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kishorxox · 4 months ago

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Big Data Security Market to be Worth $60.1 Billion by 2031

Meticulous Research®—a leading global market research company, published a research report titled, ‘Big Data Security Market by Component (Solutions [Data Encryption, Security Intelligence, Data Backup & Recovery], Services), Deployment Mode, Organization Size, End User (IT & Telecom, BFSI, Retail & E-commerce), and Geography - Global Forecast to 2031.’

According to this latest publication from Meticulous Research®, the big data security market is projected to reach $60.1 billion by 2031, at a CAGR of 13.2% from 2024 to 2031. The growth of the big data security market is driven by the emergence of disruptive digital technologies, the increasing demand for data security and privacy solutions due to the rise in data breaches, and the growing data generation in the e-commerce industry. However, the high implementation costs of big data security solutions restrain the growth of this market.

Furthermore, the growing need for cloud-based security solutions and the increasing integration of AI, ML, and blockchain technologies in security solutions are expected to generate growth opportunities for the stakeholders in this market. However, the lack of knowledge about big data security solutions and the shortage of skilled IT professionals are major challenges impacting the growth of the big data security market.

The big data security market is segmented by component (solutions [data discovery and classification, data encryption {data protection, tokenization, data masking, other data encryption solutions}, security intelligence, data access control & authentication, data governance & compliance, data backup & recovery, data auditing & monitoring, other solutions], services [professional services, managed services]), deployment mode (on-premise deployments, cloud-based deployments), organization size (large enterprises, small & medium-sized enterprises), end user (IT & telecom, healthcare & pharmaceutical, BFSI, retail & e-commerce, energy & utilities, government, manufacturing, media & entertainment, transportation & logistics, and other end users). The study also evaluates industry competitors and analyzes the market at the regional and country levels.

Based on component, the big data security market is segmented into solutions and services. The solutions segment is further segmented into data discovery and classification, data encryption, security intelligence, data access control & authentication, data governance & compliance, data backup & recovery, data auditing & monitoring, and other solutions. In 2024, the solutions segment is expected to account for the larger share of the big data security market. The large market share of this segment is attributed to the increasing concerns regarding data security and privacy, the increasing adoption of data security solutions by SMEs, and the rising demand for encryption solutions for data protection across IoT devices. Big data security solutions include tools and measures to process or safeguard data and analytics processes. In March 2024, CrowdStrike, Inc. (U.S.) partnered with Rubrik, Inc. (U.S.) to transform data security solutions and stop breaches of critical information. Moreover, this segment is also projected to register the highest CAGR during the forecast period.

Based on deployment mode, the big data security market is segmented into on-premise deployments and cloud-based deployments. In 2024, the on-premise deployments segment is expected to account for the larger share of the big data security market. The large market share of this segment is attributed to the higher preference for on-premise deployments among large enterprises and increasing data generation in large enterprises. The on-premise model of deployment is majorly adopted by well-established and large companies that are capable of making capital investments toward the required hardware and hosting environments. In addition, these organizations also have sufficient in-house IT expertise to maintain software efficiency. Internal big data security is one of the major benefits of on-premise deployments.

However, the cloud-based deployments segment is projected to register the higher CAGR during the forecast period. The growth of this segment is driven by the rapid evolution of new security avenues for cloud-based deployments, the superior flexibility offered by cloud-based deployments, and the increase in security breaches. Cloud-based security solutions provide social networking privacy, system optimization, online storage, regulatory compliance, and connected device security. The adoption of cloud computing and storage systems is gaining popularity among small and medium-scale enterprises, supporting the growth of this segment.

Based on organization size, the big data security market is segmented into large enterprises and small & medium-sized enterprises. In 2024, the large enterprises segment is expected to account for the larger share of the big data security market. The large market share of this segment is attributed to the strong IT infrastructure of large enterprises, the growing adoption of advanced technologies such as AI, IoT, and blockchain, and the availability of skilled IT personnel to manage data security platforms. With larger budgets and a keen focus on developing strategic IT initiatives, large enterprises have a competitive advantage over small and medium-scale enterprises in terms of technology adoption. Large enterprises have a stable financial backup and can easily procure customized data security solutions, contributing to this segment's growth.

However, the small & medium-sized enterprises segment is projected to register the higher CAGR during the forecast period. The growth of this segment is driven by increasing digital transformation, government initiatives to promote security solutions, and the rising incidence of data breaches. SMEs are also increasingly becoming targets of cybercrime and therefore adopting suitable and strong security solutions.

Based on end user, the big data security market is segmented into IT & telecom, healthcare & pharmaceutical, BFSI, retail & e-commerce, energy & utilities, government, manufacturing, media & entertainment, transportation & logistics, and other end users. In 2024, the IT & telecom segment is expected to account for the largest share of the big data security market. The large market share of this segment is attributed to the increasing data breaches in IT companies as they store a vast amount of customer data, strict regulatory compliance forcing companies to implement stricter data security measures, and the increasing adoption of cloud-based solutions in the IT industry. In March 2023, IBM Corporation (U.S.) collaborated with Cohesity, Inc. (U.S.) to address increased data security and resiliency issues in hybrid cloud environments. With this collaboration, IBM launched its new IBM Storage Defender solution, including Cohesity's data protection, cyber resilience, and data management capabilities in the offering.

However, the healthcare & pharmaceutical segment is projected to register the highest CAGR during the forecast period. The growth of this segment is driven by the rising adoption of telemedicine devices and remote healthcare services, growing cyberattacks on connected devices, and the increasing demand for secure medical connected devices. A vast amount of medical data is generated in the healthcare sector. It is stored to improve patient outcomes, personalize treatment plans, and develop new drugs, among other applications. However, this sensitive data requires robust security measures to protect patient privacy and prevent unauthorized access. In November 2021, Armis, Inc. (U.S.) partnered with Nuvolo (U.S.) to improve data interoperability and the overall risk posture of healthcare organizations.

Based on geography, the big data security market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. In 2024, North America is expected to account for the largest share of the big data security market. The market growth in North America is driven by the presence of prominent players offering advanced big data security solutions & services, the early adoption of disruptive technologies, and growing awareness regarding data security. North America is home to several major players that provide products and services to improve big data security measures for IT assets, data, and privacy across different domains. Thus, big data security companies operating in the North America region are investing heavily in R&D activities to develop new & advanced security solutions that can address rising security challenges. In February 2024, Cyberhaven, Inc. (U.S.) launched Linea AI, an AI platform designed to combat the critical insider risks threatening vital corporate data.

However, the Asia-Pacific region is projected to record the highest CAGR during the forecast period. The growth of this market is driven by the growing data breaches, supportive government initiatives, and growing awareness regarding data security among small and medium-scale organizations. In December 2023, Safetica a.s. (U.S.) partnered with Kaira Global (Singapore) to deliver Safetica's Data Loss Prevention (DLP) solutions for enterprises of all sizes to safeguard their data against insider risks and data breaches in Singapore. APAC is the fastest-growing big data security market due to rapid investments in IT infrastructure, extensive use of the Internet, and growing security challenges.

