Henry Finkelstein, CEO & Founder of Cannabis Big Data
Henry Finkelstein, CEO & Founder of Cannabis Big Data, empowers colleagues and clients by spinning data into gold with intuitive, actionable insights. After working in e-commerce, consulting, healthcare and government contracting, Henry saw the opportunity to create a modern-day data toolkit for licensed cannabis businesses that connects all the data dots with simple, intuitive, one-click reports & dashboards.
Henry’s person-centric approach to the power of data is summed up as “Let’s count what counts & celebrate our successes because the only relevant data is actionable data”.
Inventory Lunch & Learn - https://cannabisbigdata.co/inventory-lunch-and-learn/ (Geared for licensed retailers)
Data consultation - https://cannabisbigdata.co/data-consultation/ (For every industry stakeholder)
Trial of Cultivation Yield module - https://cannabisbigdata.co/grow-yields/ (Geared for licensed cultivators in METRC states)
[00:00:01] You're listening to Thinking Outside the Bud where we speak with entrepreneurs investors thought leaders researchers advocates and policymakers who are finding new and exciting ways for cannabis to positively impact business society and culture. And now here is your host Business Coach Bruce Eckfeldt.
[00:00:30] Welcome everyone this is Thinking Outside The Bud, I’m Bruce Eckfeldt I’m your host and our guest today is Henry Finkelstein who is CEO Founder of Cannabis Big Data. And I'm excited for this conversation because I think this is one of the areas that the cannabis industry really is kind of going through in terms of maturing and really kind of thinking about from a business point of view so we can talk a little bit about data. We're going to talk about making good business decisions. We're going to learn a little bit about Henry and his business. And with that Henry. Welcome to the program.
[00:00:57] Thank you Bruce it's a pleasure to be here and I'm very grateful to have this conversation with you and your audience.
[00:01:02] Yeah so I was like to start with just learning a little bit about the person. So tell me about your background professional background and how you got into cannabis. What's the story. Everyone seems to have a story in this.
[00:01:14] Sure. I'm a data guy through and through data entrepreneur I should say but really a data guy through and through. I have been working in startups for a while I've done three startup incubators the last of which was it why did one called Yale Entrepreneurial Institute when I graduated from university did one called lean startup in the tradition of Eric Reece and all those guys. Exactly. Eric Reece Steve Blank sort of get out the building guys you know the lean methodology in short and so I'm a deep acolyte of the lean methodology. And then most recently through Carnegie Boulder is in a startup accelerator focus specifically on ancillary cannabis businesses software and product but but also software. And so I'm an entrepreneur for sure but I've also worked in big corporations doing frankly data analytics but in various departments I did data for marketing and sales teams. I did data and workflow automation CRM as I've done data for UI UX and manage the creative side as well of course. But at the end of the day the decisions are made by the numbers. In many large organizations and so I have a lot of experience taking that methodology of a data driven perspective on insert X department and applying that both through my own experience as a management consultant when I had my own consulting firm for many years as well as working in larger corporate environments. Most recently I worked in the healthcare industry with government contracting so very comfortable and heavily regulated environments.
[00:02:47] And of course you know to answer the second half of your question of how I came into the cannabis industry by and large I see an opportunity I see a real opportunity of course financially there's no question about that and folks talked about the green rush and so on. But if we extend the analogy of the green rush you know who made the most money and had the greatest impact on the trajectory of the gold rush.
[00:03:10] And I'll give you a hint it wasn't the Panthers Levi's Levi Strauss exactly Levi's as every and or needs a pair of jeans.
[00:03:19] But not every panic strikes gold. So too I believe about the cannabis industry. If we think of all of the license cannabis businesses that are panning for gold right now a lot of them are going to strike it because we're early on in this industry. But at the same time not every pander strikes gold but every pander needs a pan and so every 2 Cannabis Company both licensed and ancillary needs a data driven methodology for how to make decisions. And that's why we started this company that's how I got into this industry I saw an opportunity to apply a data tool kit to a budding industry so to speak and in that way found a hole as it related to data from an operational standpoint. The day to day tool kit what's my cost per gram. What's my batch yield for manufacturing. What's my unit margins across the industry or across the location and so on. These types of day to day questions it just there isn't a tool kit yet for that in the cannabis industry. Until we started cannabis Big Data.
