Home > Resources > Articles > Business Intelligence for Actionable Insights: Top BI books to Explore

Business Intelligence for Actionable Insights: Top BI books to Explore

Business Intelligence for Actionable Insights: Top BI books

In the fast-paced world of data-driven decision-making, having a solid understanding of BI is essential. Explore a handpicked selection of top books that cover everything a data professional needs.

In today's data-driven landscape, businesses face the ongoing challenge of deriving meaningful insights from vast amounts of data to facilitate informed decision-making. This is where the field of business intelligence (BI) becomes crucial. Business intelligence encompasses a range of processes, technologies, and strategies that empower organizations to transform raw data into valuable insights, thereby driving business success. Access to comprehensive and insightful information is critical whether you're new to the field and seeking foundational knowledge or an experienced professional aiming to enhance your skills. This article will explore a curated selection of the best business intelligence books that offer valuable knowledge, practical guidance, and strategic insights. These books cover various facets of BI, including fundamental concepts, methodologies, agile approaches, data mining techniques, and cultural considerations. By delving into these essential resources, you will learn the tools and understanding necessary to navigate the complex realm of business intelligence, harness the full potential of your organization's data assets, and propel your business forward.

1. Business Intelligence

Elizabeth Vitt, Michael Luckevich, Stacia Misner

‘Business Intelligence, 1st Edition’ presents a comprehensive approach to empower readers with the vital knowledge and expertise needed to thrive in the dynamic field of business intelligence (BI). Co-authored by Elizabeth Vitt, Michael Luckevich, and Stacia Misner, this BI book is an invaluable resource catering to both beginners and seasoned professionals within the realm of business intelligence. Setting the stage, the book lays a solid foundation by illuminating fundamental concepts in business intelligence. It adeptly demonstrates how organizations can harness the power of massive data repositories to glean invaluable business insights, enabling them to make informed decisions swiftly and effectively regarding customers, partners, and operational aspects. 'Business Intelligence' guides readers through the intricacies of leveraging business intelligence insights to seamlessly amalgamate information, individuals, and cutting-edge technologies. Armed with this knowledge, readers gain the confidence to devise and execute successful business strategies with precision.

2. Business Intelligence: Data Mining and Optimization for Decision Making

Carlo Vercellis

In the book 'Business Intelligence: Data Mining and Optimization for Decision Making,' Carlo Vercellis explores the crucial intersection between data mining, optimization techniques, and decision-making processes within the field of business intelligence. The author starts by laying the groundwork for business intelligence and introduces key concepts such as data warehousing, data mining and its applications, machine learning, supply optimization models, decision support systems, and analytical methods for performance evaluation, setting the stage for a holistic approach to business intelligence. The book emphasizes the practical application of data mining and optimization techniques through real-world examples and case studies. 'Business Intelligence: Data Mining and Optimization for Decision Making' though aimed at postgraduate students, is an essential resource for professionals and researchers interested in harnessing the power of data mining, optimization, and decision support systems.


3. Business Intelligence: An Essential Beginner’s Guide to BI

Richard Hurley

'Business Intelligence: An Essential Beginner's Guide to BI, Big Data, Artificial Intelligence, Cybersecurity, Machine Learning, Data Science, Data Analytics, Social Media, and Internet Marketing,' provides a comprehensive introduction to the diverse and interconnected world of modern business intelligence. As the title suggests, this business intelligence book goes beyond traditional BI and explores emerging technologies shaping the business landscape. It delves into big data, providing insights into the challenges and opportunities associated with processing and analyzing massive datasets. Hurley introduces artificial intelligence (AI), machine learning (ML), and pattern recognition to explain their potential applications in driving business intelligence and decision-making. The book also addresses the critical roles that social media and internet marketing play in the growth of BI and how these platforms can be leveraged to gather valuable business intelligence insights and engage with customers effectively.


4. Business Intelligence: And How It Can Help You Grow Your Business

Johan Faerch

This book offers a practical and insightful guide to leveraging business intelligence strategies and techniques to drive business growth. This BI book is designed to empower entrepreneurs, business owners, and managers with the knowledge and tools necessary to make informed decisions and unlock the full potential of their organizations. It begins by introducing the concept of business intelligence and its significance in today's competitive marketplace and explains how BI goes beyond mere data analysis and reporting, acting as a catalyst for growth and innovation. The author highlights the transformative power of BI, demonstrating how it can provide a deep understanding of market trends, customer preferences, and internal operations. 'Business Intelligence: And How It Can Help You Grow Your Business' is a highly accessible and practical resource that bridges the gap between theory and real-world application. Johan Faerch's expertise and experience in the field, shines through as he provides valuable business intelligence insights and actionable strategies for harnessing the power of BI to achieve business growth.


