#big data technology

[webmethodology]

https://www.reverbtimemag.com/blogs_on/big-data-technology-manage-large-volumes-of-data-easily

Discover the benefits of leveraging advanced big data analytics to efficiently manage and get valuable insights from large volumes of data. Improve your business processes with the latest Big Data technologies.

[gwl55]

GoodWorkLabs uses a mix of technologies to build a data science software platform for Analysts and Data Scientists to explore, prototype, and analyze tons of unstructured data in an efficient way. Our Big Data consulting services help organizations draw valuable insights from large data sets and boost their professional efficiency. 

[1stepgrow]

Reasons to learn Data Science

Data science has become the backbone of the all the companies and industries like healthcare, finance, and more. Every company started to hire Data Scientists and Analysts nowadays because of the evolution of Data Science in all sectors. It is a new platform in the market, and people are learning to get better opportunities. Here this great infographic design shows the top 5 reasons to learn Data Science. For more information, please visit: 1stepGrow

[v2softindia]

Businesses are facing immense competition from their rivals. It is very important for businesses to keep up with the latest products and services in order to survive in the digital world. However, emerging technologies such as artificial intelligence, Big Data analytics, and machine learning are making it increasingly difficult for business owners to secure their market share. This can be an advantage for you as a businessman as you will be able to create a sense of urgency among your customers. The use of Big Data can help businesses gain an edge over their competitors by identifying customer preferences and understanding customer behavior.

[fake-destiel-news]

[pb-dot]

Some Thoughts on the Reddit Blackout

Like many new arrivals on Tumblr these days, I used to be a Redditor until recent developments encouraged me to take my business elsewhere, and I have been following the development of the story as thoroughly as I can without actually giving Reddit any more traffic. With the most recent development of the Reddit admin corps taking on a suite of strategies lifted straight from the depression-era railroad baron playbook, I figured the time has come to talk a little about the wider implications of this whole story.

The Tech sector is, to the best of my understanding, in a vulnerable place right now. After the Web 2.0 gold rush and years of consolidation and growth from the biggest actors, your Alphabets, Twitters, Metas, and so on, many of the larger sites and services are reaching the largest size they can expect to grow to. How, for instance, could Facebook or Twitter grow much more now that everyone and their mother is on Facebook and Twitter? Prior to the Musk buyout, Twitter seemingly settled on upping engagement, making sure people were on Twitter longer and invested more energy and emotion in the platform, usually by making damn sure the discourse zapping through that hellhole was as polarizing and hostile as possible. Meta, meanwhile, has been making bank on user data as advertisers, AI folks, and any number of other actors salivate over getting their hands on the self-updating contact and interest registry that is Facebook.

With the rise of what we apparently have decided to call AI, data is now more valuable than ever. I consider this to be yet another Tech Hype Bubble on the level of NFTs or Metaverses, but, like with the two above, I can imagine it's hard to explain that when you are a Tech CEO and your shareholders ask you "Hey, how do you plan on earning us money off of this AI/NFT/Metaverse thing?" This is not to say CEO Steve Huffman isn't handling this whole thing with the grace of a three-legged hippo, but merely to suggest that his less-than-laudable decisions and actions in this mess don't arise from his character alone but also is a result of wider systemic issues.

One of these issues is the complicated role user data plays in modern websites and -services. Since its inception as a publicly accessible space, the question of how to monetize the Internet has been a tricky one for site and service owners. Selling ad space on your website or service has long been the go-to, but this in itself presents its own issues, having to curate content that is considered ad-friendly, malicious or careless actors making using said service or website less attractive for customers, and finally how to convince your advertisers that they get what they pay for in the first place, ie. "how do I know people even look at our ads?" All of this is before you even stop to consider how ads massively favor large, established actors.

It's no small wonder, then, that several startups in the era of internet mass adoption chose to forgo ads, or at least massively deprioritize them and/or relaunch them as "promoted posts," in an attempt to escape the stigma around ads. Meta/Facebook is probably the biggest fish in this particular pond, but we also see other services such as Twitter and Reddit follow the same pattern.

What makes this work is that the data these platforms collect from their users isn't all that valuable on a person-to-person basis, knowing that so-and-so is 32 years old, lives in a traditionally conservative part of the city, goes to Starbucks a lot, and listens to Radiohead isn't particularly useful information for anyone but a dedicated but lazy stalker; When viewed as an aggregate, however, large collections of data on a large population becomes quite valuable. This is especially true if you're working with, say, targeted ads or political campaigns. Look no further than the Cambridge Analytica data scandal for an example.

