Businesses around the world are deeply engaged in their digital transformation journey, as they digitize and automate outdated processes. To achieve this, they are increasingly investing in data analytics and business intelligence tools to analyze large sets of data and make the right business decisions.

Hence, the data analytics market is booming and now exceeds $200 billion in annual spendingaccording to IDC analysts.

Similarly, an upward trend is also seen in the data analytics job market. The US Bureau of Labor Statistics predicts a strong growth of more than 30% in data science positions by 2030. Moreover, according to Gartner, it is estimated that almost all companies (up to 90%) value information as an essential asset and data analytics as a critical competitive advantage.

Several factors are fueling this exponential growth in the field of data management. Here we take a look at the top seven trends driving the data management market in 2022 and beyond as enterprises strive to meet every data-centric demand for competitive advantage.

Also Read: Best Big Data Tools and Software for Analytics 2022

Top Data Management Trends in 2022

1. Intercloud and multicloud technologies

More and more data and applications are migrating to the cloud, and this data migration requires business leaders to implement complex data management strategies and technologies. Some include managing data within the same cloud ecosystem, managing different cloud services, or using an on-premises data management system.

In fact, a 2021 IDC survey found that nearly 82% of companies currently use or plan to use multiple clouds in the next 12 months.

Multi-cloud technology allows a data management service to operate on more than one cloud ecosystem. On the other hand, intercloud technology enables data management systems to collaborate seamlessly using different cloud services running on various cloud ecosystems.

Thus, multi-cloud and cross-cloud data management is becoming increasingly crucial to support various data management strategies.

Also read: Successful cloud migration with automated discovery tools

2. Artificial Intelligence

The COVID-19 pandemic and the culture of remote working have dramatically changed the way companies around the world collect and analyze data, creating a new data-driven corporate culture. Therefore, this new data-driven corporate culture is fueling investments in artificial intelligence (AI)-based analytics.

AI, machine learning (ML) and automation are game changers for all businesses around the world. These technologies increase human capabilities in data analysis and help create better business value. For example, AI can help increase sales by predicting market demand and maintaining an appropriate supply of products in warehouses.

Read also: The best artificial intelligence (AI) software

3. Analytics Ops

AnalyticsOps is the only way to handle highly complex AI and other advanced data analytics approaches. Simply put, AnalyticsOps is an information technology (IT) framework that monitors analytics automation in a business organization.

It includes a series of steps, integrated processes, and technologies that help a company successfully deliver business value from advanced AI-powered analytics models. As a result, AnalyticsOps frameworks break down silos and accelerate time to value by bringing data science, IT engineering, and business together.

4. Data structure

As the volumes and types of data continue to increase as businesses migrate to the cloud, the seamless weaving of a network’s data is necessary to make a business more efficient and profitable.

The Data Fabric is a cloud-based architecture that uses a data storage ecosystem in theory and in practice. It offers extensive toolsets, granting centralized access to data from multiple sources. This single view of data can be used across the entire network.

The Data Fabric system provides several benefits, such as eliminating data silos, enabling hybrid cloud, simplifying data management, reducing data disparity, and increasing scalability.

5.Blockchain Technology

Bitcoin introduced Blockchain technology, also known as Distributed Ledger Technology (DLT). It helps businesses maintain more secure transaction records, audit trails, and asset creation. DLT, with blockchain technology, stores data in a decentralized way without tampering but with improved authenticity and accuracy.

In simpler terms, DLT and blockchain technology is about creating a decentralized network beyond conventional centralized networks and systems, which rely on a third-party authority. Therefore, these technologies have far-reaching implications on different industries and sectors and their data management strategies.

Also Read: Potential Use Cases of Blockchain Technology for Cybersecurity

6. Advanced Computing

the The edge computing market is booming at a compound annual growth rate (CAGR) of nearly 20% each year. It is also estimated to grow from $36.5 billion in 2021 to $87.3 billion in 2026. As computing power moves to the edge i.e. smartphones and Internet of Things (IoT) devices, technologies such as data analytics are more likely to reside at the edge. edge.

Therefore, edge computing brings speed, agility, and flexibility by supporting real-time data analysis. Moreover, it also provides autonomy to IoT devices.

Moreover, the data analysis potential of edge computing is so vast that Gartner predicts that 50% of data analysis work will be on data created, managed and analyzed at the edge by 2023.

See also: Edge AI: The Future of Artificial Intelligence and Edge Computing

7. The transition from big data to small and wide data

AI, data structure, and composable analytics enable businesses to collect and analyze the combination of micro and macro data and structured and unstructured data, applying techniques that derive valuable insights from it.

Composable data analytics combine and use multiple analysis techniques from multiple data sources. As a result, it helps businesses make more efficient and smarter decisions.

Additionally, tools such as composable data analysis provide greater agility than traditional approaches and tools. They also allow organizations to use reusable and exchangeable modules that can be deployed anywhere, including containers.

Businesses are more likely to continue to leverage their ability to access data sources large, small, and broad in the years to come. According to a Gartner study, by 2025, 70% of companies shift their focus from big data to small and big data— data from a wide range of sources. This gives more space for comprehensive analyzes and intelligent decision making.

Prioritize data management for effective decision-making

Effective data management in a complex, data-driven digital world enables successful operations for every organization in every industry worldwide. The digital world is cluttered with large volumes of data. However, if your business has access to effective data management and analysis, it opens the door to seizing more opportunities, raising more questions, and solving more problems.

Since nearly every business collects data today, it makes sense to manage it well to deliver better insights. Additionally, the need for real-time data analysis will also increase with the increase in volume, variety, and speed of data. And these trends will put businesses under enormous pressure to make effective data management their top priority.

In a data-driven world, only companies that successfully gain actionable insights by leveraging core data management technologies can innovate faster, design better strategies, and manage change more effectively.

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