[00:04:17] Yeah so. And talk to me more about toolkit. So when you talk about data toolkit What are you talking about actually building or providing.
[00:04:26] Absolutely. So cannabis Big Data is a connect the data dots model. So our soft plugs into any data source seed to sale point of sale accounting batch logs hardware sensors internal spreadsheets custom databases. I mean you name it we'll take it. We pull all of that information into a central data warehouse and organize it and then add a visualization layer for reporting and dashboard thing so that it's simple intuitive and actionable.
[00:04:55] That's really the bottom line. That's what we provide. We provide actionable data driven insights from all the information in your organization. And the reason I say a tool kit you know in my mind data is not the answer. It's a hammer. It's a better hammer. It's a way to build your empire and can have very talented and crafts person or carpenter build a home with a shoe and a box of nails. Yeah if they're good enough. Absolutely. Well they build a better stronger house with a really solid Hammer. Yeah absolutely. And in that way a data tool kit is just that it is a better hammer to build your empire.
[00:05:32] Yeah it's the one thing I find about kind of data or you know taking kind of this data view of businesses and processes and things like that as always kind of starts with what decisions are you trying to make. How do you approach this when when when you're working with companies on using data to make better actionable insights to make better decisions. What's the process that you use or when a company comes to you saying hey look we've got all this data we're trying to we're trying to do something with it. How do you how do you start. Whereas the first step in organizing all this.
[00:06:06] Yeah you nailed it Bruce and you hit it you really outlined one of the key questions and in fact you answered it in your question. You ask us where we start because it's so important to know what the goal or objective of the business is. And that's exactly where we start by asking about the goal or objective of the business. That is number one thing and you know I always say that we do not expect our clients to be technical by any stretch of the imagination and in fact we do all of the technical work specifically so that we can have a plain English conversation. And the data tool kit much like the hammer analogy I often bring up the concept of a bright spotlight data as a tool is a bright spotlight. It doesn't tell you what to do. It just shines light on the path and shows you the dark spots and the bright spots within your own organization. And more often than not the answer reveals itself as a function of what the core objectives are and what you now see. Given the data even if what you see is that you need to clean up the data that in and of itself is something to see within an organization. And so oftentimes when folks come to us and say oh like a proposal and I'd like this module in that module and all these things I always come back and say great absolutely we'll build whatever you're asking for but first and foremost wave a magic wand. Tomorrow you wake up everything is perfect in your organization. Everything is as perfect as you imagine it. Walk me through your day and what's different from what you have today. And that magic wand exercise really provides a clear perspective on the gap between what you are today and what you want to be tomorrow. And data can help plot the map. It can be a navigational system from here to there but really at the end of the day the business operators or the folks that are driving that that distance.
[00:07:50] Yeah I liked that I wasn't one of the things I always say as a coach that I'm a I'm a flashlight not a hammer. I'm going to help you up you shine light and it may be slightly uncomfortable but you know sunlight is the best cure for most situations you know if we can figure out where to focus on that's going to drive our attention and it's going to drive our our process. So I'm totally onboard with that.
[00:08:11] What. So you know I think abstractly I get the you know figure out what questions you need to find answers to to make better decisions.
[00:08:19] Therefore we start looking at the data with literally I guess what data are you finding in the cannabis phase as being you know kind of relevant or pertinent to the kind of questions that cannabis business leaders are trying to manage or trying to come up with insights from.
[00:08:33] Yeah absolutely. As it relates to cultivation is one of the key questions is yield. What is a given strain or cultivar of a plant yielding. And you might normalize that by square footage by kilowatt by number of plants by whatever metric is most relevant in your organization. But on some very basic level revenue is tied to production output. And as such folks are really tracking production output. If you're going one step higher in the order of sophistication you might collate that against your cost structures to really identify a cost per gram of cultivation. You might also think about other resource expenditures like waste if social impact or carbon footprint is an issue you might think about energy utilization you might think about air quality considerations and so on as you get sort of more and more sophisticated in the scope of data overlay. You might also have sensors automated sensors or manual sensors for light humidity P.H. other environmental considerations. And of course as part of the throughput you're certainly tracking cannabinoid profiles thc cbd perhaps even Turpin levels. So cultivation tend to start with a somewhat narrow focus as it relates to revenue and as they get more comfortable with that they can sort of build out from there. Manufacturing has a similar trajectory in thought process. We start with throughput efficiency yields how much product Am I making. Oftentimes normalized by the amount of raw material required to make that final number in its output. If we go one order further in the circle of onion of consideration we can think about the cost structures around that production throughput. We might also think about labor other consumable inventory considerations and so on. We get further and further out market level demand and sales trends from the wholesale accounts and so on.
