Every cannabis business is collecting information, but not every company has the resources to crunch the numbers. Today we’ll explore the topic of data analysis and how it can reveal hidden value in your process streams.
Cultivation schedules, production yields, sales metrics; thanks to seed-to-sale inventory tracking requirements, almost every cannabis business is gathering data in these categories and more. It’s a treasure trove of numbers that not everyone has the time—or the resources—to sort effectively, much less analyze for meaningful, actionable insights.
While companies across the budding cannabis industry continue to grow and thrive, more so than their peers in legacy industries, they often leave efficiencies hiding in the data in favor of prioritizing steering the ship in a shifting storm of regulation, opportunity, and consumer demand. Legacy industries rely on structured data analysis to eke out profits year over year. The case study below illustrates how these same principles can be applied to the cannabis industry:
Company A is a highly respected hash oil producer with limited presence in other categories. Company A has an in-house genetics program (more on the pros and cons of having your own program in a future post). Their lead Geneticist has considerable control over operational plans and processing allotments.
Enter Strain X. Strain X is a delightfully fruity, candy-gas-profiled hybrid, an in-house spin on recently released genetics from a notable breeder. Given this information and this alone, having Strain X in Company A’s lineup is an objectively good decision.
Enter the Hashmaker. He has washed every strain in the company’s library multiple times. A few months and therefore harvests of Strain X have passed when the Geneticist comes to visit. The Hashmaker is washing Strain X. Enticed by the smell, the Geneticist asks what strain is washing. The Hashmaker replies truthfully: Strain X. What ensues highlights a major flaw in data utilization.
The Geneticist smiles and mentions how much he enjoys the strain. The Hashmaker frowns.
It’s not good.
What do you mean? The Geneticist is appalled. It’s flavorful, fragrant, the buds nicely structured, the THC testing strong.
It doesn’t wash.
The Geneticist is stunned. The Hashmaker elaborates.
Due to a fruity, alcohol-based terp profile, the hash does not form well. Solventless yields for Strain X are at or below 2%, a notable lower threshold for viability from a hashmaking perspective. Other strains in Company A’s library yield 3.5%+, respectable-if-not-impressive solventless conversions from fresh frozen weight.
A decision needs making. Either the flavors and name recognition win out, or yields, with their more direct impact on profit, do.
Yield data is present throughout the company’s work logs, but comparative analysis and performance monitoring that utilized that data was missing. While, from the Geneticist’s perspective Strain A was a no-brainer, important data was sequestered, unintentionally, within the hash production lab. While lab management could have made a case to never grow a second run of Strain A after the first test wash, they were unable to effectively quantify and communicate not only the underperformance of Strain A in the production lab, but also the less accessible yet more substantive evidence of missed profits and resource misallocation that stretched back to the moment the first full production crop of Strain A went into grow media instead of a higher-performing strain.
Again, Company A is thriving still, even growing; this inefficiency was plain to one production-level employee, and the data was there for all to see, but identifying and addressing this issue took a rare, chance meeting of two talented and respected professionals who may otherwise never have had the opportunity to share this information. Catching this kind of issue after only three or four crops was lucky. Catching the fact that whole production cycles were running at 50% of the efficiency of parallel processing after a year or two? The effects on margins could be unconscionable.
With persistent data analytics, it’s never an issue.
At Fire Business Strategies, these are the hidden nuances of operations we seek to illuminate by developing thoughtful, data-driven solutions for the cannabis space. Whether it’s utilizing the power of our Forecaster to plan a big operational shift or custom-built solutions for tracking metrics across your specific use-case, we’re here to help light things up. Reach out to set up a chat about the ways Fire’s Forecaster tool and other custom data solutions can benefit your business.
