Data Management Makes or Breaks AI Success for Small Businesses

Consolidated, clean and well-governed data is crucial to artificial intelligence implementations at scale.

Dominick Sorrentino is web editor for StateTech.

Small businesses have been embracing artificial intelligence to streamline operations and better serve customers. Restaurants are using AI to optimize supply chains, accounting firms rely on AI to detect fraud and retailers are leveraging it to personalize marketing campaigns.

But as companies face tighter budgets and increasing competition, opportunities exist to deploy AI more broadly and strategically — provided their data is ready for it.

“Many small businesses start their AI journeys with a specific use case, something simple such as a chatbot,” says John Whippen, regional vice president for U.S. public sector at Snowflake. “As they show the value of those individual use cases, they’ll attempt to make it more prevalent across the entire organization.”

For smaller companies, readying data for AI-driven initiatives can seem daunting. However, data consolidation, governance and management are foundational steps that determine whether AI drives growth or creates operational headaches.

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Data Consolidation Is More Important (and Easier) Than Ever

“The first thing to do is to consolidate,” Whippen says.

Many small businesses use multiple cloud platforms or apps — from payroll software to marketing automation tools. While this flexibility is valuable, it often creates data silos.

Where the data is physically stored matters less than the ability to centralize access, management and analytics.

“Today, you can extract all of that data much more easily, from a user interface perspective, and manipulate it the way you want, then put it back into the system of record, and you don't need a data scientist for that,” says Mike Hurt, vice president of state and local government and education for ServiceNow. “It’s not your grandmother’s way of tagging anymore.”

Platforms such as ServiceNow’s Workflow Data Fabric and Snowflake allow businesses to bridge data virtually, eliminating the need for costly, physical data migrations.

For example:

SnowConvert can automate the migration of legacy SQL code to modern standards, simplifying the process of moving older applications into the cloud.
ServiceNow’s Workflow Data Fabric connects to different data sources and apps, allowing teams to manipulate and clean data without having to leave the ServiceNow environment.

These solutions give small businesses the agility to scale without hiring large IT teams.

“Smaller organizations often look at data more holistically,” Hurt explains. “They don’t have the luxury of siloed resources, so a unified approach becomes a necessity.”

Simplifying Data Policy and Governance

Once data is consolidated, it becomes easier to establish and enforce data policies.

“A strong data policy is the backbone of AI readiness,” Whippen says. “If your end goal is to identify use cases where AI can drive efficiency and customer satisfaction, you must ensure that the data you have is accurate and secure.”

For small businesses, managing data policies across different software platforms can be tricky. Each system may have unique settings and permissions, making uniform policies hard to implement.

The solution? A unified data management platform.

“We can look at our role-based access control from a data policy perspective,” Whippen says. “You can create policies once, implement them, and apply them across every app and department.”

A strong data policy that prioritizes privacy, security and accessibility often reduces the need for a separate AI policy because good data governance naturally protects against misuse.

In addition to governing the data itself, businesses must also govern the AI tools accessing it.

“You need governance for the large language models that will be touching that data,” Hurt says. This helps prevent shadow AI — unsanctioned tools that could create compliance or security risks.

If your end goal is to identify use cases where AI can drive efficiency and customer satisfaction, you must ensure that the data you have is accurate and secure.”

John Whippen Regional Vice President, U.S. Public Sector, Snowflake

Classify, Clean and Monitor Data

Centralized data management also simplifies three essential practices for preparing data for AI:

Data classification. Implement standardized metadata policies and access controls so team members can quickly find and use the right data for their roles.
Data cleaning. Remove duplicate or outdated records. Clean data ensures AI models generate consistent, accurate insights.
Data sharing. Enable secure cross-departmental sharing so marketing, sales, finance and operations can collaborate using shared, real-time data.
Data monitoring. Continuously track data quality and usage to protect against breaches, flag anomalies and keep AI outputs relevant.

While many of these processes can be automated, human oversight remains crucial.
“You want to make sure you actually have hands-on experience and that you’re looking at that data to do a pulse check against automated processes,” Whippen says. “It can seem like a heavy lift initially, especially if you’re not prepared for it, but there are a lot of native platforms out there that make it easier.”

Ultimately, data quality and security dictate AI success.

“It all begins with solid data management,” Whippen emphasizes. “Breaking down silos and creating consistent, reliable data lays the foundation for small businesses to grow AI from a single use case into a transformative force.”

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