Key Players

The key players operating in the big data security market are Check Point Software Technologies, Ltd. (Israel), Cisco Systems, Inc. (U.S.), Fortinet, Inc. (U.S.), Oracle Corporation (U.S.), IBM Corporation (U.S.), Microsoft Corporation (U.S.), Hewlett Packard Enterprise Development LP (U.S.), Intel Corporation (U.S.), Palo Alto Networks, Inc. (U.S.), Thales Group (France), Juniper Networks, Inc. (U.S.), Broadcom, Inc. (U.S.), Dell Technologies, Inc. (U.S.), CyberArk Software Ltd. (U.S.), and Rapid7, Inc. (U.S.).

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=4984

Key Questions Answered in the Report:

What are the high-growth market segments in terms of the component, deployment mode, organization size, and end user?

What is the historical market size for the global big data security market?

What are the market forecasts and estimates for 2024–2031?

What are the major drivers, restraints, opportunities, challenges, and trends in the global big data security market?

Who are the major players in the global big data security market, and what are their market shares?

What is the competitive landscape like?

What are the recent developments in the global big data security market?

What are the different strategies adopted by major market players?

What are the trends and high-growth countries?

Who are the local emerging players in the global big data security market, and how do they compete with the other players?

Contact Us: Meticulous Research® [email protected] Contact Sales- +1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

#Big Data Security Market #Big Data Security Management #Big Data Security and Privacy #Big Data Security Technologies #Big Data Security Solutions #Big Data Security Platform

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basheeransari · 5 months ago

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Understanding Data Insights: How Businesses Can Use Data for Growth

In today's digital world, data is everywhere. Every interaction, transaction, and process generates information that can be analyzed to reveal valuable insights. However, the real challenge is using this data effectively to drive informed decision-making, improve efficiency, and predict future trends.

What Are Data Insights?

Data insights refer to the meaningful patterns, trends, and conclusions that businesses derive from analyzing raw data. These insights help organizations understand past performance, optimize current operations, and prepare for future challenges. By leveraging data, companies can make strategic decisions based on facts rather than intuition.

Why Are Data Insights Important?

Data-driven decision-making has become a key factor in business success. Here’s why:

Better Decision-Making – Businesses can use data to evaluate market trends, customer preferences, and operational efficiency.

Enhanced Customer Experience – Understanding customer behavior helps companies tailor products and services to meet specific needs.

Operational Efficiency – Identifying inefficiencies allows organizations to streamline processes and reduce costs.

Risk Management – Analyzing data helps in detecting fraud, assessing financial risks, and improving security.

Competitive Advantage – Companies that leverage data effectively can anticipate market shifts and respond proactively.

Types of Data Analytics

There are several types of analytics, each serving a different purpose:

Descriptive Analytics – Examines historical data to identify trends and patterns. Example: A retail store analyzing sales data to determine seasonal demand.

Diagnostic Analytics – Explains why something happened by finding correlations and causes. Example: A company investigating why customer engagement dropped after a website update.

Predictive Analytics – Uses historical data and statistical models to forecast future outcomes. Example: Predicting customer churn based on past interactions.

Prescriptive Analytics – Recommends the best course of action based on predictive models. Example: An airline optimizing ticket pricing based on demand trends.

Cognitive Analytics – Uses artificial intelligence (AI) and machine learning to interpret complex data and generate human-like insights. Example: A chatbot analyzing user sentiment to improve responses.

How Different Industries Use Data Insights

Data insights are widely used across industries to improve efficiency and drive innovation.

Healthcare : Data insights help predict disease outbreaks and improve patient care by analyzing health patterns and trends. They also play a crucial role in personalized treatment, allowing doctors to tailor medical plans based on a patient's history. Additionally, data-driven approaches accelerate drug development, helping researchers identify effective treatments and potential risks more efficiently.

Retail & E-Commerce : Analyzing customer behavior enables businesses to personalize recommendations, enhancing the shopping experience. Additionally, real-time demand forecasting helps in efficient inventory management, ensuring that products are stocked based on consumer needs.

Finance & Banking : Financial institutions use anomaly detection to identify fraudulent transactions and prevent unauthorized activities. Additionally, analyzing customer spending patterns helps assess credit risk, allowing for better loan and credit approval decisions.

Manufacturing : Predictive maintenance helps prevent equipment failures by analyzing performance data and detecting potential issues early. Additionally, data-driven insights optimize supply chain management and production schedules, ensuring smooth operations and reduced downtime.

Marketing & Advertising : By analyzing consumer data, businesses can create targeted marketing campaigns that resonate with their audience. Additionally, data insights help measure the effectiveness of digital advertising strategies, allowing companies to refine their approach for better engagement and higher returns.

Telecommunications : Predicting potential failures helps improve network reliability by allowing proactive maintenance and reducing downtime. Additionally, analyzing customer feedback enables service providers to enhance quality, address issues efficiently, and improve user satisfaction.

Education : Tracking student performance helps create personalized learning paths, ensuring that each student receives tailored support based on their needs. Additionally, data-driven insights assist in curriculum planning, allowing educators to design more effective teaching strategies and improve overall learning outcomes.

Logistics & Transport : Optimizing delivery routes helps reduce fuel costs by identifying the most efficient paths for transportation. Additionally, predictive analytics enhances fleet management by forecasting vehicle maintenance needs, minimizing downtime, and ensuring smooth operations.

How to Implement Data Insights in a Business

For organizations looking to integrate data analytics, here are key steps to follow:

Define Business Objectives – Identify what you want to achieve with data insights.

Collect Relevant Data – Ensure that you gather high-quality data from various sources.

Choose the Right Tools – Use analytics software and machine learning algorithms to process data efficiently.

Ensure Data Security �� Protect sensitive information through encryption and compliance measures.

Interpret Results Accurately – Avoid misinterpreting data by considering multiple perspectives.

Train Employees – Build a data-literate workforce that understands how to use insights effectively.

Continuously Improve – Regularly refine analytics processes to stay updated with new trends.

Data Analytics in Advanced Technologies

Space Technology : AI-driven data analytics enhances satellite imaging, real-time Earth monitoring, and space exploration by processing vast amounts of astronomical data efficiently.

Quantum Computing : Quantum-powered analytics enable faster simulations and predictive modeling, improving data processing for scientific and financial applications.

Large Data Models : AI-driven large data models analyze massive datasets, extracting valuable insights for businesses, healthcare, and research.

Research & Analytics (R&A) Services : AI enhances R&A services by automating data collection, trend analysis, and decision-making for industries like finance and healthcare.

Big Social Media Houses : Social media platforms use AI analytics to track user behavior, detect trends, personalize content, and combat misinformation in real-time.

The Future of Data Analytics

The field of data analytics is evolving rapidly with advancements in artificial intelligence, cloud computing, and big data technologies. Businesses are moving towards automated analytics systems that require minimal human intervention. In the coming years, expect to see:

AI-powered decision-making – Machines making real-time business decisions with minimal human input.

Edge computing – Faster data processing by analyzing information closer to the source.

Ethical data practices – Increased focus on privacy, transparency, and responsible AI usage.

Data insights have transformed how businesses operate, enabling smarter decision-making and improved efficiency. Whether in healthcare, finance, or marketing, data analytics services continue to shape the future of industries. Companies that embrace a data-driven culture will be better positioned to innovate and grow in a highly competitive market.

