As an industrial data management solution provider, we believe in the value of data. We've built our organization on helping operations teams collect, analyze, and act on operational information. But we also recognize a truth that's easy to overlook in the excitement of digital transformation: data is necessary but not sufficient. 

Recently, we were reminded of this idea while reading Damon Centola's How Behavior Spreads: The Science of Complex Contagions. In the book, Centola provided two clear examples of data constraints. The first is the stock market. We have more financial data than at any point in human history: trading information, corporate filings, economic indicators, and sentiment analysis. Yet, we still can't fully predict market movements. The problem isn't the lack of data; it's that we're dealing with an extraordinarily complex system where we can currently capture only some of the context, some of the interdependencies, and some of the human factors that drive what will or will not happen.  

The second example is planetary observations. In the late 16th century, the astronomer Tycho Brahe devoted his lifetime to collecting extraordinarily accurate observations of planetary motion. Brahe built upon the conceptual frameworks of Ptolemy and Copernicus and then methodically gathered precise data. It was his focus on gathering accurate data and making precise observations that created his legacy. His data would later become foundational for Kepler's laws of planetary motion and, eventually, Newton's theory of gravitation.  

These examples show the duality of data: insufficient when we treat it as a magic solution to complex systems we don't fully understand (e.g., the stock market) and also essential when used systematically to build knowledge over time (e.g., Brahe data). What does this mean for industrial operations in the age of digital transformation? 

Data and the Digital Transformation

In digital transformation, data is necessary but not sufficient. This doesn't mean industrial operations should abandon data initiatives. Instead, it's about adopting a more thoughtful, interrogative approach to industrial digitalization. As Socrates famously stated, "an unexamined life is not worth living." The same holds for industrial operations. An unexamined process, system, or facility isn't worth running in the long term.

Before diving into data collection and dashboards, we need to ask fundamental questions and interrogate ourselves, our organizations, and our industry in general: 

  • What are we trying to solve and why? This seems basic, but too many digital transformation initiatives begin with "we need more data" rather than "we need to understand why our reliability is below expectations." 
  • Why haven't we solved it yet? This question cuts deeper. Often, the barriers are a combination of technical, organizational, and cultural factors. 
  • What is the limiting resource, and can it be resolved? Is it budget, expertise, equipment capability, or management attention? 
  • Can an initiative be started now with existing resources? There's tremendous value in beginning with what you have rather than waiting for perfect conditions. 

These questions help frame the problem, but we need to go further. To understand why data alone isn't sufficient for digital transformation, we need to examine what makes data "necessary" and what additional elements make insights "sufficient" for driving real operational improvements. 

What is Necessary?

Let's establish what necessary actually means for operational data. Three criteria help define essential data:

1. Accurate

Your data must measure what you're expecting and reflect actual operations. But accuracy isn't absolute; it's contextual. Consider room temperature. In a college dorm room, ±5°F might be perfectly acceptable. In a commercial bakery, a temperature variation of ±2°F can significantly impact product quality. In a pharmaceutical laboratory, a temperature variation of ±0.5°F may be critical. Context determines the required precision. As a result, the appropriate accuracy for the application matters, not some arbitrary standard of perfection.

2. Consistent

Are you measuring the same way over time? This seems obvious until you discover that half your team records temperature in Celsius and half in Fahrenheit, or that different shifts use different calculation methods for OEE. Consistency is why dietitians recommend weighing yourself at the same time every day; it removes variables. Did you actually gain five pounds, or did you eat a large meal? Consistent measurement practices answer that question.

3. Complete

Complete doesn't mean perfect. It means incorporating the necessary data points to make informed decisions. You can make reasonable extrapolations, but you need foundational information to support them. If you're analyzing equipment startup performance but you've never collected startup-specific data, you're starting from a position of ignorance. Build your data collection around the questions you're asking, not around what's easy to measure. 

What Is Sufficient? 

Having the necessary data is the starting point. Making your insights sufficient requires three additional elements:

1. Conceptual Framework

Like Brahe building on Ptolemy and Copernicus, you need a mental model before diving into data. This comes from two sources: theoretical understanding of the underlying principles and operational experience of how systems actually behave. The best operations specialists combine both; they understand the science at play while also knowing how equipment actually performs under real-world conditions, with all its quirks and edge cases.

2. Operational Validation

Data can be transformed into knowledge through testing of hypotheses against reality. This involves designing controlled experiments where you isolate variables, make specific predictions based on your mental model, and verify them against operational results. Challenge your assumptions in real operational conditions, not just in theory. Document what works, what doesn't, and critically, why. Was your hypothesis wrong, or were there other factors you didn't account for? Testing separates insights from coincidental correlations and transforms raw data into actionable understanding.

3. Reproducible Results

One successful test may not be the entire story. Repeatability demonstrates that you've uncovered meaningful relationships rather than statistical noise or exceptional circumstances. Can you reproduce the same results across different shifts, equipment, operating conditions, or time periods? Repeatability builds confidence that your insights are robust enough to support operational decisions. It also reveals the boundaries of your understanding; under what conditions does your model hold, and when does it break down? This iterative process of validation and repetition is what transforms preliminary findings into reliable operational knowledge. 

Creating a Data Culture 

Finally, Centola's work offers one more crucial insight for industrial operations: creating a data-driven culture isn't like spreading a virus or a social media trend. It doesn't transfer through weak ties or casual exposure. It requires broad, sustained engagement across your organization. You can't simply mandate that everyone "uses data for decisions" and expect transformation. You need to build deep understanding, provide context, demonstrate value, and create widespread buy-in. 

Data is necessary for modern industrial operations, but it's not sufficient alone. Combine accurate, consistent, complete data with rigorous questioning, conceptual frameworks, systematic testing, and repeatable validation. That's how you move from simply having data to understanding and improving your operations.