Envisioning the Future of Data Modernization in Business Operations

Business Operations that are still running on legacy data platforms often experience performance issues, slow turnaround times, high upgrade costs, and increasing maintenance expenses. ‘Data Modernization’ involves helping businesses adopt modern data architectures to create a seamless data platform that delivers unified access, ensures data trust, and gets the data ready for AI.

Modern data architectures offer diverse computing capabilities—batch, streaming, and in-memory, with multiple service options to choose from to meet the required scale, cost and flexibility, while also effectively managing a wide range of data types, including documents, media files, and device data.

Three pivotal aspects to consider in Data Modernization are ‘unified data access through data fabric architecture,’ ‘investments in newer cloud strategies’ and the ‘integration of AI in metadata-driven approach.’

Unified Data Access Through Data Fabric Architecture

Data Fabric architecture ensures seamless access to data regardless of where it resides. Metadata, Data Governance, Virtualization, and Federation are the key capabilities of a Data Fabric Architecture.

A global financial services provider had their data scattered across multiple on-premises databases and cloud platforms. The data fabric architecture with Dremio as the core query engine allowed for the integration of these disparate data sources into a single, unified view enabling the analysts with quicker access to data.

Cloud data platform providers like Microsoft Azure, AWS, and Google Cloud are offering robust solutions that support data fabric implementation as well as cloud agnostic SaaS platforms Snowflake and Databricks. All of them enable querying and sharing data across clouds ensuring seamless data access and governance.

Focus on Investment in Newer Cloud Strategies

Multi-cloud strategy is becoming a norm; businesses have started leveraging a combination of cloud capabilities to meet their varied needs. The top five drivers for multi-cloud adoption include

  

•    Risk of a single cloud service provider

•    Cost efficiency

•    Business Continuity 

•    Compliance

•    Best-in-Class niche cloud services

A life sciences firm had their SAP system and data platform running on Azure, while their ML and AI models were running on Google Cloud Platform (GCP). This multi-cloud strategy leveraged both Azure and GCP services to optimize performance and cost, reflecting an industry trend where 72% of organizations are expected to adopt multi-cloud in the next five years.

Cloud providers are driving this trend by offering a range of services that facilitate seamless integration across cloud platforms and support various open-source standards. For example, Microsoft Fabric provides access to AWS S3 as a shortcut in its OneLake, while AWS, GCP, Snowflake, and Databricks offer external table support. Additionally, all major cloud providers support Spark integration, open-source formats like Delta, Parquet, Iceberg, and multiple large language models (LLMs).

Integration of AI in Metadata-Driven Approaches

Artificial Intelligence (AI) and Generative AI are increasingly getting embedded in data governance, a key component of data modernization. Ensuring Data Readiness for AI now heavily relies on metadata, data quality, and robust governance processes. AI enhances metadata-driven processes by automating tasks such as data classification, data quality assessment, and business data cataloging.

A healthcare provider implemented an AI-driven data governance solution that automated the extraction and classification of metadata from various data sources, including electronic health records (EHRs), clinical notes, and imaging systems. This approach reduced manual data management and significantly improved data accuracy.

All major cloud providers support AI in data governance and metadata management. AWS Glue Catalog, DataBrew, and Glue Data Quality offer AI-driven data profiling, cleansing, and cataloging. Microsoft Purview uses AI to discover, classify, and protect sensitive data, while Google Cloud’s Data Catalog employs machine learning to automatically discover and tag data.

Conclusion

The future of data modernization in business operations is being shaped by the convergence of unified data access, innovative cloud strategies, and AI-driven metadata management. As cloud data platform technology continues to evolve, organizations that quickly adopt these newer capabilities will lead in transform data into a strategic asset, driving growth and innovation.

Written By - Muneeswara Pandian, Vice President – Data Engineering, Ascendion