Five Important Trends in Big Data Analytics
Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business
Opinions expressed by Entrepreneur contributors are their own.
You're reading Entrepreneur India, an international franchise of Entrepreneur Media.
New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is not new. What's new is the extent to which big data analytics is being implemented by organizations worldwide and the growing number of professionals who are now specializing in the field. Today, thanks to skyrocketing computing power, accelerated digitization, and widespread data migration to the cloud, we are discovering the true potential of big data analytics. While it provides a goldmine of opportunities for businesses, big data trends, like all other technologies, are ever-changing. Here are five trends to watch out for in 2023.
1. The Evolution of Streaming Analytics
Streaming analytics is a new trend in data analysis that has become more popular in recent years. It is based on the idea that real-time data can be analyzed as it arrives rather than waiting for all of the data to be collected.
Two major factors have contributed to the rise of streaming analytics. First, more businesses are moving their operations online where a significant amount of business is being conducted. It means companies now have more real-time access to data than ever before. Second, the amount of data produced has increased exponentially over the past decade. Also playing a role is the growing list of technical tools for working with real-time analytics, which includes Spark, Kafka, Kinesis, and HubSpot Operation Hub.
2. The Rise of AI-Powered Big Data Analytics
The future will be driven by artificial intelligence (AI), and the next generation of big data analytics, too, is AI-driven, which brings several factors into play. For instance, technology has grown, and memory cost has decreased significantly. It enables storage and processing to meet various use cases that were unthinkable a decade ago. Next is the speed at which AI tools process data. Parallel processing, Graphics Processing Units (GPUs), on-demand processing power spin-up tools, and faster and more accessible data movement between various phases have all opened the door for AI use cases.
Furthermore, AI has increased the precision with which it can interpret data. AI has expanded algorithm and library options, improved efficiencies, and enabled the combination of algorithm pipelines to yield better predictions and results.
The level of automation has also expanded. After the necessary data models and hyperparameters have been defined, automation is prevalent from the collection of data to feeding it to the data pipeline, as well as triggering the machine learning (ML) algorithm from start to final analytic dashboard or notifications making the whole process error-free and speedy.
3. The Expansion of Edge Computing
In the simplest terms, edge computing is the processing of data at the network's or device's edge rather than in a centralized location. The increasing popularity of Internet of Things (IoT) devices drives growth in edge computing. With so many connected devices, managing all the data from one centralized location is difficult. As a result, many small businesses are building third-party networks capable of handling edge computing. With an increasing need to retrieve analytics faster and in real-time, edge computing and analytics are becoming the need of the hour. Most cloud vendors have IoT edge analytics tools, and certain IoT platforms, like PTC Thingworx, are popular for such use cases.
4. Significant Dependence on Cloud Storage
Although it has some disadvantages, cloud storage is a practical option for storing large amounts of data. When dealing with vast or particularly sensitive data, it's not always the ideal option. Furthermore, it can be challenging to keep track of high quantities of cloud storage accounts. However, one of the biggest developments in big data is still cloud storage. Nowadays, people are more concerned with who has access to their information rather than where it's kept. "Noisy neighbor" is a common concern where customers are worried about their data being accessed by one of their competitors or a bad actor.
Still, the industry has some way to go before such concerns are put to ease. The regulations provide challenges that must be considered when companies design how and where their data is stored. Shared Software-as-a-Service (SaaS) companies are thinking about becoming cloud-agnostic, meaning the coding and data can live in the customers' cloud sans data privacy concerns.
5. DataOps for Data
DataOps has brought structure to the ever-growing madness of data management and enables an organization to recognize the ROI that has gone into the data paradigm. The SaaS wave brought DataOps into the spotlight.
Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business. There are now several aspects of data management within a company, and the list keeps growing. These include data volume, use cases, frequency of updates, data model changes, security and privacy tests, the addition of data sources and consumers, and customizable analytics. Data management has become the responsibility of the organization, which influences the data garnered in every conceivable way.
Valued at $271.83 billion (USD) in 2022, the global big data analytics market is projected to reach $655.53 billion by 2029. Together, these trends will be game-changers for the big data analytics field, and their impacts will be sizable.