Zero Waste Data Spending: The 2025 Imperative

Data budgets waste away in silence. Most firms have little visibility into who uses their purchased data, how they use it, or what value it creates. This blindspot costs organizations millions while starving critical initiatives of resources they desperately need.

We're approaching a critical inflection point. Organizations face mounting pressure to eliminate waste in their data spending while simultaneously needing more data to fuel AI and other strategic initiatives. The solution isn't bigger budgets but smarter spending through accountability systems that track, measure, and optimize data investments.

The Hidden Costs of Untracked Data

The most obvious hidden cost is purchasing data that creates little or no value for the organization. It's surprising how little information most firms have about who uses particular datasets, what they're using that data for, or the value it provides. Most organizations completely lack any KPIs for measuring data value.

This information vacuum creates a painful renewal process. At renewal time, it becomes an exercise of emailing people throughout the organization, having 20 different conversations to figure out where the value is. This approach is fundamentally counterproductive.

Data usage information needs to be tracked in a centralized place and tied into information coming from vendors so you can quickly understand where the value lies. Without this visibility, organizations have no way to ensure purchased data assets are properly utilized. Even when data has potential value, without visibility into usage patterns, the likelihood of realizing that value approaches zero.

Building a Centralized Data Inventory

To properly assess data value, organizations need to track specific information in a centralized system. At minimum, you need to know:

• Who is using each data set
• How they're using the data
• What specific purpose it serves
• Some quantification of the value derived
• Usage frequency and patterns

This centralization is crucial because often the people buying data and seeing its potential value aren't the same people responsible for using that data to produce outcomes. If you can't see what happens after purchase, you're setting yourself up for failure.

The first step toward solving this problem is implementing a centralized system like a Data Relationship Manager to serve as the hub for aggregating usage information. Organizations should also define the KPIs they want to track, recognizing that different datasets have different dynamics. Success using one dataset might require different usage patterns than another, so it's critical to establish measurements that accurately reflect each dataset's actual value.

Implementing Data Value Scoring Systems

The most effective approaches to quantifying data value are automated. At a minimum, organizations should connect their central tracking system directly to information from vendors. If users access data through a UI, track login velocity and regularity. For data loaded into databases, implement instrumentation to send usage information back to your central system.

While less sophisticated, surveys can also provide valuable insights. Building periodic surveys into your data management system allows you to ask users about their experience with different datasets, rating value on various scales. Though crude, this approach still provides substantially more visibility than most organizations have today.

For basic metrics, start by tracking:

• List of users accessing each dataset
• Number of access events per user per month
• Total volume of data consumed
• Query frequency
• Login frequency

These metrics provide a starting point for meaningful conversations about data value, particularly at renewal time.

Connecting Data Spending to Business Outcomes

The ability to tie data spending directly to business outcomes varies significantly by use case. In some businesses, the connection is relatively straightforward. For example, if you implement a new dataset to power a pricing strategy, you can compare resulting sales using the new pricing versus previous results.

Quantitative investment firms can more easily perform attribution analysis since their investment strategies often rely directly on specific datasets. However, many business cases present significant measurement challenges. In these situations, you can often get a directional sense of value. People typically have a reasonable gut check on whether data is valuable, but generating precise ROI figures can be difficult without resorting to arbitrary calculations.

The clearest sign of waste is when no one accesses a data portal, makes API calls to endpoints, or queries databases where data has been imported. However, this waste often stems from good intentions. Organizations purchase data with plans to use it, but without visibility into usage patterns, they can't identify when adoption fails or take corrective action.

Technology Solutions for Data Spending Accountability

Nomad Data’s Data Relationship Management (DRM) platform is becoming increasingly important as a central hub for tracking data usage and value. DRM systems connect to various APIs to pull in usage information from both external and internal systems, then display that information in accessible dashboards so stakeholders can easily assess data value.

The shift toward consumption-based data models will actually simplify tracking efforts. As more data consumption occurs through APIs, these systems will likely include reporting endpoints. With a centralized system connected to these endpoints, organizations can more easily see exactly where different datasets are being used, how they're being used, which systems and people are using them, and ultimately tie that back to value produced.

Overcoming Implementation Challenges

The biggest obstacle to implementing data spending accountability systems is often urgency, or rather, the lack thereof. Companies juggle numerous competing priorities, and while inefficient data spending creates real problems, it may not feel like a crisis demanding immediate attention.

For some businesses, the lack of centralized data inventory actively impedes progress today. For others, it represents a significant but not immediate problem, leading them to continually postpone addressing it. This procrastination results in ongoing poor economic decisions around data, leaving budgets consumed by underutilized assets while preventing investment in new, potentially valuable data sources.

As this cycle continues, it creates increasingly serious problems for the business, limiting innovation and hampering growth.

The 2025 Data Spending Landscape

Looking toward the future, we anticipate significant shifts in how organizations approach data budgeting and value assessment. The rise of AI will substantially impact how all systems consume data, likely accelerating the transition to consumption-based models that better accommodate diverse systems and use cases.

This shift will give companies, agents, AI systems, and other entities access to more data, making it even more critical to measure the value of consumed data. The trend toward increasing AI adoption will simultaneously increase demand for data, putting upward pressure on data budgets.

Since most companies lack the financial flexibility to simply increase data spending, efficiency becomes paramount. The question becomes: Are you spending efficiently on data?

The Unexpected Benefit of Data Spending Accountability

Beyond cost savings, the most significant benefit of implementing data spending accountability systems is having budget available for new initiatives. When team members request data for new projects, features, or products, organizations with optimized data spending can actually fulfill those requests.

This may seem minor, but in today's environment where data budgets are typically maxed out, new requests are often denied or deferred to future budget cycles. As a result, data-driven projects—which increasingly means virtually all projects—struggle to move forward.

By eliminating waste and optimizing data spending, organizations create the financial flexibility to support innovation and growth, turning data from a cost center into a true strategic asset.

As we move through 2025, the organizations that thrive will be those that implement robust data spending accountability systems, eliminating waste while ensuring every dollar spent on data delivers maximum value. The era of zero waste data spending isn't just an aspiration—it's becoming a business imperative.