Digital Acceleration and Public Benefit
The world has been noticeably going digital since the early 1990s, though it’s a process that actually goes back much further. However, it was in the time of grunge that the corner was really turned and the process has seriously kicked into overdrive thanks to the response to COVID. Suddenly, millions of people were working from home, making use of cloud servers to prepare documents, apps for work meetings, all of which pushed the digitization of society ahead by years. As restrictions lift in many places, conversations are being had about just how much of that transformation should be continued. Should people go back to work at the office? Should we go back to the more set schedules of 2019?
At the same time, TARTLE is still going through certifications as a public benefit corporation. How do these two seemingly disparate things relate to one another?
One of the major things that those certifications look at is how TARTLE as a company takes care of its people. How do we treat those who work with us to transform how people view data? One of the major things we do is allow people a great deal of flexibility. For the most part, our team doesn’t have fixed hours. There are certain activities that require multiple people to be working at the same place at the same time, but they are few. Filming episodes of TARTLEcast is probably the most labor intensive activity and that requires only a handful of people to get done. Otherwise, people get their work done as they are able. If they like to be up at night, they can get it done when the sun is down, or vice versa. If someone likes to break up their day with a visit to the gym or a walk in the park, there is nothing stopping them. Since nearly everyone works from home, they can get all their errands done when traffic is light, saving time and frustration. There is no sitting around an office either. How much time is wasted in the typical office building as people walk around the rows and rows of cubicles just to get the blood flowing again? Sure, the people are there for eight hours, but is eight hours of work getting done? Does eight hours of work even need to happen? Can someone get it done in six hours when they don’t have to deal with the company copy machine?
That all makes vacations incredibly flexible as well. If someone needs a week or two away, they can do that. So long as the work gets done, it doesn’t matter. Whether it gets done before vacation, during, or things get caught up after, it isn’t really a problem, the individual gets to make those calls on his own.
Speaking of staying out of the office, there is another benefit – the environment. Without the need for all those office buildings, there is much less environmental impact. After all, a person’s home is almost always heated or cooled whether they are there are not. Not to mention the emissions saved by not having to drive back and forth to the office. That also allows all those buildings to be used for some other purpose. Low cost housing springs immediately to mind. Suddenly, there is all kinds of construction that doesn’t need to happen, tons of resources that can stay in the ground. All because TARTLE and other businesses are letting people adopt a life and work style that is more flexible and suited to their own needs and desires.
So, how are digital acceleration and public benefit certifications related? They directly feed into each other. Digital technology allows us to treat our collaborators better, allowing them to live a life they want to live, which in turn feeds the digital acceleration in general. The net result is a cleaner world and people with a greater amount of individual freedom in their daily lives than ever before.
What’s your data worth?
We’ve spoken a lot lately about how agriculture is making use of Big Data to help improve its operations. Everyone from big companies like Monsanto and John Deere to little ones like Trimble are busy collecting and analyzing data to improve their operations. In and of itself, this is a very good thing. Anything that will help agriculture companies work more efficiently will be beneficial to everyone. After all, more efficient farming means more crops, which means being able to feed more people at hopefully a reduced cost. At least, that is what it should mean. What it winds up meaning in practice is often something different entirely.
A recent article on the Agriculture Analytics Market report attempts to analyze all the current conditions and make predictions on what is to come. Right away, you should notice something, namely the presence of that pesky little ‘p’ word, ‘predictions’. As long-time readers (or listeners of our podcast) will know, predictions are another word for ‘guessing’. At best, they are an educated guess. You can gather all the data you want about what happened in the past and what is happening right now and predicting what will happen in five years based on that is not likely to be reliable. To some extent of course, any organization has to plan for the future and that means making your best guess on what that future will be. So that isn’t the problem, the problem is that too many call their guesses ‘predictions’ and treat those ‘predictions’ as though they are prophecies. Projecting more than a year out has always been dicey but especially in the wake of COVID even a year seems like a distant, unknown land.
As annoying as the drive to try to predict the future can be, that isn’t the main problem we’ve noticed in the article. We identified a running theme, and one that we see far too often – how do we get more? How can we get 20% year-over-year growth every year forever? We touched on an example of this recently in the way that John Deere has been operating and interacting with data. They’re selling expensive farm machines loaded with sensors to gather information to help improve farming efficiency. All well and good, right? Wrong. Why? Because they sell that to other parties without rewarding the farmers for generating all that data in the first place. Rather than constantly looking for ways to double-dip, why not be content with making money, even if it’s only the same amount as last year? Why does it always have to be more? Why not, once the profit is made, turn some of those leftover resources to more directly helping people? Not just as a tax write-off but because helping people is the right thing to do.