By understanding and applying data insights, businesses can navigate challenges, seize opportunities, and remain ahead in an increasingly digital world.

FAQs:

What are data insights?Data insights are patterns and trends derived from analyzing raw data to help businesses make informed decisions.

Why are data insights important?They improve decision-making, enhance customer experience, optimize operations, and provide a competitive advantage.

How do businesses use data insights?Companies use them for customer behavior analysis, fraud detection, predictive maintenance, targeted marketing, and process optimization.

What tools are used for data analytics?Common tools include Python, R, SQL, Tableau, Power BI, and Google Analytics.

What is the future of data analytics?AI-powered automation, edge computing, and ethical data practices will shape the future of analytics.

#technology #software #data security #data science #ai #data analytics #databricks #data #big data #data warehouse

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infosecnexus · 5 months ago

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How Tarun Wig Innefu Labs are Transforming India’s Defence with AI & Big Data

Tarun Wig's Innefu Labs are revolutionizing India’s defense sector with advanced AI and big data technologies that strengthen national security and military capabilities. Recently, Innefu Labs, under Tarun Wig's leadership, hosted a session with over 80 senior officers from the Indian Armed Forces at the Military Institute of Technology (MILIT), discussing how emerging technologies like generative AI and big data analytics can improve defense strategies.

AI-Powered Big Data for Enhanced National Security

The session focused on AI-driven big data analytics, showcasing how Innefu Labs, led by Tarun Wig, is helping the military process vast data at high speeds for real-time decision-making. One key innovation discussed was the Intelligence Fusion Platform, a system that integrates multiple data sources to improve intelligence sharing and security operations across agencies.

Open-Source Intelligence (OSINT) for Strategic Insights

The session also highlighted OSINT (Open-Source Intelligence), which uses publicly available data to identify security threats. Innefu Labs, with Tarun Wig’s leadership, is integrating OSINT with AI and Big Data to give military personnel valuable insights into emerging vulnerabilities.

Generative AI in Defense: A Technological Edge

Another major topic was generative AI, which is helping enhance military cybersecurity, training, and strategic defense. Tarun Wig and Innefu Labs are pioneering its use to support both offensive and defensive strategies, giving India’s military a technological advantage.

Future of Defense: AI and Big Data Integration

The session concluded with a focus on the practical application of these technologies in the Indian Armed Forces. Tarun Wig emphasized the importance of integrating these solutions into daily operations, ensuring the military stays ahead of potential threats and remains prepared for future challenges.

Innefu Labs, under Tarun Wig's guidance, continues to lead the way in transforming India’s defense capabilities through innovation. Their efforts ensure that India remains at the forefront of military technology, prepared for the evolving challenges of national security.

#Tarun Wig #Innefu Labs #Tarun Wig Innefu Labs #AI in Defence #Big Data Technology #National Security #Generative AI

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eye2future · 9 months ago

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youtube

#enviornment #science #social media #technology #data analytics #datascience #big data #data privacy #online privacy #internet privacy #worldwide privacy tour #internet #safety #security #Youtube

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zomb13s · 10 months ago

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The Illusion of Influence: An Examination of the Media, Security Agencies, and Technological Power in Shaping Public Perception

Introduction In today’s digital age, the boundary between reality and illusion has blurred significantly. This essay explores how the perception of magical influence, akin to saying “hocus pocus” and seeing changes unfold, mirrors the intricate interplay between journalism, security agencies, state agencies, and information specialists in contemporary society. By examining these mechanisms and…

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uvation · 11 months ago

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The Data Economy: A Balancing Act in the Age of AI Enhanced Devices

The AI Revolution: A New Era of Computing

AI is no longer a distant concept; it's woven into the fabric of our digital lives. Smartphones and computers, once mere tools, are evolving into intelligent companions, capable of understanding, learning, and anticipating our needs. Companies like Apple, with its focus on privacy and user experience, and Microsoft, with its aggressive push into AI, are at the forefront of this transformation. Apple Intelligence and Microsoft's Copilot+ PC are prime examples of how AI is being integrated into everyday devices.

The Allure of AI: Benefits and Promises

AI-powered devices offer a myriad of benefits:

Personalized Experiences: Tailored recommendations, content, and services based on individual preferences and behaviors.

Enhanced Productivity: Automation of routine tasks, freeing up time for more creative and strategic endeavors.

Improved Accessibility: Tools and features designed to assist individuals with disabilities.

Healthcare Advancements: AI-driven diagnostics, drug discovery, and personalized treatment plans.

Data: The Fuel for AI

To function effectively, AI algorithms require vast amounts of data. This data is meticulously collected from user interactions, sensor data, and third-party sources. AI-powered devices are constantly learning and adapting based on the data they collect, creating a symbiotic relationship between technology and human behavior.AI-driven devices meticulously collect data about user preferences, habits, and interactions. This data is used to train AI models, improve device performance, and deliver targeted advertising.

Brands like Apple and Microsoft and Their Approach to AI

Apple’s Pioneering Intelligence

Apple has been a pioneer in integrating AI into its devices, with Apple Intelligence enhancing the capabilities of Siri and other services. Siri's AI-driven features include voice recognition, contextual understanding, and proactive suggestions, making it a robust personal assistant. Apple also focuses on privacy, ensuring that user data is processed on-device whenever possible, minimizing the risk of data breaches.

Microsoft's Copilot Approach

Microsoft has introduced the Copilot+ PC, a new line of AI-enhanced laptops designed to integrate AI seamlessly into everyday tasks. These devices come with AI features that assist with productivity, security, and personalization. For example, the Copilot+ PC can anticipate user needs, suggest actions, and automate routine tasks, significantly enhancing the user experience.

The Data Privacy Dilemma

The extensive data collection practices of AI-powered devices raise significant privacy concerns. Users may be unaware of the extent to which their data is being collected, shared, and used.

Data Breaches: The concentration of vast amounts of personal data on these devices creates a tempting target for cybercriminals. Data breaches can have devastating consequences for individuals and organizations.

Surveillance Capitalism: The business model of collecting and monetizing user data raises ethical questions about surveillance and privacy.

Consent and Control: Users should have greater control over their data, including the ability to opt out of data collection and usage.

Mitigating the Risks

To harness the benefits of AI while minimizing its risks, a responsible approach is essential:

Data Privacy Regulations: Enforceable data protection laws like GDPR and CCPA are crucial to safeguarding user rights.

Transparency and Accountability: Companies must be transparent about data collection practices and accountable for data breaches.

User Control: Providing users with clear choices about data sharing and usage is essential.

Ethical AI Development: AI systems should be developed and deployed with ethical considerations in mind, avoiding bias and discrimination.

Cybersecurity: Robust cybersecurity measures are necessary to protect user data from breaches.

The Future of AI and Data

The relationship between AI and data is complex and constantly evolving. As AI technology advances, so too will the volume and complexity of data collected. It is imperative to strike a balance between innovation and privacy, ensuring that AI benefits society while protecting individual rights.

