{"id":31515,"date":"2023-02-01T06:00:00","date_gmt":"2023-02-01T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=31515"},"modified":"2023-02-02T09:24:57","modified_gmt":"2023-02-02T17:24:57","slug":"heard-on-the-street-2-1-2023","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/","title":{"rendered":"Heard on the Street \u2013 2\/1\/2023"},"content":{"rendered":"
\n
\"\"<\/figure><\/div>\n\n\n

Welcome to insideBIGDATA\u2019s \u201cHeard on the Street\u201d round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning. Enjoy!<\/p>\n\n\n\n

The Power of Data and the Airline Industry.<\/strong> Commentary by  CEO and co-founder of DataScalp<\/a>, Dwight Harris Jr. <\/p>\n\n\n\n

\n

We have seen the power that data has to make large-scale changes in business operations. It is imperative that the next area that needs this large-scale change is the airline industry. For decades, customers have been at the mercy of major airlines. From massive corporations like United Airlines and JetBlue to small carriers like Spirit and Frontier, the belief in delivering a positive travel experience to customers has fallen to the wayside. Rich crowdsourced data is the key to drive a major overhaul in flight experience. Through data collected by those who are actually experiencing travel issues like delays, cancellations, lost baggage, or slow refund time, these airlines can fully understand where they are lacking. Airlines must pay attention to what customers are saying in these channels and adjust their operations accordingly. Otherwise, a competitor is just one click away.<\/p>\n<\/blockquote>\n\n\n\n

The importance of data quality in benefits.<\/strong> Commentary by Peter Nagel, VP of Engineering, Noyo<\/p>\n\n\n\n

\n

The benefits industry brings together a wide range of business models in varying states of modernization. As a result, data quality issues are a persistent and pervasive problem. With EDI still the most common method for collecting enrollment and eligibility data, many companies have learned how to work around its limitations. That\u2019s now beginning to change with growing adoption of API-enabled data exchange. Building ETL capabilities that clean and standardize benefits data on top will help address the quality issues that impact everyone in the industry \u2014 drastically reducing coverage-impacting issues and opening up new pathways for innovation.<\/p>\n<\/blockquote>\n\n\n\n

Intersection of AI and Staffing: How to Scale Your Business Without Adding Headcount<\/strong>. Commentary by Rebecca Jones, general manager of Mosaicx<\/a><\/p>\n\n\n\n

\n

Operating a business is a balancing act. Businesses are navigating a complex labor market<\/a> that impacts how they scale their businesses and optimize their existing resources. AI-enabled tools allow business leaders to introduce automation to reduce the burden on understaffed teams and identify growth opportunities. Intelligent virtual agents and other AI tools automate internal and external operations and communications. For example, these conversational tools can automate shift scheduling, reporting, and payroll discrepancies, and they can also identify opportunities to generate revenue. Companies that turn to these solutions reduce the burden on team members, which improves employee retention, while also saving the company money and scaling services.<\/p>\n<\/blockquote>\n\n\n\n

Hybrid cloud workflows remain key. <\/strong>Commentary by David Feller, Vice President of Product Management and Solutions Engineering, Spectra Logic<\/a><\/p>\n\n\n\n

\n

Bandwidth, access and cost concerns mean that very large and active data lakes will remain on-premises for sectors such as genomics, life sciences, university institutes, and government research, that generate and store massive volumes of data. In other environments, hybrid cloud workflows remain key. The media and entertainment industry leads the way here, in part due to its distributed workflows and the huge public cloud investment made by AWS to support this market. And with data backup rapidly transitioning to cloud interfaces (S3) and leveraging the combination of cloud compute, databases, and long-term storage, on-premises solutions will need to adapt for cloud interoperability.<\/p>\n<\/blockquote>\n\n\n\n

Apache Arrow Makes Data Move, Which Makes The World Move<\/strong>. Commentary by  Alex Merced, Developer Advocate at Dremio<\/a><\/p>\n\n\n\n

