COVID has changed a lot about the way we go through the world and navigate even simple, mundane tasks. No matter how someone feels about all the different things that have gotten wrapped up in the virus, things like masks, lockdowns, vaccines, its origins and so on, the fact is, the effects have been immense. Truth be told, the full effect of the shifts in the economy, distribution networks, and how and where we get our work done won’t be known, much less understood for years to come.
One thing we do know is that people’s habits have changed immensely in the last year. While at one time only a relative handful of people were doing curbside pickup or home delivery, now, almost everyone has done that at least once. Things that we never thought we would buy online are now in our cart and at our doorstep before we even think about going to an actual store for them.
That means all the information that companies had on us as a society pre-COVID has virtually no descriptive power for what we are doing now. Such information is now a historical curiosity rather than something that could be a guide for what people will generally be doing this summer. Realizing this, companies are looking to recalibrate how they get their data and how they analyze it.
One significant element getting a fresh look is of course data management. Many companies do a bang up job of collecting tons of data. The problem lies not in whether they are collecting it, but in what they do with it. All too often, data sits in different servers and split up between different departments, siloed, making cross referencing the data difficult. How much time and energy gets wasted because different departments in a company can’t look at each other’s data? Or what about different companies under a larger, umbrella company? They are probably each gathering all their down data, duplicating a ton of effort that could be better spent elsewhere. This is exacerbated by the rush to gather new data to capture all those changed behaviors.
In managing it, they also clearly need to secure that data a lot better. There are simply too many data breaches to justify any level of complacency. Especially since that data doesn’t really belong to the companies collecting it. The individuals who generated that data are the ones who own it. At best, these companies might look at it as being on loan to them. As such, they have to take better care of it than if it were truly theirs.
That’s what we do at TARTLE, we understand that when someone stores their data with us, we are acting as stewards of that data. We keep it secure and don’t just share it with anyone. The only person who shares the data is you, no one else. What do you think we are? A bank loaning out your money to people without you knowing about it?
What is also apparent is that the first problem, the hoarding of data, leads to the attitude on the part of a company that they are the ones who own the data. With server racks full of it, the temptation definitely arises to find ways to monetize it, first to make enough to pay for the servers themselves and then the question becomes ‘why not more’?
What if there were a way to get the data you need without hoarding it? What if you could get it straight from the source, the individuals generating it? What if you did it with their consent? You don’t have to just store it in racks, you can get exactly the data you want, when you want it, avoiding all the extraneous noise, and so save yourself a lot of the costs of data hoarding as well. You can get all of that with TARTLE. Sign up and get the data you need when you need to do the analysis you need now. Then, if you want, you can sell the analysis if it is beneficial to someone else. That helps create a system that works not just for a few but for everyone.
What’s your data worth? Sign up for the TARTLE Marketplace through this link here.
Data and Servant Leadership
On the latest interview episode of T-CAST, Alex and Jason joined Robert Sieger for a conversation. Robert wears a lot of hats. He has been Chief Information Officer, Chief Technology Officer, and VP of Technology and currently employed as a CIO at Boomerang Carnets. Unless you are up on some of the finer points of international law you might be wondering what a carnet is. As Sieger describes them, they are a passport for stuff. Most countries have pretty tight controls on what you can and can’t bring in and out of them. This exists at least in part because of the desire to control trade. However, there are certain businesses that frequently bring goods in and out of a country with no intention of selling them.
An example based on some of Boomerang’s clients is news crews. These have a large amount of equipment that go with them into a number of different countries around the world. The carnets that Boomerang provides are essentially a way to record the movements of that equipment in order to ensure CNN or FOX isn’t selling random camera equipment to the locals.
However, this conversation wasn’t about such esoteric matters. Robert is also a strong advocate for servant leadership. Sounds good, but what is it? Servant leadership is simply the idea that the leader should be empowering others to help them be their best. Part of the way this works is that it is really a two way street. The leader helps those he leads be their best and they in turn help the leader be his best. This requires a certain degree of humility on the part of the leader, who has to be willing to hire and develop people who are smarter than him in certain areas. He has to then be willing to learn from them. This is certainly true in regards to the technology world in which Siegel operates. He doesn’t need to know every aspect of coding and website design, in fact, he can’t. What he can do is direct all of the necessary elements to accomplish the specific goals of the company. Taking the time to also develop the people, to understand them, their talents, their desires, and their ideas helps them, helps the leader, helps the company and of course helps the clients.
