3 steps to better data quality

Businesses are creating, capturing, and consuming data at a rapid rate. Statista forecasts the amount of data generated globally will reach 527.5 zettabytes by 2029.¹

Within an organisation, this data can be siloed across hundreds of systems and applications, from enterprise resource planning (ERP) to HR tools. It can also be duplicated in records that tell users different things. For example, one system might list a regional consultant as a full-time employee, while another shows they transitioned to a part-time contractor months ago.

To get the most value from your AI investments, it’s important to feed high-quality data into AI systems. Here’s a three-step approach to enhancing data quality.

1. Connect your data

When data is siloed, it’s challenging for teams and AI agents to use it to inform actions and decision-making. Employees spend their time manually moving data between systems, setting up integrations, and working with outdated information. For the best results, data must be consolidated onto a single, accessible platform.

Unstructured data may be stored in a variety of formats, such as PDFs, videos, and streamed content. This can be converted, accessed, and combined with structured data held in relational SQL databases, spreadsheets, JSON files, and more. Application programming interfaces (APIs), bots, and real-time data pipelines should also be brought onto the platform and linked.

ServiceNow Workflow Data Fabric enables businesses to connect data from any system. Information is instantly available to employees and AI agents, whether it’s hosted in apps, databases, data lakes, or elsewhere—without the need for manual copying or lengthy integration projects.

2. Contextualise your data

Many data platforms can move data around, but they can’t directly tie that data and its context into workflows. Without context, isolated data points are meaningless.

Giving data context helps AI agents learn what data means, where it comes from, and whether it can be trusted. This makes it possible for AI agents to automate complex, cross-departmental workflows that deliver real business outcomes—from streamlining the supply chain to delivering seamless customer service.

Constructing a unified, searchable data catalogue, enriched with business context, is a great way to centralise your metadata. AI agents can then easily find relevant data, reason across datasets, and take immediate, autonomous action.

Workflow Data Fabric uses Knowledge Graph to organise enterprise data by relationships, enabling AI agents to recognise how data connects across people, processes, and systems. Using Knowledge Graph as a map to the organisation’s data, AI agents can build deep domain awareness, improve reasoning, and optimise processes.

3. Control your data

AI agents may struggle to distinguish between a trustworthy and an untrustworthy source without built-in governance controls. If an AI agent accesses unverified or sensitive data to automate a workflow, it could provide inaccurate advice to a customer or inadvertently expose sensitive data that violates data protection regulations.

Robust data governance is essential to unlocking AI deployments that deliver value without compromising security or compliance. As legislation rolls out to govern responsible AI use, such as the EU AI Act, ensuring effective technology governance will become a business imperative.

Workflow Data Fabric provides easy-to-use workflows to track data and enforce governance policies. This can give teams confidence to implement AI and trust the outputs it delivers.

Use cases for high-quality data

High-quality data enables employees and AI agents to take fast, meaningful action. Customer service representatives can resolve issues in a single call. Finance workers can approve expense requests instantly. Let’s unpack some use cases in more detail.

A financial services business wants to automate its credit extension process. By centralising its data, the organisation eliminates the need to navigate disparate systems for customer information, order history, and credit checks. The relevant information, enriched with real-time borrower data, is surfaced in a single user interface.

An AI agent can use this data to automatically evaluate customer credit risk and pass its recommendation to a manager for the final credit decision. This streamlined approach allows the organisation to process loans faster and deliver an enhanced borrower experience.

When a technology company faces a critical IT issue after a product update, disjointed systems can make it challenging to achieve a swift resolution. The resulting downtime leaves customers frustrated.

Connecting key systems and performance data enables the organisation to implement AI agents for speedy incident resolution. With critical system information instantly available, an AI agent can triage and route the case to the correct support team. The problem gets resolved faster, and the AI agent uses historical resolution patterns to predict issues before customers detect them.

Realising AI return on investment is easier if your data is connected, contextualised, and controlled effectively. ServiceNow Workflow Data Fabric brings data across the organisation onto a single platform—so you can tame the data chaos.

Find out how ServiceNow can help put your data to work.

¹ Statista, Volume of data or information created, captured, copied, and consumed worldwide from 2010 to 2029, November 19, 2025