[00:10:32] Yeah. Yeah I mean I always say cannabis is a business just like any other business. So there's a lot of things you can learn just some of the business fundamentals and obviously then there's a whole bunch of specific things around the cannabis business both from dealing with you know the agricultural side of it and also the sort of the regulation and kind of tells exactly like retail you know like think of any Gap store and it tracks largely the same metrics that you would expect a cannabis retail store with some regulatory additions to that.
[00:11:01] But largely the same sort of metrics. But I think to answer a subtext in your question Bruce around Where should folks be putting their attention or where are we seeing a gap in attention in this industry. And I think one of the areas that we're seeing a real friction and pain point around understanding the true scope of the metrics is around margins. And that largely relates to some of the regulatory complexity around taxation and 280 e and charts of accounts are not all we set up in a traditional operational concept. Oftentimes they're set up with a different function in mind and as such true operators that are focused on business concepts that come from other industries or have more traditional gap accounting or more traditional accounting practices that really help them drill into unit costing that is hard in this industry to really have a true unit cost that is fully reflective of all the considerations requires a lot of thought and diligence that the data architecture level that certainly we provide. But that very few organizations have the mental and resource bandwidth to tackle in the way that it truly needs to be to get to the drill down per penny per SKU per unit per batch level that this industry will invariably get to within the next couple of years.
[00:12:22] Yeah I totally agree. I think one of the biggest challenges I think I find at most businesses that I come in and work with is the churn of accounts is typically set up for taxation purposes. You know to the accountant running end of year tax liability not to really help you develop inside around what's working what's not working in your side your business so getting a getting an operational chart of accounts set up as is can usually be a first task and then yeah I and I think it's this whole idea of cohort analysis is very similar. I mean we we use the software side a lot in the software as a service. You know it applies to the cannabis base in terms of the grows and understanding. Well you know that the grow that you started you know four months ago you know has has a lifecycle and I need to understand well what did that grow kind of experience versus the next grow and how do I really analyze all my per batch or per per cohort know what worked and what didn't. Because it's very easy to kind of get that a skew to see a result and kind of assign it or attribute it to the wrong set of initial criteria or national circumstances that caused that result. So teasing that apart I think is one of the bigger challenges from just kind of operational analysis point of view and it sounds like good data. I certainly find that much easier than actually I can associate those results to the different timeframes.
[00:13:37] Bruce I'd love to dig into that just for. Yeah. I don't want to derail your questions. No it's good data good data. You know that's it's such a subjective concept of what good data is and in a lot of people think that clean data is good data and in some ways perhaps but but I really want to be careful there for yourself and all the listeners because that's a subjective term that I sort of reject on a qualitative level. To me the only relevant data is actionable total control.
[00:14:08] It's I do something with that information because if not it's worse than useless.
[00:14:11] It's distracting. And it takes my attention away from that which I do have agency and action over. And if it's not presented to me in a format that is actionable the underlying data might be totally valid but the presentation layer is not actionable. And as such the reporting sucks but the data itself is just as good as any other. And so we I always try to tease that out a little bit because you know so many people have a negative emotional experience of the concept of data. Yeah like with a capital D the way they have a negative emotional experience with math and a capital M and like that one algebra teacher that was a total asshole and gave me a deal on that quiz when I really should've done just fine and now I hate math for the rest of my life and I get it and that's fine. But but when one of the beautiful things I think about the trajectory of data science and data toolkits outside of the cannabis industry and why I was so excited to start a data company in the cannabis industry as it relates to the democratization of data and making it more accessible. Visa V better ways to see understand and internalize the natural intuitive insights of a data set as a function of just having a better window into that pool of wisdom. Information and so we're in the business of making it much more simple much more intuitive and ultimately what's the most important actionable data to us. That's what quote unquote good data is. It's when we can do something and make money or stress less. Really that's what we're trying to accomplish and make more money or stress less doing exactly what we're doing today. And if we can accomplish either or both of those then we're in a good place.
