Start Building a Data Foundation With 3 Essential Steps

Start Building a Data Foundation With 3 Essential Steps

Learn how to build a unified approach to data with a step-by-step guide from our Agentic Enterprise Playbook.

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This practical guide is inspired by Chapter 5 of our comprehensive playbook "Becoming an Agentic Enterprise: A Step-by-Step Guide," where we dive deeper into data strategy alongside six other critical steps for AI transformation. Read the complete step-by-guide.

Think of the best restaurant experience you've ever had. The service was flawless, the ambience unforgettable, but what truly made you a customer for life was how every bite exceeded your expectations.

Behind that extraordinary meal was a chef who understood that exceptional dining experiences don't happen by accident - they emerge from what culinary professionals call mise-en-place, the meticulous practice of having every ingredient sourced, cleaned, and organized in its place well before service begins.

Your enterprise AI agents need the same mise-en-place approach to data.

Just as messy kitchen prep leads to disappointing meals and frustrated diners, scattered enterprise data creates confused AI agents and underwhelmed customers. But when you apply discipline to your data foundation - carefully curating information sources, organizing them systematically, and understanding how each piece contributes to exceptional experiences - your AI agents can deliver the kind of personalized, intelligent service that drives customer loyalty.

And the best part is, all the ingredients you need are already in house. Your CRM records, support cases, product catalogs, and customer interaction data represent a complete pantry of information. Here's how to turn your data into competitive advantage - a unified knowledge foundation for AI agents that consistently exceeds expectations.

Step 1: Clean and organize your data

Every organization has two types of data, structured and unstructured. Structured data pertains to information that is easily archivable, like client records or profit trends. Unstructured data, like social media and emails, don't easily fit into the rows and columns of a spreadsheet.

Now imagine a prep cook with two pantries in the same restaurant: one is meticulously organized with labeled containers and clear shelving (structured data), while the other is a cluttered mess where ingredients are scattered, unlabeled, and nearly impossible to find when you need them (unstructured data). Each type of data requires specific prepwork.

Prepare structured data for AI agents

Typically an organization's structured data comprises items like customer records, transaction histories, and inventory databases. These data types are labeled and sorted, but even here challenges remain. For example, the "Text-to-SQL" task of translating natural language prompts into database queries requires specific preparation. Or when your AI agent encounters a "customer_id," it needs to recognize that it's not just a random database field, but that it corresponds to specific customer records in your CRM system. Achieving this connection requires fulfilling three prerequisites:

• Semantic mapping: Create metadata descriptions explaining field meanings and relationships (e.g., "customer_id" links to "Customer records in CRM system")
• Query patterns: Document how your business calculates lifetime value or identifies churn risk (e.g., "How do we typically calculate customer lifetime value?" or "What data points indicate churn risk?")
• Data validation: Implement consistent rules ensuring accuracy and completeness (e.g., (e.g., required fields, data format standards, acceptable value ranges)

Convert unstructured data into agent knowledge

According to the IDC, unstructured data - emails, PDFs, images, chat transcripts, video files - will soon represent 90% of the 400 billion terabytes of global data, yet most organizations treat it like it's that expired half-empty bottle of ketchup shoved into the refrigerator door. The problem is, this data is highly valuable to the customer experience. It just needs better organization.

Smart enterprises navigate this chaos through:

• Content extraction: Use AI-powered tools to automatically extract text, entities, and relationships from various file formats (PDFs, Word docs, images, etc.), making previously locked information searchable and usable.
• Semantic organization: Apply consistent tagging and categorization to make content discoverable (for example, tagging customer support transcripts by issue type, product, or resolution status).
• Version control: Establish clear processes for handling document versions and updates to ensure agents always access the most current, authoritative information.

Once your unstructured and structured data have been prepped, they're ready to connect.

Step 2: Create a unified knowledge architecture

What separates master chefs from their competition? They understand not just that exceptional dishes require excellent ingredients but also how they complement each other. In data terms, this is ontology - the structured map showing how information relates to each other. A great example of ontology is how customer data relates to product information, sales processes, and broader business outcomes.