\n

Whether a for-profit, nonprofit or any other type of entity, an organization\u2019s use of data will make or break its growth and impact. The Apache Arrow project has transformed the way our applications talk to data by creating standards on how to represent that data in-memory and transport it between applications. When I use a tool that leverages Apache Arrow not only will I see better performance, but I also know that the room for interoperability with other tools is likely\u2014making it easier for me to create the workflows I need with the tools right for the job. Performance and interoperability are becoming the cornerstone of what open-source projects like Apache Arrow, Apache Parquet and Apache Iceberg bring to the modern data stack.<\/p>\n<\/blockquote>\n\n\n\n

Vertical Industries Slow to Innovate in the Cloud<\/strong>. Commentary by Jeff Robbins, Founder and CEO, LiveData<\/p>\n\n\n\n

\n

Several risks are facing businesses today including the threat imposed on specific vertical industries if they don\u2019t innovate in the cloud to take advantage of the benefits and solve long-standing problems with on-premises computing. Looking to 2023, some highly regulated vertical industries will continue to experience dynamic tensions inherent in a high-performance cloud-native data strategy. However, few verticals are impacted like healthcare. For example, enabling hospital decision-makers with current and accurate data while navigating the many security and privacy constraints (e.g., those imposed by HIPAA) is often still somewhat aspirational, but the trend is underway. Hospitals need to do more with the resources they have. Moving actionable analytics to the cloud gives hospital administrators the platform to improve many factors in a nurse\u2019s workplace, contributing to job satisfaction and, critically, retention. CIOs and data teams in vertical industry markets need to partner with that industry\u2019s experts who can harness broadly accepted horizontal technologies (e.g., Tableau) in a safe way for their vertical. Failing to prepare risks your organization getting left behind the curve as your peers and competitors enhance their workplaces to keep pace with the heightened expectations of today\u2019s (and tomorrow\u2019s) workforce. While cybersecurity, governance, regulatory compliance, and other facets of avoiding risk make it tempting to adopt a cloud native strategy slowly, the rapid changes in our world require some purposeful initiatives to make a real impact on what working at your organization means to your employees.<\/p>\n<\/blockquote>\n\n\n\n

Don’t Miss the Real-time Wave. <\/strong>Commentary by Gary Hagmueller, CEO of Arcion Labs<\/a><\/p>\n\n\n\n

\n

Businesses that miss the real-time wave will undoubtedly experience competitive disadvantages when compared to adopting firms. These disadvantages will grow over time. Real-time data availability is an innovation phase or wave, and as has been seen many times before, such waves tend to create material winners and losers. It\u2019s well known that adopting an innovation wave early in such a cycle can create a material separation between the fortunes of early adopters and those that adopt in the mainstream (or later). Think Walmart vs. Sears, Airbnb vs Hilton, and Netflix vs. Blockbuster. Smart technology-aware executives from the winners noted above had cemented unsurmountable leads by the time their more mainstream competitors even realized something needed to change. Aside from competitive factors, firms that fail to adopt these techniques face the risk of feeling and appearing old very quickly. In the modern data age, it\u2019s getting easier by the day to detect a data leader and a data laggard. And tolerance of the laggards will begin to decline precipitously as the leaders enhance their pace. You don\u2019t want the threat of becoming a data laggard.<\/p>\n<\/blockquote>\n\n\n\n

Regulating AI \u2013 Ensuring fair practices and integrity<\/strong>. Commentary by Alan Cross, Chief Commercial Officer, Diveplane<\/a><\/p>\n\n\n\n

\n

The provisions in the EU AI Act<\/a>, especially the all-important Article 13 requirement that users be able to understand and interpret AI, are essential for a future in which AI is accountable to its users. Regulatory requirements for auditable, explainable, interpretable technology are urgently needed to prevent unalignable \u201cblack box\u201d neural networks from being deployed on socially critical systems. For similar reasons, the goals of the US AI Bill of Rights Blueprint<\/a> are important. In particular, the fourth principle asserts that users should be able to understand the automated systems that affect their lives, and this is of critical importance to protecting the public from the real risks that uninterpretable<\/em> AI will become unaligned<\/em> AI. However, Diveplane has some concerns about whether the Blueprint is the best path forward for AI regulation in the United States. We believe that a more robust regulatory structure will be required to promote enterprise best practices and protect consumers. Companies need the reliability afforded by clear and objective regulations that can allow them the confidence to invest resources in innovative systems. Consumers need the security of legal protection for their data, privacy, and safety. We believe the American public would be better off if the United States created a comprehensive, balanced framework that both businesses and consumers could rely on. We hope that the next step forward in the development of the Blueprint for an AI Bill of Rights will work toward that.<\/p>\n<\/blockquote>\n\n\n\n

