Enterprises worldwide are deeply engaged in their digital transformation journey, as they digitize and automate antiquated processes. To get there, they are increasingly investing in data analytics and business intelligence tools to analyze extensive datasets and make the right business decisions.
Consequently, the data analytics market is surging, and now tops $200 billion in annual spending, according to IDC analysts.
Similarly, a rising trend is also seen in the data analytics job market. The U.S. Bureau of Labor Statistics predicts a strong growth of over 30% in data science positions by 2030. Moreover, according to Gartner, nearly every business (up to 90%) is estimated to value information as a critical asset and data analytics as an essential competitive edge.
Several factors are fueling this exponential growth in the data management arena. Here we look at the top seven trends that determine the data management market in 2022 and beyond, as enterprises strive to meet every data-centric demand for competitive edge.
Also read: Best Big Data Tools & Software for Analytics 2022
Top Data Management Trends in 2022
1. Intercloud and multi-cloud technologies
More and more data and applications are moving 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, handling different cloud services, or using an on-premises data management system.
In fact, a 2021 IDC survey found that nearly 82% of businesses currently use or plan to use multiple clouds within 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 lets data management systems to seamlessly collaborate using different cloud services running on diverse cloud ecosystems.
As such, multi-cloud and intercloud data management are becoming more crucial to support diverse data management strategies.
Also read: Successful Cloud Migration with Automated Discovery Tools
2. Artificial intelligence
The COVID-19 pandemic and remote work culture have significantly changed the way enterprises all over the globe collect and analyze data, creating a new data-driven business culture. As a result, this new data-driven business culture fuels investments in analytics based on artificial intelligence (AI).
AI, machine learning (ML), and automation are game-changers for every business all over the globe. These technologies augment human capabilities in data analytics and help create better business value. For example, AI can help increase sales by predicting market demand and keeping an appropriate supply of products at warehouses.
Also read: Top Artificial Intelligence (AI) Software
3. AnalyticsOps
AnalyticsOps is the only way to manage the highly complex AI and other advanced data analysis approaches. Simply put, AnalyticsOps is an information technology (IT) framework that monitors the automation of analytics across a business organization.
It comprises a series of steps, integrated processes, and technologies that helps an enterprise successfully deliver business value from AI-based advanced analytics models. As a result, AnalyticsOps frameworks eliminate silos and speed up a time to value by collating data science, IT engineering, and the business.
4. Data fabric
As volumes and data types continue to increase as businesses migrate to the cloud, seamlessly weaving together a network’s data is necessary to make a company more efficient and profitable.
Data fabric is a cloud-based architecture that uses a data storage ecosystem in theory and practice. It offers large sets of tools, granting centralized access to data from multiple sources. This single view of data can be used across the network.
Data fabric system offers several benefits, such as eliminating data silos, enabling hybrid cloud, simplifying data management, reducing data disparity, and augmenting scalability.
5. Blockchain technology
Bitcoin introduced Blockchain technology, also known as Distributed Ledger Technology (DLT). It helps enterprises keep more secure transaction records, audit trails, and create assets. DLT, along with blockchain technology, stores data in a decentralized way devoid of alteration but with improved authenticity and accuracy.
In simpler terms, DLT and blockchain technology are all about creating a decentralized network beyond the conventional centralized networks and systems, which rely on a third-party authority. As a result, these technologies have far-reaching consequences on different industries and sectors and their data management strategies.
Also read: Potential Use Cases of Blockchain Technology for Cybersecurity
6. Edge computing
The edge computing market is expanding at nearly a 20% compound annual growth rate (CAGR) every 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—that is, smartphones and Internet of Things (IoT) devices—technologies such as data analytics are more likely to reside at the edge.
Therefore, edge computing brings speed, agility, and flexibility by supporting real-time data analytics. In addition, it also provides autonomy for IoT devices.
Moreover, the data analytics potential of edge computing is so vast that Gartner predicts that 50% of the data analytics job will be done on the 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 fabric, 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.
Composable data analytics combine and utilize several analytics techniques from multiple data sources. As a result, it helps enterprises make more effective and intelligent decisions.
In addition, tools like composable data analytics provide greater agility than traditional approaches and tools. They also let organizations utilize reusable and swappable modules that can be deployed anywhere, including containers.
Enterprises are more likely to continue leveraging and harnessing their capability to access big, small, and broad data sources in the coming years. According to a Gartner study, by 2025, 70% of enterprises will shift their focus from big data to small and wide data—the data derived from a wide array of sources. It gives more space for comprehensive analytics and intelligent decision-making.
Prioritize Data Management for Effective Decision Making
Managing data efficiently in a complex, data-driven digital world empowers the successful operations of every organization across all industries all over the globe. The digital world is cluttered with heavy chunks of data. However, if your enterprise has access to efficient data management and analytics, it opens the door to seize more opportunities, raise more questions, and solve more problems.
Since almost all enterprises collect data today, it makes sense to manage it well to provide better insights. Moreover, the need for real-time data analysis will also rise with expanding volume, variety, and velocity of data. And those trends will put enterprises under tremendous pressure to make efficient data management their highest priority.
In a data-driven world, only the businesses that successfully derive actionable insights by harnessing core data management technologies can innovate faster, devise better strategies, and manage change more effectively.
Read next: Top Data Quality Tools & Software
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