Now, all this is to illustrate the strange position the user occupies in Web 2.0. We tend to think of ourselves as the customer of Facebook, Reddit, Tumblr, and so on, but it isn't the case. After all, we don't pay for these services, and if we do it's to buy freedom from ads or other minor service modifications. It is more correct to say that we make up the product itself. This is true in two respects, first, an active social community is vital for social media to not be entirely pointless, and second, we generate the data that the platform holder seeks to monetize. This hybrid product/participant role doesn't map cleanly to traditional understandings of "worker," but I argue it is a closer fit than "customer."

All of this is to say that it is immensely gratifying to see the Reddit Blackout taking the shape of a strike rather than the more typical boycott model we've seen in the internet-based protests of yesteryear. Much of this, I think, we can thank the participating Reddit moderators. While the regular platform user can be *argued* to be a worker, the moderator inarguably is one, and the fact that they aren't paid for their efforts is more a credit to the prosocial nature of humans than to the corporate acumen of the platform holders. Either way, moderating a subreddit is work, if the subreddit is large, it's quite a lot of work, and moderators keeping malicious actors, scammers, and hatemongers out of everyone's hair is a must for any decently sized social space to not be an objectively terrible experience. So, if you were to, for example, withhold your labor (moderating for free) which you as a worker can do, it would be plain irresponsible to leave the place open for said bad apples to ruin everyone's bunches, thus the shutdowns.

I don't think it's a controversial take to claim that the Reddit admins also view this more as a strike than a boycott, given their use of scabs, intimidation, and other strikebreaking tactics in an attempt to break the thing up. This is nothing new, and the fact that Reddit admins are willing to stoop to these scumbag tactics tells us that their bluster about the shutdown not affecting their bottom line is nothing more than shareholder-placating hot air.

As this entire screed has perhaps demonstrated, I believe the Reddit Blackout is important. My stay at Tumblr so far has been excellent and will probably continue past this strike no matter what outcome it has, but for others in my situation, or perhaps entirely alien to the Reddit biome, I ask you to consider: If we do not stop this level of consumer and user-unfriendly bullshit Reddit have been pulling on the API change, where will it pop up next? Who's to say the next bright idea in corpo-hell isn't "Hey boss, how about we charge these nerd losers a dollar per reblog? And maybe a fiver for a Golden Reblog (TM)?"

This is perhaps getting into grandstanding, but I believe we are way past due for a renegotiation of what it means to be a platform holder and -user on this hot mess of an internet. If we as users do not take an active, strong stance on the matter, the Steve Huffmans, Elon Musks, and Mark Zuckerbergs of the world will decide without us. One does not have to be a fortune teller to see that the digital world this would create would not have our best interests in mind any more than the current one does.

So, in closing, I wish to extend my wholehearted support to the participating Moderators of Reddit and everyone who has decided to take their business elsewhere for the duration of the shutdown. Even without getting into the nitty-gritty of the API situation, this is a fight worth having, and may we through it make a world that's just a little bit less shitty.

Become Ungovernable

Become Unprofitable

Stay that way.

[uthra-krish]

Exploring Data Science Tools: My Adventures with Python, R, and More

Welcome to my data science journey! In this blog post, I'm excited to take you on a captivating adventure through the world of data science tools. We'll explore the significance of choosing the right tools and how they've shaped my path in this thrilling field.

Choosing the right tools in data science is akin to a chef selecting the finest ingredients for a culinary masterpiece. Each tool has its unique flavor and purpose, and understanding their nuances is key to becoming a proficient data scientist.

I. The Quest for the Right Tool

My journey began with confusion and curiosity. The world of data science tools was vast and intimidating. I questioned which programming language would be my trusted companion on this expedition. The importance of selecting the right tool soon became evident.

I embarked on a research quest, delving deep into the features and capabilities of various tools. Python and R emerged as the frontrunners, each with its strengths and applications. These two contenders became the focus of my data science adventures.

II. Python: The Swiss Army Knife of Data Science

Python, often hailed as the Swiss Army Knife of data science, stood out for its versatility and widespread popularity. Its extensive library ecosystem, including NumPy for numerical computing, pandas for data manipulation, and Matplotlib for data visualization, made it a compelling choice.

My first experiences with Python were both thrilling and challenging. I dove into coding, faced syntax errors, and wrestled with data structures. But with each obstacle, I discovered new capabilities and expanded my skill set.