5. Growing Business Intelligence

Larry Burns

'Growing Business Intelligence: An Agile Approach to Leveraging Data and Analytics for Maximum Business Value' offers a comprehensive guide to unlocking the full potential of business intelligence (BI) through an agile and adaptable framework. This BI book provides practical insights and strategies for effectively utilizing data and analytics to drive continuous growth and optimize business value. With a keen focus on core principles, the book highlights the importance of navigating the complexities of BI architecture to find the most suitable path for each unique organization. The book serves as a trusted resource, guiding readers on effectively managing the risks associated with disruptive technologies and adopting agile methodologies to deliver on the promises of BI and analytics in a rapid, concise, and iterative manner. 'Growing Business Intelligence' is an invaluable asset for business leaders, managers, and data professionals involved in BI, analytics, or Big Data projects. It also caters to organizations aiming to maximize the value derived from their data and investments in BI technology.


6 Business Intelligence: A Comprehensive Approach to Information Needs, Technologies, and Culture

Rimvydas Skyrius

'Business Intelligence: A Comprehensive Approach to Information Needs, Technologies, and Culture' by Rimvydas Skyrius delves deep into the multifaceted realm of business intelligence from various perspectives. This book looks at BI as a process driven by the synergistic blend of human capabilities and technological advancements and emphasizes the complex nature of information needs and decision-making support within organizations. It begins with a comprehensive introduction to the fundamental concepts of BI and related areas of information processing, navigating through the intricacies of BI, addressing data integration, information integration, and the processes & technologies involved. It further explores the maturity and agility of BI, delving into the components, drivers, and inhibitors of BI culture, as well as the soft factors like attention, sense, and trust that shape the BI landscape. Rimvydas, in this book, presents a holistic perspective view on business intelligence, possible structures and tradeoffs within the field of BI, providing readers with valuable insights.


7. Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence

John Boyer, Bill Frank, Brian Green, Tracy Harris, Kay Van De Vanter

'Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence' serves as a comprehensive and practical guide for organizations aiming to develop and implement a successful business intelligence strategy that drives excellence and delivers tangible results. The book begins by emphasizing the strategic importance of BI in modern organizations, highlighting its role in enabling informed decision-making, improving operational efficiency, and fostering a data-driven culture. It guides readers through the process of creating business alignment strategies that help prioritize business requirements, build organizational & cultural strategies, increase IT efficiency, and promote user adoption. The authors emphasize the importance of engaging stakeholders and fostering collaboration between business and IT teams to ensure the strategy's effectiveness and long-term success. 'Business Intelligence Strategy' equip readers with the right tools and strategies to develop and implement a robust BI strategy that drives excellence and delivers measurable value to their organization.


8. Fundamentals of Business Intelligence (Data-Centric Systems and Applications)

Wilfried Grossmann, Stefanie Rinderle-Ma

'Fundamentals of Business Intelligence' serves as a comprehensive and systematic introduction to the dynamic field of business intelligence , providing readers with strong foundational knowledge. This business intelligence book focuses on the transformation of process-oriented data into valuable information crucial for decision-making across diverse domains. The authors, Grossmann and Rinderle-Ma, follow a step-by-step approach to develop models and analytical tools that enable the acquisition of high-quality data structured in a manner conducive to applying complex analytical techniques. Covering a wide range of essential topics, the book delves into the fundamental concepts of business intelligence, the data-centric nature of BI, exploring various approaches to modeling in BI applications, data provisioning, data description, visualization, reporting, and more. The book seamlessly blends theoretical explanations with practical examples and compelling case studies to further enhance comprehension.


9. Business Intelligence: The Savvy Manager's Guide 1st Edition

David Loshin

'Business Intelligence: The Savvy Manager's Guide' is an insightful resource offering, practical guidance and strategic insight to assist managers in comprehending, implementing, and maximizing the benefits of BI. Loshin initiates the journey by clearly describing the fundamental architectural components of a business intelligence environment. Topics covered range from traditional subjects such as business process modeling and data modeling to more contemporary areas like business rule systems, data profiling, information compliance and data quality, data warehousing, and data mining. The book follows a logical progression, starting with the establishment of a robust data model infrastructure, followed by data preparation, analysis, integration, knowledge discovery, and ultimately the practical utilization of the acquired knowledge. Loshin adeptly provides clear explanations devoid of technical jargon, coupled with in-depth descriptions that articulate the business value of emerging technologies while offering the necessary introductory technical background. The true strength of this book lies in its ability to bridge the gap between technical and managerial perspectives.


