Few topics are as discussed or as important as big data. As I’ve noted in the past, the amount of data being generated is so enormous that the modifier “big” is a totally inadequate — maybe even laughable — modifier. Fortunately, with ever-improving analytics, data size is not as important as it was in the past. Nevertheless, “big data” is a term that has demonstrated what marketers call “stickiness” over the past decade. Tech writer Sumana Bhattacharya explains, “Big data has grown more popular for businesses that seek to better understand their consumers and operational possibilities, especially with technologies like cloud computing, Internet of Things (IoT) devices, and streaming.”[1] Data lies at the very heart of the Digital Age and data is carving the path to the future for most industries.
Big Data Trends
Big Data Becomes a National Security Issue. Everyone is aware of growing tensions between China and the West. One area of growing concern is how big data is collected, stored, and used. Increasingly, authoritarian governments, like China’s, are using personal and other data to control both their civilian populations and companies operating within their borders. In 2022, journalist Devin Partida predicts, “Governments will regulate big data more closely as it becomes a larger industry. This trend has already begun to take shape with laws like the GDPR and China’s Data Security Law, but government interest will expand in 2022. China’s recently announced plan to triple its big data industry by 2025 is a sign of things to come. Big data will become a foreign policy issue as more governments take steps to regulate the industry and support their local sectors. Nations may start to draw lines and issue digital trade restrictions relating to the industry. Operations will have to navigate increasingly complex regulatory issues as a result.”[2]
Data Fabric and Data Mesh Improve Data Availability and Security. Gartner analysts insist the top trend in big data is the rise of data fabric. They explain, “Data fabric provides a flexible, resilient integration of data sources across platforms and business users, making data available everywhere it’s needed regardless where the data lives. Data fabric can use analytics to learn and actively recommend where data should be used and changed. This can reduce data management efforts by up to 70%.”[3] Journalist Sayantani Sanyal adds, “As data becomes increasingly complex and digital business accelerates, data fabric will become the architecture that will support composable data and analytics in its various forms. Data fabric reduces the time for integration by 30%, and for development by 70% since the technology designs will draw on the ability to reuse and combine different data integration styles.”[4]
Journalist Jelani Harper writes, “Conceptually, a data mesh is an architectural approach that is both similar and assistive to an enterprise data fabric. … A data mesh builds on this distributed architectural approach by including domain specific information about data’s creation, storage, and cataloging so it’s applicable to users across domains.”[5] Gartner analysts insist a data mesh can be particularly useful in a cybersecurity setting. They explain, “[A] cybersecurity mesh is a flexible, composable architecture that integrates widely distributed and disparate security services. Cybersecurity mesh enables best-of-breed, stand-alone security solutions to work together to improve overall security while moving control points closer to the assets they’re designed to protect. It can quickly and reliably verify identity, context and policy adherence across cloud and non-cloud environments.”
Real-time Data Helps Make Sense of a Volatile World. Occasionally, the business world faces a paradigm shift that makes reliance on historical data problematic. The pandemic created that kind of paradigm shift for many industries. As a result, real-time data reflecting changing conditions has grown in importance as has predictive analytics. Arti Chaudhary, a journalist and content analyst, explains, “With the help of statistical tools and techniques leveraging past and existing data, predictive analytics predict future trends and forecasts. With predictive analytics, companies can make insightful decisions for immense growth and progress. Therefore, predictive analytics is one of the top big data trends in 2022.”[6] Journalist and data scientist Daniel D. Gutierrez (@AMULETAnalytics) adds, “Predictive analytics will drive new, emerging use cases around the next generation of digital applications. The technology will become more immersive and embedded, where predictive analytics capabilities will be blended seamlessly into the systems and applications with which we interact.”[7]
Rising Importance of Small Data. Business consultant and futurist Bernard Marr (@BernardMarr) notes, “The concept of ‘small data’ has emerged as a paradigm to facilitate fast, cognitive analysis of the most vital data in situations where time, bandwidth, or energy expenditure are of the essence. It’s closely linked to the concept of edge computing. Self-driving cars, for example, cannot rely on being able to send and receive data from a centralized cloud server when trying to avoid a traffic collision in an emergency situation. TinyML refers to machine learning algorithms designed to take up as little space as possible so they can run on low-powered hardware, close to where the action is. In 2022 we will see it appearing in an increasing number of embedded systems — everything from wearables to home appliances, cars, industrial equipment, and agricultural machinery, making them all smarter and more useful.”[8] Sanyal adds, “Large enterprises can save massive amounts of time by just evaluating the most vital data instead of entire lots of the generated data. This can be efficiently achieved if businesses shift from big data to small data. It can enable more streamlined, fast, and bandwidth-sparring innovations to take place.”
Concluding Thoughts
Obviously, a lot more could be said about trends in the big data arena. What I hope to convey, however, is that, far from being a tired topic of discussion, big data remains a vibrant area in which advances are constantly being made. Bhattacharya concludes, “The true value of big data developments will not be harnessed until and unless it can be well-utilized to transform into actionable data (a usable form of data), as businesses realize that collecting more and more data from various sources will not be of much significance until and unless it can be well-utilized to transform into actionable data.” Making data actionable generally falls under the subject of data science — a subject I will discuss in a subsequent article.
Footnotes
[1] Sumana Bhattacharya, “Top Big Data Trends for Data Scientists,” Analytics Insight, 11 August 2021.
[2] Devin Partida, “5 Big Data Disruptions Coming in 2022,” Dataconomy, 13 December 2021.
[3] Staff, “Gartner Top Strategic Technology Trends for 2022,” Gartner, 2021.
[4] Sayantani Sanyal, “The Evolution of Big Data Analytics in 2022: Top 10 Hidden Trends,” Analytics Insight, 13 December 2021.
[5] Jelani Harper, “2022 Trends in Big Data: The Data Marketplace Evolution,” insideBIGDATA, 7 December 2021.
[6] Arti Chaudhary, “Top 10 Big Data Trends Will Drive the Digital World in 2022,” Analytics Insight, 22 December 2021.
[7] Daniel D. Gutierrez, “Big Data Industry Predictions for 2022,” insideBIGDATA, 15 December 2021.
[8] Bernard Marr, “The 5 Biggest Data Science Trends in 2022,” Forbes, 4 October 2021.