III. R: The Statistical Powerhouse

In the world of statistics, R shines as a powerhouse. Its statistical packages like dplyr for data manipulation and ggplot2 for data visualization are renowned for their efficacy. As I ventured into R, I found myself immersed in a world of statistical analysis and data exploration.

My journey with R included memorable encounters with data sets, where I unearthed hidden insights and crafted beautiful visualizations. The statistical prowess of R truly left an indelible mark on my data science adventure.

IV. Beyond Python and R: Exploring Specialized Tools

While Python and R were my primary companions, I couldn't resist exploring specialized tools and programming languages that catered to specific niches in data science. These tools offered unique features and advantages that added depth to my skill set.

For instance, tools like SQL allowed me to delve into database management and querying, while Scala opened doors to big data analytics. Each tool found its place in my toolkit, serving as a valuable asset in different scenarios.

V. The Learning Curve: Challenges and Rewards

The path I took wasn't without its share of difficulties. Learning Python, R, and specialized tools presented a steep learning curve. Debugging code, grasping complex algorithms, and troubleshooting errors were all part of the process.

However, these challenges brought about incredible rewards. With persistence and dedication, I overcame obstacles, gained a profound understanding of data science, and felt a growing sense of achievement and empowerment.

VI. Leveraging Python and R Together

One of the most exciting revelations in my journey was discovering the synergy between Python and R. These two languages, once considered competitors, complemented each other beautifully.

I began integrating Python and R seamlessly into my data science workflow. Python's data manipulation capabilities combined with R's statistical prowess proved to be a winning combination. Together, they enabled me to tackle diverse data science tasks effectively.

VII. Tips for Beginners

For fellow data science enthusiasts beginning their own journeys, I offer some valuable tips:

Embrace curiosity and stay open to learning.

Work on practical projects while engaging in frequent coding practice.

Explore data science courses and resources to enhance your skills.

Seek guidance from mentors and engage with the data science community.

Remember that the journey is continuous—there's always more to learn and discover.

My adventures with Python, R, and various data science tools have been transformative. I've learned that choosing the right tool for the job is crucial, but versatility and adaptability are equally important traits for a data scientist.

As I summarize my expedition, I emphasize the significance of selecting tools that align with your project requirements and objectives. Each tool has a unique role to play, and mastering them unlocks endless possibilities in the world of data science.

I encourage you to embark on your own tool exploration journey in data science. Embrace the challenges, relish the rewards, and remember that the adventure is ongoing. May your path in data science be as exhilarating and fulfilling as mine has been.

Happy data exploring!

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

Hey there! 🚀 Becoming a data analyst is an awesome journey! Here’s a roadmap for you:

1. Start with the Basics 📚:

- Dive into the basics of data analysis and statistics. 📊

- Platforms like Learnbay (Data Analytics Certification Program For Non-Tech Professionals), Edx, and Intellipaat offer fantastic courses. Check them out! 🎓

2. Master Excel 📈:

- Excel is your best friend! Learn to crunch numbers and create killer spreadsheets. 📊🔢

3. Get Hands-on with Tools 🛠️:

- Familiarize yourself with data analysis tools like SQL, Python, and R. Pluralsight has some great courses to level up your skills! 🐍📊

4. Data Visualization 📊:

- Learn to tell a story with your data. Tools like Tableau and Power BI can be game-changers! 📈📉

5. Build a Solid Foundation 🏗️:

- Understand databases, data cleaning, and data wrangling. It’s the backbone of effective analysis! 💪🔍

6. Machine Learning Basics 🤖:

- Get a taste of machine learning concepts. It’s not mandatory but can be a huge plus! 🤓🤖

7. Projects, Projects, Projects! 🚀:

- Apply your skills to real-world projects. It’s the best way to learn and showcase your abilities! 🌐💻

8. Networking is Key 👥:

- Connect with fellow data enthusiasts on LinkedIn, attend meetups, and join relevant communities. Networking opens doors! 🌐👋

9. Certifications 📜:

- Consider getting certified. It adds credibility to your profile. 🎓💼

10. Stay Updated 🔄:

- The data world evolves fast. Keep learning and stay up-to-date with the latest trends and technologies. 📆🚀

. . .