Without this connective tissue, even advanced AI would be like a skilled chef wearing a blindfold. They may be still able to chop the onion well, but they lack the clarity needed to pull together cohesive dishes for their patrons.

So how do you create a unified knowledge foundation that connects structured and unstructured data effectively? Three key methods make this possible:

API-first approach

Develop standardized APIs ensuring consistent data access across all systems

Embedding strategies

Implement consistent vector embedding approaches across data types to convert all data into numerical patterns that AI can understand and compare. Think of this as creating a common "language" for text, images, and database records

Integration patterns

Establish repeatable connection methods across systems, including real-time sync and data transformation rules

You've now organized and connected your data. Next, ensure a system for maintaining the accuracy and helpfulness of your data.

Step 3: Build scalable data governance that works

At fine dining establishments, executive chefs don't simply leave their staff to their own devices. They watch, instruct, and step in when needed. Likewise, establishing a solid data foundation isn't a one-and-done task. Successful enterprise data depends on continual quality assurance and updates. Here's what to keep in mind when establishing governance systems for AI agents in your business.

Your three-part data governance framework

Access controls:

In any impeccably run kitchen, pastry chefs wouldn't have access to the saucier's station, and only the head chef has keys to the wine cellar. Similarly, your data governance requires precision and hierarchy.

• Role-based access: Define clear roles for agents just as you would for employees
• Attribute-based access: Control access based on data attributes and need-to-know classifications
• Audit trails: Maintain comprehensive logs of all agent data access
• Purpose limitation: Restrict data usage to specific, documented purposes

Privacy safeguards:

Great chefs protect their secret recipes-sharing only what's necessary with each team member and carefully guarding proprietary techniques. Your enterprise data deserves the same thoughtful protection, especially when it contains sensitive customer information.

• Data minimization: Limit agent access to only data necessary for their specific function
• Anonymization/pseudonymization: Apply appropriate masking techniques for sensitive data
• Retention policies: Implement clear data retention and deletion procedures
• Consent management: Ensure proper consent tracking for data use

Security measures:

The world's finest restaurants don't just lock their front doors-they secure ingredient deliveries, protect recipe vaults, monitor kitchen access, and train staff on safety protocols. Similarly, protecting your enterprise data requires multiple layers of security that safeguard information at every stage of the AI agent process.

• Encryption: Protect data in transit and at rest with appropriate encryption
• Multi-factor authentication: Apply strong authentication for sensitive system access
• Backup and recovery: Ensure comprehensive backup procedures for all data sources
• Security awareness: Train teams on security practices specific to AI agents

Lessons learned from winning CIOs

Now that you have a solid foundation for understanding your data, resolving its issues, and keeping your information safe and secure, it's time to think about an implementation strategy. As you plan this out, though, keep in mind some of the most important takeaways from early AI adopters:

• Invest in semantic organization from the start; otherwise, agents find facts but miss meaningful insights that create real value
• Ensure data quality across every department. Messy data creates confused agents. Watch for content collisions where sources contradict each other
• Leverage decades of past decisions. Rather than starting from scratch; institutional knowledge dramatically improves decision quality
• Build for broader use cases than your first implementation; clean, connected data delivers unexpected value beyond initial plans. Smart small. Stay focused. And set yourself to learn and grow along with your agents.

Data quality determines outcome quality

Your AI agents deserve better and so do your customers. Future success depends on treating data preparation with the same rigor as a master chef preparing a five-course meal for their most important patron. AI has literally made what we previously thought impossible, possible. And while you should be inspired by that, it's important to remain thoughtful about how data is connected and how these connections translate to customer experience.

The difference between a happy customer and an unhappy one lies entirely in how well you prepare your data. In the agentic AI era, data quality determines outcome quality.

Bottom line: Clean and organize your data. Create your "secret sauce" - a unified knowledge architecture. Govern your data with executive chef efficiency. Your AI agents - and your customers - will taste the difference.

Try this activity: Score essential data sources

Assess your data sources before deploying AI agents. This systematic evaluation ensures your agents have access to the highest-quality information needed to deliver exceptional customer experiences.

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