Hemp Batch Tracker is a tracking solution, powered by CAN, that combines a robust platform, data structure, rapid reporting, and the flexibility to meet the needs of a changing environment.
Hemp Batch Tracker is also positioned to be the “middle ground” for the producers and the regulators in Hemp. The solution will offer a law enforcement portal to help identify highly regulated cannabis and largely de-regulated hemp. This will help reduce the strain on law enforcement as they police a new industry.
If you or someone you know is in the Hemp space and would like to talk to CAN about what we can do for them, please send them our way!
Justin Trowbridge, our Business Development Officer, will be presenting Data Hierarchy at this years Word Camp Omaha.
We see this as an opportunity to help developers and companies start the road down data driven decisions. Developers are the tip of the spear when it comes to data collection. By exposing developers to the concepts of Data Hierarchy they can build stronger data sets for future use.
Going a step further web designers/developers tend to make great data scientists. With our Omaha Data Science Academy they have the ability to add to their core competencies. In a competitive field the difference is in what you can deliver.
A designer who can deliver a strong data minded solution and then execute the analysis is worth their weight in gold to a company just getting started with Data.
The conference is August 24th & 25th at UNO’s Mammel Hall. More information about Word Camp Omaha and ticket sales through the button below.
Contemporary Analysis has been working on our solution Inventory Batch Tracker for the bulk of 2019. During the marketing phase we realized there was an opportunity to (potentially) be the first to market with a Hemp/CBD specific tracking solution.
Over the course of the last few months we’ve had conversations with groups in Montana, California, Utah, Colorado, Nebraska, Tennessee, and even the DEA about our Hemp solution. Our goal is to provide a solution that supports the producers, manufacturers, consumers, and law enforcement efforts around commercial Hemp.
Commercial hemp is forecast to be a 20 Billion dollar industry by 2024. The applications for hemp products are almost too numerous to count. We’re excited to hear overwhelmingly positive feedback from the groups so far.
Justin Trowbridge, Business Development Officer for CAN, will be speaking AIM’s Heartland Developer Conference. During the Friday breakout session he will be going over Data Hierarchy.
We launched the series on our Blog and have the E-Book available here. Data Hierarchy helps frame the bigger picture for people who don’t work with the data day to day. By helping the C-suite understand the bigger picture the support and expectations are in line with deliverables.
Companies who follow Data Hierarchy principles can do more with their data with less wasted effort. This increases the ROI and helps reinforce making data driven decisions.
In keeping with the mix of humor and high level concepts the breakout promises to be a great Friday afternoon session.
If you’ve gone through all 6 of the levels of Data Hierarchy hopefully you see exactly why it’s important to do these steps. So much of each step is contingent on the previous steps. Poor execution of one step can compound issues down the line.
If you remember in the first part of this series the goal of Data Hierarchy is to see the distant goal. The goal that might even be over the horizon from where you stand today. The codependency of the levels will always be there, but their functions are all dependent on the base data set.
Now for the mind- blowing part of this whole series. …take a deep breath…you may even want to sit down for this. Ready? Yes, the steps are codependent…No, you don’t always have to do them in order.
Think of it like building a house. You have to pour the foundation before you put the shingles on the roof. There’s just no getting around that. But, when you’ve got the framing in the house you can work in one room for a bit and then shift focus if needed.
So when the company who cut your marble countertops, because you’re worth it, was loading them onto the truck and slipped and broke into 1000 pieces. You don’t stop building the house while a new slab is quarried in Italy, you work somewhere else. You can hang cabinets without a countertop, you can tile a bathroom, you can start the landscaping early. All of which move you toward a fully built house without cutting serious corners.
Picking and choosing which sub-project to work on without skipping an essential part of the process is part of the long term plan. The Data Hierarchy is a 6 tier concept. However, in execution this could be 60 smaller projects, it all depends on what you’re trying to accomplish.
