In the ever-evolving landscape of technology, the realm of big data stands as a dynamic force, continuously shaping the way businesses operate and innovate. With the advancement of big data and analytics (BDA), tech gurus have begun with a multitude of predictions for the year 2024 and beyond. Tech Gurus focuses on the big data trends that a business should know as we are involved more in the age of AI.
As we step into 2024, it's crucial to stay ahead of the curve by understanding the latest trends that will influence the big data sphere. Let's delve into key predictions backed by current stats and reports that are set to redefine the big data landscape.
Some Stats Supporting the Big Data Trend
Businesses have already benefited greatly from the aggressive use of big data to remove obstacles. Netflix is in the winning circle; by sifting through its massive consumer data, it retains subscribers and saves $1 billion a year (TechJury, 2021).
IoT will eventually save different businesses $1.2 trillion (American Family Insurance). When intelligent buildings, machinery, and devices handle the majority of the required modifications on their own, businesses with extremely complicated operations, several branches, departments, and hundreds of teams will stand to gain the most.
Top 10 Big Data Trends That Will Change Your Thought Process
Data Volume Will Grow and Mitigate The Clouds
The vast majority of big data specialists concur that future data generation will increase at an exponential rate. IDC predicts that by 2025, the world's atmosphere will cross 175 zettabytes in its Data Age 2025 report for Seagate. To put this sum into perspective, let's quantify it in 128GB iPads. For instance, the stack would have covered two-thirds of the distance between Earth and the Moon in 2013. This stack would have been 26 times longer by 2025.
Secondly, a plethora of IoT data analytics are generated, gathered, and disseminated globally every day by billions of embedded systems and linked devices.
Businesses will soon have the ability to develop and manage 60% of big data as they receive the chance to work with real-time big data. Individual customers, however, also contribute significantly to the growth of data. IDC predicts in the same analysis that by 2025, 6 billion people, or 75% of the global population, will engage with online data daily. Put otherwise, every eighteen seconds or so, each connected user will engage in at least one data transaction.
Digital Transformation Wave
The global currency driving technology forward is digital transformation.
It will keep expanding as IaaS companies rush to establish data centers and cover ground. The constant difficulty faced by data centers is dissipating heat, and they will do so from the depths of the ocean to the polar regions, which are the actual ends of the globe (Forbes, 2016).
Big data, machine learning, artificial intelligence (AI), and the Internet of Things (IoT) all go hand in hand with digital transformation.
It is simple to understand where the big data is coming from, as the number of IoT-connected devices is predicted to increase from 10.07 billion in 2021 to a startling 25.44 billion in 2030 (Statista, 2021).
To keep the vast amounts of big data that are coming out of the enormous data centers under control, machine learning and artificial intelligence (AI) tools will strive to store and project insights while keeping them within the realm of human comprehension.
Nevertheless, businesses still have a lot of work to do to maximize the utilization of data servers. For instance, the cost of poor data quality is estimated to be as high as $3.1 trillion annually in the US economy alone (IBM). However, how these businesses plan to handle that is still to be seen.
Fast And Real-Time Data With More Traction
Fast data enables real-time stream processing, in contrast to big data, which usually relies on Hadoop and NoSQL databases for information analysis in batch mode. Real-time big data analytics are made possible by stream processing in as little as one milliseconds. Organizations that can move quickly upon the arrival of data and make business decisions would benefit more from this.
Users have also been spoiled with fast data, becoming dependent on in-the-moment interactions. Consumers anticipate being able to access data on the go as businesses become more digitally connected, which improves the customer experience. They also want it to be customized. According to IDC's analysis mentioned above, by 2025, about 30% of global data will be real-time.
On the other hand, real-time actionable data is the crucial component that separates big data and business value. As was previously noted, big data is too complicated, multi-structured, and massive to be of any use without analysis. Through the use of analytical platforms, organizations may process data to produce reliable, standardized, and actionable information. These insights assist businesses in streamlining their operations, designing additional big data use cases, and making better business decisions.
