Why is Data Modernization Key to a Winning Data Strategy?

With world economies becoming more complex and intertwined, the amount of data generated, stored, and used globally has increased exponentially, reaching a staggering 59 zettabytes in 2020. Therefore, adopting a modern data architecture that can cope with the growing data volumes is a competitive necessity for enterprises today.
For most organizations, the evolution of data architecture has been largely guided by shifts in operational and business requirements. However, there are some key aspects of data modernization that are common across businesses. For instance, the need to improve traditional use cases and make them more cost-efficient. In addition, this new generation of data architecture rests primarily on a few shared data management tools. These include the classical data warehouse, data lake, and real-time streaming.
But whatever the industry or the use case, the path to modernizing the data architecture involves revolutionizing data-centric technology. This includes migrating siloed data from complex legacy databases to modern cloud-based data lakes to ensure agility, efficiency, and a rapid ingestion process.
1. The Need for a Modernized Data Architecture
Before we dive into why businesses need to modernize their data system, let’s briefly discuss the workings of legacy data management tools. The traditional data stack involves a wide range of tools that need to be aligned perfectly with each other to deliver outcomes. These systems had been designed to deal with static and predictable data from a fixed source.
However, with the diversification of data sources, structured and unstructured, there has been an exponential rise in the unpredictability and complexity of data. This shift caused the traditional data management tools to be deemed complex, costly, and incapable of solving modern data problems. And change has been swift.
Current data and analytics platforms can help businesses create a flexible and scalable data stack. Data modernization facilitates productivity by revolutionizing how enterprises collect, process, analyze, and use data and make better business decisions quickly.
They provide a cohesive view of diverse data assets and a single point of access to its users while at the same time:
Improve data governance by ensuring compliance with data protection and privacy regulations
Ensure access to the right data at the right time
Bridge organizational silos by bringing together data from diverse source points
Provide valuable, actionable insights that can meet the needs of a dynamic business environment
Lower costs, reduce data latency, accelerate time to market, and improve decision making
Accommodate unstructured data and open-source technologies
From the perspective of data strategies too, data modernization can have significant benefits. By enabling real-time, enterprise-wide democratization of data, a modernized data architecture can help derive strategies that support lower OpEx costs, enhanced data security and governance, and improved data quality.
Hybrid infrastructure: As enterprise data continues to grow by leaps and bounds, companies need to consciously develop robust data retention strategies and governance policies around that data. Most importantly, companies need to figure out feasible data storage architecture, be it multi-cloud or a mix of cloud and on-prem data hubs, to ensure optimal analytics delivery.
This makes data modernization an even more critical task. A modern enterprise must systematically plan, update, and build the right data storage architecture to modernize the data core.
Cross country data: Another key driver for data modernization is frequent change in cross-country regulatory requirements surrounding data practices. Today the majority of companies do not have their operations restricted to one particular location or country.