Understanding Data Procurement: Process, Management, Benefits, and More


Table Of Contents

Procurement involves the entire supply chain of an organization, including every activity with regard to obtaining goods and services that a company needs to maintain daily operations, negotiating terms, purchasing items, and more. Data procurement is a business function that both strategic and tactical.

Business leaders already know that it’s important to make data-driven decisions. Analyzing internal data provides objective information on a variety of aspects surrounding business performance, helping decision-makers make choices that are based on concrete evidence rather than gut feelings.

But these days, relying exclusively on internal data to make important decisions will cause your business to fall behind. Modern businesses work with a variety of stakeholders, such as:

  • Business partners
  • Resellers
  • Channel partners
  • Regulators
  • Suppliers
  • Customers

These networks of external stakeholders are often globally distributed, with different economic climates, business procedures, and environmental factors contributing to the way they do business. The opportunities and additional insights that can be harnessed by analyzing external data from these stakeholders are invaluable. This collected first- or second-party data can highlight patterns or trends, like shifting consumer preferences and behaviors, competitor initiatives, new industry best practices, and much more.

In some cases, businesses may also want to gain access to other types of data, like weather or demographics, that they feel would be useful. This type of data is called third-party data and is unique because it results from data aggregation and is sold by data brokers or identity solutions providers that didn’t create the data themselves. Instead, they’ve made a market of aggregating data from different sources and packaging it together to sell, usually through a data marketplace or data exchange.

But procuring and using data effectively is often a challenge for businesses. Most companies still need to modernize their data infrastructure to handle increasing amounts of data and contend with the regulations and laws that exist to protect sensitive data and ensure that data isn’t used outside of its intended purpose. Further, ensuring that access to data is democratized without compromising on security and data governance policies needs to be considered as well.

This article outlines what data procurement is, explains what the different types of data mean, the typical process behind procuring data, challenges, benefits, and more.

What is Data Procurement?

When businesses acquire datasets from various sources to enhance business operations throughout the organization’s supply chain, it’s called data procurement.

Data discovery is an essential part of data procurement. Organizations often need to “discover” their own data by using a data discovery tool to find and classify internal data from various distributed systems.

From an external data-gathering perspective, data discovery involves finding data that can be used to augment internal organizational data. The benefit, of course, is that businesses can better understand their overall business performance by having more data points to compare their internal data to.

Examples of sources where relevant data can be discovered include:

  • Data marketplaces
  • Data exchanges
  • Direct data sharing between two organizations
  • From IoT devices, such as machine sensors and smart devices

Different Types of Data

It’s essential that businesses understand what kind of data they are seeking out. The table below outlines three types of data that can be gathered through data procurement processes:

Type of Data Explanation
First-party data Data that a business collects from customer interactions or transactions. This data can originate from websites, apps, CRMs, and more.
Second-party data Refers to first-party data from a different organization. Second-party data can come from partners, suppliers, distributors, and more.
Third-party data Data sets that are collected and managed by organizations that don’t directly interact with customers. This can include datasets that are built using data from various sources, like IoT devices, government, or academia.

Procurement Data Management

Effective procurement data management must consider all aspects of how data is gathered, stored, analyzed, and categorized. This often involves utilizing different technologies like a data lakehouse, data procurement software, and ERPs.

Tools that Assist with Data Procurement

Various technologies are available that help businesses manage their data procurement. These technologies are outlined in the table below.

Tool Use Case Explanation
Enterprise resource planning (EPR) systems Designed to assist with managing business procurement functions like supplier selection, ordering, and tracking payments and deliveries
Business intelligence and data analytics tools Offer a range of procurement data analytics capabilities, from analyzing large amounts of data to visualizing trends and insights that can inform supply chain decision making
Big data platforms These platforms allow businesses to collect, store, and analyze large amounts of data in real-time

The Data Procurement Process Explained


Understanding data procurement management requires a closer look at the process behind it. While the data procurement process can be more complex than the steps listed below, it will include at least the following steps:

1. Identifying Needs

Determining what data your organization needs from external stakeholders in your network, or could use from third-party vendors like data brokers, is the first step in the data procurement process. Choosing what data should be obtained needs to align with established business outcomes. An effective enterprise data governance plan should also be in place to ensure that data is handled appropriately and that access and use cases are monitored and controlled.

2. Planning & Budgeting

Effective data procurement has to be backed by a robust data infrastructure that supports the management of incoming data. This may mean investing in data catalog tools, data governance tools, BI and analytics tools, initiatives related to data science and strategy, and more. Planning for investment into these tools and initiatives is important to ensure that once data is collected, it can be used effectively and efficiently by those who need it.