By fostering a culture of data responsibility, transparency, and user empowerment, we can shape a future where AI serves as a force for good. At Uvation, we are committed to helping businesses navigate the complexities of AI technology and data security. Our expert team provides tailored solutions to ensure responsible AI deployment, protecting user data and enhancing operational efficiency.

#artificial intelligence #information technology #cyber security #data privacy #cybersecurity #big tech

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techtoio · 1 year ago

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Emerging Tech Trends in the Internet of Things (IoT)

Introduction

The Internet of Things (IoT) is transforming our world by connecting devices and enabling smarter, more efficient interactions. In everything from smart homes to industrial automation, the IoT is leading a revolution in our living and working environments. In this article, TechtoIO explores the emerging tech trends in IoT, highlighting the innovations and advancements that are shaping the future. Read to continue link

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enduradata · 1 year ago

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#Linux #Linux data replication #cloud solutions #Big data security #cloud computing #secure data handling #data privacy #cloud infrastructure #cloud technology #Data Analytics #data integration #Big data solutions #Big data insights #data management #File transfer solutions

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kishorxox · 4 months ago

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Big Data Security Market by Size, Share, Forecast, & Trends Analysis

Key Players

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=4984

Key Questions Answered in the Report:

What are the high-growth market segments in terms of the component, deployment mode, organization size, and end user?

What is the historical market size for the global big data security market?

What are the market forecasts and estimates for 2024–2031?

What are the major drivers, restraints, opportunities, challenges, and trends in the global big data security market?

Who are the major players in the global big data security market, and what are their market shares?

What is the competitive landscape like?

What are the recent developments in the global big data security market?

What are the different strategies adopted by major market players?

What are the trends and high-growth countries?

Who are the local emerging players in the global big data security market, and how do they compete with the other players?

Contact Us: Meticulous Research® [email protected] Contact Sales- +1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

#Big Data Security Market #Big Data Security Management #Big Data Security and Privacy #Big Data Security Technologies #Big Data Security Solutions #Big Data Security Platform

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jcmarchi · 2 months ago

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AI in education: Balancing promises and pitfalls

New Post has been published on https://thedigitalinsider.com/ai-in-education-balancing-promises-and-pitfalls/

AI in education: Balancing promises and pitfalls

The role of AI in education is a controversial subject, bringing both exciting possibilities and serious challenges.

There’s a real push to bring AI into schools, and you can see why. The recent executive order on youth education from President Trump recognised that if future generations are going to do well in an increasingly automated world, they need to be ready.

“To ensure the United States remains a global leader in this technological revolution, we must provide our nation’s youth with opportunities to cultivate the skills and understanding necessary to use and create the next generation of AI technology,” President Trump declared.

So, what does AI actually look like in the classroom?

One of the biggest hopes for AI in education is making learning more personal. Imagine software that can figure out how individual students are doing, then adjust the pace and materials just for them. This could mean finally moving away from the old one-size-fits-all approach towards learning environments that adapt and offer help exactly where it’s needed.

The US executive order hints at this, wanting to improve results through things like “AI-based high-quality instructional resources” and “high-impact tutoring.”

And what about teachers? AI could be a huge help here too, potentially taking over tedious admin tasks like grading, freeing them up to actually teach. Plus, AI software might offer fresh ways to present information.

Getting kids familiar with AI early on could also take away some of the mystery around the technology. It might spark their “curiosity and creativity” and give them the foundation they need to become “active and responsible participants in the workforce of the future.”

The focus stretches to lifelong learning and getting people ready for the job market. On top of that, AI tools like text-to-speech or translation features can make learning much more accessible for students with disabilities, opening up educational environments for everyone.

Not all smooth sailing: The challenges ahead for AI in education

While the potential is huge, we need to be realistic about the significant hurdles and potential downsides.

First off, AI runs on student data – lots of it. That means we absolutely need strong rules and security to make sure this data is collected ethically, used correctly, and kept safe from breaches. Privacy is paramount here.

Then there’s the bias problem. If the data used to train AI reflects existing unfairness in society (and let’s be honest, it often does), the AI could end up repeating or even worsening those inequalities. Think biased assessments or unfair resource allocation. Careful testing and constant checks are crucial to catch and fix this.

We also can’t ignore the digital divide. If some students don’t have reliable internet, the right devices, or the necessary tech infrastructure at home or school, AI could widen the gap between the haves and have-nots. It’s vital that everyone gets fair access.

There’s also a risk that leaning too heavily on AI education tools might stop students from developing essential skills like critical thinking. We need to teach them how to use AI as a helpful tool, not a crutch they can’t function without.

Maybe the biggest piece of the puzzle, though, is making sure our teachers are ready. As the executive order rightly points out, “We must also invest in our educators and equip them with the tools and knowledge.”

This isn’t just about knowing which buttons to push; teachers need to understand how AI fits into teaching effectively and ethically. That requires solid professional development and ongoing support.

A recent GMB Union poll found that while about a fifth of UK schools are using AI now, the staff often aren’t getting the training they need:

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Finding the right path forward

It’s going to take everyone – governments, schools, tech companies, and teachers – pulling together in order to ensure that AI plays a positive role in education.

We absolutely need clear policies and standards covering ethics, privacy, bias, and making sure AI is accessible to all students. We also need to keep investing in research to figure out the best ways to use AI in education and to build tools that are fair and effective.

And critically, we need a long-term commitment to teacher education to get educators comfortable and skilled with these changes. Part of this is building broad AI literacy, making sure all students get a basic understanding of this technology and how it impacts society.

AI could be a positive force in education – making it more personalised, efficient, and focused on the skills students actually need. But turning that potential into reality means carefully navigating those tricky ethical, practical, and teaching challenges head-on.

See also: How does AI judge? Anthropic studies the values of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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business-sites · 1 year ago

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IMR Green Smokeless Gun Powder

IMR green smokeless gun powder is a revolutionary advancement in ammunition technology. Developed by IMR (Improved Military Rifle) Powders, alliant green dot is designed to offer cleaner and more efficient burning compared to traditional powders.

Winchester Small Pistol Magnum Primers

Green’s composition minimizes waste, facilitating firearm maintenance. Greendot powder smokeless nature reduces environmental impact and improves shooter visibility, which is crucial in tactical and competitive situations. Hodgdon Varget Smokeless Gun Powder

Green dot shotgun powder improves accuracy and consistency and is favored by shooters and hunters alike.

With Ramshot Big Game environmentally friendly profile and consistent performance, IMR Smokeless Green Powder represents a significant step forward in firearms propellant technology, meeting the demands of modern shooters for reliability and sustainability.

Key Features

Clean Burning : Designed to generate minimal waste, ensuring easier firearm maintenance.

Consistent Performance: Delivers reliable ignition and even combustion, improving shot-to-shot consistency.

Accurate measurement: The granular structure allows for precise measurement of Powder, ensuring accurate loading.

IMR Enduron 4166 Powder

Versatility: Suitable for various firearms and applications, from competitive shooting to hunting.

Imr Enduron 4451 Powder

Smokeless Operation: Improves shooter visibility and reduces environmental impact.

Improved Accuracy: Facilitates tighter groupings and improves shooting accuracy.

Modern Technology: This green dot powder represents a significant advancement in ammunition technology and meets the needs of modern shooters.