This is why it’s important to find someone to help develop your data vision. Someone who has been there before. Executed hundreds of projects, stood up multiple data science teams. Someone who’s website is www.canworksmart.com for example. (Shameless plug!
All joking aside, we are here to help and be with you every step of the way. Contact us today to learn what we can accomplish together.
About Contemporary Analysis (CAN):
Having a third party partner who can hold your hand through this trying process is unbelievably helpful. With most of these levels, it takes 10 people to build it, but only 2 people to run it long term.
Additionally, one of our key roles is to fill in a company as they move through the process is the “Data Visionary” role. This role helps you see over the horizon and provides strategy of that vision. It needs to be an employee at some point, but not at first. These individuals are hard to find and even harder to keep. CAN has the ability to fill this role while the initiative is still new and vulnerable. We have no desire to retain it, however. Our goal is to, at the appropriate time, train an individual and transition all of the IP, the leadership, and the know-how to an internal champion–someone who fits the culture and the design of the particular company. Because, in the end, the hardest part of this whole process is getting the decision makers in an organization to embrace the culture of change.
“We’ve done it this way for X years.” Is becoming the leading indicator of a dying business. If you’re 40 years old the technology available today wasn’t even conceptualized when you were in grade school. “We’ve done it this way for 40 years…” means you’re already behind the curve.
So, how do you start, and when? Now–Yesterday actually. You can’t wait any longer. And with a phone call to us. We want to help. We were built for it.
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The year is 2056 and all of humanity has been enslaved by robots…ok that’s enough of that.
Hollywood has done a lot to help educate the public about technology. Artificial Intelligence (AI) seems to be the one that is “scariest” to humanity because of all the ways it can go wrong
So lets start there with de-mystifying AI and why it’s not going to try and conquer the world without you. Artificial Intelligence is a program that gets smarter every time you run it. If you’ve ever seen a Roomba robotic vacuum…everyone would assume that is AI. It is not. The robot is simply running the program: if you hit a wall, change direction. If you hit another wall, change direction again. When full or when low on power, find docking station with sensors. Repeat until obsolete with newer model found in Skymall.
If you’ve ever owned one of these, (non-weaponized Roomba) you know they randomly attempt to clean your floor. They don’t run 1000 cycles and are significantly better on the 1001 cycle versus the first. If a Roomba were Artificial Intelligence, it would “learn” every time it ran a cycle. If the robot was running that kind of programming it would learn where your furniture is and never run into objects. It would also find the optimal pattern to cover 100% of the floor over time
Until now we’ve not used the term Machine Learning (ML). We did that on purpose to help make a clear delineation of what ML consists of. Oddly enough, ML and AI get confused all the time, even among technologists. Most examples of AI is just machine learning done really well.
Everyone’s friends, and unofficial wiretapping devices, Siri and Alexa, are not AI. They are glorified Roombas running a scripted program. So how where do you draw the “line in the sand” between machine learning and artificial intelligence?
Machine Learning: Machine learning is a program that looks for patterns and provides recommendations for those patterns. It’s up to the human being to test out the recommendations to see which one is the best option.
Artificial Intelligence: AI is a much more complicated decision making program than machine learning. It has multiple decisions it could/can make when presented with a scenario. Not only does it have the ability to make multiple decisions, in most cases, it remembers what it did previous and looks for ways to improve using those different decisions.
So let’s put this into an example people can understand: a Roomba robot vacuum. Your Roomba is using machine learning to figure out how to cover a 100 sq foot room with furniture. It bounces around and eventually will clean the entire floor. In the process its hit some items multiple times and essentially uses “dumb luck” to complete the task.
Now, you rip out the computer chip and install AI because that’s how you roll. (you rebel you) Your Roomba now starts running random patterns and hits a table leg. It then circles the table leg, mapping out its location, and then moves on. After dozens or even hundreds of cycles like that it starts running parallel lines like its mowing grass, missing every table leg in the room. It then changes to diagonal lines, then circles, then a spiral square, then herringbone pattern. Eventually, the AI learns the most efficient pattern to clean your floor, all while avoiding furniture legs, and still gets close enough to clean the corners and avoid cords.