[00:15:52] Yeah I think that's an excellent point. I think it is. It's easy to get sucked into kind of the allure of just more data. If I have more data like somehow that's better and I always know it's gonna be it's gonna help you make a better decision right. And if it's not helping you make a better decision or you can't make any any better of a decision with more data than you've kind of reached. You've reached the balance of your job of your effort there.
[00:16:15] Talk to me a little bit about kind of internal versus external data.
[00:16:19] I mean I think a lot of people sort of dive into the business and they start looking at the databases and the things that they have the reports the spreadsheets and stuff. Where do you see opportunities to actually bring in a third party or external data sets as part of this process because I think that's that's oftentimes where I've see some really interesting instances when you actually actually bring third party or external stuff into that analysis.
[00:16:40] Yeah absolutely. That's a great question and I think that's a line in the sand that a lot of folks conflate or sort of mixed up in it and it might be worth teasing out a little bit. The difference between internal and external data at least it as as it relates to our vocabulary is the concept of data that's generated within your four walls of your business so to speak your sales numbers your accounting numbers your manufacturing numbers your cultivation and so on your customers yours basket sizes and so on and then external data would be sort of market level trending. If you get a report from New Frontier or b the s analytics or headset or cannabis benchmark chart there's a lot of great companies out there bright field group is another one that's worth mentioning there's a lot of great cannabis data companies out there that publish the leaflet just published another sort of wholesale spot pricing report either great companies that are doing awesome work in that market level analysis standpoint but that's different than being internal. And to answer the question around when it's internal versus external data relevant I find that external data is really good for understanding trends. General trends around what's happening around you but it doesn't always inform exactly what the best choice is for you concentrates maybe on the up and up as a general market but your part of town might be particularly keen on top Nichols and so you're topical is are growing way faster and really you should be serving your customers and their needs recognizing and understanding what you're places in the greater context of the industry at large. But I find that there's often a reach for external metrics and sort of market level trends to drive internal decisions before folks have really grasped and internalized their own experience first and foremost and that I find can create tension and misalignment in the easiest path because if you really understand your own world first before you compare it to the outside world it can yield a lot greater insight and value from understanding the outside experience because you've reflected on the inside experience.
[00:18:53] Yeah. It's interesting. I would agree. I mean I think one thing that I've always found is that you know you exist within sight of a market so I think it is important that you know you're your sales are up 50 percent. Great while if the market is up 200 percent you know that's a difference to market like you exist within a context but you're right. I think that understanding it's easy to get overly summarize I guess that kind of the world using this big data using the data that's out there from third parties and forgot to really take a look at your specific situation and that micro environment that you're working in and making sure that you you need to operate with your customers and your clients and making good decisions at that level. So yeah I think that's I think that's smart. I think it makes sense.
[00:19:32] I'll give you a perfect example. It's just a yes and on what you're saying. I love it as it relates to to forecasting exercises. We always when we do you know we have a forecasting module for each of the verticals whenever we go into a forecasting exercise to really model out expectation for a business. It always starts with the core nugget of past experience of historical. But we also know that in the cannabis industry the only constant is change. Exactly right. Regulations change competitors change vendors change products change everything changes always. You can't expect it to stay still. So yes you want to start with. Last year last month but we also know that because everything is constantly changing last year and last month is only directionally accurate for next year or next month it's not going to be linearly accurate. And so in that way we want to make sure to start with our experience knowing what was done while also recognizing the greater context of what and what could be looking ahead.
[00:20:30] So let me ask about that because I think that's one thing that I find really really valuable when I'm working with folks and we're we're going to do this kind of forecasting. I'm a I'm a big fan of scenario forecasting right. So take four or five six different potential outcomes. Usually looking at highly uncertain variables within a business and look at what what happens if. Right. How how do you approach I guess dumbly a little bit about how your tools or how you've kind of approached this kind of forecasting and scenario planning to help businesses you know sort of build better strategies or better tactics around how they're going to deal with markets.