[atozofsoftwareengineering]

Intelligent Automated Risk Management (IARM): Enhancing Risk Identification and Decision-Making

Unlock the future of risk management with Intelligent Automation: smarter, faster, and more proactive. #RiskManagement #AI #MachineLearning #Automation #BigData #Fintech #Healthcare #CyberSecurity

Introduction In an era where businesses face a myriad of risks—from financial uncertainties to cyber threats—traditional risk management approaches often struggle to keep up with the pace and complexity of emerging risks. Intelligent Automated Risk Management (IARM) offers a transformative approach by integrating cutting-edge technologies to enhance risk identification, assessment, and…

[emptyanddark]

what's actually wrong with 'AI'

it's become impossible to ignore the discourse around so-called 'AI'. but while the bulk of the discourse is saturated with nonsense such as, i wanted to pool some resources to get a good sense of what this technology actually is, its limitations and its broad consequences. 

what is 'AI'

the best essay to learn about what i mentioned above is On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? this essay cost two of its collaborators to be fired from Google. it frames what large-language models are, what they can and cannot do and the actual risks they entail: not some 'super-intelligence' that we keep hearing about but concrete dangers: from climate, the quality of the training data and biases - both from the training data and from us, the users. 

The problem with artificial intelligence? It’s neither artificial nor intelligent

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

The Values Encoded in Machine Learning Research

Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research

AI Now Institute 2023 Landscape report (discussions of the power imbalance in Big Tech)

ChatGPT Is a Blurry JPEG of the Web

Can we truly benefit from AI?

Inside the secret list of websites that make AI like ChatGPT sound smart

The Steep Cost of Capture

labor

'AI' champions the facade of non-human involvement. but the truth is that this is a myth that serves employers by underpaying the hidden workers, denying them labor rights and social benefits - as well as hyping-up their product. the effects on workers are not only economic but detrimental to their health - both mental and physical.

OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic

also from the Times: Inside Facebook's African Sweatshop

The platform as factory: Crowdwork and the hidden labour behind artificial intelligence

The humans behind Mechanical Turk’s artificial intelligence

The rise of 'pseudo-AI': how tech firms quietly use humans to do bots' work

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

workers surveillance

5 ways Amazon monitors its employees, from AI cameras to hiring a spy agency

Computer monitoring software is helping companies spy on their employees to measure their productivity – often without their consent

theft of art and content

Artists say AI image generators are copying their style to make thousands of new images — and it's completely out of their control  (what gives me most hope about regulators dealing with theft is Getty images' lawsuit - unfortunately individuals simply don't have the same power as the corporation)

Copyright won't solve creators' Generative AI problem

The real aim of big tech's layoffs: bringing workers to heel

The Exploited Labor Behind Artificial Intelligence

AI is already taking video game illustrators’ jobs in China

Microsoft lays off team that taught employees how to make AI tools responsibly/As the company accelerates its push into AI products, the ethics and society team is gone

150 African Workers for ChatGPT, TikTok and Facebook Vote to Unionize at Landmark Nairobi Meeting

Inside the AI Factory: the Humans that Make Tech Seem Human

Refugees help power machine learning advances at Microsoft, Facebook, and Amazon

Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make

China’s AI boom depends on an army of exploited student interns

political, social, ethical consequences

Afraid of AI? The startups selling it want you to be

An Indigenous Perspective on Generative AI

“Computers enable fantasies” – On the continued relevance of Weizenbaum’s warnings

‘Utopia for Whom?’: Timnit Gebru on the dangers of Artificial General Intelligence

Machine Bias

HUMAN_FALLBACK

AI Ethics Are in Danger. Funding Independent Research Could Help

AI Is Tearing Wikipedia Apart  

AI machines aren’t ‘hallucinating’. But their makers are

The Great A.I. Hallucination (podcast)

“Sorry in Advance!” Rapid Rush to Deploy Generative A.I. Risks a Wide Array of Automated Harms

The promise and peril of generative AI

ChatGPT Users Report Being Able to See Random People's Chat Histories

Benedetta Brevini on the AI sublime bubble – and how to pop it   

Eating Disorder Helpline Disables Chatbot for 'Harmful' Responses After Firing Human Staff

AI moderation is no match for hate speech in Ethiopian languages

Amazon, Google, Microsoft, and other tech companies are in a 'frenzy' to help ICE build its own data-mining tool for targeting unauthorized workers

Crime Prediction Software Promised to Be Free of Biases. New Data Shows It Perpetuates Them

The EU AI Act is full of Significance for Insurers

Proxy Discrimination in the Age of Artificial Intelligence and Big Data

Welfare surveillance system violates human rights, Dutch court rules

Federal use of A.I. in visa applications could breach human rights, report says

Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI

Generative AI Is Making Companies Even More Thirsty for Your Data

environment

The Generative AI Race Has a Dirty Secret

Black boxes, not green: Mythologizing artificial intelligence and omitting the environment

Energy and Policy Considerations for Deep Learning in NLP

AINOW: Climate Justice & Labor Rights

militarism

The Growing Global Spyware Industry Must Be Reined In

AI: the key battleground for Cold War 2.0?