Ecological profile: supports sustainability efforts by minimizing the environmental footprint.

These PPU M1 Garand Ammo features make IMR Smokeless Green Powder the preferred choice for shooters looking for reliability, performance and environmental responsibility.

IMR 7828 SSC Smokeless Gun Powder

Specifications

Composition: Designed with advanced formulation for clean combustion and minimal waste.

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Ignition: Reliable ignition characteristics for consistent performance from shot to shot.

Ramshot True Blue

#gunshot #guns n roses #big arms #arm #bullets #social security #information technology #data security #cyber security #five nights at freddy's security breach

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emergingindiaanalyticsnoida · 1 year ago

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How Data Science is Different from Data Analytics

Unlocking the power of data has become a crucial aspect for businesses and industries in today’s digital era. The abundance of information available at our fingertips holds immense potential to drive informed decision-making, optimize processes, and gain valuable insights. As organizations strive to harness this vast ocean of data, two fields have emerged as game-changers: Data Science and Data Analytics. In this blog post, we will delve into the world of data science and data analytics, exploring their key differences, applications across various sectors, job opportunities they offer, required skills for a successful career in these domains, and future growth trends. So fasten your seatbelts as we embark on an exciting journey through the fascinating realms of data!

Understanding Data Science and Data Analytics

Data Science and Data Analytics are two distinct disciplines that play a crucial role in extracting meaningful insights from data. While they share similarities, such as their reliance on data and statistical analysis, there are key differences between the two. Data Science involves the use of various techniques, algorithms, and tools to extract knowledge or insights from structured or unstructured data. It encompasses a wide range of skills including programming, machine learning, statistics, and domain expertise. Data scientists employ complex models to uncover patterns and make predictions that help organizations optimize operations or solve complex problems. On the other hand, Data Analytics focuses more on exploring historical datasets to identify trends, analyze performance metrics, and gain actionable insights for decision-making purposes. It involves using descriptive analytics techniques like visualization or statistical analysis to understand past trends or current scenarios. In simpler terms: while data science is focused on discovering hidden patterns within large datasets through advanced algorithms and modeling techniques; data analytics primarily deals with analyzing existing information to provide valuable business intelligence for strategic decision-making. Both fields rely heavily on technology but have different objectives — one aims at prediction while the other focuses on interpretation. Understanding these distinctions is essential when choosing a career path in either field. So let’s take a closer look at some of their distinguishing characteristics!

Key Differences Between Data Science and Data Analytics

Data science and data analytics are two terms that are often used interchangeably, but they actually represent distinct fields with their own unique characteristics. While both involve working with data to gain insights and drive decision-making, there are key differences between the two. Data science is a multidisciplinary field that combines elements of statistics, mathematics, computer science, and domain knowledge. It focuses on extracting meaning from large volumes of complex data by utilizing advanced algorithms and predictive modeling techniques. Data scientists are skilled in developing innovative approaches to solve problems using machine learning algorithms. On the other hand, data analytics primarily involves analyzing past data to understand trends, patterns, and correlations. It is focused on exploring historical datasets to uncover actionable insights that can be used for strategic planning or process optimization. Data analysts use statistical tools and visualization techniques to present findings in a clear and understandable manner. While both roles require strong analytical skills and proficiency in programming languages like Python or R, the emphasis differs. Data scientists need more extensive knowledge of machine learning algorithms as they build models from scratch whereas data analysts focus on exploratory analysis using existing tools. Another difference lies in the scope of work involved. Data science projects typically involve identifying business problems or opportunities where predictive modeling can provide valuable insights. In contrast, data analytics projects tend to be more descriptive in nature — examining historical patterns for reporting purposes or making recommendations based on observed trends. Furthermore, the outputs generated also vary between these disciplines. A typical output of data science would be a predictive model capable of making accurate predictions about future events or behaviors based on historical patterns identified during the training phase. On the other hand, The output from a typical analytic project would often take the form as dashboards containing visualizations depicting key metrics derived from historical datasets Overall, data science tends to have a broader scope encompassing various stages like problem definition, data collection, model building, fine-tuning while -data analytics is more focused on analyzing past data to uncover valuable insights.

Applications of Data Science and Data Analytics

Data science and data analytics have a wide range of applications across various industries. Let’s explore some of the key areas where these fields are making a significant impact. In healthcare, data science is revolutionizing patient care by analyzing vast amounts of medical data to identify patterns and trends. These insights help in diagnosing diseases more accurately, predicting outcomes, and developing personalized treatment plans. Data analytics also plays a crucial role in optimizing hospital operations, improving resource allocation, and enhancing patient experience. In finance, both data science and data analytics are used for risk assessment, fraud detection, investment strategies optimization, and algorithmic trading. By analyzing historical financial data combined with real-time market information, businesses can make informed decisions to mitigate risks and maximize returns. E-commerce companies leverage data science techniques to gain insights into customer behavior such as purchase history or browsing patterns. This enables them to create targeted marketing campaigns that drive customer engagement leading to increased sales conversions. The transportation industry relies on data analytics for route optimization based on traffic patterns analysis which helps reduce travel time and fuel consumption while increasing efficiency. Furthermore, data scientists work on creating predictive maintenance models ensuring timely repairs reducing downtime & operational costs The entertainment industry uses both fields extensively to understand audience preferences through sentiment analysis of social media feeds or recommendation engines suggesting personalized content tailored to each viewer’s taste. These are just a few examples highlighting the diverse applications of data science and data analytics in today’s world. As technology continues to advance at an exponential rate there will be countless other opportunities for these fields in sectors such as manufacturing supply chain management agriculture energy among others

Job Opportunities in the Fields of Data Science and Data Analytics

The fields of data science and data analytics offer a wide range of exciting job opportunities. With the growing importance of data in today’s digital age, professionals skilled in analyzing and interpreting large datasets are highly sought after. Data scientists play a crucial role in extracting valuable insights from complex datasets. They utilize statistical analysis techniques, machine learning algorithms, and programming skills to uncover patterns and trends that can inform business strategies. From predicting customer behavior to optimizing supply chain operations, data scientists have the ability to drive innovation across various industries. On the other hand, data analysts focus on collecting, cleaning, and organizing raw data for further analysis. They excel at utilizing tools like SQL or Excel to process large volumes of information efficiently. Data analysts often work closely with stakeholders to understand their specific requirements and generate reports or visualizations that help decision-making processes. Both fields require strong analytical skills but differ in terms of depth and breadth of expertise required. While data scientists typically possess advanced degrees in computer science or mathematics, data analysts may enter the field with a bachelor’s degree coupled with relevant experience or certifications. In terms of career paths, both roles offer diverse options such as working as consultants for specialized firms or joining established companies’ internal teams dedicated to handling their extensive datasets. Additionally, there is also ample opportunity for freelance work or starting one’s own consultancy firm catering specifically to organizations seeking expert guidance on leveraging their data assets effectively. As technology continues to advance rapidly and more businesses recognize the value in harnessing big data capabilities, job prospects in these fields are expected to grow significantly over the coming years. Industries ranging from healthcare to finance are increasingly relying on accurate insights derived from vast amounts of information — creating a high demand for skilled professionals who can navigate this complex landscape. To succeed in either field requires continuous learning given how fast-paced they are evolving disciplines. Staying updated with new methodologies such as deep learning algorithms and familiarizing oneself with emerging technologies like artificial intelligence (AI) and the Internet

