Crafting a blueprint for data-first application modernization

Data-first application modernization is more than a technological upgrade from legacy infrastructure — it is a strategic move for businesses to undergo architectural improvements that make them future-proof. At its core, data-first application modernization provides the seamless connectivity and usability of all data and deploys analytics and intelligence to come up with innovative business process enhancements.

We are currently witnessing a rapid adoption of data-centric strategies. Data architecture is now a core business focus. And organizations are now relying on big data tools to further their objectives. There is a rise in the adoption of event-driven architecture, data streaming and application programming interfaces (APIs) for efficient data processing. The focus on addressing challenges of data monetization, accuracy and governance has also become paramount.

To that end, a data-first application modernization strategy, in effect, helps businesses reach their end goal of improving customer experience, operational efficiency and market positioning.

The advantages evidently are compelling.

So, how do you get there?

Here are a few key technological and architectural considerations for making the move towards data-first application modernization.

• Leverage cloud migration for enhanced data management
  Data-centric applications, powered by serverless platforms and containerized solutions, provide scalable and reduced time-to-market deployment. With data lakes and warehouses, facilitating storage, management and processing of extensive datasets gets simplified. Data warehouses help businesses excel in processing structured data at speed, while data lakes help streamline processing and analytics for vast unstructured datasets. But the landscape is evolving, and with convergence in the capabilities of data lake and data warehousing technologies already underway, the case for data-first application modernization will only become stronger.
  
  
• Elevate your workflows with real-time data streaming and messaging services
  Robust tools of data modernization seamlessly transition batch workloads into real-time scenarios, offering scalable, durable and fault-tolerant publish-subscribe (pub-sub) services. Moreover, they empower you with real-time analytics and efficient alerting mechanisms, enhancing your ability to respond promptly to dynamic industry demands. By staying ahead of the curve with these technologies, you can optimize your processes for heightened efficiency and responsiveness.
  
  
• Build end-to-end business components as highly modular platforms
  Data pipelines facilitate seamless data flow, making sure that information moves effortlessly across your system. API-based interfaces provide a standardized and efficient means of communication between diverse components, fostering interoperability. Meanwhile, analytics workbenches empower you with robust insights, steering your decision-making processes towards data-driven precision.


• Keep data secure behind an API gateway
  Secure your data fortresses behind an API gateway, where access is meticulously regulated through the implementation of data-centric APIs. The API gateway safeguards sensitive information, while data-centric APIs standardize and streamline interactions. This not only protects your business against potential breaches but also offers a user-friendly pathway for authorized data use. By adopting this approach, you can instill confidence in your data security protocols, ensuring compliance with evolving standards and regulations. In an era where data is an invaluable asset, this can also be an additional means for secure, controlled and efficient data management.
  
  
• Shift workloads to data lakes or distributed data mesh
  Optimize your data strategy by harnessing the power of data lakes or distributed data mesh, strategically diverting select workloads away from core systems. Data lakes provide a repository for vast, unstructured datasets, allowing for flexible and dynamic processing. Meanwhile, distributed data mesh architectures streamline data processing, enhancing resilience and responsiveness. By embracing these solutions, businesses can alleviate the strain on core systems and build more adaptable and robust data infrastructure that aligns with the demands of businesses today.
  
  
• Speed up storage management with data infrastructure as a service
  Accelerate your storage management implementation with Data Infrastructure as a Service (DIaaS). This simplifies the process of handling and optimizing your data storage, providing businesses with a robust and efficient data framework. DIaaS not only facilitates storage management but also introduces scalability and flexibility, allowing you to adapt to evolving data demands. As organizations navigate the complexities of modern data ecosystems, embracing DIaaS becomes a strategic move, providing swift and effective storage solutions while freeing up valuable resources for strategic initiatives.


• Integrated distributed data sets with data virtualization
  Through data virtualization capabilities, you can seamlessly bridge the gap between disparate data sets. Data virtualization allows businesses to unlock the full potential of their data by creating a cohesive and accessible layer. By removing data silos and enabling interoperability, business leaders are empowered to make informed decisions based on a comprehensive, real-time view.


• Provide metadata management capabilities with data cataloging
  These tools simplify the complexities involved in handling extensive datasets. With a comprehensive metadata system, data cataloging facilitates efficient organization, retrieval and understanding of data assets. With intuitive search functions, users can navigate vast datasets effortlessly and foster a data-driven environment.
  
  
• Allow for greater flexibility and a competitive edge with schema-light and denormalized data models
  Graph databases, with their schema-light nature, break free from the constraints of rigid structures, allowing for dynamic adaptation to evolving data requirements. Adopting these models empowers organizations with the agility needed to extract meaningful insights and gain a competitive edge in the rapidly changing analytics landscape.

Keen on taking a deeper dive?

Check out our whitepaper for more insights on embracing data-first application modernization. Unlock all these benefits and more with NTT DATA’s Digital Application Services. Elevate your business to new heights through cutting-edge solutions that provide agility, scalability and enhanced performance.

Contact us today.