới của Semrush cho thấy

Semrush 新报告显示，小型企业正在利用人工智能进行内容营销

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