Required Skills for a Career in Data Science or Data Analytics

To excel in the field of data science or data analytics, there are certain skills that one must possess. These skills not only help professionals stand out but also ensure they can handle the complexities and challenges of working with large datasets. First and foremost, a strong foundation in mathematics and statistics is essential. Proficiency in areas such as linear algebra, calculus, probability, hypothesis testing, and regression analysis provides the necessary framework to understand complex algorithms and models used in data analysis. In addition to mathematical aptitude, programming skills are crucial for both data scientists and analysts. Python and R are two popular programming languages used extensively in these fields due to their versatility and extensive libraries for statistical computing. Data visualization is another critical skill that professionals should develop. The ability to present complex findings visually through charts, graphs, or dashboards helps stakeholders understand insights more effectively. Domain knowledge is also vital when it comes to analyzing datasets within specific industries like healthcare or finance. Understanding the nuances of a particular domain enables professionals to ask relevant questions while interpreting data accurately. Problem-solving abilities coupled with critical thinking play a significant role. Data scientists and analysts need to approach problems analytically by breaking them down into smaller components before arriving at solutions. Developing expertise in mathematics/statistics, programming languages (such as Python/R), data visualization techniques, domain knowledge and problem-solving/critical thinking will equip individuals with the necessary skills for a successful career in either data science or data analytics.

Future Growth and Trends in Both Industries

Data science and data analytics are two fields that continue to experience significant growth and show no signs of slowing down. As technology advances, the amount of data being generated increases exponentially, creating a need for professionals who can analyze and make sense of this vast amount of information. In the field of data science, one trend that is expected to continue growing is the use of machine learning algorithms. These algorithms allow computers to learn from data without being explicitly programmed, enabling them to make predictions and decisions based on patterns they discover. This has applications in various industries such as healthcare, finance, marketing, and more. Another area that holds great potential for both data science and data analytics is the Internet of Things (IoT). With the proliferation of connected devices, there is an enormous amount of valuable data being generated every second. Professionals in these fields will play a crucial role in extracting insights from IoT-generated data to drive informed decision-making. Furthermore, as businesses become increasingly aware of the importance of leveraging data for strategic decision-making, there will be a growing demand for skilled professionals who can not only analyze large datasets but also communicate their findings effectively. The ability to translate complex technical concepts into actionable insights will be highly sought after by employers. Additionally, with advancements in cloud computing technologies and storage capabilities, companies are now able to store massive amounts of structured and unstructured data at a fraction of previous costs. This opens up opportunities for both aspiring practitioners looking to gain hands-on experience with big datasets as well as established professionals seeking new challenges. Ethical considerations surrounding privacy concerns and responsible use of personal or sensitive information are likely to shape future trends in both industries. Data scientists and analysts must navigate these ethical dilemmas while ensuring compliance with regulations such as GDPR or CCPA. In conclusion, the field’s future growth trajectory looks promising due to technological advancements like machine learning algorithms integration into diverse industries, the rise of IoT-generated data, the need for effective communication skills in

Conclusion

Data science and data analytics are two distinct fields that play crucial roles in extracting insights and making informed decisions from vast amounts of data. While they share some similarities, such as the use of statistical techniques and programming skills, their primary focus areas differ significantly. Data science encompasses a broader scope, combining computer science, mathematics, statistics, and domain knowledge to uncover patterns and trends in complex datasets. It requires expertise in machine learning algorithms, predictive modeling, data visualization techniques, and a deep understanding of business objectives. Data scientists are often responsible for developing new methodologies to solve real-world problems using cutting-edge technologies. On the other hand, data analytics is more focused on analyzing historical data to gain actionable insights into past performance or current trends. Analysts utilize various tools like SQL queries or visualization software to identify patterns that can help organizations make strategic decisions. They work closely with stakeholders to understand their needs and provide them with valuable reports or dashboards that drive business growth. Both fields offer promising career opportunities for those who possess the necessary skills and passion for working with big data. However, it’s important to note that these industries are constantly evolving due to advancements in technology and an increasing demand for data-driven decision-making. To succeed in either field, individuals need a strong foundation in mathematics/statistics along with proficiency in programming languages like Python or R. Additionally, having good communication skills is vital as professionals must effectively translate complex findings into meaningful insights for non-technical stakeholders. Looking ahead at future growth prospects for both industries shows no signs of slowing down. As businesses continue to generate massive amounts of data every day across various sectors such as finance, healthcare, marketing, and e-commerce; the need for skilled professionals who can extract value from this wealth of information will only increase. In summary, data science focuses on solving complex problems through advanced mathematical models, machine learning algorithms, and domain expertise. Data analytics, on the other hand, is geared towards deriving actionable insights by analyzing historical data.

#data science #machine learning #cyber security #deeplearning #big data #blockchain #blockchain technology #data scientist

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glasshomewrecker · 1 year ago

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i rlly hate that so much software's "pRivAcY pOLiCy" amounts to "hey, we're gonna take your usage statistics and personal data and keystrokes to iMpRoVe oUr pRoDuCtS and there's nothing you can do about it 🤗 fuck you 🥰"

#technology #tech #data harvesting #big tech #privacy #data #data security #cyber security #cybersecurity #software

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thedigitalhorizon · 2 years ago

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The Role of Technology in Modern Democracy: A Double-Edged Sword

In an era where the digital landscape is ever-evolving its inclufence on our lives in inescapable. From social networks that keep us connected 24/7, to platforms that have democratized knowledge, technology seems to be the driving force of modern society. But have you ever stopped to consider its impact on our democracy? Let's delve into the complex relationship between technology and modern democracy—a relationship that's both promising and fraught with peril.

Increased Accessibility: Democracy at Your Fingertips

One of the shining achievements of technology is how it has made democracy more accessible. Gone are the days when town hall meetings and door-to-door campaining were the only ways to engage with your community. Today, you can Tweet your local representative, participate in online forums, and even host digital petitions. Apps like Nextdoor and platforms like Change.org are empowering local communities to have a say in their governance.

Transparency and Accountability: Can Big Data Keep Big Brother in Check?

We often hear about "Big Data" in the context of marketing or even surveillance. But the flip side is that it can serve as a powerful tool for transparency. Various countries are implementing blockchain technology to ensure transparent voting systems. Moreover, data analytics can track how funds are used in public projects, rooting out corruption and fostering accountability.

Information Overload: The Dark Side of Digital Freedom

While it's amazing to have information at our fingertips, the downside is the sheer volume that we have to sift through. The rise of "fake news" and the spread of misinformation poses serious threats to an informed electorate. Recall the Cambridge Analytica scandal? It perfectly illustrates how information can be manipulated to subvert democratic processes.

Data Security: The Achilles Heel of Digital Democracy?

With great power comes great responsibility. This is especially true for technology’s role in safeguarding our democratic systems. From concerns about foreign interference in elections to alarming data leaks, digital democracy is vulnerable. It poses a difficult question: How can we balance the need for open, accessible systems with the need for security?