3. Researching Suppliers

Performing research for suppliers for data procurement can refer to two different things, depending on whether you’re looking for second or third-party data:

  • For second-party data, you’ll want to do research on which suppliers, distributors, and channel partners, business partners, and other external stakeholders your organization does business with. From there, you can determine what kind of data would be useful for you to obtain from them. Useful data could include product purchasing, shipping, or delivery information.
  • For third-party data, research potential vendors that provide aggregated data, ensure they are reputable, and sell data in compliance with appropriate regulations and laws.

4. Negotiation & Contracts

If you’re establishing a data sharing or exchange relationship with an external organization within your network, then you’ll want to discuss and negotiate the terms of the agreement and draw up a written contract.

Suppose you’re considering purchasing data from a data broker or vendor that sells third-party data. In that case, they should have licensing agreements and general terms and conditions for data use. In this case, you’d have to agree with their terms and ensure that you follow the licensing requirements for the data that you’ll obtain from them. Neglecting to do so can result in fines from regulatory authorities or even result in a lawsuit.

5. Handling Relationships

Ongoing relationship management is important for dealing with external stakeholders and vendors. If issues arise, they should be addressed promptly to ensure the data-related transactional relationship can continue as smoothly as possible. Further, it’s important that everyone understand their obligations and responsibilities from the beginning to help prevent any issues from occurring in the first place.

6. Performing Quality Assurance

When receiving data from vendors or external stakeholders, it’s important to perform the appropriate checks and balances to ensure the quality and integrity of the data that you’ve received. This is especially important at the beginning of the transactional relationship with your suppliers, distributors, or partners or if you’re using a new vendor to procure third-party data.

7. Ensuring Available Inventory

Of course, you can’t retrieve data from an external stakeholder if they don’t have the data to provide. Once you determine what data you need, you should discuss the availability of this data with them so you can plan for how you will use it effectively. For instance, a supplier may only have complete purchasing data available every business quarter as they may have to gather it from their customers. That should be factored into your plans regarding how this data will be used in your business.

Procurement Data Management Challenges

The purpose of gathering data is to increase the information that your business has access to so that better decisions can be made and so improvements can be made across the supply chain. However, the dynamics behind procurement data management isn’t an easy task.

To manage large amounts of data, organizations need to consider a variety of challenges and come up with solutions to meet them. These challenges include:

Bridging the Data Gap

Data sources vary widely in the typical organization. It’s not uncommon for businesses to have multi-cloud and hybrid systems for data storage. Because of the differences in how these different systems approach security and access, it can be difficult to enable fast access to data for all users.

As a data marketplace solution, Revelate is platform agnostic, meaning that it can extract data from any source and prepare it to be delivered to any target, provided that target can accept the data format. Combined with centralized security and access control through our partner Immuta, democratized data access is possible regardless of the data source.

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Data Exploration

Visibility into organizational data is crucial to ensure that it’s correct, to identify gaps, and to determine where external data would provide benefits. But for most organizations, finding data and performing manual data categorization would take an astronomical amount of time and resources.

To find and organize data internally, organizations need to be in a position to invest in data discovery tools and data catalog tools so that data from various sources can be categorized and searchable. This can be a time-consuming process in the beginning as categorization standards are developed and libraries of business terms are created.

Enrich Data for a Comprehensive Analysis

Gathering data from partners and marketplaces is good, but time needs to be taken to determine where those external data sources can add value to internal data. This requires robust data analysis tools, especially ones that can present a visualization of datasets so that non-technical employees can glean insights and determine whether it would add value to an internal dataset. Once again, companies must be in a position to invest in these types of data tools and prioritize the time that will be spent on gleaning insights and augmenting internal data.

Tracking Usage Allocating Cost of Procurement

Gathering, storing, preparing, packaging, and distributing data all have costs associated with processing power and storage. It’s easy to get lost in the weeds with cost, as the focus will likely be on how to use incoming data effectively. For most organizations, providing the processing power and storage needed isn’t feasible from a cost and resources perspective.

Organizations like Microsoft, Amazon, and Google provide processing and storage solutions for data procurement via monthly subscriptions or pay-per-use. Big data service providers also provide solutions for processing large amounts of incoming data, typically via a pay-per-use model where processing power scales to your use case.

Usage Rights Management

One of the most important considerations when businesses are gathering external data is to ensure that they are using it correctly and that if it’s sensitive, it is protected as per applicable regulatory requirements and laws. Without the right tools and approach to data governance in place, organizations place themselves in a precarious position, risking large fines and even company shutdowns.

