4 Big Data Trends for 2022 - Photo by Joshua Sortino on UnsplashBig data has been steadily making a name for itself over the last few decades. The 21st century heralded a new era of technological advancements ranging from self-driving cars to biometric security systems such as fingerprint and facial recognition.

An integral part of the fourth industrial revolution, big data continues to transform the world we live in. With around 218 or 2,000,000,000,000,000,000 bytes of data being generated across all industries each day, the big data industry is forecast to grow in value to $77 billion by next year.

The number of jobs requiring data scientists is also increasing exponentially. Many predict this will be the most in-demand profession over the next five years. Regardless of the area of speciality, foundational data science skills can give you the breadth of knowledge needed to begin a dynamic and promising career in this field. This article will look at some of the current big data trends for 2023.

Hybrid Cloud Storage

The sheer volume of data flowing into many organizations no longer makes data storage on-premises a viable option. Cloud computing is a current big data trend that offers hybrid cloud solutions to businesses without investing in expensive on-site storage infrastructure or relying solely on public cloud providers due to custodian and security concerns.

As a result, many are looking to hybrid cloud solutions where they store some information on servers such as Microsoft Azure, Google Cloud and Amazon Web Service and other, more sensitive data on-premises. The strategy ensures they retain full custody of any private data while benefiting from the potent features offered by the public cloud.

Edge computing is another big data trend that works alongside cloud computing and is used to process data closer to where it is collected or at the network’s edge. Rather than sending it back and forth to the cloud, response times can be minimized, making edge computing ideal for processing time-sensitive data, for example, data collection from sensors on self-driving cars.


DataOps is a methodology that combines data management processes to improve efficiencies in the flow of data throughout an organization by removing obstacles in their data lifecycle and analytics processes. In doing so, business processes are streamlined, ensuring they are better able to access and interact with their data. It also allows them to use artificial intelligence (AI) and other disruptive technologies. Some of the ways DataOps improves the speed and quality of data include the following:

  • Increased data analysis: With the huge volume of data generated from IoT Devices, businesses have a huge potential for new insights. Analyzing such data sets to glean meaningful insights will require new technologies such as DataOps.
  • Predictive analysis: Businesses will seek to extract the most value from their data to facilitate the process of decision-making. There is also a demand to maximize the value of this data for customers through self-service predictive analysis These can generate insights without reliance on experts allowing them to make fast and informed data-driven decisions.
  • Data management: Extremely large volumes of data are generated in various forms each day. Current estimations stand at 5 quintillion bytes of data daily. Managing this data is a challenge. However, machine learning is making it simpler. By applying historical patterns to new data, machine learning (ML) can enrich data, thereby eliminating the need for labor and increasing productivity.

AI/ML- Analytics

AI/ML is one of the biggest trends in big data analytics and has widespread applications for businesses everywhere. Organizations that integrate this technology will be able to automate more of their workflows and processes, increasing the accuracy and speed of their decision-making.

Value can be derived from AI/ML solutions in numerous ways.

  • forecasting consumer habits and demand to drive sales
  • managing inventory in supply chain processes
  • improving order fulfilment and delivery times to improve overall customer satisfaction

Vector Similarity Search

Vector similarity search involves translating images, text or audio as vectors. These are used in ML to make the searching and retrieving of connected data faster and more accurate. Vector similarity search is a growing trend in big data as it combines algorithms with deep learning techniques to search for and find items based on a conceptual understanding rather than traditional search methods, which may look for keywords. Real-world applications of this technology include eCommerce, where visitors to websites are given exact or similar matches to their search criteria to increase shopper engagement and, ultimately, the chances of a sale.

As outlined in this article, these are just some of the trends in big data that businesses can harness to stay ahead of their competitors and ensure long-term success.

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