Automation and AI: Algorithmic Democracy or Digital Despotism?

Technology isn’t just limited to social media or data analytics; it also includes automation and AI. Estonia, for instance, uses a machine learning algorithm to allocate its police forces, leading to a dramatic reduction in crime rates. However, the use of algorithms for public decision-making also raises serious ethical concerns. Who writes the code? Who ensures it is free from bias?

Conclusion: The Road Ahead

So, is technology a boon or a bane for modern democracy? The answer, as with most things, is not black and white. Technology offers incredible opportunities for enhancing democratic processes but also introduces challenges that require vigilant oversight and ethical considerations.

The road ahead is filled with both challenges and opportunities. But one thing is clear: technology is here to stay, and it's up to us to ensure that we harness its immense power for the greater good of our democratic systems. After all, democracy is of the people, by the people, and for the people—even in the digital age.

Thank you for reading! Stay tuned to The Digital Horizon for more insights, tips, and recommendations on navigating the digital world.

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mostlysignssomeportents · 7 months ago

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Bossware is unfair (in the legal sense, too)

You can get into a lot of trouble by assuming that rich people know what they're doing. For example, might assume that ad-tech works – bypassing peoples' critical faculties, reaching inside their minds and brainwashing them with Big Data insights, because if that's not what's happening, then why would rich people pour billions into those ads?

https://pluralistic.net/2020/12/06/surveillance-tulip-bulbs/#adtech-bubble

You might assume that private equity looters make their investors rich, because otherwise, why would rich people hand over trillions for them to play with?

https://thenextrecession.wordpress.com/2024/11/19/private-equity-vampire-capital/

The truth is, rich people are suckers like the rest of us. If anything, succeeding once or twice makes you an even bigger mark, with a sense of your own infallibility that inflates to fill the bubble your yes-men seal you inside of.

Rich people fall for scams just like you and me. Anyone can be a mark. I was:

https://pluralistic.net/2024/02/05/cyber-dunning-kruger/#swiss-cheese-security

But though rich people can fall for scams the same way you and I do, the way those scams play out is very different when the marks are wealthy. As Keynes had it, "The market can remain irrational longer than you can remain solvent." When the marks are rich (or worse, super-rich), they can be played for much longer before they go bust, creating the appearance of solidity.

Noted Keynesian John Kenneth Galbraith had his own thoughts on this. Galbraith coined the term "bezzle" to describe "the magic interval when a confidence trickster knows he has the money he has appropriated but the victim does not yet understand that he has lost it." In that magic interval, everyone feels better off: the mark thinks he's up, and the con artist knows he's up.

Rich marks have looong bezzles. Empirically incorrect ideas grounded in the most outrageous superstition and junk science can take over whole sections of your life, simply because a rich person – or rich people – are convinced that they're good for you.

Take "scientific management." In the early 20th century, the con artist Frederick Taylor convinced rich industrialists that he could increase their workers' productivity through a kind of caliper-and-stopwatch driven choreographry:

https://pluralistic.net/2022/08/21/great-taylors-ghost/#solidarity-or-bust

Taylor and his army of labcoated sadists perched at the elbows of factory workers (whom Taylor referred to as "stupid," "mentally sluggish," and as "an ox") and scripted their motions to a fare-the-well, transforming their work into a kind of kabuki of obedience. They weren't more efficient, but they looked smart, like obedient robots, and this made their bosses happy. The bosses shelled out fortunes for Taylor's services, even though the workers who followed his prescriptions were less efficient and generated fewer profits. Bosses were so dazzled by the spectacle of a factory floor of crisply moving people interfacing with crisply working machines that they failed to understand that they were losing money on the whole business.

To the extent they noticed that their revenues were declining after implementing Taylorism, they assumed that this was because they needed more scientific management. Taylor had a sweet con: the worse his advice performed, the more reasons their were to pay him for more advice.

Taylorism is a perfect con to run on the wealthy and powerful. It feeds into their prejudice and mistrust of their workers, and into their misplaced confidence in their own ability to understand their workers' jobs better than their workers do. There's always a long dollar to be made playing the "scientific management" con.

Today, there's an app for that. "Bossware" is a class of technology that monitors and disciplines workers, and it was supercharged by the pandemic and the rise of work-from-home. Combine bossware with work-from-home and your boss gets to control your life even when in your own place – "work from home" becomes "live at work":

https://pluralistic.net/2021/02/24/gwb-rumsfeld-monsters/#bossware

Gig workers are at the white-hot center of bossware. Gig work promises "be your own boss," but bossware puts a Taylorist caliper wielder into your phone, monitoring and disciplining you as you drive your wn car around delivering parcels or picking up passengers.

In automation terms, a worker hitched to an app this way is a "reverse centaur." Automation theorists call a human augmented by a machine a "centaur" – a human head supported by a machine's tireless and strong body. A "reverse centaur" is a machine augmented by a human – like the Amazon delivery driver whose app goads them to make inhuman delivery quotas while punishing them for looking in the "wrong" direction or even singing along with the radio:

https://pluralistic.net/2024/08/02/despotism-on-demand/#virtual-whips

Bossware pre-dates the current AI bubble, but AI mania has supercharged it. AI pumpers insist that AI can do things it positively cannot do – rolling out an "autonomous robot" that turns out to be a guy in a robot suit, say – and rich people are groomed to buy the services of "AI-powered" bossware:

https://pluralistic.net/2024/01/29/pay-no-attention/#to-the-little-man-behind-the-curtain

For an AI scammer like Elon Musk or Sam Altman, the fact that an AI can't do your job is irrelevant. From a business perspective, the only thing that matters is whether a salesperson can convince your boss that an AI can do your job – whether or not that's true:

https://pluralistic.net/2024/07/25/accountability-sinks/#work-harder-not-smarter

The fact that AI can't do your job, but that your boss can be convinced to fire you and replace you with the AI that can't do your job, is the central fact of the 21st century labor market. AI has created a world of "algorithmic management" where humans are demoted to reverse centaurs, monitored and bossed about by an app.

The techbro's overwhelming conceit is that nothing is a crime, so long as you do it with an app. Just as fintech is designed to be a bank that's exempt from banking regulations, the gig economy is meant to be a workplace that's exempt from labor law. But this wheeze is transparent, and easily pierced by enforcers, so long as those enforcers want to do their jobs. One such enforcer is Alvaro Bedoya, an FTC commissioner with a keen interest in antitrust's relationship to labor protection.