AI \u2013 The Future of eCommerce Accounting<\/strong>. Commentary by Rohan Thambrahalli, CEO and founder of DimeTyd<\/a><\/p>\n\n\n\n

\n

Amazon is a primary channel for B2C and B2B vendors, yet the e-commerce giant\u2019s accounting systems can result in significant lost profit and inefficiency. Amazon\u2019s accounting is complicated, and there are virtually no software platforms designed for vendors to manage the volume and complexity of transactions. With up to 5% loss on the line with every $100,000 generated through Amazon, sellers and vendors need to catch up and meet Amazon with the same advanced technology. Amazon has long recognized the benefits <\/a>of artificial intelligence (AI), machine learning and related technologies, but vendors have struggled to match its complexity for true clarity in invoicing and reconciliation. Advanced accounting solutions leveraging AI and automation can match computation to computation, including creating accurate invoices, tracking shipping, and automatically determining what should and should not be invoiced. Ultimately, Amazon vendors are turning to accounting solutions featuring AI technology that is able to process millions of data points \u2013 and play a vital role in making the accounting and reconciliation process error-free, cost-efficient, and logistically seamless.<\/p>\n<\/blockquote>\n\n\n\n

How will AI implementation grow in 2023?<\/strong> Commentary by Ganesh Shankar, CEO and Co- Founder of RFPIO<\/a> <\/p>\n\n\n\n

\n

AI shows immense promise in 2023. As we finally get away from the pandemic haze, companies are buckling down and refocusing on how to do more with less \u2013 especially amid a recession. We are confident knowing that AI implementation has made a new mark for the tech industry trend, but some businesses are curious where AI is going. Factors such as advancements in AI research and development, increased access to data sets and more robust computing power, as well as changing economic and regulatory environments will all impact how far AI reaches in 2023. More businesses will invest in AI implementation due to its popularity and success as companies shift to a permanent, hybrid workforce. Due to this, AI will become more accessible to employees globally. Especially in industries like healthcare, financial services, and manufacturing, AI adoption can create more efficiency. Businesses will learn to utilize AI more and will also encourage their employees to embrace it daily. AI is set to see significant growth across computer vision, natural language processing and reinforcement learning in the next few years and will forever impact how employees work. By embracing AI, the future of work will evolve, and employees will be able to be more efficient and up-level their skills and knowledge base.<\/p>\n<\/blockquote>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n

