.svg)
Which of the following statements best describes your current knowledge about procurement data management?
Jokes aside, many procurement teams struggle to get the most out of their data, citing roadblocks like poor data quality or fragmented systems. But the real issue is that most organizations lack a formal procurement data strategy.
Creating a data strategy sounds complicated and cumbersome, but it really isn’t. It simply means you are clear about the data you do have, the data you want to have, what you want to do with it, and how you want it to influence your actions. This blog explains the key components needed to build your very own procurement data strategy.
When you start working on a data strategy for procurement, you will come across the terms data lake and data ontology. Before we discuss how to develop a data strategy, let’s ensure we have a clear understanding of these concepts.
A data lake is a centralized repository that can store structured, semi-structured, and unstructured data — all in one place. That could include ERP data, supplier portal records, emails, invoices, contracts, and even external market feeds. The term 'data ocean' might be more appropriate these days.
Unlike traditional databases or data warehouses, which require data to be organized and formatted according to a predefined schema before storage, a data lake allows you to store everything in its raw form and process it only when needed, giving you maximum flexibility.
To create a data lake, you must select a scalable data storage solution and processing tools that fit your organization’s size and needs. Cloud platforms like AWS, Azure, or Google Cloud are common choices and offer good flexibility. Your IT team will have the foundational tools needed to build your data lake, but likely lack domain-specific procurement knowledge.
Did you know that ornithology is the term for studying birds? What does this have to do with data? Nothing, but the terms are really close, and we want to make sure you can correct your colleague the next time he says data ornithology. *chuckles*
When speaking about data, the correct term to use is ontology. A data ontology is a structured framework that defines the concepts, categories, and relationships between different data types within a specific domain. Think of it as a shared vocabulary that helps people and machines understand and use data in a consistent and meaningful way.
Unlike simple data schemas or taxonomies, a data ontology not only organizes data hierarchically but also captures complex associations and rules between concepts, supporting a richer and more flexible representation of knowledge.
This structure becomes especially important when working with a data lake. A data lake can store large volumes of raw procurement data, but it doesn’t provide structure or meaning on its own. Without a data ontology to define key concepts — like suppliers, contracts, and risk scores — the lake can quickly become a disorganized “data swamp.”
Ontologies bring consistency by connecting data across systems and formats, making it easier to query, analyze, and apply AI. This is often done through metadata tagging, which links raw data to ontology-defined entities and relationships, and vector engines that enable systems to interpret not just what the data is, but also what it means.
For example, a procurement-specific ontology might define entities like “Supplier,” “Purchase Order,” and “Contract,” and map their relationships, such as a Supplier fulfilling a Purchase Order. It can also include attributes like region, category, or risk score. Tagging your data with this structure ensures consistency across sources — like ERP, supplier portals, and contract systems — enabling deeper insights and more intelligent automation.
For a more in-depth look at all the key terms discussed here (and more), head over to our Procurement Aicademy — self-paced video series to help you build procurement AI literacy.
Building a robust procurement data strategy requires a structured, step-by-step approach. We often partner with our clients and leverage our proven approach to help them build a data lake and strategy that exploits their data most effectively. Here is how we do it.
Every successful data strategy begins with a clear understanding of the outcomes it’s designed to achieve. This means aligning your data efforts with specific business goals, such as boosting cost savings, enhancing supplier performance, reducing risk exposure, or meeting ESG compliance targets. These objectives should be linked to measurable KPIs to ensure the strategy focuses on delivering tangible value and impact across the organization.
Once you know what you are looking to achieve, evaluate your existing data environment to understand your baseline. Identify the systems where procurement data is generated and stored, such as your ERP, procurement suite, contract management system, or supplier platforms. Then, catalog the types of data available, including supplier, spend, and contract data, and assess typical quality issues like duplicates, missing fields, or inconsistent formats. It's also essential to understand data ownership and usage across departments. This assessment should include a gap analysis to highlight what data you have versus what you need to meet your objectives.
With a clear understanding of your objectives and current state, you can begin to design your ideal data model. This involves defining key procurement entities, such as suppliers, purchase orders, contracts, and categories, and mapping the relationships between them. As explained above, a data ontology takes it further by creating a structured, shared vocabulary that both people and machines can understand. This model becomes the blueprint for what well-structured, meaningful procurement data should look like across your organization.
Your data architecture is the technical backbone of your strategy, defining how data flows from source systems into central repositories (like your data lake) and analytics platforms. The key is to ensure procurement data from multiple systems is integrated, enriched, and readily available for analysis. Tools that support data mapping, transformation, and visualization will be essential to make this architecture truly functional.
Data quality is often the biggest barrier to progress, and it won’t improve without deliberate action. There are two approaches: implement a strategy for managing only new data going forward, or use AI to cleanse and enrich historical data as well. The latter is far more valuable. Historical procurement data holds insights into supplier behavior, pricing trends, and risk signals. With the help of AI or rules-based engines, you can standardize supplier names, categorize spend more accurately, fill in missing fields, and link related records. This step lays the groundwork for advanced analytics and automation.
Sustaining data quality requires a strong data governance framework. This includes assigning clear ownership of datasets, defining standards, and setting policies around access control and compliance. Governance also covers ongoing stewardship — ensuring your data remains accurate, relevant, and well-managed over time through continuous oversight and accountability.
Even the best data strategy falls flat if people can’t access and interpret the data. Ensure buyers, category managers, finance teams, and other stakeholders can access insights through intuitive dashboards, reports, or self-service search tools. Advanced models can surface deep insights — from supplier risk to demand trends — but only if data is available in a format that’s easy to access and understand. Ease of use is key to driving adoption and value.
Creating a data strategy isn’t a one-time effort. It’s an ongoing process. Feedback loops, regular audits, and performance metrics help keep the strategy aligned with your evolving goals. As your business changes, your data strategy should adapt, keeping pace with new technologies, challenges, and priorities.
Many procurement teams still operate without a formal data strategy or a centralized data lake. At Procure Ai, we partner with our clients to understand their current landscape, strategic goals, and specific needs, helping them define a tailored data strategy that drives measurable outcomes. Drawing on our experience, we offer a procurement-specific data ontology as a starting point, streamlining the strategy development process and reducing complexity.
Once your strategy is in place, we support the implementation of AI to enhance data cleansing, harmonization, enrichment, and analysis. Our AI-powered Procurement Automation Platform helps organizations overcome fragmented data structures and disconnected toolsets, enabling them to exploit their data to realize its full potential. We bring your data strategy to life through powerful capabilities such as automated cleansing and enrichment agents, an advanced spend and data visualization engine, and our 'Universal Search'—a self-service tool that unlocks insights from years of internal data that makes all procurement information instantly accessible.
A well-defined data strategy is essential for any procurement team aiming to move beyond poor data quality and fragmented systems. It gives you clarity on what data you have, what data you need, and how to use it to drive business outcomes. With the right structure in place, you can significantly improve data quality, unlock value from historical records, and enable smarter, faster decision-making.
Ready to exploit your procurement data to drive insights and savings? Get in touch with Procure Ai today to build a robust data strategy and start turning your data into tangible business value.
%201.svg)
.svg)
