Since big data first entered the tech scene, the concept, strategy, and use cases for it have evolved significantly across different industries.
Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential.
See below to learn about current big data trends and what we can expect in the future for big data.
What’s Trending in Big Data?
- Stronger reliance on cloud storage
- The growth of data fabric technology
- Ethical customer data collection
- AI/ML-powered automation
- The evolution of vector similarity search
Read next: The Pros and Cons of Edge Computing
Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, and increased knowledge of how disparate data types can be used strategically, big data storage capacity is an issue.
In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.
Ben Gitenstein, VP of product at Qumulo, an unstructured data management platform, believes cloud migration brings storage and additional benefits to corporate big data:
“Cloud solutions are now the name of the game, particularly hybrid cloud solutions for workloads that demand multiple storage environments,” Gitenstein said. “And as data continues to inevitably grow, enterprises require the flexibility and scalability only cloud services currently provide.
“The cloud helps put accessible information into the hands of more people, and in real-time. Leveraging the cloud can help establish a new database or application, spin up a server, or build new clusters in a split second. The cloud also consolidates resources, so you don’t have to worry about buying additional servers and having IT teams install and maintain them.”
With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes.
Joe DosSantos, chief data officer at Qlik, a Fortune 500 analytics company, believes that this increased focus helps organizations achieve new real-time data goals:
“In recent years we’ve seen the rise of modern data warehouses and data lakes that leverage the cost structure, scalability and flexibility of the cloud,” DosSantos said. “When combined with data catalogs, access to more relevant and real-time data is now a reality for more and more organizations.”
Another important development that focuses on expanding the space available for digital transformation in an enterprise, data fabrics are progressively developing in the cloud and being adopted by organizations that need additional real estate and increased accessibility for their growing pools of big data.
With a data fabric architecture, they can easily store and retrieve needed data sets across distributed on-premises, cloud, and hybrid network infrastructure.
Robert Eve, former senior data management strategist at TIBCO, a top-ranked data analytics and management platform, emphasizes the importance of data fabrics in organizations that crave both real-time analytics and data democratization:
“Data fabrics — modern distributed data architectures, provide enterprises with a competitive advantage that allows them to be most impactful with their data,” Eve said. “For example, it accelerates time to value by unlocking distributed on-premises, cloud, and hybrid cloud data — no matter where it resides — and delivering it at the pace of business. The technology also democratizes data access to arm business users with all the data they need to make faster and more accurate business decisions.
“In an ever-changing regulatory landscape, data fabrics allow enterprises to embrace new data and analytics technology advancements, while ensuring the right data is securely provided. It’s also nimble and allows for organizations to embrace new data and analytics technology advancements such as data science, real-time data, and the cloud faster to stay ahead of competition.”
Data fabric technology is also trending in the world of artificial intelligence (AI) and machine learning (ML) automation for big data, primarily because the distributed design discourages the data silos that make data annotation and machine learning more difficult.
Scott Gnau, VP of data platforms at InterSystems, a data analytics and integration company, describes this functionality in smart data fabrics, explaining that data fabrics are key to the data quality necessary for automation:
“The next generation of innovation and automation must be built on strong data foundations,” Gnau said. “Emerging technologies, such as artificial intelligence and machine learning, require a large volume of current, clean, and accurate data from different business silos to function.
“Yet, seamless access across a global company’s multiple data silos is extremely difficult and with more and more data pouring in from disparate sources, organizations are in need of architectures that bring the composable stack and distributed data together for actionable real-time insights.
“Organizations of all sizes are turning to smart data fabrics as it presents one such reference architecture that provides the capabilities needed to discover, connect, integrate, transform, analyze, manage, utilize, and store data assets to enable the business to meet its myriad of business goals faster and with less complexity than previous approaches, such as data lakes.”
Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media.
Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.
It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.
“With big tech recently making privacy a huge selling point, data will be harder to come by,” said Christian Adams, co-founder of Coffee Affection, a blog for baristas.
“When something becomes more scarce, what happens to the price? That’s right, it goes up. So, as the next few years unfold, expect to see first party data be bigger than ever. That’s to say, if companies want data, they will likely have to collect it themselves.”
One of the biggest big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.
“AI and machine learning specialties are expanding and growing at a rapid pace,” said Jared Peterson, SVP of engineering at SAS, a top analytics and AI company.
“There are multiple reasons for that and numerous ways to look at the expansion. First, advances in deep learning, the compute necessary to enable those advancements (e.g., GPUs), and the frameworks that make it all accessible have brought about a renaissance in the world of computer vision and NLP. The pace of research and publishing in these areas has been staggering.”
AI and ML solutions are exciting on their own, but the automations and workflow shortcuts that they enable are business game changers.
Nir Kaldero, executive global head of data science at NEORIS, a digital transformation company, sees AI and automation together:
“AI by itself is very powerful, but AI + automation is the new opportunity to create smart systems that react automatically to the technology in a seamless way to reach a higher level of intelligence and complete end-to-end services.”
With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.
Perhaps the least-known and most interesting trend for the future of big data comes with vector similarity search, a new approach to finding and retrieving data through deep learning and other smart data practices.
Edo Liberty, founder and CEO of Pinecone, a managed vector database solution, explains why he thinks vector similarity search is growing and what it will mean for the future of data results:
“Vector similarity search is a new method of searching through big data,” Liberty said. “Unlike traditional search methods, it indexes and searches through vector representations of data. It uses a combination of deep learning models and state-of-the-art algorithms to find items by their conceptual meanings rather than keywords or properties.
“Machine Learning teams are starting to use vector search to drastically improve results for semantic text search, image/audio search, recommendation systems, feed ranking, abuse/fraud detection, deduplication, and other applications.”