Big Data has been around for quite some time now, and it will only grow in prominence in 2023 and beyond. In this digital world, more data is being generated every second. This humongous amount of data is of no use unless analyzed to find trends, patterns and arrive at conclusions.
In association with artificial intelligence and other technologies, big data is fueling what is called the Fourth Industrial Revolution. It is one of the most powerful technology trends in recent times and reshaping numerous business processes across the globe. Global Big Data market revenue is slated to hit USD 103 billion by 2027, as per the DataToBiz report.
The year 2023 is around the corner, and Big Data Analytics will only become bigger. Let’s have a look at some of the Big Data Analytics and Trends for 2023.
Big Data Analytics Trends
● Driverless Technology
Although fully autonomous cars are still far from becoming a reality, there has been significant progress in this segment. For example, Apple did more testing on their self-driving cars, and the disengagement rates improved from 8.35 disengagements per 1000 miles in 2019 to 6.91 disengagements per 1000 miles in 2020.
With accurate analytic tools, the enormous traffic of Big Data will catapult the progress toward having fully autonomous vehicles in the future.
● Natural Language Processing(NLP)
NLP is a part of Artificial Intelligence and is used for communication between humans and computers. The technology is meant to understand and read the human language and is based on Machine Learning. NLP requires algorithms to obtain the data from each sentence by applying grammar rules. Syntactic Analysis & Semantic Analysis are the two techniques that are used in NLP.
Syntactic analysis takes care of grammar and sentence structure, whereas semantic analysis handles the meaning of data or text.
● Edge Computing
Edge processing is all about running processes and moving those processes to a local system, which can be a user’s device, IoT device, or server. The technology brings computation to a network’s edge, reducing the long-distance communication between a customer and a server. Big Data augments edge computing by boosting data streaming, including real-time data streaming and processing. The technology enables the device to respond immediately.
Edge computing is an efficient way to process massive data by consuming less bandwidth. It reduces software costs for organizations and helps run software at remote locations.
● Data as a Service
Data is stored in data stores, from where specific applications access it. When Software as a Service(SaaS) was popular, Data as a Service had just made a beginning. Likewise, SaaS, Data as a Service, uses cloud technology to give users and applications on-demand access to information without depending on where the users or applications are.
Data as a Service will be trending in 2023 and beyond. It will make the task of analysts to obtain data for business analysis and for businesses to share data, easier.
Data Fabric is an assortment of services and architecture which provide consistent functionality across different endpoints that span multiple clouds. It is a powerful architecture that creates a common data management practice and practicality that can be scaled across a number of on-premises cloud and edge devices.
Data Fabric optimizes the use of data within a company and cuts design, deployment, and data management tasks by as much as 70%. With the pace of business constantly increasing, data becomes more complex, and more & more organizations will use this technology, as it is easy to use and repurpose. It can be combined with other technologies and data hub skills.
Data Governance is ensuring data quality and securely sharing it across an organization. The technology also ensures compliance with laws related to Data Security and privacy.
Without a data governance program, there are chances of poor quality data, compliance violations, penalties, issues in finding the right data, missed opportunities, delays in analyzing, and poorly trained AI models. Providing Data to all individuals of the organization, irrespective of their technical expertise, embeds data into every aspect of decision-making and creates trust among users.
In this era of predictive analytics, augmented analytics is one of the trends you are most likely to see. Augmented Analytics uses machine learning & Natural Language Processing to derive insights from it. Without Augmented Analytics, it would require a Data Scientist or a specialist to do so.
An augmented analytics tool can help business users better understand their business context and quickly derive insights. Moreover, Augmented Analytics helps analysts, and advanced users, perform more thorough analysis and data preparation tasks without having deep analytical expertise
Data Analytics automation is all about automating analytical tasks done by computer systems or processes, minimizing human involvement. The automation of data analytics can significantly impact the productivity of organizations.
The technology has led to Analytic Process Automation (APA), known to help a lot in getting predictive and prescriptive insights for faster gains and higher ROI. It will increase productivity and data utilization. One of the notable features of the tool is it can search categorical data to come up with a set of relevant features. IBM Analytics, Apache Hadoop, SAP, and IBM Analytics are some of the popular Data Analytics software.
On a Closing Note…
There is more to data than just stored information. As the world increasingly becomes digital, with the internet reaching all corners of the world, more data will be generated with every ticking second. These tonnes of data, when processed and analyzed, can give insights that could have never been realized.
As a result, Big Data Analytics is attracting the attention of all companies, no matter small, mid-sized or big. We are already seeing an increasing demand for Data Scientists and software professionals specializing in Data Analytics. Big Data Analytics is only going to become bigger in 2023 and beyond.
Frequently Asked Questions (FAQs)
Big Data is huge sets of complex data, which are of different types, and difficult to analyze manually.
Big Data Analytics is a procedure of analyzing complex sets of data with the help of software tools to gain actionable insights into it.
All organizations, big or small, can use Big Data to understand their target audience and make decisions.
Victor Ortiz is a Content Marketer with GoodFirms. He likes to read & blog on technology-related topics and is passionate about traveling, exploring new places, and listening to music.