[00:21:08] Absolutely. My poor guys hear me say this all the time. Simple is not easy. We keep it simple. It's simple it's not easy.
[00:21:16] And what that means for us is having a core set of assumptions that are a either an input as a quantified number. Growth rates certain categories or trends or so on or a quantification of a qualitative concept no changes in strategic leadership changes in vendor profiles and so on.
[00:21:37] So we have a set of assumptions that then cascade and drive the modeling exercise. And when we think about scenario modeling we can dynamically filter and change our assumptions based on whatever core thesis we have around the differential in this scenario as distinct from the last one.
[00:21:55] And again anytime we have a view of quote unquote different then that needs to be in some way quantified in the model Visa V the assumptions that inform and drive the model. And so we create different assumption sheets that then subsequently cascade through to yield different outcomes which then gives us a range of. Okay. So in the worst case scenarios in any direction this is sort of what we can expect to happen in those areas those arenas.
[00:22:23] Yeah that's great. So I think that's a particularly in a market like this where there are things that are uncertain and can have a big impact on the business having some tool to understand you know what what would happen to your business left unchanged and what a reasonable strategic responses are going to be you know in those scenarios it really helps a company whether weather the storm that comes out talk to me a little bit about A.I.. Do you have any other views opinions I mean you know there's so much talk out there right now about this artificial intelligence and you know feeding learning systems lots and lots of data and having them spit back to you know this synthesize higher level conclusions.
[00:23:02] What what's your kind of what's your take. Have you played around with this at all of you. Is it part of your kind of product roadmap in any way.
[00:23:10] Yeah I always laugh on this question because in the spirit of Elon Musk I don't want to piss off our future leaders. You know I know me personally I feel like there is nothing more powerful than the human intuition just period. There's nothing in my world I do not. And this is you know I'm a I'm a data guy by trade. I am a technologist by philosophy. We can go into more roots spiritual considerations if you wanted to get there. But on a very basic level I find that we as humans cannot conceivably program anything as nuanced and and intricate as the human intuition and the web of interconnectivity of one human connected to another has implications that I really on a just a route philosophy level do not believe can be recreated in an artificial network of synthesized robotic concepts. Now when when we unlock the the algorithm to create consciousness that's a whole other ball game and I'll punt on that for now although it's a fascinating kind of future dialogue.
[00:24:17] Yeah yeah.
[00:24:18] I don't think we're anywhere near it yet. I think it is conceivable to stumble upon something that approximates consciousness and that's both fascinating and terrifying in many many ways. But I'll sort of punt on that for now as it relates to the tools that are within reach within the next I don't know 20 to 50 years at least as far as my futurist mind can grok them. I feel like the human intuition is incomparable and insurmountable. Period. I believe that fully and if that's the case I also equally believe that the human intuition is only as powerful as the inputs into that information. It's it's it's own meta algorithms so to speak it's something that connects all the pieces in ways no we still don't fully understand yet. And if that's the case if you have a filter or an inaccurate set of inputs into the intuitive process it stands to reason you would have a skewed output and it's in that way increasing the accuracy and speed of processing of information into the intuitive engine that is the human consciousness is the most and the most powerful way the most important and the most powerful way to generate decision to maximize value.
[00:25:34] Yeah. So kind of a day data is a tool to get there.
[00:25:38] We can use the machines for what they're good at not to be classes so to speak. But there is just a set of functions that machines do way better than we can do with our machine but our machine is capable of things that no mechanical component can can create. And so I see a synthesis and are working together more so than I see a division in that way.
[00:25:59] Yes. Data augmented human intelligence absolute you know to make better faster.
[00:26:05] Just give us a little insight on where your kind of product roadmap is going. What are things that you have kind of in the works that you're working on kind of developing and launching to help businesses in the space.
[00:26:14] Yeah. So a couple of things that we've recently launched that are pretty bad ass and then they've been getting some good acclaim. We recently launched an inventory management module for licensed retailers that folk made it and rec that focus focuses one part on understanding inventory management what products do we reorder when from what vendors what's our opportunity cost of having a stock out event some really. And then of course how many what do we need to reorder so that we can have the desired inventory on hand write some of these basic functions and other industries that we don't want inventory management for most other companies but it's fascinating it's just it's still happening.
