What is Data Modernization?

What is a data modernization?

Data modernization is a strategic approach and often describes the data transfer from legacy databases to modern databases. Data platform modernization is critical to turning large volumes of data, much of which may be useless when siloed, into actionable insights that drive results. As the demand for data modernization services has grown, the data modernization market size has swelled to unprecedented levels. More and more companies are realizing the value of modernizing data and, as such, are seeking solutions to help them understand and make good use of their data. You can have all the data in the world at your disposal, but if you don’t know how to use it, it won’t do your business any good.

Understanding data modernization trends is a great way to learn more about data and what the modernization process looks like. One of the trends to watch for in 2022 and beyond is uniform implementation, which aligns all those within an organization on how to transfer and handle data. By keeping everyone on the same page, companies can ensure better alignment and continuity with their data systems. In addition, more companies are switching to cloud-based platforms. These serve as a modern alternative to traditional data warehouses and offer greater access to storage.

Data modernization questions center around how to make data more meaningful and accessible. Data observability platforms like Acceldata provide comprehensive visibility to improve data efficiency, allowing users to modernize their data systems. The platform offers heightened data observability for complex environments. Acceldata provides the tools for businesses to optimize their data systems and get more out of their assets. Data observability equals 300% higher data engineering productivity and 90% fewer data-related incidents, and Acceldata offers a more detailed look at data systems so that companies can modernize their data and streamline their operations for greater efficiency.

Data modernization according to Gartner

Data modernization Gartner resources offer helpful tips for modernizing your data center. For example, the Gartner Legacy Modernization Magic Quadrant provides information on modernizing legacy systems. Viewing legacy modernization examples can give you a better idea of how this process works and why it’s an important part of data management. Additionally, by searching “data fabric architecture Gartner,” you can find helpful resources on data fabric architecture. This site can be incredibly useful for those wanting to learn more about data modernization and how they can best implement the process in their own organization.

But what is the data fabric definition? Data fabric refers to the services and architecture that supports and integrates a variety of endpoints across cloud environments. You can browse different data fabric examples to get a better understanding of how it works. It’s also important to understand the concepts of data fabric vs. data lake and data fabric vs. data warehouse. A data lake is a collection of storage instances of data assets, and a data warehouse refers to storage architecture built for data that’s been extracted from various sources, so while each of these deal with the management of data, there are some key distinctions between the three to be aware of.

Data modernization services

Data modernization services exist to provide modernization help to clients. Data can be difficult to store and manage, and even harder to use in actionable ways, but data modernization platforms like Acceldata help simplify the process and grant deeper visibility into users’ data. Many of these platforms use a data modernization framework. Whatever your data modernization initiative you’re working towards, a service like this can be extremely helpful as you modernize your data. While this process can be tricky, especially for beginners, these services make it easy for anyone to optimize their data.

There are multiple data modernization service offerings you can choose from, depending on your platform of choice. Acceldata offers solutions for data engineers, data executives, and Cloudera and Snowflake users. Your data modernization strategy can be improved with Acceldata’s services, which predict issues early on, correlate events, and prevent unreliable data from negatively impacting business outcomes. The platform also offers three unique products. Pulse is used for compute performance and data pipeline monitoring, Torch is designed for data reliability, and Flow enables data pipeline observability. These tools allow users to get more out of their data and refine their data modernization strategy for greater efficiency.

Data modernization benefits

Data modernization benefits are extensive, impacting multiple aspects of business. First and foremost, it enables more efficient data processing. By modernizing your data you can more easily find and access high-quality data and correlate it appropriately. This process also promotes greater standardization, helping all those within an organization to remain aligned on common goals and objectives. Understanding the data modernization meaning in context, as well as cloud analytics modernization and the numerous benefits it provides, can empower you to make smarter business decisions about how to handle your data.

Implementing a data modernization framework can help you get started with the process. For example, it can be helpful to begin by assessing your data architecture, including what data is currently available. You might also consider your data management practices and what you might do differently once the modernization process is complete. While there’s a lot that goes into modernizing data, it can fundamentally transform the way you manage your data, allowing you to get more out of that data. Acceldata offers data observability tools that can help your business achieve greater success with data modernization and enable you to optimize your systems for maximum efficiency.