Rather than taking all the data and selling it, John Deere could offer it back to the farmer, or at least it could do both. As an alternative to finding new mechanical means of squeezing every possible crop out of the soil, why not look for more natural solutions?
With TARTLE being in over 70 countries, it is possible to gather data on any number of small farms in a variety of climates. While there are averages to crop yields, it’s important to remember that those are averages. Those who do unusually well or unusually poor could be studied to find out that they are doing right or wrong. Such knowledge could then be duplicated elsewhere and possibly scaled up to work on the massive farms that inhabit the American Heartland. That is the real genius of TARTLE, we provide a data marketplace that allows the vast resources of the corporate world to support and amplify the knowledge and skill of the smallest farmer in a nearly forgotten valley, while still allowing that farmer to benefit from the exchange. That’s what we are about, that’s the TARTLE way.
What’s your data worth?
Carvana and Data
You know what unifies almost everyone? We all hate having to go buy a car. Unless you have so much money it doesn’t matter how much you spend, the whole thing is incredibly nerve-wracking and annoying. Sure, you can do your research on what kind of car you want and where to find it online these days, so it’s a lot easier than it was twenty years ago. Yet, at the end of the day, you’re still going to the dealership to walk around the car, check it over, and then haggle with the salesperson, trying to skim a few thousand off the price or get an extra or two thrown in. You never know for sure if he’s being honest with you on the condition of the car or how much they can afford to cut the price.
Those last points in particular demonstrate that the typical car buying experience is anything but transparent. What if it was though? What if there was a simple price that you knew was the price it was actually going to be sold at, there wasn’t any haggling to do, no salesperson, and strong guarantees on the quality of the vehicle? What if you could do it all online and have the vehicle delivered right to your driveway? In short, what if buying a car was like buying something on Amazon?
That is the experience that Carvana set out to create. Using data the right way they managed to create a user experience that allows for buying a vehicle in as little as ten minutes while actually increasing their profits. They were already doing well before COVID hit, but as soon as it did, Carvana really took off with their stock price going from $29 to $280. How did they manage to do this?
One of the big things they did was create an online shopping experience that works for everyone. By foregoing the car lot, they avoid a lot of overhead as well as having salespeople. Not having salespeople does more than just save money by having one less person to pay. It takes the subjectivity out of the equation. Other than the obvious temptation on the part of the salesperson to oversell a vehicle or steer someone to a more expensive vehicle for the larger commission, there are personality issues. If the salesperson and the buyer have incompatible personalities, there is a high likelihood that the sale doesn’t happen, even if the car is actually exactly what the buyer wants. Carvana does away with that subjectivity and keeps the focus on helping the buyer find the car that will best suit his needs.
When asked about how they use data so well, the CEO had a great, TARTLEesque answer. He said it wasn’t really about having an AI and machine learning, it was about getting the right data and doing the right things with it. That is exactly what we’ve been saying, it isn’t about the quantity of the data but the quality of it and being able to use it effectively.
It isn’t that they don’t make use of AI and machine learning, it’s that they use them very intentionally. For example, they have programs that monitor used car auctions, looking for cars that are in demand in certain areas and waiting for them to get to the right price so they can turn a profit. When that happens, Carvana gets a notification from its AI and purchases the vehicle. Then any needed restoration work is done and it is put up online and if needed, shipped to the region where it is most likely to sell.
Carvana uses their algorithms and data so effectively because they understand that they are more than a car company. They grasp that they are really a tech and data company before they are a car company. That, plus the desire to help get people in the car that they want to be in, makes Carvana one of the best and most interesting companies in the digital age.
If they can use data to revolutionize something as mundane as selling a car, imagine what can be done by millions of people all sharing their own data through TARTLE.
What’s your data worth?
Buyer FAQ Part 2
Most people would agree that quantity is less important than quality. What good is it to have a lot of something if none of it does the thing you need it to do? Data is no different. It’s a lot better to have a relatively small amount of data that has a lot of identifiable markers that can help give you the answers you need rather than a mountain of data that tells you nothing. If you are a buyer that is just hoarding data without getting answers, you’re just wasting your cash.