Creating an effective data governance model, investing in the right security and access tools (preferably with centralized management), and facilitating an organizational culture that prioritizes data security must be in place.

Benefits of Data Analytics in Procurement


The purpose of data analytics in procurement is to glean actionable insights from procurement data that can then be used to improve business performance and deliver value. These insights are the most important reason businesses invest in the tools, processes, and procedures involved in utilizing procurement data.

Below are the main benefits of data analytics in procurement, explained in more detail:

1. Reduce Costs and Improve Margins

By analyzing data from various external sources across your organization’s supply chain, valuable insights can be gleaned that allow improvements in how processes are executed at every stage. For instance, let’s say a delivery company augments internal delivery data with routing data from third-party vendors and discovers optimizations to delivery routes that save thousands of dollars in fuel and vehicle maintenance costs every month and time to deliver a product.

Using data analysis can make more effective decisions at the start of a process or procedure, with cascading positive effects that save time and money.

2. Increase Savings by Learning More about Suppliers

Data analysis is important for supplier monitoring. To ensure that organizational values are being reflected in suppliers, as well as ensuring that quality and consistency standards are being met, it’s essential to utilize data analysis to capture the details, anomalies, and performance information for all suppliers that your organization works with. Doing so helps ensure that expenses are being allocated appropriately and that your organization isn’t wasting money on non-compliant suppliers.

3. Better Understand Risks

By analyzing patterns and trends, organizations can better prepare for future risks that may occur throughout the supply chain by developing strategies to mitigate them. For instance, let’s say a manufacturing company uses a specific type of machine to fabricate a product, but trends from data analysis of third-party data regarding this machine show that a particular component will likely fail at around the five-year period. By replacing that component before the five-year mark, the manufacturer can mitigate the risk of halting or slowing operations that would cost the company money and time.

3. Increase Planning Accuracy

Analyzing customer data helps organizations make better plans for the future. For instance, by understanding trends in customer demand for a product, an organization can decide when manufacturing for a specific product should be slowed or increased.

4. Increase Collaboration

By sharing analytics information with the appropriate stakeholders, collaborative relationships can be developed that benefit all involved parties. For example, car dealerships can share vehicle repair information with car manufacturers so they can better determine appropriate warranty lengths.

Why is Big Data Procurement Important?

Big data is increasingly being utilized by companies of all sizes and in all industries. The insights that can be gleaned from big data to make effective business decisions are just too valuable to ignore.

Traditionally, procurement analytics has involved various manual processes, including collating historical spending, contract, and supplier data with transactional data sources, then manually enriching this data with spend categorization. But with big data, the breadth and scope of effective decision-making increases exponentially.

With regards to supply chains, big data has the transformative potential to:

  1. Improve decision making
  2. Predict better entry scenarios
  3. Provide more mature market analysis
  4. Improve purchasing strategies
  5. Create more transparency between suppliers
  6. Provide more flexibility across the supply chain
  7. Improve process efficiency

That being said, organizations need to prioritize building the data infrastructure needed to support effective management of data procurement. Utilizing big data SaaS specialists is a viable solution for most organizations for building out this initial infrastructure and handling the costs associated with processing and transforming large amounts of data into usable sets.


Data procurement is essential for modernizing the insights an organization can glean from their supply chain. Organizations will need to utilize data from their direct stakeholders, including suppliers, distributors, business partners, and more, as well as other data sources that may be helpful that can be obtained from third-party data vendors.

Effectively extracting and combining this data together results in cost savings through better operational efficiency throughout the supply chain, potentially better relationships with stakeholders, and identification of opportunities that simply could not have been found without the insight provided by these external data sources.

But to realize the benefits that external data can provide to your organization’s supply chain, the people who understand particular aspects of your business, from marketing to finance, need to have easy access to this data.

That’s where Revelate comes in as a solution.

Through your Revelate data marketplace, you can democratize access to data throughout your organization and provide on-demand self-service fulfillment. This is done through a fully customizable data marketplace that can present different UIs depending on who is accessing the system. For example, internal employees would see a different interface compared to external business partners. Further, you can completely customize how your datasets are displayed on the marketplace, ensuring that data consumers will be able to find the datasets they are looking for, and understand their contents. Data is also never stored on Revelate’s servers. Instead, temporary S3 buckets are used to enable secure extraction and delivery of datasets when they are requested.

In addition to all of this, you can centrally manage the security, access, and data governance of your marketplace, ensuring that the right people get access to the right data.

Interested in trying Revelate out for yourself? Get Started today!

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started