‘Machines set loose to slaughter’: the dangerous rise of military AI

AI: The New Frontier of the EU's Border Extranalisation Strategy

The A.I. Surveillance Tool DHS Uses to Detect ‘Sentiment and Emotion’

organizations

AI now

DAIR

podcast episodes

Pretty Heady Stuff: Dru Oja Jay & James Steinhoff guide us through the hype & hysteria around AI

Tech Won't Save Us: Why We Must Resist AI w/ Dan McQuillan, Why AI is a Threat to Artists w/ Molly Crabapple, ChatGPT is Not Intelligent w/ Emily M. Bender

SRSLY WRONG: Artificial Intelligence part 1, part 2

The Dig: AI Hype Machine w/ Meredith Whittaker, Ed Ongweso, and Sarah West

This Machine Kills: The Triforce of Corporate Power in AI w/ ft. Sarah Myers West

[shrinkrants]

The technological society… will not be a universal concentration camp, for it will be guilty of no atrocity. It will not seem insane, for everything will be ordered, and the stains of human passion will be lost amid the chromium gleam. We shall have nothing more to lose, and nothing to win. Our deepest instincts and our most secret passions will be analyzed, published, and exploited. We shall be rewarded with everything our hearts ever desired. And the supreme luxury of the society of technical necessity will be to grant the bonus of useless revolt and of an acquiescent smile. (p. 427)

This sharp paragraph was not written in the 1990s, nor in the 2010s, nor in recent years. It appeared in French sociologist and philosopher Jacques Ellul’s treatise The Technological Society, originally published in 1954 and translated into English with Robert Merton’s introduction in 1964. Ellul was concerned with the emergence of a technological tyranny over humanity and the totality of efficiency, and in this classic book he masterly elaborated on these substantial issues. Quite a few claimed 70 years ago and even decades later that Ellul was too provocative, that he overstated and exaggerated. Rereading his significant and penetrating observations in the current age evokes different thoughts and reactions. What is certain is that we are only at the beginning of this age. So is it possible that even we are far from understanding the essence of Ellul’s alerting words?

To reflect more on this crucial topic dive into these insightful books for which Ellul’s The Technological Society laid the groundwork: — Beer, David. 2019. The Social Power of Algorithms. Routledge. — Fisher, Eran. 2022. Algorithms and Subjectivity: The Subversion of Critical Knowledge. Routledge — Fisher, Max. 2022. The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds and Our World. Little, Brown and Company. — Schwarz, Ori. 2021. Sociological Theory for Digital Society: The Codes that Bind us Together. John Wiley & Sons. — Zuboff, Shoshana. 2018. The Age of Surveillance Capitalism. Profile Books

[lbhslefttiddie]

I haven’t seen you around much here lately, so I just wanted to say I hope you’re doing well ^^ . If you have any life challenges going on right now, I believe in you to conquer them!!!

thank you!!! the life challenges is my phone and computer both had a stroke and died within a month of each other 😔 it was super cursed but im cool im being very brave about it

[dhivyakrishnan107667]

From Beginner to Pro: A Game-Changing Big Data Analytics Course

Are you fascinated by the vast potential of big data analytics? Do you want to unlock its power and become a proficient professional in this rapidly evolving field? Look no further! In this article, we will take you on a journey to traverse the path from being a beginner to becoming a pro in big data analytics. We will guide you through a game-changing course designed to provide you with the necessary information and education to master the art of analyzing and deriving valuable insights from large and complex data sets.

Step 1: Understanding the Basics of Big Data Analytics

Before diving into the intricacies of big data analytics, it is crucial to grasp its fundamental concepts and methodologies. A solid foundation in the basics will empower you to navigate through the complexities of this domain with confidence. In this initial phase of the course, you will learn:

The definition and characteristics of big data

The importance and impact of big data analytics in various industries

The key components and architecture of a big data analytics system

The different types of data and their relevance in analytics

The ethical considerations and challenges associated with big data analytics

By comprehending these key concepts, you will be equipped with the essential knowledge needed to kickstart your journey towards proficiency.