Yet, you’d be surprised how many people just keep looking for more data with no thought to its quality, persisting in the false hope that if they just have enough data, they’ll get what they need. No, you won’t. What you need is a place to buy some quality data. Fortunately, we happen to know a place by the name of TARTLE.
However, you might ask what is our long-term vision of the relationship between the buyer and the seller? Our vision is nothing less than a world of perfect data transfer. We want to be able to seamlessly get data from the individual silos that are the sellers to our buyers who can use it for some greater good. Or to put it more simply, to help get data from people who have it to people who need it. It also helps people find causes they believe in to share their data with.
Imagine you’re the American Heart Association and they are doing a new study on the best habits for keeping your heart healthy. You could put the request out into our system and sellers would respond, knowing that their data will be used for a good cause. This creates a double incentive for the seller to share his data.
Think about it. How much incentive does a person have to share his data with a stranger? Not much, especially for free. In the TARTLE data marketplace, a seller actually gets paid for sharing his data. That’s one incentive, but the other is based on the reason that you, the buyer, wants the data. If you want the data for something a seller is concerned with himself, then you’ve made a connection. You are now not completely strangers. That connection, based around a common cause is the other incentive. It’s the desire of the buyer and the seller both to help make the world just a little better that really makes the transaction happen.
This creates a model that is logical, efficient, and fair. How is it logical? Quite simply, the seller owns the data that you want. Like anything else, you simply offer a certain amount of money for that. The transfer of data logically works the same as any other purchase. How is it efficient? All our transactions operate on a 24hr bid cycle so if the needed sellers are already part of the system, it is very fast and efficient to get the needed information. The buyer also only pays for exactly what he gets; there is no overspending or overcharging in our system. Finally, in the TARTLE data marketplace, both parties are getting something that they want – the seller gets paid and the buyer gets his data. Everybody wins.
As such, TARTLE offers the opportunity for a company to build goodwill with customers by inviting sellers to sell their data while being transparent about how it will be used. When people see these interactions and see how the company will use that data, it can be a strong motivator to use that business over another one that is less open about how they operate.
TARTLE is a system that treats all of its users with equity, inviting all of them, buyers and sellers, to change the world while sharing data and earning money all at the same time. Which only leaves one more thing –
What’s your data worth?
Antitrust and Amazon
Recently, the European Union filed an antitrust lawsuit against Amazon. The premise is that Amazon’s ubiquity gives it an unfair advantage in selling their own products. Amazon is of course most famously known for selling everyone else’s stuff. Tons of retailers and manufacturers use Amazon as both a storefront and distribution network. You can even get products from Sam’s Club on Amazon. That isn’t the main issue in the lawsuit though. What the EU is alleging is that Amazon’s vast network and data gathering capabilities give it an advantage in that they use all that data to better refine their own products and then market and distribute them. The fear of course is that no one could possibly compete with the way that Amazon operates.
There are a couple problems with this lawsuit. One, Amazon doesn’t actually make that many branded products. It’s a smattering of smart devices like their tablets, streaming sticks, and the Alexa devices. That’s about it. It’s also worth pointing out that despite the marketing and distribution advantages, the Fire Phone was hardly a raging success. In all honesty, this case could be better made against Walmart that has a whole like of products of many kinds that it distributes through its massive network of brick and mortar stores and online shopping.
Finally, this is just Amazon being smart with the data that they can gather. Why wouldn’t you pay attention to what sells, what features are most important, and what price points people buy at when you are designing your own products? It’s as if you were building a house and someone gave you a free blueprint for exactly what you were looking for, but instead you threw it away and figured it out from scratch. See how that doesn’t make any sense?
Amazon’s success isn’t really a matter of forcing competition out but looking at the way things are going and getting there first. In the early days of the internet, they saw the potential in selling small items like books. Suddenly, bibliophiles didn’t need to spend years combing used book stores for a particular work, they could just look it up and order it. And anyone could do it, used bookstores, major publishers, or even just the soccer mom with a few old books to unload.
As capabilities increased, they branched out, streaming music, movies, and of course selling ebooks and their own e-readers, practically speaking the tablet market into existence. And let’s not forget that they developed partnerships with hordes of retailers around the world allowing them to sell nearly anything under sun.
