A Practical Guide to Big Data: Concepts, Drivers and Techniques
Principles of Big Data PDF Download
Big data is one of the most influential and disruptive phenomena in the modern world. It has transformed how we communicate, work, learn, shop, play and live. But what is big data exactly? Why is it so important? How can we process it? And what are the principles that guide its use?
In this article, we will answer these questions and more. We will also show you how to download a free or discounted copy of Principles of Big Data, a book by Kenneth Cukier and Viktor Mayer-Schönberger that explains the key concepts and implications of big data in an accessible and engaging way.
What is Big Data?
Big data is a term that refers to the massive amounts of digital information that are generated, collected, stored and analyzed every day. According to IBM, 90% of the world's data was created in the last two years alone. This data comes from various sources, such as social media, online transactions, sensors, cameras, satellites, smartphones, etc.
Big data has four main characteristics:
Volume: The size of big data is measured in terabytes, petabytes or even exabytes (one exabyte equals one billion gigabytes).
Variety: The diversity of big data includes structured, semi-structured and unstructured data, such as text, images, audio, video, etc.
Velocity: The speed of big data refers to how fast it is generated, processed and analyzed.
Veracity: The quality of big data refers to how accurate, reliable and trustworthy it is.
Depending on the purpose and context, big data can be classified into different types, such as:
Descriptive: This type of big data describes what has happened or what is happening in a given situation.
Diagnostic: This type of big data explains why something has happened or why something is happening in a given situation.
Predictive: This type of big data predicts what will happen or what is likely to happen in a given situation.
Prescriptive: This type of big data prescribes what should happen or what is the best course of action in a given situation.
Why is Big Data Important?
Big data is important because it offers many benefits, challenges and opportunities for businesses and society. Some of the benefits of big data are:
Improved decision making: Big data enables better and faster decisions based on data-driven insights and evidence.
Enhanced customer experience: Big data enables personalized and customized products, services and recommendations for customers.
Increased efficiency and productivity: Big data enables optimized processes, operations and performance for organizations.
Innovation and discovery: Big data enables new and novel solutions, products, services and knowledge for various domains and fields.
Some of the challenges of big data are:
Data management: Big data requires adequate infrastructure, technology, tools and techniques to store, process and analyze it.
Data quality: Big data requires methods and standards to ensure its accuracy, completeness, consistency and validity.
Data security: Big data requires policies and practices to protect it from unauthorized access, use, disclosure and modification.
Data ethics: Big data requires principles and values to ensure its responsible, fair and transparent use.
Some of the opportunities of big data are:
New business models: Big data enables new ways of creating and delivering value for customers and stakeholders.
New markets and niches: Big data enables new segments and audiences for products and services.
New social and environmental impacts: Big data enables new ways of addressing and solving global and local issues and challenges.
How to Process Big Data?
To process big data, we need big data analytics, which is the process of applying various tools and techniques to extract meaningful insights from big data. Big data analytics can be divided into four stages:
Data collection: This stage involves acquiring, capturing and ingesting big data from various sources.
Data preparation: This stage involves cleaning, transforming and integrating big data to make it ready for analysis.
Data analysis: This stage involves applying various methods and algorithms to big data to discover patterns, trends, correlations, anomalies, etc.
Data visualization: This stage involves presenting and communicating the results of big data analysis using charts, graphs, dashboards, etc.
To perform big data analytics, we need various tools and techniques, such as:
Distributed computing: This technique involves using multiple computers or nodes to process big data in parallel.
Hadoop: This tool is an open-source framework that enables distributed storage and processing of big data using a cluster of nodes.
Spark: This tool is an open-source framework that enables fast and flexible processing of big data using in-memory computing.
NoSQL databases: These tools are non-relational databases that enable scalable and flexible storage and retrieval of big data.
Machine learning: This technique involves using algorithms that learn from big data to perform tasks such as classification, regression, clustering, etc.
Natural language processing: This technique involves using algorithms that understand and generate natural language from big data.
Data mining: This technique involves using algorithms that extract useful information from big data.
What are the Principles of Big Data?
The principles of big data are the fundamental concepts and guidelines that shape how we use and understand big data. In their book Principles of Big Data, Kenneth Cukier and Viktor Mayer-Schönberger propose 10 principles of big data that capture the essence and implications of this phenomenon. These principles are:
Principle 1: Collect Everything
data for different purposes and contexts in the future.
Principle 2: Messiness is OK
This principle states that we should accept and embrace the imperfections and inconsistencies in the data, rather than trying to eliminate or correct them. By accepting messiness, we can leverage the scale and variety of the data, rather than sacrificing them for accuracy and precision. We can also deal with uncertainty and ambiguity in the data, rather than ignoring or avoiding them. We can also adapt to changes and dynamics in the data, rather than relying on fixed and static assumptions.
Principle 3: Correlation Beats Causation
This principle states that we should focus on finding and exploiting correlations in the data, rather than seeking and explaining causation. By focusing on correlation, we can discover and predict patterns and trends in the data, rather than understanding and justifying them. We can also use data to answer what and how questions, rather than why questions. We can also use data to inform and guide actions, rather than to prove and validate hypotheses.
Principle 4: Datafication Changes Reality
This principle states that we should recognize and appreciate the impact of datafication on reality, rather than ignoring or denying it. Datafication is the process of transforming social and natural phenomena into quantifiable data. By datafying reality, we can measure and monitor aspects of reality that were previously invisible or inaccessible. We can also manipulate and influence aspects of reality that were previously immutable or uncontrollable. We can also create and shape aspects of reality that were previously nonexistent or unimaginable.