10. Business Intelligence: The Savvy Manager's Guide 2nd Edition

David Loshin

'Business Intelligence: The Savvy Manager's Guide' by David Loshin is a comprehensive resource that equips managers with the knowledge and insights necessary to effectively navigate the field of business intelligence.The book covers the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. The author also explores key factors to be taken into account in the planning and execution of a successful BI program, considerations for developing a BI roadmap, the platforms for analysis, such as data warehouses, and the concepts of business metadata. 'Business Intelligence: The Savvy Manager's Guide' serves as an accessible resource for BI professionals, including senior and middle-level managers, Chief Information Officers, Chief Data Officers, senior business executives, business staff members, database or software engineers, and business analysts seeking to harness the power of BI in their organizations.


Conclusion

The field of business intelligence is ever-evolving and plays a vital role in the current data-driven business landscape. The books highlighted in this article provide a wealth of knowledge and insights for individuals at various stages of their BI journey. These business intelligence books collectively offer a comprehensive and diverse range of perspectives on business intelligence to promote growth and expertise. Whether you are a beginner seeking fundamental knowledge or a seasoned professional aiming to enhance your skills, these resources provide valuable insights and guidance for harnessing the power of BI to drive success in the modern business landscape.

Spotlight

Mev, Llc

MEV® is a NYC-based web technologies provider servicing global business and technology initiatives. We manage and create both modular and full spectrum projects for Medium and Enterprise level businesses at custom rates. We specialize in complex web and mobile software development as well as data analysis and visualization tools.

OTHER ARTICLES
Business Intelligence, Big Data Management, Big Data

Data-driven Content Marketing for 2022

Article | July 10, 2023

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

Read More
Business Intelligence, Big Data Management, Big Data

Saurav Singla, the machine learning guru, empowering society

Article | July 18, 2023

Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker. While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart. In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well. Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets. He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years. After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years. He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him. Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science. He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology. In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR. During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models. In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years. At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases. Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network. Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product. Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence. His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career. Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.