[00:26:55] One of my favorite simulations to run is the beer game you know really helping helping leadership teams understand you know the the effect the ball with the fact of inventory ordering and lag and stock and things like that. But it's I think it's great that you've got actually some systems are doing this because I think that you know it's costly. If they don't get the stuff right. And it's sometimes not intuitive sometimes it's not they don't without the data to kind of see the difference on those stuff it's is it's tough to stuff to intuitively get it without some of the data analysis.
[00:27:29] Absolutely. I should actually thank you. Well let me take a step back and give you just one thread of what we're sort of reaching for and then I think the rest of all of the examples will make sense. One thing we realized because myself my co-founders were all sort of tech heads were all data guys from various industries and companies large scale companies and and so all of us sort of have lived the value and power of data. But what we realized when we started this company is that it's not as self intuitive self evident. It's business owners here as it is in other industries. And that's totally OK. And so our core function we believe is to help educate this marketplace around what where data has value and how it can be helpful and frankly where it doesn't have value so that you can focus on the relevant actionable pieces and let go of the attachment or the or the focus on all this other stuff that's just noise in the system. And so we started this team the research outreach and insights team are a wide team and the whole purpose of the ROIC y team is to add value by having conversations you know helping folks get it. So we have this inventory Lunch and Learn where we share best practices around canvas inventory management no sales no gimmicks no nothing just really. We bring lunch. We share with you best practices if you learned awesome you give us a high five and we say thank you.
[00:28:47] Good luck. So again these types of concepts and we have many ROIC campaigns that we're staging over the course of 2019 to help educate and inform the market around areas where data can help soft in an ease in the path of making business decisions in a qualified and profitable format. And so one of the questions we get all the time as you know I'm sure you've heard this pain point as well is around the process what's the best process for my specific use case. Well I think there's over 40 distinct processes now in the marketplace focused on messaging something around cannabis. Golly of course they're struggling to find the one that's relevant for them. And so we're putting together this P Os guide to help businesses understand sort of different processes have different pros and cons which ones are most relevant to you. Not that anyone is the best per say but they all have the things they do well and the things that aren't their core skill set their core functions set and so knowing those more and so on. We've got campaigns for manufacturers for cultivators we've got a really interesting run one around sustainability. So really the entire product roadmap is geared towards what we hear as pain points of the customer of the end users in the industry the lean methodology right.
[00:30:03] The feedback from the people that are actually in the field doing a little go to the gamba see what's going on with the problem is exactly exactly alignment generated.
[00:30:12] If you want to find about more about you about cannabis big data some of the tool sets the tool kits that you've developed. What's the best way to get more information.
[00:30:21] Yeah really our Web site is a great place to start. www.CannabisBigData.co. We're based out of Boulder Colorado. You can email me directly. Henry@CannabisBigData.co. You can also schedule a free data consultation on our website. There's a resources section blogs really there's a lot of ways to get engaged in and click into gear as well.
[00:30:47] Perfect. I'll make sure that those links in your email addresses all shown out so people can get a hold of you
[00:30:52] And I hear that you're out you're doing a little fundraising in 2019 here.
[00:30:57] Yeah absolutely we're doing our Series A we already closed our series seed back in 2017 built out and hit some core milestones in 2018 so we're opening our Series A round now in 2019. And if you're interested in that just go ahead and give me an email. Henry@cannabisbigdata happy to connect with anyone that's interested on the investor side of the equation.
[00:31:18] Yeah. I'm curious to see how that goes. I think you've got a great angle in this industry.
[00:31:24] So I don't think you'll have a huge problem but I think you see who actually gets involved. Henry this has been a pleasure. Thank you so much for taking the time. Great conversation and I've learned a lot and a huge amount of value for the folks listening here. So I really appreciate it.
[00:31:37] Absolutely. Thank you Bruce really appreciate being on the call.
[00:31:41] You've been listening to Thinking Outside the Bud with Business Coach Bruce Eckfeldt to find a full list of podcast episodes. Download the tools and worksheets and access other great content. Visit the Web site at thinkingoutsidethebud.com. And don't forget to sign up for the free newsletter at thinkingoutsidethebud.com/newsletter.