Your Roomba has taken basic programming concept of “run until hit wall, turn left” to “find the most efficient way to become full without running out of power or hitting furniture”.
The interesting thing is the line between a high function machine learning solution and a low level AI is tough to discern. As consultants we’ve seen a lot of AI solutions that are just machine learning.
There’s a bartender joke that applies here. “The difference between a rum and coke and a Cuba Libra? …$1.50 and a lime.” There is a significant cost difference between machine learning and AI. There’s also more marketability of AI over machine learning.
So, looking back at the Data Hierarchy we’ve already covered and how you get to AI. Your incoming data Reporting is comprehensive and all inclusive, it’s not being filtered or scrubbed. Your Business Intelligence is able to report the real time analytics from your data. Descriptive Data is diving deeper into the why behind your BI. Predictive Data is narrowing the “what if” scenarios down to a manageable number of options. Prescriptive Data is running up and down the data looking for how to optimize the outcomes to your desired results. And now *drum roll* you flip the switch on Artificial Intelligence and it does everything else for you…right? Wrong
AI takes all the data results you’ve had previously and learns your business. It learns what happens when you move any given variable. It learns what kind of outcomes you are looking for. It is the eccentric and task obsessed employee everyone needs but never finds. It learns how to make your business better 24/7 and it isn’t afraid to fail.
The “scary” AI from Hollywood is autonomous and/or sentient–meaning it comes up with a solution and executes it on its own. It inevitably determines humans are virus, they need bio electricity to themselves, humans become batteries, Neo is the one, and he can’t die because Trinity loves him. Hollywood AI is just that–Hollywood.
As it turns out, AI isn’t scary. AI is the super helpful employee nobody knows about. AI, when done right, will recommend well thought out data supported options for human evaluation and action.
Instead of HAL, from 2001 a Space Odyssey, asking (ominously) “What are you doing Dave?”. HAL says “Dave, you should buy blue teddy bears in bulk in October. Red teddy bears should never be bought in bulk and only in January. If you do those two things you’ll earn an extra $127,438.43 this year.”
The real kicker with AI is how important the preceding Data Hierarchy elements are to have a smart, successful recommender. If you skip steps, If your data isn’t accurate, if you haven’t had multiple iterations of your team correcting interpretations by your Machine Learning, your AI will be worthless. It will simply learn “bad habits” and suggest things that don’t make sense or work for that matter. The morale of the story is, you probably could skip steps–but don’t. There is no shortcut to AI. All the more important to start now.
If you wait until your competitor has AI, and your not already at ML, your doomed. You are just the next bit of dirt the Roomba is coming to pick up. Make your decision to start up the hierarchy now. The Roomba is on its way and it won’t slow down just because there is a table leg in its way…
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I didn’t want to say it…but using the last levels Predictive Data analysis, we were able to predict you’d want to read this one too. Obviously that’s a joke, but prediction is pretty powerful when its correct.
So you can now predict the future with your data because you’ve followed the Data Hierarchy steps. Congratulations, you’re still not the coolest kid on the block.
Predictions are more accurate “what if” type of scenarios. You as a person have to make the decision on how to get there. This is actually the difference between levels 6 and 7 which we will get to in a second.
Prescriptive Data is the “what should we do about it” phase. (for example) Your Predictive model spit out 5 highly likely outcomes. Your team all agree they would be great if you could make them a reality. Prescriptive Data takes the same data set and looks for adjustments and/or variables to help achieve those outcomes. Prescriptive Data gives you the actionable items that increase the likelihood of your predictions even more.
Anybody who’s ever hit a golf ball knows there are dozens of split seconds in a golf swing that equate to hitting the ball correctly. Get them all right, and you’ll beat Tiger Woods. He’s good, but he’s not perfect. Get 2 or 3 of those moments wrong, and you’ll probably end up in the woods at the worst possible time.