Principle 5: Value is in the Long Tail
This principle states that we should explore and exploit the long tail of the data, rather than focusing on the head or the average. The long tail is the part of the data distribution that contains many low-frequency or low-value items, while the head or the average is the part that contains few high-frequency or high-value items. By exploring the long tail, we can uncover and utilize hidden or overlooked opportunities and niches in the data. We can also cater to and satisfy diverse and individual preferences and needs in the data. We can also generate and capture more value from the data.
Principle 6: Privacy is a Trade-off
This principle states that we should balance and negotiate the trade-off between privacy and utility in the data, rather than choosing one over the other. Privacy is the right or ability to control one's personal information, while utility is the benefit or value derived from using or sharing information. By balancing privacy and utility, we can respect and protect individual rights and interests in the data, while also enabling and enhancing social and collective benefits and values in the data. We can also establish and maintain trust and transparency in the data, while also fostering and promoting innovation and collaboration in the data.
Principle 7: Think Differently
and contribute to the new values and ethics of big data. Some of the new skills, mindsets and ethics for big data are:
Data literacy: The ability to read, write and communicate with data.
Data curiosity: The attitude of being interested and eager to explore and learn from data.
Data creativity: The skill of being able to generate and apply novel and useful ideas from data.
Data responsibility: The duty of being accountable and respectful for the consequences and impacts of data.
Data justice: The principle of ensuring fairness and equity in the access, use and distribution of data.
Principle 8: Be Skeptical
This principle states that we should be critical and cautious of big data, rather than trusting and accepting it blindly. By being skeptical, we can avoid and prevent the limitations and pitfalls of big data, such as:
Data errors: The mistakes and inaccuracies in the data that can affect its quality and reliability.
Data biases: The prejudices and distortions in the data that can affect its representation and interpretation.
Data gaps: The missing or incomplete information in the data that can affect its coverage and completeness.
Data misuse: The inappropriate or unethical use of data that can affect its security and privacy.
Principle 9: Experiment More
This principle states that we should use big data to experiment more, rather than relying on intuition or tradition. By experimenting more, we can use big data to test and validate ideas, assumptions and hypotheses. We can also use big data to discover and learn new things, insights and knowledge. We can also use big data to innovate and create new solutions, products and services.
Principle 10: Embrace Paradox
This principle states that we should acknowledge and accept the paradoxes and complexities of big data, rather than simplifying or resolving them. By embracing paradox, we can appreciate and understand the multiple and contradictory aspects of big data, such as:
More is less: Big data can provide more information, but also less meaning.
Less is more: Big data can require less human intervention, but also more human judgment.
Simple is complex: Big data can enable simple solutions, but also create complex problems.
Complex is simple: Big data can reveal complex patterns, but also simplify reality.
How to Download Principles of Big Data PDF?
and principles of big data. It also provides examples and case studies of how big data is used and applied in various domains and fields.
To download a free or discounted copy of Principles of Big Data PDF, you can follow these steps:
Go to this link, which is a website that offers free PDF downloads of books.
Click on the green button that says "Download (PDF)".
Wait for a few seconds until the download starts automatically.
Save the file to your device or cloud storage.
Enjoy reading the book!
Alternatively, you can also buy a hardcopy or an ebook version of Principles of Big Data from Amazon or other online retailers. You might be able to get a discount or a free trial if you are a Prime member or a Kindle Unlimited subscriber.
In conclusion, big data is a phenomenon that has changed and will continue to change the world. It has many benefits, challenges and opportunities for businesses and society. It also has many principles that guide its use and understanding. By learning and applying these principles, we can make the most of big data and its potential.
If you want to learn more about the principles of big data, we recommend you to read the book Principles of Big Data by Kenneth Cukier and Viktor Mayer-Schönberger. You can download a free or discounted copy of the PDF from the link we provided above, or buy a hardcopy or an ebook version from Amazon or other online retailers.
We hope you enjoyed this article and found it useful. If you have any questions or feedback, please let us know in the comments below. Thank you for reading!
Here are some frequently asked questions about principles of big data:
What are the 10 principles of big data?The 10 principles of big data are: collect everything, messiness is OK, correlation beats causation, datafication changes reality, value is in the long tail, privacy is a trade-off, think differently, be skeptical, experiment more and embrace paradox.
Who wrote the book Principles of Big Data?The book Principles of Big Data was written by Kenneth Cukier and Viktor Mayer-Schönberger. Kenneth Cukier is a journalist and the data editor of The Economist. Viktor Mayer-Schönberger is a professor and the director of the Oxford Internet Institute.
How can I download Principles of Big Data PDF for free?You can download Principles of Big Data PDF for free from this link: https://www.pdfdrive.com/principles-of-big-data-e158418258.html. You just need to click on the green button that says "Download (PDF)" and wait for a few seconds until the download starts automatically.
How can I buy Principles of Big Data book?You can buy Principles of Big Data book from Amazon or other online retailers. You can choose between a hardcopy or an ebook version. You might be able to get a discount or a free trial if you are a Prime member or a Kindle Unlimited subscriber.
Why should I read Principles of Big Data book?You should read Principles of Big Data book if you want to learn more about the concepts and implications of big data in an accessible and engaging way. The book covers topics such as the history, definition, characteristics, sources, types, benefits, challenges, opportunities, tools, techniques and principles of big data. It also provides examples and case studies of how big data is used and applied in various domains and fields.