5 Data Management Trends Data Practitioners and CIOs Should Know in 2024 and 2025

By on
Read more about author Yash Mehta.

Data has always been a driving factor for organizations. Over the past decade, the value of data has increased exponentially. Organizations across all categories and sizes (MNCs and SMEs) have started making critical business decisions based on insights gathered from collected data. The data, most commonly referred to as historical data, is collected over sufficient time to gather meaningful insights.

Of late, due to the growth of huge amounts of data and the processing of collected data, organizations are facing the pressing issue of managing data effectively. This article looks forward to some trending ways to manage data that will optimally share the spotlight in 2024-25.

Cloud-Based Data Management and Dockerization

Cloud-based technologies for data protection can offer numerous advantages over conventional data persistence and management methods. To name a few, scalability on demand, zero hardware maintenance, 24/7 availability of customer support, and cost as per data utilization – which is very cost-effective as compared to on-premise data management – are some of the best advantages that provide a winning edge for cloud-based data storing and management services. Amazon Web Services (AWS) and Google Cloud Platform (GCP) are two of the most popular cloud services provided among various others in the market.

Because cloud service providers offer the above-mentioned competitive advantages, companies rapidly adopt cloud technologies across various business verticals.

Gartner studies have shown that the adoption of the cloud market has reached almost $600 billion from around $300 billion in one year(2022-2023). The provision of Infrastructure-as-a-Service (IaaS) was found to be the most prevalent reason for the rapid growth of the adoption of cloud services.

When it comes to data management, being able to effectively replicate the data and still being able to generate the same output across different environments plays a major role. Here is where dockerization comes into the picture.

In simpler terms, containerization (with technologies like Kubernetes and Docker) supports deployments of code based on hardware without making any changes. This, in turn, requires minimal resources for maintenance, enabling the companies to utilize resources in other aspects of business processes like sales and marketing.

Artificial Intelligence and Machine Learning

The advent of artificial intelligence has become more prevalent by the day in the tech realm. One of the prominent reasons for this is that with AI, organizations can process and analyze enormous amounts of data and get useful insights while requiring absolutely no human intervention in the process at very small amounts of time.

With AI being enabled in almost all business solutions, the global artificial intelligence market is expected to reach around 1812 billion USD by the end of 2023.

Apart from this, the combination of AI and ML can benefit from custom-made algorithms that help identify specific patterns in data and anticipate the possibilities of upcoming events. Moreover, it can also be used to process massive amounts of unstructured data and structure it to provide meaningful and relevant information that is easily understood and accessed by non-technical professionals as well.

Synthetic Data Generation

One of the most intriguing topics that pop up while on data management is synthetic data generation.

Synthetic data generation aims to create synthetic data that resembles every aspect and characteristic of the actual data but has no impact or correlation with the real data (production data). This helps ensure the data is well protected and can also assist in training data models to perform data analytics or generate faux data for software testing.

Since the development uses synthetic data that resembles the underlying patterns of production data, it is easier to integrate the code into a production environment. Organizations with futuristic vision have already started adopting the synthetic data generation method because of its capability to address many business use cases optimally.

Large-scale enterprise-level data management platforms provide full-fledged synthetic data management solution that combines the potential of generative AI, rules engine, entity cloning, and data masking to provide accurate synthetically generated data.

Data Privacy and Security

As the amount of data generated and processed only increases exponentially, handling the processed data with utmost concern is very important. For example, if a hospital collects information about patients, their medical history, and their family medical history and maintains it for every single patient, it is usually referred to as “PII” (personal identifiable information). When this information becomes accessible on the internet, it can do potential damage to the individual, and the organization will have to take necessary responsibilities for the damages that occurred to the individual. Because of this reason, businesses tend to prioritize data protection and invest heavily in ensuring data security.

Researchers have identified that almost 33% of global consumers have been subjected to data breaches in some form over the past year.

Server-side encryption services store enterprise-grade data and backup and recovery options. These solutions also provide easy migration of data across public cloud services.

Data Decentralization

In recent years, the evolution of technology and the change in the data realm have been more rapid than ever. This brings the pressing need for rapid adoption capabilities to be updated with modern technologies and improvised methods. Eventually, organizations figured out that the best way to do this would be to follow a decentralized approach to manage data effectively.

In a decentralized approach, designated teams maintain the data. A few of the most important aspects of a decentralized approach are:

  1. Provide sufficient permissions for users to access the data whenever required and understand the characteristics of the data they are dealing with.
  2. Device a data management architecture that bridges all data sources and components of 
    data management through defined methods (mostly using metadata).

According to a recent study, by 2025, almost 75% of organizations will have adopted data decentralization.

As we progress through the era of massive data growth, it is difficult to conclude that one particular method of managing data will solve all the enterprise issues related to data management. The above-mentioned methods have their own shortcomings as well. But collectively, they can solve most of the issues organizations face. In the future, with advanced technologies and more clarity in data management, one single method could solve most or even all of the data management-related concerns.