Read More
Business Intelligence, Big Data Management, Big Data

Importance of Encryption in the Business World

Article | May 15, 2023

Almost every day, we come across some news about data breaches and cyber-attacks, which has forced us to discuss and debate more about the importance of data and how we can protect it. Some of the most significant data breaches of 2021 and 2022 were the Microsoft Software data breach (2021), Facebook data breach (2021), Bureau Veritas cyberattack (2021), Gloucester Council Cyberattack (2022), and many more. Cyber-attacks were rated as the fifth-top risk in 2020 by the World Economic Forum. The COVID-19 pandemic has increased cybercrime by 600% compared to the pre-pandemic period. According to Accenture, 43% of cyber-attacks target small businesses, but only 14% are prepared to defend themselves. As per Cybersecurity Ventures, cybercrime will cost businesses worldwide $10.5 trillion annually by 2025 up from $3 trillion in 2015. Unfortunately, we still don’t have a concrete solution, which can be called a one-size-fits-all approach for preventing security breaches or even handling them when they happen. However, there are numerous methods for minimizing data exposure. Data encryption and regular data back-ups have become two of the most effective and widely used methods for protecting against data exposure. Importance of Encryption The facts and figures mentioned above highlight the growing importance of encryption for data security. No business, regardless of size, is immune to the risk of a data breach. Encryption has become the need of the hour because it is considered the last line of defense. Many applications and websites depend upon user passwords and password verification software to access sensitive information. Apart from knowing how to generate a safe password, users have minimal options to encrypt their password. This is why they use a password manager to keep their passwords secure. A good password manager must use strong encryption to protect what is a gold mine of data. Businesses can choose an encryption type as per their preference and requirements. There are two types of encryption for scrambling or masking data. They are as follows: Symmetric Encryption Symmetric encryption is the simplest way to protect data from hackers. It has just one key, and everyone uses it to encrypt and decrypt data. For example, you encrypt a file and send it to your manager, who uses the same key to unlock or decrypt it. Asymmetric Encryption Asymmetric encryption encrypts using two keys. The public key is used for encryption, whereas the private key is used for decryption. For example, the private key can be used to encrypt a file, but only the manager can use the private key to decrypt it. Reasons why encrypting your data is crucial Encryption has evolved as a critical component in securing data from malicious attacks of any kind. However, some organizations are still hesitant about encryption because they are unaware of the benefits. So let’s look at the top reasons why businesses should encrypt their data. Encryption is the Last Line of Defense When we talk about cyberattacks, companies are often helpless when it comes to preventing them. In this case, encryption acts as a protector making it difficult to encrypt data without the decryption key. This is one of the significant implications of encryption, and hence we call it the last line of defense. Encryption is Cheap to Implement From smartphones to Microsoft Windows, almost every device, software, and operating system today has encryption technology. Also, there are many encryption programs available for free download, programs like LastPass, TunnelBear, HTTPS and others. Encryption protects data on the go One of the biggest data security threats companies face is when data is on the move. It means portable devices, whether mobile phones, USBs, laptops, or tablets containing sensitive data, move outside a company’s security network. A misplaced USB, a laptop left unsupervised, or a mobile phone forgotten in a coffee shop can sometimes be disastrous. Encryption makes sure that if a device is lost or stolen, its data can't be read or misused by anyone who doesn't have a key to decrypt it. Encryption Algorithms to Secure Your Business Network & How Encryption Works Various encryption algorithms help secure your business networks. But before we dive into the details of encryption algorithms, it is important to know the workings of encryption. How Encryption Works Unencrypted information or data, such as blogs like the one you are reading, is written in plaintext. At its core, data encryption employs an encryption algorithm to distort or mask plaintext, resulting in “ciphertext”, which humans interpret as alphanumeric nonsense. An encryption algorithm is incomplete and cannot convert plaintext to ciphertext and vice-versa. Encryption Algorithms to Secure Your Business Data As data security threats have become more sophisticated and aggressive, maintaining online security has become critical. Therefore, modern encryption has grown more complex to protect private data. Different types of encryption algorithms can help you enhance your data encryption strategy. If required, you can create your own algorithm. However, there are a few standard encryption algorithms that you can consider. Data Encryption Standard (DES) The data encryption standard is an older symmetric-key method of encrypting data that was utilized as a standard method by the United States government. But it was withdrawn later as it was not considered secure enough for many modern applications. A DES key has 64 binary digits (also known as bits), 56 of which are randomly generated by the algorithm. The other eight are utilized for the detection of errors. People who use DES know the encryption algorithm, but unauthorized entities do not have the decryption key. Data encryption standard is insecure because the 56-bit key is too small. Triple Data Encryption Standard (Triple DES) The triple data encryption standard (also called Triple DES, or TDES or 3DES) is the newer and safer version of the data encryption standard. There are two kinds of triple DES: two-key and three-key, based on the number of generated keys. Triple DES runs DES three times; the data is encrypted, decrypted, and then again encrypted before it is sent to the receiving party. Rivest-Shamir-Adleman (RSA) Popularly known as RSA, it is named after its creators, Ron Rivest, Adi Shamir, and Len Adelman. RSA is an asymmetric encryption algorithm primarily utilized to share data over insecure networks. RSA is a popular option for secure data transmission. It leverages a robust algorithm for data scrambling. Advanced Encryption Standard (AES) Today the advanced encryption standard (AES) is extensively used and supported in both hardware and software in today's encryption. There have been no realistic cryptanalytic attacks against AES identified so far. Additionally, AES includes built-in key length flexibility, which provides some 'future-proofing' against advancements in the capacity to execute exhaustive key searches. Twofish In terms of encryption techniques, Twofish is regarded as a highly safe solution. Any encryption standard that employs a key length of 128 bits or more is theoretically immune to brute force attacks. This is where Twofish comes into play. Twofish is vulnerable to side channel attacks because it employs "pre-computed key-dependent S-boxes." This is because the tables have already been calculated. Creating these tables key-dependent, on the other hand, helps to limit that danger. Conclusion Cybercrimes constantly evolve, compelling security experts to come up with new strategies and methods. Irrespective of the size or industry, every business can benefit from taking extra steps to protect its data. Whether it is about protecting your email communication or storing data, you should be sure that you include encryption in your lineup of security tools. FAQ What are public and private keys? Both public and private keys are employed in asymmetric encryption. A public key is a key that is known by everyone and is not a secret. Anyone can use it to encrypt data. But, the data can only be decrypted by the user who has access to the private decryption key. Is it possible to break encryption? Yes, in a word. While decrypting encrypted data would require a significant amount of processing power and expertise, it is still possible. It is, however, extremely unusual due to the resources needed. Is it safe to use encryption? Encryption is extremely secure. The majority of encryption standards provide a degree of protection that is unrivaled by other cybersecurity precautions. The U.S. National Security Agency (NSA) has authorized the AES 256 encryption standard due to its fantastic dependability.

