What is data modernization?

Business processes are being made faster, smarter, and more efficient with modern technologies. Learn about data modernization, one of the core pillars of the digital enterprise.

Data is a valuable commodity for companies because of its potential to power better decision-making. However, data management tools and processes must be equipped to handle the volume, velocity and variety of data that companies face today. The exploding volume of data and the many new applications that generate and consume it have created an urgent need for data management programs to help organizations stay on top of their data.

Data management initiatives, including data integration and analytics, help organizations optimize their use of data as a strategic asset. Data modernization is a critical component of these initiatives. It addresses the challenges of legacy data management systems by moving away from rigid siloed systems and increasing the value of your data.

In this guide to data modernization, learn how it works and some of the best strategies for modernizing your own organizational data.

Data modernization is the process of converting an organization’s data assets into more usable formats for the modern workplace. One way to do this is to move data from legacy systems to a new system that operates on a cloud-based platform.

Data modernization can also involve extracting data from legacy applications or databases, cleaning up the data, and importing it into a modern system. Once data has been modernized and moved to a newer platform, users can explore it with features like interactive visualization tools, advanced analytic functions, machine learning capabilities and mobile access.

There are many data modernization strategies to choose from, including open data management or data governance. Your choice of strategy will depend on the needs of your company or organization. In most cases, however, you’ll want to take these steps to optimize your data modernization efforts.

Data is frequently scattered across numerous systems and applications within a business. A consolidated database is a good solution to make sure data is easier to find, accessible and up-to-date. Creating a consolidated database that accesses disparate data sources can yield new insights, enabling companies to analyze all the different types of data sets they have collected over time.

Knowing what data assets are available within your company is an essential step in any data modernization effort. A clear understanding of the types of data being stored by different departments, where data is located across the network, and data age and format will help you to identify the best course of action for modernizing that data.

Machine learning and artificial intelligence technologies offer many benefits for data modernization. Some of the most important functions of AI/ML for data include automated extraction of unstructured data, predictive analytics and workflow automation.

Modernizing data often involves moving it around and exposing it to new applications and users.