Step 2: Mastering Data Collection and Storage Techniques

Once you have a firm grasp on the basics, it's time to dive deeper and explore the art of collecting and storing big data effectively. In this phase of the course, you will delve into:

Data acquisition strategies, including batch processing and real-time streaming

Techniques for data cleansing, preprocessing, and transformation to ensure data quality and consistency

Storage technologies, such as Hadoop Distributed File System (HDFS) and NoSQL databases, and their suitability for different types of data

Understanding data governance, privacy, and security measures to handle sensitive data in compliance with regulations

By honing these skills, you will be well-prepared to handle large and diverse data sets efficiently, which is a crucial step towards becoming a pro in big data analytics.

Step 3: Exploring Advanced Data Analysis Techniques

Now that you have developed a solid foundation and acquired the necessary skills for data collection and storage, it's time to unleash the power of advanced data analysis techniques. In this phase of the course, you will dive into:

Statistical analysis methods, including hypothesis testing, regression analysis, and cluster analysis, to uncover patterns and relationships within data

Machine learning algorithms, such as decision trees, random forests, and neural networks, for predictive modeling and pattern recognition

Natural Language Processing (NLP) techniques to analyze and derive insights from unstructured text data

Data visualization techniques, ranging from basic charts to interactive dashboards, to effectively communicate data-driven insights

By mastering these advanced techniques, you will be able to extract meaningful insights and actionable recommendations from complex data sets, transforming you into a true big data analytics professional.

Step 4: Real-world Applications and Case Studies

To solidify your learning and gain practical experience, it is crucial to apply your newfound knowledge in real-world scenarios. In this final phase of the course, you will:

Explore various industry-specific case studies, showcasing how big data analytics has revolutionized sectors like healthcare, finance, marketing, and cybersecurity

Work on hands-on projects, where you will solve data-driven problems by applying the techniques and methodologies learned throughout the course

Collaborate with peers and industry experts through interactive discussions and forums to exchange insights and best practices

Stay updated with the latest trends and advancements in big data analytics, ensuring your knowledge remains up-to-date in this rapidly evolving field

By immersing yourself in practical applications and real-world challenges, you will not only gain valuable experience but also hone your problem-solving skills, making you a well-rounded big data analytics professional.

Through a comprehensive and game-changing course at ACTE institute, you can gain the necessary information and education to navigate the complexities of this field. By understanding the basics, mastering data collection and storage techniques, exploring advanced data analysis methods, and applying your knowledge in real-world scenarios, you have transformed into a proficient professional capable of extracting valuable insights from big data.

Remember, the world of big data analytics is ever-evolving, with new challenges and opportunities emerging each day. Stay curious, seek continuous learning, and embrace the exciting journey ahead as you unlock the limitless potential of big data analytics.

[thefreethoughtprojectcom]

A large collective and imaginative effort is needed to resist data colonialism’s new injustices. This effort is a crucial step on the longer journey to confronting and reversing colonialism itself.

Read More: https://thefreethoughtproject.com/be-the-change/ai-companies-want-to-colonize-our-data-heres-how-we-stop-them

#TheFreeThoughtProject #TFTP

[yellowpaintpalette30]

The Legal Side of Big Data

After seeing "The Legal Side of Big Data," I can say with certainty that it presented me with a very thorough understanding of knowledge relating to big data and its concerns, with a special focus on the legal side of it with relation to the corporate sector and its benefits and drawbacks. 

Businesses should be aware of the very serious risks associated with big data use, including data privacy and data protection. When organizations acquire huge data, this is the first risk associated with big data. At all costs, it must be kept private and secured, as its loss would be catastrophic for the company. The organization must be able to meet the financial requirements because protecting huge data comes at a significant cost. Additionally, the business should take the proper, legal precautions to carefully handle data storage, retention, and big data analysis. 

However, consumers have a right to know how their data is used, saved, and shared, therefore businesses must maintain transparency, which is another important factor. As a result, consumers should be informed of specific company behaviors. It is important to provide consumers with clear and straightforward privacy policies and terms of use so they may decide how their data will be used.

Moreover, the best way for a company to balance the opportunities and threats brought on by the growth of big data is to develop a thorough strategy and a plan in advance, carefully develop and carry out the strategy, successfully stay focused on business needs and goals, and put strong access and governance controls in place to be in compliance with all laws that apply.