Read More
Data Science

Man Vs. Machine: Peaking into the Future of Artificial Intelligence

Article | March 15, 2021

Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI. As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end? It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories: A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc. However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines! Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively. B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today. While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality. C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’. It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task. If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak. While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI. D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these. Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy. In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.

Read More
Business Intelligence, Big Data Management, Big Data

Implementing Data Analytics: Emerging Big Data Tools in 2023

Article | July 10, 2023

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

Read More
Business Intelligence, Big Data Management, Big Data

Leveraging Big Data for Competitive Advantage: Benefits

Article | July 18, 2023

Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker. While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart. In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well. Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets. He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years. After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years. He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him. Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science. He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology. In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR. During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models. In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years. At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases. Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network. Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product. Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence. His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career. Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.

Read More
Business Intelligence, Big Data Management, Big Data

Harnessing the Power of Big Data: Events to attend in 2023

Article | May 15, 2023

Almost every day, we come across some news about data breaches and cyber-attacks, which has forced us to discuss and debate more about the importance of data and how we can protect it. Some of the most significant data breaches of 2021 and 2022 were the Microsoft Software data breach (2021), Facebook data breach (2021), Bureau Veritas cyberattack (2021), Gloucester Council Cyberattack (2022), and many more. Cyber-attacks were rated as the fifth-top risk in 2020 by the World Economic Forum. The COVID-19 pandemic has increased cybercrime by 600% compared to the pre-pandemic period. According to Accenture, 43% of cyber-attacks target small businesses, but only 14% are prepared to defend themselves. As per Cybersecurity Ventures, cybercrime will cost businesses worldwide $10.5 trillion annually by 2025 up from $3 trillion in 2015. Unfortunately, we still don’t have a concrete solution, which can be called a one-size-fits-all approach for preventing security breaches or even handling them when they happen. However, there are numerous methods for minimizing data exposure. Data encryption and regular data back-ups have become two of the most effective and widely used methods for protecting against data exposure. Importance of Encryption The facts and figures mentioned above highlight the growing importance of encryption for data security. No business, regardless of size, is immune to the risk of a data breach. Encryption has become the need of the hour because it is considered the last line of defense. Many applications and websites depend upon user passwords and password verification software to access sensitive information. Apart from knowing how to generate a safe password, users have minimal options to encrypt their password. This is why they use a password manager to keep their passwords secure. A good password manager must use strong encryption to protect what is a gold mine of data. Businesses can choose an encryption type as per their preference and requirements. There are two types of encryption for scrambling or masking data. They are as follows: Symmetric Encryption Symmetric encryption is the simplest way to protect data from hackers. It has just one key, and everyone uses it to encrypt and decrypt data. For example, you encrypt a file and send it to your manager, who uses the same key to unlock or decrypt it. Asymmetric Encryption Asymmetric encryption encrypts using two keys. The public key is used for encryption, whereas the private key is used for decryption. For example, the private key can be used to encrypt a file, but only the manager can use the private key to decrypt it. Reasons why encrypting your data is crucial Encryption has evolved as a critical component in securing data from malicious attacks of any kind. However, some organizations are still hesitant about encryption because they are unaware of the benefits. So let’s look at the top reasons why businesses should encrypt their data. Encryption is the Last Line of Defense When we talk about cyberattacks, companies are often helpless when it comes to preventing them. In this case, encryption acts as a protector making it difficult to encrypt data without the decryption key. This is one of the significant implications of encryption, and hence we call it the last line of defense. Encryption is Cheap to Implement From smartphones to Microsoft Windows, almost every device, software, and operating system today has encryption technology. Also, there are many encryption programs available for free download, programs like LastPass, TunnelBear, HTTPS and others. Encryption protects data on the go One of the biggest data security threats companies face is when data is on the move. It means portable devices, whether mobile phones, USBs, laptops, or tablets containing sensitive data, move outside a company’s security network. A misplaced USB, a laptop left unsupervised, or a