Why Procurement Platforms Miss the Mark in Data Tracking

Nomad Data
October 8, 2024
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The Hidden Complexities of Data Asset Management

In today's data-driven business landscape, organizations are increasingly reliant on vast amounts of information to drive decision-making and gain competitive advantages. However, many companies are struggling with a critical blind spot in their data management strategies - the limitations of traditional procurement platforms in tracking data purchases and usage.

These platforms, designed primarily for tangible assets, fall short when it comes to the unique characteristics and lifecycle of data products. The result? A significant gap in understanding the true value, usage, and potential of data assets within organizations.

The Pre-Procurement Blind Spot

Unlike traditional assets, data requires extensive evaluation and testing before purchase. This crucial phase often goes unrecorded in procurement systems. Engineers and analysts may spend weeks or months assessing a dataset's quality, running tests, and determining its potential value to the organization. Yet, procurement platforms typically only capture the final purchase decision, leaving a wealth of valuable insights and learnings unrecorded.

This oversight can lead to inefficiencies and repeated work. Without a system to track pre-purchase evaluations, organizations risk wasting resources by repeatedly testing the same datasets. In some cases, companies have evaluated the same dataset multiple times, unaware that it had already been deemed unsuitable for their needs.

The Complexity of Data Licensing

Data products come with intricate licensing terms and usage restrictions that traditional procurement systems struggle to capture. Unlike a physical asset with clear-cut ownership and usage rights, data licenses can be highly specific and variable. For instance, a dataset might be licensed for use by a particular department, for solving specific problems, or with restrictions on its application in AI model training.

These nuanced terms are critical for compliance and maximizing the value of data assets. However, procurement platforms often lack the flexibility to record and communicate these details effectively across an organization. This gap can lead to unintentional misuse of data or missed opportunities to leverage existing assets fully.

The Post-Purchase Knowledge Vacuum

Perhaps the most significant shortcoming of procurement platforms in data tracking is their inability to capture the evolving value and challenges of data usage over time. Once a purchase is recorded, these systems typically offer little support for tracking how the data is actually being used, the insights gained, or the problems encountered.

This limitation creates a knowledge vacuum where valuable learnings about data assets are scattered across the organization, often residing only in the minds of individual employees. When staff members leave or change roles, this crucial information can be lost, leading to a fragmented understanding of the organization's data assets.

Accessibility and Knowledge Sharing Barriers

Traditional procurement systems are designed primarily for use by procurement teams, not for broader organizational access. This restricted access creates significant barriers to knowledge sharing about data assets. The average employee seeking information about available datasets within the company will find little useful information in a procurement platform.

The lack of a centralized, accessible repository for data asset information hinders effective data discovery and utilization across the organization. Teams may be unaware of valuable datasets already purchased by the company, leading to redundant purchases or missed opportunities for cross-functional collaboration.

The Need for a Holistic Approach

Addressing these challenges requires a shift in how organizations approach data asset management. What's needed is a system that can track the entire lifecycle of a data asset, from initial discovery through evaluation, purchase, and ongoing usage.

Such a system would ideally capture:

  • Initial vendor interactions and dataset discovery
  • Evaluation processes and test results
  • Detailed licensing terms and usage restrictions
  • Purchase information and renewal dates
  • Ongoing usage patterns and derived value
  • Implementation challenges and solutions

By maintaining this comprehensive record, organizations can make more informed decisions about future data purchases, avoid redundant acquisitions, and maximize the value of their existing data assets.

The Dynamic Nature of Data Value

Unlike physical assets that typically depreciate over time, the value of data can fluctuate and even increase as new use cases are discovered. A dataset initially purchased for one purpose might later prove invaluable for an entirely different application. Traditional asset valuation methods struggle to account for this dynamic nature of data value.

For example, a company might purchase a research corpus for sector analysis, only to later realize its potential for training an internal AI model. However, if the original licensing terms didn't account for this use case, the company might be unable to capitalize on this opportunity. A more flexible tracking system could help organizations stay aware of both the potential and limitations of their data assets as new technologies and use cases emerge.

Real-World Consequences of Inadequate Tracking

The shortcomings of current data tracking methods can have significant real-world consequences. Organizations may find themselves repurchasing datasets they already own, wasting substantial resources in the process. In extreme cases, companies have been known to buy the same dataset multiple times, each time with an organizational license, resulting in unnecessary expenditure.

Moreover, the lack of comprehensive tracking can lead to missed opportunities for data monetization or collaborative projects. Valuable datasets might sit unused simply because the right people in the organization are unaware of their existence or potential applications.

Moving Beyond Procurement Platforms

As organizations continue to invest heavily in data assets, it's clear that relying solely on procurement platforms for tracking these investments is inadequate. The future of effective data asset management lies in specialized systems designed to handle the unique characteristics of data products.

These systems should be accessible to a wide range of users within the organization, facilitate knowledge sharing, and provide a comprehensive view of the data asset lifecycle. By implementing such solutions, companies can unlock the full potential of their data investments, avoid costly redundancies, and foster a more data-savvy organizational culture.

In an era where data is often touted as the new oil, having the right tools to manage this valuable resource is not just an operational necessity - it's a strategic imperative. Organizations that can effectively track, manage, and leverage their data assets will be well-positioned to thrive in the increasingly data-driven business landscape of the future.

To address this issue, many companies are turning to Nomad Data's Data Relationship Manager (DRM). The DRM tracks all interactions with data vendors and datasets, including meetings, tests, licenses, and implementation details. It serves as the central repository for this information, acting as the main source of truth for all data assets a firm is aware of, has tested, or purchased. While not intended to replace procurement platforms, the DRM provides a comprehensive overview of an organization's data-related activities and relationships.