Bedoya understands that antitrust has a checkered history when it comes to labor. As he's written, the history of antitrust is a series of incidents in which Congress revised the law to make it clear that forming a union was not the same thing as forming a cartel, only to be ignored by boss-friendly judges:

https://pluralistic.net/2023/04/14/aiming-at-dollars/#not-men

Bedoya is no mere historian. He's an FTC Commissioner, one of the most powerful regulators in the world, and he's profoundly interested in using that power to help workers, especially gig workers, whose misery starts with systemic, wide-scale misclassification as contractors:

https://pluralistic.net/2024/02/02/upward-redistribution/

In a new speech to NYU's Wagner School of Public Service, Bedoya argues that the FTC's existing authority allows it to crack down on algorithmic management – that is, algorithmic management is illegal, even if you break the law with an app:

https://www.ftc.gov/system/files/ftc_gov/pdf/bedoya-remarks-unfairness-in-workplace-surveillance-and-automated-management.pdf

Bedoya starts with a delightful analogy to The Hawtch-Hawtch, a mythical town from a Dr Seuss poem. The Hawtch-Hawtch economy is based on beekeeping, and the Hawtchers develop an overwhelming obsession with their bee's laziness, and determine to wring more work (and more honey) out of him. So they appoint a "bee-watcher." But the bee doesn't produce any more honey, which leads the Hawtchers to suspect their bee-watcher might be sleeping on the job, so they hire a bee-watcher-watcher. When that doesn't work, they hire a bee-watcher-watcher-watcher, and so on and on.

For gig workers, it's bee-watchers all the way down. Call center workers are subjected to "AI" video monitoring, and "AI" voice monitoring that purports to measure their empathy. Another AI times their calls. Two more AIs analyze the "sentiment" of the calls and the success of workers in meeting arbitrary metrics. On average, a call-center worker is subjected to five forms of bossware, which stand at their shoulders, marking them down and brooking no debate.

For example, when an experienced call center operator fielded a call from a customer with a flooded house who wanted to know why no one from her boss's repair plan system had come out to address the flooding, the operator was punished by the AI for failing to try to sell the customer a repair plan. There was no way for the operator to protest that the customer had a repair plan already, and had called to complain about it.

Workers report being sickened by this kind of surveillance, literally – stressed to the point of nausea and insomnia. Ironically, one of the most pervasive sources of automation-driven sickness are the "AI wellness" apps that bosses are sold by AI hucksters:

https://pluralistic.net/2024/03/15/wellness-taylorism/#sick-of-spying

The FTC has broad authority to block "unfair trade practices," and Bedoya builds the case that this is an unfair trade practice. Proving an unfair trade practice is a three-part test: a practice is unfair if it causes "substantial injury," can't be "reasonably avoided," and isn't outweighed by a "countervailing benefit." In his speech, Bedoya makes the case that algorithmic management satisfies all three steps and is thus illegal.

On the question of "substantial injury," Bedoya describes the workday of warehouse workers working for ecommerce sites. He describes one worker who is monitored by an AI that requires him to pick and drop an object off a moving belt every 10 seconds, for ten hours per day. The worker's performance is tracked by a leaderboard, and supervisors punish and scold workers who don't make quota, and the algorithm auto-fires if you fail to meet it.

Under those conditions, it was only a matter of time until the worker experienced injuries to two of his discs and was permanently disabled, with the company being found 100% responsible for this injury. OSHA found a "direct connection" between the algorithm and the injury. No wonder warehouses sport vending machines that sell painkillers rather than sodas. It's clear that algorithmic management leads to "substantial injury."

What about "reasonably avoidable?" Can workers avoid the harms of algorithmic management? Bedoya describes the experience of NYC rideshare drivers who attended a round-table with him. The drivers describe logging tens of thousands of successful rides for the apps they work for, on promise of "being their own boss." But then the apps start randomly suspending them, telling them they aren't eligible to book a ride for hours at a time, sending them across town to serve an underserved area and still suspending them. Drivers who stop for coffee or a pee are locked out of the apps for hours as punishment, and so drive 12-hour shifts without a single break, in hopes of pleasing the inscrutable, high-handed app.

All this, as drivers' pay is falling and their credit card debts are mounting. No one will explain to drivers how their pay is determined, though the legal scholar Veena Dubal's work on "algorithmic wage discrimination" reveals that rideshare apps temporarily increase the pay of drivers who refuse rides, only to lower it again once they're back behind the wheel:

https://pluralistic.net/2023/04/12/algorithmic-wage-discrimination/#fishers-of-men

This is like the pit boss who gives a losing gambler some freebies to lure them back to the table, over and over, until they're broke. No wonder they call this a "casino mechanic." There's only two major rideshare apps, and they both use the same high-handed tactics. For Bedoya, this satisfies the second test for an "unfair practice" – it can't be reasonably avoided. If you drive rideshare, you're trapped by the harmful conduct.

The final prong of the "unfair practice" test is whether the conduct has "countervailing value" that makes up for this harm.

To address this, Bedoya goes back to the call center, where operators' performance is assessed by "Speech Emotion Recognition" algorithms, a psuedoscientific hoax that purports to be able to determine your emotions from your voice. These SERs don't work – for example, they might interpret a customer's laughter as anger. But they fail differently for different kinds of workers: workers with accents – from the American south, or the Philippines – attract more disapprobation from the AI. Half of all call center workers are monitored by SERs, and a quarter of workers have SERs scoring them "constantly."

Bossware AIs also produce transcripts of these workers' calls, but workers with accents find them "riddled with errors." These are consequential errors, since their bosses assess their performance based on the transcripts, and yet another AI produces automated work scores based on them.

In other words, algorithmic management is a procession of bee-watchers, bee-watcher-watchers, and bee-watcher-watcher-watchers, stretching to infinity. It's junk science. It's not producing better call center workers. It's producing arbitrary punishments, often against the best workers in the call center.

There is no "countervailing benefit" to offset the unavoidable substantial injury of life under algorithmic management. In other words, algorithmic management fails all three prongs of the "unfair practice" test, and it's illegal.

What should we do about it? Bedoya builds the case for the FTC acting on workers' behalf under its "unfair practice" authority, but he also points out that the lack of worker privacy is at the root of this hellscape of algorithmic management.

He's right. The last major update Congress made to US privacy law was in 1988, when they banned video-store clerks from telling the newspapers which VHS cassettes you rented. The US is long overdue for a new privacy regime, and workers under algorithmic management are part of a broad coalition that's closer than ever to making that happen:

https://pluralistic.net/2023/12/06/privacy-first/#but-not-just-privacy

Workers should have the right to know which of their data is being collected, who it's being shared by, and how it's being used. We all should have that right. That's what the actors' strike was partly motivated by: actors who were being ordered to wear mocap suits to produce data that could be used to produce a digital double of them, "training their replacement," but the replacement was a deepfake.

With a Trump administration on the horizon, the future of the FTC is in doubt. But the coalition for a new privacy law includes many of Trumpland's most powerful blocs – like Jan 6 rioters whose location was swept up by Google and handed over to the FBI. A strong privacy law would protect their Fourth Amendment rights – but also the rights of BLM protesters who experienced this far more often, and with far worse consequences, than the insurrectionists.

The "we do it with an app, so it's not illegal" ruse is wearing thinner by the day. When you have a boss for an app, your real boss gets an accountability sink, a convenient scapegoat that can be blamed for your misery.

The fact that this makes you worse at your job, that it loses your boss money, is no guarantee that you will be spared. Rich people make great marks, and they can remain irrational longer than you can remain solvent. Markets won't solve this one – but worker power can.

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

#pluralistic #alvaro bedoya #ftc #workers #algorithmic management #veena dubal #bossware #taylorism #neotaylorism #snake oil #dr seuss #ai #sentiment analysis #digital phrenology #speech emotion recognition #shitty technology adoption curve

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