Talking big data — and the effect its use will have on the entire marijuana industry — with the CEO of Cannabis Big Data
In the increasingly competitive cannabis industry, finding efficiencies, streamlining processes, recognizing trends and targeting the right consumers is becoming more important with every new state that creates a marijuana marketplace.
But that means sifting through data, which can be daunting to farmers and retailers alike, and then putting it into practice, something that can be even more difficult.
That’s where Henry Finkelstein comes in. As CEO and founder of Cannabis Big Data, Finkelstein has been mining, sifting and — more importantly — creating strategies using every bit of information the industry has to offer.
“Cannabis Big Data is a company that takes data throughout the cannabis industry and makes it actionable,” Finkelstein says. “The key concept here is that the only relevant data is actionable data. If it doesn’t in some way inform the business decisions, what is the purpose?”
Cannabis Big Data accesses data from clients’ traceability software, point-of-sale systems, hardware sensors, logs from testing facilities and other inputs, then develops insights and modules for specific components of that company, such as cultivation, batch extraction, store management and inventory management.
“We have predictive analytics that say, given all your historical cultivation data, what plants yield the most in what context to maximize profitability,” he says. “Instead of market-level aggregations, such as what products are trending in what regions, we do much more of an integrative-within-the-business perspective to inform future decisions.”
Marijuana Venture recently talked with Finkelstein about how the use of data analysis will be the future of the cannabis industry.
Marijuana Venture: What have you learned about the industry from looking at all these data sets over the past three years?
Henry Finklestein: What’s really fascinating and exciting is the potential for growth. The sophistication around data best practices the last handful of years has been relatively low. Any professional in a different vertical coming into the cannabis industry will likely be surprised at how far behind the industry is today from general best practices in agriculture, retail or manufacturing.
There’s just a lot of room for growth to implement best practices. This is the growth cycle where the business gets refined and gets more dialed in as more industry professionals come in with expertise at doing it at a completely different scale.
Part of the reason we started Cannabis Big Data is that all of those professionals will need to have the data tool kit to prove the concepts, establish best practices and customize what we know from other industries for the cannabis industry. There can be lessons learned, but it’s a unique plant, a unique chemical, a unique ecosystem, a unique regulatory landscape, so there will need to be tools that are specific to this industry.
It’s a good time to be a pick-and-shovel, so to speak, that every sector needs to grow their efficiency as we go on this march of maturation in the cannabis industry.
MV: Have you found that this is an eye-opening process for the business owners and managers as they start to see their data in a way they can analyze?
HF: We find that there are largely two types of businesses.
There are the folks that are already doing a lot of analyses already, and our tool kit helps them do it faster and more efficiently, and they just hit refresh in their browser instead of exporting reports and so on. For those folks, they’ve typically had experience with business intelligence and data visualization in other industries or at least they have a very clear sense of how easy it could be.
The other type of customer doesn’t necessarily know or have the bandwidth to run reports and try to collate information from different sources and figure it out all on the fly. Those folks are grateful and typically very excited because it’s almost like the experience of wiping off their glasses when they’re foggy. You know you can see okay, but it’s not quite as clear as it could be, but when you wipe off that fog, you can see what’s happening in the business and where there are opportunities, efficiencies, cost savings and so on. It just makes it that much easier to do their daily job.
MV: When they see this information, does it typically line up with what they believed about their business anecdotally?
HF: There are two rounds that they typically go through. The first round is, “Oh yeah, I see that. That makes sense.” Or, “I remember that strain didn’t do well that harvest cycle.” The next wave is, “Oh, that’s interesting.” That’s a really beautiful moment, because that’s that next level of awareness. Maybe they saw 80% or even 90% of what was happening with their business, but when they look at the data, they realize just how well one strain was actually doing or they see exactly how poorly a specific product was selling.
MV: Can you give an example of a situation like that?
HF: I’ll give an example from one of our customers using the inventory management module. It was a general manager working with a number of store managers and different locations. Each of them had their own directive to manage their store inventory as they saw fit. Some of them were better at it than others.
One of the direct objectives that our client stated when he turned on some of our inventory management tools was generating consistency and clarity around what was happening between the different locations and how to make improvements. Fast-forward three months and he’s recognizing that one of the locations has an outstanding manager and the others were struggling with various aspects. He realized that centralizing management would create further efficiencies on bulk ordering and packaging. Now they’ve completely changed their workflow in the organization as a function of what they saw when they popped under the hood. They’re making more profit as a function of that.
MV: So this data can be used to monitor and evaluate the proficiency of employees in certain categories?
HF: Absolutely, whether it’s on the cultivation side or extraction, manufacturing, retail, distribution; it’s a window. The data tool kit is a window into the specifics of what was happening that can be beneficial in a lot of different contexts.
MV: Have you seen companies that are doing particularly well even without in-depth data analysis?
HF: Absolutely. One of our core values is celebrating successes. The concept that we can be better is not against the idea that they’re already doing well. Having the data, we can underscore places where companies are already doing quite well and celebrate those, because they know their business well and are doing well in certain contexts.
What the concept of celebrating successes underscores is accountability and control mechanisms — to really see when companies are doing a good job — and also to see the areas where there’s room for improvement and to be honest about those as well, not in a deprecating way, but in an honest way.
MV: What are the important things you look for on the cultivation and production sides of things?
HF: On a very basic level, we try to understand the current culture of decision making. Is the culture organized around a subjective concept of “best”?
Does Farmer Joe like this product the best? Or does he like the color when it gets cultivated?
Or is there a culture of focusing on metrics: yields, profitability and so on. Both of them are valid in various ways and both of them have their drawbacks in various ways, and they need to be melded together. Knowing how the company currently operates can help inform us how to communicate information using a data-driven methodology.
There’s definitely going to be a rapid acceleration as Big Agriculture techniques start to come into play in a more industrialized, monoculture farming infrastructure, like we’re seeing in other commodities. Cannabis ultimately will become a commodity when it’s legalized. In that way, I think we know where the forward trajectory of the agricultural side is going, and like any other agricultural industry, it’s toward big data and toward massive thresholds of thought around maximizing profitability.
This interview has been edited for length and clarity.