Data: Build a Smarter Data Collection System for Your Coffee Business
In this first instalment of a two-part series on smart data in coffee, ELISA CRISCIONE, Founder & CEO of Digital Coffee Future, shares practical strategies for how specialty coffee companies can collect, manage, and visualize data. These features are part of a broader, new, series on SCA News, where industry leaders share practical guidance designed to keep coffee professionals informed and ahead in a quickly evolving industry.
Data collection might not sound like the most thrilling aspect of running a specialty coffee business. But, businesses that collect data systematically discover insights that can transform and improve how they operate.
The reality is, whether you're a farm owner tracking harvest yields, a roaster monitoring batch consistency, or a café owner analyzing customer patterns, you're already swimming in data. Every receipt from your point-of-sale system captures purchasing behavior. Each roasting batch generates sensory profiles and technical parameters. Logistics create trails of delivery times, quality scores, and the market value of a lot. The question isn't whether you have data; it's whether you're collecting it strategically. Businesses that collect data systematically discover insights that can transform and improve how they operate.
The quality vs. systematization balance
Most coffee professionals are aware that poor-quality data leads to poor-quality insights. Conversely, good results start with good, relevant data that fits actual business needs. It's like cooking: follow a great recipe with mediocre ingredients, and you'll get not-so-wonderful results. Use great ingredients with the same care, and the outcome transforms entirely.
But here's where it gets interesting: data quality alone isn't enough. What matters more is systematization, which means understanding which data elements are truly essential before you start collecting anything.
What is the fundamental purpose behind your data collection efforts? Are you gathering information for internal assessment and operational improvements? Is a key partner or buyer requesting specific metrics for certification or relationship requirements? Has a new sustainability regulation or traceability standard created reporting obligations? Or perhaps you're exploring ways to differentiate your product in an increasingly competitive market?
The answers to these questions shape everything that follows. They help determine not just what data you collect, but how you structure that collection, who needs access to the information, and what systems you'll need to support ongoing management. Identifying your internal requirements allows you to design collection methods that serve both your requirements and those of your external partners, if needed.
Understanding your "why" also helps you avoid the most common pitfall in coffee industry data collection: many companies collect too much data, hoping more will solve problems, but instead face confusion and neglect without clarity around how to utilize it.
The frustration cycle (and why it happens)
As a data strategy expert, who's worked in the space for six years, I've collaborated with coffee businesses throughout the sector. I've noticed a widespread pattern in how businesses approach their data, something that I call "the frustration cycle."
Companies enter this cycle when their first response to a business challenge is to begin collecting data. For instance, perhaps a customer complained that the last batch of roasted coffee they received was inconsistent with previous batches. A reasonable and common response is, "We need better data on this."
In problem-solving situations like this, a team often starts collecting data from multiple touchpoints without developing a clear strategy first. Their intention is excellent; they hope to use information to improve operations and avoid future problems. So, they add several parameters to roasting logs and capture additional records from inventory and point-of-sale transactions. After several weeks of data collection, they analyze what they've gathered and realize they can’t draw data from the scattered or insufficient datasets, or they have collected irrelevant data that doesn't answer their original need.
The natural response is to assume they need more information. So they add more metrics, thinking having more data will solve the problem. Nevertheless, the result is not what they hoped for. The data project gets shelved with promises to "revisit this when we have more time." Six months or a year later, the cycle restarts.
Breaking the cycle: a strategic approach
Instead of beginning with data collection, you can break free from this frustration cycle following a fundamentally different path. Start with strategic questions and work backward to identify the minimum viable information needed to answer those questions effectively.
First, identify the problem you are trying to solve: To do so, ask yourself questions directly related to those issues. These questions will be informed by your unique context and your role in the coffee sector. For example, a roaster might ask: "Which coffee origins perform best with our customer base?" A café owner might wonder: "What factors influence our peak times?" A processor might ask: "Which processing methods are my clients most interested in?" An importer could ask: "What coffee attributes are roasters in our market seeking?"
Next, deliberately limit the number of data you collect: This feels counterintuitive, since our instinct suggests that more data provides better insights. However, focused datasets reveal patterns more quickly, and anomalies are easier to identify and investigate than when assessing long and multiple spreadsheets. Therefore, limit your data collection to three to five key metrics that directly relate to your primary questions, consciously ignoring everything else during the initial phase.
Evaluate initial data results and iterate: After collecting focused data for one to two months (the timeline depends on your business cycle and the nature of the metrics) analyze what you have. Only then, evaluate whether additional indicators would enhance your understanding of the problem. This might mean adding one or two new metrics, or removing ones that proved less useful than expected.
Good and successful data collection is systematic and iterative. This process might make you feel like data collection setups take longer than what you initially expected. However, to see real returns from this investment, I suggest you spend at least a few months refining your collection approach before expanding your efforts any further. The time invested in this phase builds the full foundation to guide your future operational decisions.
A structured and iterative approach allows for constant learning: make a plan based on your goals, test it, adapt, and repeat.
Strategic collection and stakeholder relationships
This strategic approach to data collection creates an unexpected benefit: it naturally addresses the complex stakeholder and ethical considerations that coffee supply chains present.
When you collect data systematically rather than broadly, you're more likely to consider fundamental questions upfront. Whose information are you gathering? What constitutes sensitive versus non-sensitive data in your specific context? How does your data collection impact relationships with suppliers, employees, customers, or community members?
Importers, for example, occupy an interesting position in coffee supply chains, receiving detailed lot information from providers while preparing different data formats for roaster customers. Strategic data collection helps address these challenges because it forces conversations about necessity and mutual benefit. When you can clearly articulate why you need specific information, it becomes easier to explain that purpose to data providers. Similarly, if you limit collection to essential indicators, you reduce the burden on everyone involved in providing information.
But the relationship dynamics in coffee supply chains are more complex than simple data sharing agreements can address. Power imbalances between different levels of the value chain mean that data requests from larger, downstream companies can feel extractive to smaller, upstream partners, even when the information seems routine.
Consider whether you can build data systems that deliver insight and create value throughout your sourcing chain. One way to do so is to make it a practice to share actionable results with stakeholders providing data. If you collect processing data from a farm, share feedback on how those methods correlate with prices. This reciprocal approach to data sharing helps address concerns about equitable value distribution across the supply chain. Instead of simply extracting maximum data from partners, systematic collection focuses on information flows that create insights benefiting multiple stakeholders simultaneously.
Building toward systematic management
To succeed with data, you don't need to begin with sophisticated analytics platforms or impressive visualization dashboards. Start with intentional, systematic collection that relates to solving important problems and serves clear business purposes. It’s the foundation for everything that follows in your data journey. When you understand exactly why you're gathering specific information and have established consistent collection methods, you're positioned for the next crucial challenge: managing and visualizing the information you've gathered.
Collecting the right data creates value only if you can organize it, maintain its accuracy, and analyze it efficiently when business decisions need to be made. The habits and systems you establish during collection determine whether data management becomes a strategic asset or an administrative burden.
Most importantly, strategic collection builds organizational confidence in data-driven decision making. When your team sees clear connections between information gathered and operational improvements achieved, they become more willing to invest time in data quality and more interested in exploring additional applications.
This feature is the first installment of a two-part series by ELISA CRISCIONE on effective data practices for roast and retail businesses. Stay tuned for the second feature, Data: Management and Visualization for Better Business Decision Making.