In business your “golf swing” is how you execute decisions. Lets say your biggest competitor is the Tiger Woods of your industry. They get 15 out of 20 of the little things right almost all the time. Pretty good, can’t compete with them right? Wrong! They’re still missing 20% of the things that make them good.
Prescriptive Data looks at your swing. Because you’ve used the previous steps you know you are doing 13 out of the 20 things right. Prescriptive Data runs hypothetical “tweaks” to your swing at every point with every variable until it can get 17 out of 20. (for example)
Your data will tell you at any given point in your “swing” if you do X then Y will happen more often. And its proven by the historic data you have on 1000’s of swings before. Tiger Woods won the 2019 Masters by 1 stroke…1 stroke. Xander Schauffele, the guy who LOST by 1 stroke, wasn’t beaten by a vastly superior golfer…he was beaten by a detail (or more) being wrong in 2 swings. A foot that was ¼ inch out of place, a shoulder that dipped ⅛ of an inch too low, and worst of all…he probably hit those shots and could feel it wasn’t perfect.
Hundreds of people leave cable companies every year. For what? A myriad of reasons. Better price, free HBO, wireless boxes, faster internet, Game of Thrones is finally over, and more. The ironic thing is that most people leave a cable company to get a feature the current company could have sold them if they
If you’ve enjoyed learning about what your data can do for you looking backward, you might want to sit down for this one. You probably are already, so lets just say you were standing then.
Level 4 is Predictive Data. Predictive Data takes the information learned in the previous steps and applies a “what if?” thought process. If Levels 1-3 were reactive, Levels 4-7 are Proactive. Proactive is knowing what’s going to happen before it happens and it takes a completely new way of thinking to apply it to business. If you’ve ever played with a magic 8 ball as a child, this isn’t that kind of prediction
Prediction is looking at the variables that produce an outcome. If X then Y then Z kind of relationships. The difference is prediction runs through the entire alphabet and double letters for every variable If A then Y then Z? If B then Y then R? Then runs through it again in near infinite combinations of if AB then Y then Z? What about if both A and T both happen, then Z? The human equivalent of “throwing it at the wall and seeing what sticks” but thousands of times over and over.
Your Reporting is giving the machine the raw data.
Descriptive Data is helping set the framework of what kinds of trends you should expect, and Predictive Data is taking what is already known and testing “what if” beyond what you could imagine
Back to the teddy bears for a second…because who doesn’t like this analogy? So you know there is a spike around Christmas and February for teddy bears. Great, now you also know there is an initial rush followed by lesser activity until the panic buyers kick in. You’ve proven this with your data and have 4 years of historic data to support it
Using Predictive Data you begin to look at other variables connected to the data. Is there a higher number of men vs women buying the bears, and is it tied to one or the other holidays? Is there an age group that is more likely to buy a bear? Why did we pick teddy bears for this example? Does the color of the bear make any difference? Does it even have to be a bear?
The machine looks at the data, takes into account all of the Descriptive Data, and begins to tie that data to trends and patterns that are happening currently. This gives you the ability to predict the sales volume and inventory requirements for next week (for staffing), next quarter (for sales quota setting), and next year (for inventory forecasting). So your Descriptive Data provides your Predictive Data meaningful information for the example below. (totally fictitious numbers
Men between the ages of 20-35 are 75% more likely to buy blue bears in December.
Having 10% more blue bears in November than the rest of the year will cover demand.
Women between the ages of 35-45 are 45% more likely to buy a red bear in February.
Having 5% more blue bears in January than the rest of the year will cover this demand.
Now we know how many to order, when, and how many people are needed to staff selling them.
With Descriptive Data you were able to make market adjustments for when to market and to whom. With Predictive Data you can laser focus your marketing to exactly who you want to sell your goods to and know how many you are going to sell. When you’re that accurate you have a higher return on investment (ROI) than you would if you just “guessed” with your marketing efforts
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