SMB IT Leaders Tackle Data Fragmentation Across Hybrid Clouds

Unify Data Platforms for Real-Time Access

“A unified data management platform is a centralized system that seamlessly integrates and governs diverse data sources, enabling secure, role-based access for multiple personas across the organization,” says Ben Schreiner, head of AI and modern data strategy business development at Amazon Web Services. “Platforms like Amazon SageMaker Unified Studio exemplify this by combining data integration, governance and AI capabilities in a single, collaborative workspace.”

A unified data management system also eliminates silos to improve business coordination and collaboration.

There are two main types of unified data management platforms, Schreiner says: plug-and-play and bespoke.

“Plug-and-play platforms are typically the best fit for smaller SMBs [around 50 employees], as they might prioritize automation, easy maintenance and quick onboarding over scale and customization,” he says. “Larger SMBs [around 1,000 employees] with more complex data needs may benefit from bespoke solutions, which offer greater customization, scalability and tailored governance.”

The middle option is to combine “plug-and-play ease with custom integrations,” Schreiner adds. “Careful evaluation of the options and the organization's requirements is essential to make the right decision.”

Real-World Example: Small Retailers

Smaller retailers such as Favorite Daughter and Joe’s have been investing in a unified data platform to consolidate customer data from their e-commerce sites, in-store systems and marketing tools.

This real-time data integration reduces data silos, improves inventory management and personalizes customer outreach.

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Automate Data Mapping for Compliance

For small financial service organizations, implementing automated data mapping can help track sensitive data across hybrid clouds. By integrating data governance tools, the IT team ensures regulatory compliance and mitigates the risk of data breaches.

Data mapping is about understanding the data lineage and cataloging. It answers the questions, what is the origin of this data, where is it stored and how does it move across systems?

“Data mapping is indispensable for effective data governance and regulatory compliance,” Schreiner says. “It provides the transparency, structure and documentation needed to manage data responsibly, respond to regulatory demands and build trust with stakeholders and regulators.”

Having a unified data management platform is key for automating data mapping. Before you can truly map your data, it helps to have a unified view of it.

RELATED: Unify data protection across your hybrid infrastructure.

Real-World Example: A Small Healthcare Practice

Healthcare companies implement data mapping as part of their unified data platforms. This allows them to “trace patient data from intake to reporting, ensuring HIPAA compliance,” Schreiner says.

Data mapping would also identify and secure personally identifiable information, understand its lineage and, crucially, use this information to identify and address compliance risks such as unauthorized data transfers.

Predictive Analytics for Operational Efficiency

“Comprehensive data integration and transformation supports seamless merging and joining of structured and unstructured data from multiple sources,” Schreiner says. It subsequently becomes easier to use services such as AWS Glue to “cleanse, standardize and prepare data for enrichment and analytics.”

For example, advanced metadata extraction makes it possible to enrich data catalogs with business context to “improve discoverability and enable richer data insights,” Schreiner says.

Simply put, data becomes much more valuable and traceable. For SMBs eyeing AI workflows, this prevents the “garbage-in, garbage-out” scenario. It also enhances efficiency by reducing risk and maximizing data’s utility to employees, regardless of its location in a hybrid cloud environment.

READ MORE: Small businesses can, and should, capitalize on AI opportunities.

Real-World Example: Supply Chain Analytics

Small logistics firms like Shipwell and Flowspace are adopting  AI-driven predictive analytics to analyze fragmented data streams from multiple cloud providers. The solutions identify potential delivery delays, optimize route planning and enhance supply chain visibility.

These types of predictive systems would be impossible without a clear way to track, contextualize, govern and ultimately utilize their data.