This has actually been the case on the distribution side of Amazon as well. In truth, that is the real secret of Amazon’s success, its ability to get almost anything almost anywhere in the world in just a couple of days. Sometimes, they can even get things delivered in a matter of hours. They realized that people would be willing to wait a little bit if they didn’t have to deal with going to a store, especially if they knew they were getting what they wanted, instead of just hoping to find it. Just like with internet streaming, they gradually increased their capabilities and now Amazon trucks are all over the streets of America, dropping off packages by the millions. Naturally, things haven’t stopped there. Noticing the rise in the gig economy (only natural since they helped bring it about) there is now Amazon Flex, which allows anyone to pick up and deliver packages under the Amazon banner and make a little side money. The next step of course is for Amazon to start using drones to deliver packages. That project has been underway for years already and as soon as they can get FAA approval, you can expect to see Amazon drones buzzing around the skies.
How does all of this relate to TARTLE? Like Amazon, we are a marketplace, with you the individual as the retailer. We see the trends towards accessibility in terms of ownership, the desire for greater personal control of data and the growth of cryptocurrency and are eagerly adopting them. Even better, we want to take you along on journey, to get out ahead of the trend and lead the way into a future where everyone has more direct control over what goes on in their lives.
What’s your data worth?
Have you ever been to an old library, or a used book store full of old books? It’s great. The musty smell, the feel of the yellowed pages that crinkle slightly as you turn them, there really is nothing like it. It evokes a sense of depth, wisdom, permanence.
That’s exactly the feeling I hoped to have when I visited the National Archives in Washington D.C. I was looking forward to rows upon rows of filing cabinets full of the personal writings of Washington, Franklin, Teddy Roosevelt, FDR, and more. However, I was disappointed. Instead of archives, it was full of TVs everywhere. Nearly everything was digital. Now, for many reasons this was a bit of a letdown. I was really looking forward to those poorly lit rooms full of dusty documents. Yet, looked at from another angle, this was actually hopeful.
The United States has the goal of having all of its documents completely digitized by 2022. As of this writing in the fall of 2020, this is really right around the corner and if a recent meeting of all the alphabet soup agencies on the matter is any indication, it would be wise not to hold your breath. When it comes to any significant change, all bureaucracies tend to move at a pace that would embarrass a glacier. Why might that be?
As beneficial as digitizing all the information at the government’s disposal is (which we’ll get to that shortly), the fact is that it’s difficult. While technology has improved greatly in recent years, including software from Google that will automatically strip the text from a photographed document, that technology isn’t necessarily available to everyone. In some cases, it might be expensive, there may be security concerns, and of course there is training. Think of when you first started introducing your older friends and family to email, but much, much harder. Addressing all of these issues takes both time and money.
There are also incentives to slowing down the process. One is simply money. The government of course doesn’t directly have the resources and know how to do this entirely on their own. That means contracting with major software companies, companies that are more than willing to offer their expertise for a government-sized paycheck. Anyone who has followed NASA for more than a few years knows that virtually every government-related project comes in behind schedule and over budget. Sometimes, that’s because the project in question is genuinely more difficult than expected. Other times, it’s because the contractor draws it out, getting as much money as they can.
Another incentive to delay the process of digitization lies in one of the benefits. The main purpose of putting that information in a digital format is to make it searchable, to be able to run it through an Artificial Intelligence/Machine Learning (AI/ML) program to find connections between different sets of data, to find patterns that would not be able to be identified any other way. It would also find inefficiencies, something government is infamous for. Needless to say, there may be a few people in government eager to make use of an inefficiency or two in order to keep the rest of the inefficiencies from being discovered.
Finally, there is a fear that the government would come to be run by machines, that those AI/ML programs would be doing more than just analyzing data, they’d be setting policy. In truth, the goal is simply what was said above, it will be used to analyze data and find correlations, not come to conclusions about what to do with that data. And even if that were the case, such software is limited by its programing.
Think of the old, trench coat wearing detective. He is typically shown with a wall of pictures, newspaper clippings, and scribbled notes, all connected by red strings, showing the different connections. AI/ML programs will do the same thing, yet they won’t replace the detective. He (just like the policy maker) still has to ask the questions and draw the conclusions and make the decisions. The data is only as good as the questions asked and the decisions made with it.
So, given all of these difficulties and fears, is it worth going to all the trouble just to be able to crunch data faster? The answer is clearly yes. While those musty rooms of old books are great, they have one key weakness – they are stagnant. They are full of information that just sits there, not doing anything. Digitization makes it much easier to access and make use of all that information. And information, data, is only valuable, only useful when it is used, when it is moving. To keep that data inert in a vault deprives us all of important insights and advances that can only be made through the movement of data. With data moving, its true power is unleashed through faster and better decisions.
What’s your data worth?