Tai Sunnanon Covers the Five Trends in Big Data for 2022

Data has become the world’s most valuable asset. Advancements in the accessibility and capacity of tools for collecting, transmitting, storing, analyzing, and acting upon data is making it easier to gather information and turn it into knowledge. Big Data now forms the centerpiece of various business strategies to compete, innovate, and capture value as well as to tackle industry-wide competition.

However, the pandemic greatly limited the utilization and access to big data by most businesses. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on vast amounts of data to a class of analytics that requires smaller, more manageable data. Five trends in big data are helping businesses meet those challenges in 2022 and beyond.

Trend No. 1. Scalable AI that post-pandemic businesses can tap into

Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems, and shorter time to value. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies — something that up to this point has been challenging.

Although traditional AI techniques may rely heavily on historical data, the pandemic has rendered is less relevant. This means that AI technology must be able to operate with less data via “small data” techniques and adaptive machine learning. And in today’s protective privacy world, these AI systems must also comply with regulations that ensure consumer privacy.

Trend No. 2. Continuous Intelligence to match customer needs

Continuous Intelligence (CI) integrates data pipelines with automated decision analysis, which makes big data insights accessible to all in the business besides supporting the decision-making process and encouraging automation endeavors.

Though an emerging trend, continuous intelligence aims to deliver tailored intelligent solutions that surround big data to match the customer’s need and expectations. A big data trend expected to gain momentum is supported by predictions by Gartner that forecasts 50% of new business systems to deploy CI by the end of 2022.

Trend No. 3. Data Fabric for greater operational efficiency

Data fabric is an architecture and collection of data networks. That provides consistent functionality across a variety of endpoints, both on-premises and cloud environments. To drive digital transformation, Data Fabric simplifies and incorporates data storage across cloud and on-premises environments. It enables access and sharing of data in a distributed data environment. Additionally provides consistent data management framework across un-siloed storage.

Data fabric reduces time for integration design by 30%, deployment by 30% and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles. Plus, data fabrics can leverage existing skills and technologies from data hubs, data lakes and data warehouses, while also introducing new approaches and tools for the future.

Trend No. 4. Data Lake (over Data Warehouse) for cost-effective utilization

Enterprises are shifting toward new data architecture approaches that allow them to handle the variety, veracity, and volume challenges of big data. Rather than trying to centralize data storage in a data warehouse that requires complex and time-intensive data extraction, transformation and loading, enterprises are evolving the concept of the data lake. Data lakes store structured and unstructured data sets in their native format. This approach shifts the responsibility of transformation and processing to end points that have different data needs. The data lake can also provide shared services for data analysis and processing.

The “data lake vs data warehouse” conversation has already begun, but the key differences in structure, process, users, and overall agility make each model unique. While many sectors already use data lakes, the finance and fintech industry are already considering the shift away from data warehouses for the cost-effective utilization that data lakes provide.

Trend No. 5. Immersive Experiences as a new norm

Immersive experiences are about to take a giant step forward into the present, as seen already by the gaming industry.

Today’s XR – technology that encompasses augmented, virtual, and mixed reality technologies – is much more immersive and emotionally engaging than past versions. As games, 3D films, and interactive business applications move to XR, prices for virtual reality headsets will surely drop, while also becoming far more immersive. Today’s headsets let users access spaces that combine real-world imagery in completely artificial environments, allowing users with VR headsets to explore and interact with virtual objects in 3D VR worlds.

Conclusion

While Big Data continues to be utilized by global companies, not all enterprises have the means and access to it, especially in a post-pandemic economy. How these companies take advantage of smaller, more manageable data depends largely on how to capture, interpret, and utilize volumes of data, including their customers. The promise of both big and small data exists, therefore, in understanding the five emerging trends in the industry for 2022 and beyond.

About Tai Sunnanon

Tai Esteban Sunnanon is a Cybersecurity Expert and Professional Analyst. He is a 2021 U.S. Department of Defense Fellow in Cybersecurity and has consulted for some of the largest cybersecurity firms in the country. Tai is an officer in the U.S. Air Force, where he received his cybersecurity certificate. He completed his doctoral coursework at Harvard University, where he also obtained two Master’s degrees. He is a frequent speaker and author on the topic.

This article does not necessarily reflect the opinions of the editors or the management of EconoTimes