Join us on LinkedIn: https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n

Join us on Facebook: https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Welcome to insideBIGDATA\u2019s \u201cHeard on the Street\u201d round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.<\/p>\n","protected":false},"author":37,"featured_media":26962,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,65,115,62,63,64,66,182,68,1054,87,180,67,56,1],"tags":[437,280,133,277,95],"acf":[],"yoast_head":"\nHeard on the Street \u2013 2\/1\/2023 - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Heard on the Street \u2013 2\/1\/2023 - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Welcome to insideBIGDATA\u2019s \u201cHeard on the Street\u201d round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2023-02-01T14:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-02-02T17:24:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/08\/Heard-on-the-Street.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"199\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/\",\"name\":\"Heard on the Street \u2013 2\/1\/2023 - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-02-01T14:00:00+00:00\",\"dateModified\":\"2023-02-02T17:24:57+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Heard on the Street \u2013 2\/1\/2023\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\",\"name\":\"Daniel Gutierrez\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"caption\":\"Daniel Gutierrez\"},\"description\":\"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Heard on the Street \u2013 2\/1\/2023 - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/","og_locale":"en_US","og_type":"article","og_title":"Heard on the Street \u2013 2\/1\/2023 - insideBIGDATA","og_description":"Welcome to insideBIGDATA\u2019s \u201cHeard on the Street\u201d round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.","og_url":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2023-02-01T14:00:00+00:00","article_modified_time":"2023-02-02T17:24:57+00:00","og_image":[{"width":300,"height":199,"url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/08\/Heard-on-the-Street.jpg","type":"image\/jpeg"}],"author":"Daniel Gutierrez","twitter_card":"summary_large_image","twitter_creator":"@AMULETAnalytics","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Daniel Gutierrez","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/","url":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/","name":"Heard on the Street \u2013 2\/1\/2023 - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2023-02-01T14:00:00+00:00","dateModified":"2023-02-02T17:24:57+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2023\/02\/01\/heard-on-the-street-2-1-2023\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"Heard on the Street \u2013 2\/1\/2023"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed","name":"Daniel Gutierrez","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","caption":"Daniel Gutierrez"},"description":"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/08\/Heard-on-the-Street.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-8cj","jetpack-related-posts":[{"id":19687,"url":"https:\/\/insidebigdata.com\/2018\/01\/04\/top-10-2017-big-data-white-papers-data-science-machine-learning-ai-deep-learning\/","url_meta":{"origin":31515,"position":0},"title":"Top 10 2017 Big Data White Papers: Data Science, Machine Learning, AI, Deep Learning & More","date":"January 4, 2018","format":false,"excerpt":"In this continuing regular feature, we give all our valued readers an annual heads-up for the top 10 most downloaded articles appearing on insideBIGDATA. Over the past several months, we\u2019ve heard from many members of our audience that this feature will enable them to catch up with important news and\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":27288,"url":"https:\/\/insidebigdata.com\/2021\/10\/04\/top-10-insidebigdata-articles-for-september-2021\/","url_meta":{"origin":31515,"position":1},"title":"TOP 10 insideBIGDATA Articles for September 2021","date":"October 4, 2021","format":false,"excerpt":"In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we\u2019ve heard from many of our followers that this feature will enable them to catch up with important news and features\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/05\/TOP10_iBD_new.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":26367,"url":"https:\/\/insidebigdata.com\/2021\/06\/03\/top-10-insidebigdata-articles-for-may-2021\/","url_meta":{"origin":31515,"position":2},"title":"TOP 10 insideBIGDATA Articles for May 2021","date":"June 3, 2021","format":false,"excerpt":"In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we\u2019ve heard from many of our followers that this feature will enable them to catch up with important news and features\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/05\/TOP10_iBD_new.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":25900,"url":"https:\/\/insidebigdata.com\/2021\/04\/07\/top-10-insidebigdata-articles-for-march-2021\/","url_meta":{"origin":31515,"position":3},"title":"TOP 10 insideBIGDATA Articles for March 2021","date":"April 7, 2021","format":false,"excerpt":"In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we\u2019ve heard from many of our followers that this feature will enable them to catch up with important news and features\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/05\/TOP10_iBD_new.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":32006,"url":"https:\/\/insidebigdata.com\/2023\/04\/03\/top-10-insidebigdata-articles-for-march-2023\/","url_meta":{"origin":31515,"position":4},"title":"TOP 10 insideBIGDATA Articles for March 2023","date":"April 3, 2023","format":false,"excerpt":"In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we\u2019ve heard from many of our followers that this feature will enable them to catch up with important news and features\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/05\/TOP10_iBD_new.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":31724,"url":"https:\/\/insidebigdata.com\/2023\/02\/27\/data-science-101-the-data-science-process\/","url_meta":{"origin":31515,"position":5},"title":"Data Science 101: The Data Science Process","date":"February 27, 2023","format":false,"excerpt":"Welcome to insideBIGDATA's Data Science 101 channel brining you perspectives for the topics of the day in data science, machine learning, AI and deep learning. Many of the video presentations come from my lectures for my Introduction to Data Science class I teach at UCLA Extension. In today's slide-based video\u2026","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/04\/DataScience_shutterstock_1054542323.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/31515"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=31515"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/31515\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/26962"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=31515"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=31515"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=31515"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}