Big Data For Traditional Businesses
Data-as-a-service (DaaS) is not a novel concept. Indeed, projections indicate that its revenue will reach $10.7 billion by the end of 2023. Additionally, you've likely come across malware in the form of downloaded movies, music, or picture files from other websites. Even if it isn't new, the notion is entirely altered by the arrival of numerous new actors, such as suppliers of product catalogs and producers of map data.
Not only specialized SaaS software solutions can enter the market: if your company has data that is valuable to third parties (hello, Cambridge Analytica), or if maintaining the data is a challenge for you, your best option may be to sell it by the megabyte, by file type, or by volume quotation.
Big Data implies that SaaS Software Solutions can simply offer simplified access, cost-effective solutions, and easy updates. It thus can transform the normal business structure of traditional businesses.
Big Data Ushered For Growth
Businesses have long benefited from analytics in the form of business intelligence solutions, and many have used it for daily operations.
However, the growth among businesses is due to the recent tendency to combine all vital corporate functions, including assistance, social media management, supply chain management, and advertising.
The enormous volume of information could come from consumer transactions, video feeds from several store locations, geographic origins, patterns of behavior on landing pages, customer surveys, and other sources. In any case, the new analytical tools ought to sift through them—even in real time—and yield insights that aren't achievable with many of the products available now.
Additionally, with current analytics approaches, retailers are already realizing up to 60% higher margins. When location-aware and location-based services are combined with the previously listed capabilities, the numbers ought to increase even further.
Big Data And Driverless Technology
Even while fully autonomous vehicles are still a ways off from becoming widely used, there have been some noteworthy advancements made in the sector. In 2020, for example, disengagement rates for Apple's self-driving cars improved from 8.35 per 1,000 miles in 2019 to 6.91 per 1,000 miles in 2020, according to more testing (9to5Mac, 2021). Waymo launched fully autonomous cars in October 2020, which users can use to request a ride (Unite.ai, 2020). Walmart began using autonomous trucks to transport goods from a Walmart Supercenter to a Walmart pickup location at the beginning of 2021 (Walmart, 2020).
The massive traffic big data could provide insights into commuter transportation management and trip generation with the appropriate analytic tools. Travelers should have the ability to more accurately estimate their travel times by tracking the locations and linking the origins and target destinations.
The robust algorithms ought to have no issue processing the data. One way to do this would be to track traffic in the city in real-time, spot crowded routes, and suggest using alternate routes.
The price of traffic is frightening. Traffic cost the US, the UK, and Germany $461 billion in losses just in 2017. That amounts to $975 for each individual (The Economist, 2018).
Although at first there was skepticism over the safety of self-driving automobiles, it seems that public opinion has shifted. According to a survey, 26% of US consumers now have a more positive opinion of autonomous delivery systems and self-driving cars than they had before the outbreak.
Big Data Stimulating the Quantum Realm
Quantum computing's potential for processing vast datasets is garnering attention. IBM predicts that quantum computing will solve certain big data problems exponentially faster, transforming industries reliant on complex data analysis.
The convergence of AI, machine learning, and big data is creating gateways to temporal dimensions ranging from femtoseconds to eons.
Even if doing an in-depth study into these quantum worlds does not immediately benefit businesses, it is quite probable that they will have a significant impact on the current frantic operations.
Privacy-Enhancing Computation on the Rise:
As data privacy concerns intensify, privacy-enhancing computation methods are gaining prominence. Computations can be performed on encrypted data using techniques like homomorphic encryption. Reports suggest that the global homomorphic encryption market will grow to $467 million by 2024.
Hyperautomation in Data Management
Hyperautomation, combining AI and automation, is set to revolutionize data management processes. According to Gartner, by 2024, organizations will lower operational costs by 30% by combining hyper-automation technologies with redesigned operational processes.
Focus on Data Democratization
Data democratization, ensuring data accessibility to non-technical users, will be a key focus. According to a report by MicroStrategy, organizations with a strong culture of data democratization are 58% more likely to exceed revenue goals.
Conclusion
As we navigate the evolving landscape of big data in 2023 and anticipate the trends of 2024, it's evident that the industry is marked by innovation, automation, and a commitment to ethical data practices. By staying informed and embracing these predictions, organizations can position themselves at the forefront of the data-driven revolution, unlocking new possibilities and ensuring a competitive edge in an increasingly data-centric world.
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