mobile phone forgotten in a coffee shop can sometimes be disastrous. Encryption makes sure that if a device is lost or stolen, its data can't be read or misused by anyone who doesn't have a key to decrypt it. Encryption Algorithms to Secure Your Business Network & How Encryption Works Various encryption algorithms help secure your business networks. But before we dive into the details of encryption algorithms, it is important to know the workings of encryption. How Encryption Works Unencrypted information or data, such as blogs like the one you are reading, is written in plaintext. At its core, data encryption employs an encryption algorithm to distort or mask plaintext, resulting in “ciphertext”, which humans interpret as alphanumeric nonsense. An encryption algorithm is incomplete and cannot convert plaintext to ciphertext and vice-versa. Encryption Algorithms to Secure Your Business Data As data security threats have become more sophisticated and aggressive, maintaining online security has become critical. Therefore, modern encryption has grown more complex to protect private data. Different types of encryption algorithms can help you enhance your data encryption strategy. If required, you can create your own algorithm. However, there are a few standard encryption algorithms that you can consider. Data Encryption Standard (DES) The data encryption standard is an older symmetric-key method of encrypting data that was utilized as a standard method by the United States government. But it was withdrawn later as it was not considered secure enough for many modern applications. A DES key has 64 binary digits (also known as bits), 56 of which are randomly generated by the algorithm. The other eight are utilized for the detection of errors. People who use DES know the encryption algorithm, but unauthorized entities do not have the decryption key. Data encryption standard is insecure because the 56-bit key is too small. Triple Data Encryption Standard (Triple DES) The triple data encryption standard (also called Triple DES, or TDES or 3DES) is the newer and safer version of the data encryption standard. There are two kinds of triple DES: two-key and three-key, based on the number of generated keys. Triple DES runs DES three times; the data is encrypted, decrypted, and then again encrypted before it is sent to the receiving party. Rivest-Shamir-Adleman (RSA) Popularly known as RSA, it is named after its creators, Ron Rivest, Adi Shamir, and Len Adelman. RSA is an asymmetric encryption algorithm primarily utilized to share data over insecure networks. RSA is a popular option for secure data transmission. It leverages a robust algorithm for data scrambling. Advanced Encryption Standard (AES) Today the advanced encryption standard (AES) is extensively used and supported in both hardware and software in today's encryption. There have been no realistic cryptanalytic attacks against AES identified so far. Additionally, AES includes built-in key length flexibility, which provides some 'future-proofing' against advancements in the capacity to execute exhaustive key searches. Twofish In terms of encryption techniques, Twofish is regarded as a highly safe solution. Any encryption standard that employs a key length of 128 bits or more is theoretically immune to brute force attacks. This is where Twofish comes into play. Twofish is vulnerable to side channel attacks because it employs "pre-computed key-dependent S-boxes." This is because the tables have already been calculated. Creating these tables key-dependent, on the other hand, helps to limit that danger. Conclusion Cybercrimes constantly evolve, compelling security experts to come up with new strategies and methods. Irrespective of the size or industry, every business can benefit from taking extra steps to protect its data. Whether it is about protecting your email communication or storing data, you should be sure that you include encryption in your lineup of security tools. FAQ What are public and private keys? Both public and private keys are employed in asymmetric encryption. A public key is a key that is known by everyone and is not a secret. Anyone can use it to encrypt data. But, the data can only be decrypted by the user who has access to the private decryption key. Is it possible to break encryption? Yes, in a word. While decrypting encrypted data would require a significant amount of processing power and expertise, it is still possible. It is, however, extremely unusual due to the resources needed. Is it safe to use encryption? Encryption is extremely secure. The majority of encryption standards provide a degree of protection that is unrivaled by other cybersecurity precautions. The U.S. National Security Agency (NSA) has authorized the AES 256 encryption standard due to its fantastic dependability.

Read More
Data Science

Man Vs. Machine: Peaking into the Future of Artificial Intelligence

Article | March 15, 2021

Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI. As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end? It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories: A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc. However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines! Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively. B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today. While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality. C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’. It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task. If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak. While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI. D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these. Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy. In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.

Read More
MORE ARTICLES

Spotlight

Mev, Llc

MEV® is a NYC-based web technologies provider servicing global business and technology initiatives. We manage and create both modular and full spectrum projects for Medium and Enterprise level businesses at custom rates. We specialize in complex web and mobile software development as well as data analysis and visualization tools.

Related News

Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

Read More

Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

Read More

Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

Read More

Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

Read More

Events