Building for AI: Why Data Collection Is Your Most Critical Investment

In today's rapidly evolving business landscape, Artificial Intelligence (AI) promises to revolutionize operations, enhance efficiency, and unlock unprecedented insights.

Yet, as organizations eagerly embrace AI, a fundamental truth often goes overlooked:

the success of any AI initiative hinges not just on sophisticated algorithms or cutting-edge platforms, but profoundly on the quality and integrity of its underlying data.

In my research on organizations on data and analytics initiatives, I've witnessed a consistent pattern:

Companies eager to adopt AI often underestimate the foundational importance of how they collect their data.

Indeed, recent reports underscore this, with some indicating that 85% of AI projects fail, while 70% specifically fail due to data quality and integration issues (McKinsey, 2023), and 80% encounter difficulties related to data quality and governance (Deloitte, 2024)

This highlights that while AI tools capture headlines, the unglamorous but essential work of getting data collection right is where true value is created.

The Data Foundation: A Critical Investment

While the allure of advanced AI models is strong, my research suggests that a significant portion of an AI initiative's success is determined by its data foundation.

This includes the meticulous processes of data collection, preparation, and governance. We've observed that investment priorities sometimes flip this ratio, with organizations spending substantial resources on AI tools and talent while neglecting the foundational work of data collection.

However, a growing number of forward-thinking companies are recognizing this imbalance. For instance, a recent survey revealed that, on average, businesses are investing 59% of their total AI budget specifically into training data, which encompasses collection, processing, and annotation.

Furthermore, approximately 68% of enterprises are allocating nearly 30% of their IT budget to data storage, management, and protection, underscoring the increasing recognition of data's foundational role.

"This shift in investment reflects a deeper understanding that robust, high-quality data is the essential raw material for the AI industrial revolution. This is evidenced by businesses investing an average of 59% of their AI budgets on training data (LXT.ai, 2022) and 68% of enterprises allocating nearly 30% of their IT budgets to data storage, management, and protection" (Komprise via Coherent Solutions, 2024)

Three Hard-Won Lessons

I've observed these consistent patterns that underscore the criticality of data collection:

1. You cant compensate downstream for poor collection upstream

The allure of advanced algorithms can sometimes lead organizations to believe that sophisticated AI can magically fix underlying data problems.

However, this is rarely the case. In one case study, a global retailer spent six months trying to build a customer churn prediction model before realizing their customer interaction data was being collected inconsistently across channels. This made it impossible to build an accurate, unified view of the customer journey. The solution wasn't a more sophisticated algorithm; it was redesigning their collection processes to ensure consistent data capture across touchpoints.

Poor data quality carries significant costs. According to Gartner, bad data can cost organizations an average of $12.9 million per year.

These issues often manifest as inaccurate AI predictions, wasted investments, and lost user trust.

Data issues frequently lead to time-consuming fixes, rework, and prolonged project timelines, ultimately eroding the return on investment (ROI).

2. Todays 'nice to have' data is tomorrows 'must have'

The data requirements for AI often extend beyond current reporting or analytical needs.

What might seem like extraneous data today could become crucial for future AI applications. For example, a manufacturing client initially dismissed collecting certain sensor data as unnecessary for their current analytics. Two years later, when implementing predictive maintenance AI, they had to delay implementation by months to retrofit sensors and accumulate sufficient historical data.

Forward-thinking organizations are designing collection systems not just for today's reporting needs but for tomorrow's AI possibilities, anticipating future requirements as highlighted in discussions about the data-driven enterprise of 2025.

3. Collection design is a cross-functional responsibility

Effective data collection for AI is not solely the domain of IT or individual business units.

The most successful AI implementations involve business leaders, data teams, and AI specialists collaboratively designing data collection processes. When collection is delegated solely to one department, it can inevitably produce data that's insufficient for enterprise AI needs. Gartner notes that a lack of ownership is a common challenge, with business leaders agreeing data quality matters but not viewing it as their direct responsibility.

However, data quality is fundamentally a business discipline, requiring collaboration and clear responsibility, with roles like the data steward being crucial for accountability.

A Structured Approach to AI-Ready Data Collection

For organizations serious about building an AI advantage, we recommend a systematic approach to transforming data collection:

• Start with AI use cases: Define the specific business outcomes you want AI to drive, then work backward to identify the precise data requirements.
• Assess your collection maturity: Evaluate your current collection practices against AI-readiness criteria. This involves understanding your data's consistency, trustworthiness, and accessibility.
• Prioritize for impact: Focus initial collection improvements on the data domains most critical to your priority AI use cases. Not all data is equally important, and efforts should be scoped to maximize business benefits.
• Design for scale: Create standardized collection processes that can be deployed consistently across the organization, ensuring data is systematically organized and well-documented.
• Build in governance: Embed quality controls and metadata capture directly into collection processes. Data governance is crucial for ensuring data is accurate, complete, and representative, reducing time spent on cleaning and preparing data.

Investment Guidance

If you're planning AI investments, it's crucial to allocate sufficient resources to your data foundation. As noted, organizations are increasingly dedicating significant percentages of their overall AI budgets to training data, with some allocating 70% or more, and an average of 59% on training data alone.

Research from successful examples show, dedicating a substantial portion, perhaps around 30% or more, of your AI budget to improving data collection capabilities, aligns with industry trends and is a prudent investment. This includes:

• Modernizing data capture systems
• Implementing robust data quality frameworks
• Enhancing metadata collection and management
• Training teams on new collection procedures and data literacy
• Establishing clear data governance mechanisms and ownership

The returns on this investment will multiply as you scale your AI initiatives, leading to operational efficiency, cost reduction, reduced error rates, and improved reputation.

Conversely, underinvestment will create compounding technical debt and hinder AI success.

Looking Ahead

As AI becomes increasingly embedded in business operations, the competitive advantage will shift from having the most advanced algorithms to having the most comprehensive, high-quality data foundation. Organizations that excel at collecting the right data, in the right way, at the right time will enjoy a sustainable edge in the AI economy.

I'm curious:

Where does data collection enhancement fit in your organization's AI roadmap?

What challenges have you encountered in aligning data collection with AI needs?

With 20+ years of experience in enterprise transformation, I have guided companies, across industries, through the journey from traditional setups to forward looking, results oriented business models. I specialize in helping organizations build the correct strategic foundations for building successful and sustainable enterprises

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References

• Assur, N., & Rowshankish, K. (2022, January 28). The data-driven enterprise of 2025. McKinsey & Company.
• DAMA International. (2022). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.).
• Harvard Business Review, Thomas H. Davenport, & Marco Iansiti. (2023). HBR's 10 Must Reads on AI. Harvard Business Review Press.

Works cited

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