Top 5 Big Data Trends for 2023 & Beyond
Almost all the companies today collect vast amounts of data from their daily operations. Data has become the driving force for the growth of businesses. Only those companies that make the best use of the available data can outpace their competition.
Data science, big data analytics and Artificial Intelligence are the 3 most dominant technology themes in the current scenario. Businesses’ reliance on data analytics for making fact-based decision making is increasing at a rapid pace. Big data helps companies of all sizes to benefit by using the data more effectively and this has led to operational efficiency, enhanced visibility to ever changing environments and customer-focused product enhancements.
Here are some of the top Big Data trends for 2023 and beyond.
1: Smarter and Scalable Artificial Intelligence
Covid-19 pandemic has drastically changed how businesses use the data. Gone are those days when Big Data analytics are privy only to those huge corporations that have vast amounts of data. Now the industry has evolved with various scalable Artificial Intelligence and machine learning methodologies that are capable of analysing even smaller data sets. This has enabled even smaller businesses to leverage the benefits of Big data and AI. These new inventions are not only scalable but can also be implemented quickly. They offer a better ROI in a shorter period of time compared to outdated AI solutions. These new solutions are faster, protect data privacy and are also easy to integrate with existing data sources. If big data and AI are properly leveraged, they can help a business to automate a lot of manual tasks.
Use Case: The global e-commerce giant Amazon created a special chain of convenience stores named Go Stores. Unlike traditional stores, these stores don’t have a checkout process. Customers can walk-in take the required products off the shelves and walk out and they will be automatically billed.
This was achieved using synthetic data generation made possible with AI and Big data analytics. Amazon was able to generate virtual shoppers using graphic software and AI, that in turn trained computer vision algorithms about how to learn what real-life shoppers select from the shelf.
2: Composed and agile data analytics
Agile data and analytics models have enabled businesses to innovate, differentiate and simultaneously grow faster than ever before. Composable data analytics offers a rich user-friendly, flexible and seamless experience by making use of data analytics, AI and machine learning solutions. Edge and composable data analytics offer various benefits like
- It promotes cooperation among the teams.
- Offers a higher level of agility and improves productivity.
- Expand the overall analytics capability of the organization
- Enable the top-brass to take educated decisions based on actionable insights generated by agile data analytics
3: Cloud computing and hybrid cloud solutions
Hybrid cloud services and cloud computing dominated most parts of data trends in 2022. Public cloud services are relatively cheaper but offer lower security compared to private cloud. Private clouds on the other hand are expensive. Hybrid cloud is formed as a result of striking a perfect balance between cost and security at the same time. This is made possible by using artificial intelligence and machine learning. Hybrid cloud is growing rapidly by offering a centralized database that is both secure and easily scalable at a reasonable price. Hybrid clouds are making it possible even for smaller organizations to tap on the cloud-based solutions.
Company: a large group of healthcare organizations based in Florida and throughout Latin America.
Problems: Frequent data outages due to weather disruptions, data loss and lack of regular backups.
Solution: Hybrid cloud was deployed both on local and IBM-cloud servers. Since data was distributed between local and cloud, it also helped in complying with data governance laws.
Results: With reduced human resources for maintain cloud services, they were able to increase service availability by 93%.
4: DataOps and data stewardship move to the fore
The way big data has been stored, processed and managed have evolved over the years and will continue to do so in the forthcoming years. The ever changing technologies requirements is what will drive the changes in Big Data Processing and how we see the data.
DataOps is one such recent innovation that primarily focuses on agile, iterative techniques for dealing with the whole life cycle of data in the company. DataOps methods and frameworks are a unique offering that will handle the company data flow right from the start to the end. It will take care of data as early as it is generated, processed and archived.
GSK is one of the leading Global healthcare companies in the world that specializes in drug discovery.
Problem: They have been in drug discovery and research for many decades now and have more than 10000 scientists working on research around the globe. It was difficult to manage and offer data analytics on all the data over the globe.
Solution: They decided to build a global data center of Excellence that will collate and deliver data from 100s of data sources to 10000+ scientists. They used StreamSets to automate data pipeline creation and to flexibly handle data drift.
Solution: This has enabled them to accelerate the time required to bring the drug to the market.
5: Edge Computing for Faster Analysis
Technology companies have made huge leaps in data analytics, but still there is the issue of potent data processing capabilities. This has led to the idea of quantum computing. Huge chunks of data can now be processed more quickly using a lower bandwidth thanks to computation. Edge computing not only enables quick processing of huge volumes of data but is also more secure and offers a rich level of data privacy. Data processed by utilizing quantum bits on a Sycamore processor, it can easily answer a problem within 200 seconds.
Company: ARC – A leading global tableware manufacturer.
Problem: They produce over 5 million pieces of glass daily across 2 production lines. They were unable to predict the energy needed and their energy cost was on the raise.
Solution: A dynamic Energy Efficiency Index(EEI) was created for each of its furnaces to forecast energy consumption and efficiency. This enabled them to foresee futuristic energy needs and also increase efficiency.
Results : They were able to reduce energy consumption by 8% in each of their furnaces while increasing the overall productivity.
The Bottom Lime:
Big data has become a key component in the world of technology. In this day and age, nearly all businesses and organizations have at least some access to large data sets that they can leverage to create new processes and products. With the rise of AI, big data is becoming more accessible than ever before.
Big Data analytics have continued to evolve and businesses globally are relying more on data analytics for their decision making and growth. Going forward companies of all sizes will embrace data analytics to stay ahead in the curve. To embed data analytics in their system, business needs a reliable technology partner who has the necessary experience and expertise in big data and TAFF is a one-stop shop for all your Data analytics needs. We are a Data analytics and AI consulting firm with deep knowledge on AI and ML based Big Data Analytics.