A cynic will say that all sounds great in theory but does it actually work in practice? Can Mr. Sieger produce data to show that his approach is effective? As it happens, he surely can. When he joined Boomerang, Robert took a poll of its users asking how they felt about technology and its responsiveness and impact on their lives. The results were less than promising. He took the information from that poll and worked with his team. He also began taking the same poll every year and the results are now uniformly positive. He was also able to bring in a phone upgrade $30,000 under budget simply by really listening to his team.
That is what truly separates the real servant leader from a pretender. He actually listens. He does not just have a suggestion box, or an open door policy that everyone can tell is just lip service. All you have to do is have a real conversation with people and then if they have a good idea, follow through on it. Don’t just smile and nod. Yet, few take this simple approach, a fact that baffles Sieger, and frankly us as well.
Robert was also able to take this approach to the rest of the company, getting the other VPs on board with servant leadership. Even that took plenty of patient listening. The VPs all had their own issues to deal with, which might require differences of approach, differences that you might not be able to detect unless there is real listening going on.
Sieger takes this approach even when team members don’t perform up to standards. Should an employee make a mistake, he gets coached and so long as it is addressed, that’s the end of it. In fact, the correcting of the mistake or behavior becomes a positive come the next performance review. Should there be a chronic problem, a plan gets developed in conjunction with the person to figure out how to correct the issue. Most times, this corrects things and the employee improves. Though sometimes, there will still be someone who has to be let go. As they say, you can lead a horse to water but can’t make him drink.
Robert Sieger represents exactly what a leader should be. He seeks not to control but to serve, treating people like people and not cogs in a machine, getting information, and ideas directly from them. This makes Mr. Sieger another excellent data champion.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Companies spend a ton of money on studies, both internal and from third parties. These studies are meant to evaluate their products and practices. What can they do better? What is the next thing people want to see? What do they never want to see again? All of these are important questions for any organization that exists to market any kind of product or service to others, whether it be baseball bats, rockets, or a homeless shelter.
However, often the tools they use to conduct those studies and produce those reports discussed in meeting rooms around the nation are unreliable. Not dishonest necessarily, simply the wrong tool for the job. TARTLE is proposing a tool that will allow any organization to get the information they need to get truly reliable reports, reports that will accurately reflect where the organization is at and so help provide a more reliable guide for the future. You want every report to hit it out of the park and we want to help make that a reality. If that can be possible, then you will not only be successful, you’ll be so far ahead of the competition that they’ll be wondering how you managed to pull that off.
What is it that makes those other tools so unreliable? After all, you’re spending plenty of cash for them, you would hope they would at least be reliable. The thing is, there is every incentive for these tools (other data and research companies) to make sure they get as much money out of you as they can. That means they may not always be producing the best product. Sometimes it just means they are producing a report that looks and sounds good. Be honest, a thick stack of paper with a glossy cover full of graphs and big words is impressive. While such things might be very impressive, they don’t necessarily make for good reports. Because at the end of the day what matters is not whether or not the report looks good, what matters is that it gives you the kind of information that will help your organization make effective decisions in the future.
There is also the fact that those reports are going through a filter, someone else’s filter. Yes, you give them instructions and maybe even have some oversight, yet, everyone has a filter, an interpretative lens that is all but impossible to completely eradicate. Wouldn’t you rather the filter be yours? Wouldn’t you rather do the analysis in the way that you want it done so that you are getting the answers you need, not the answers someone else thinks you need?
The tool TARTLE offers lets you do exactly that. You are the one in full control of what data you get and how you analyze it. You also know exactly where it is coming from. We put you in direct contact with the individuals who are the sources for that data that gets aggregated by all those third parties. Would you or would not rather get the raw unfiltered data? Doing so lets you be flexible, lets you adjust things on the fly as needed so you are getting the information you need when you need it. That allows you to make much better, much more timely decisions than are possible when you are going with giant reports that take weeks or months and thousands if not tens of thousands of dollars.
Can it be risky to make a change? It can certainly seem that way. Changing the way things are done always implies some level of risk. Yet, no one ever got better, no one ever made a breakthrough or got out in front of the pack by not taking a risk. Do you want to be a Fortune 500 company or a Fortune 1 company? Take the risk and see what happens.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Remember how during the end of 2019 and beginning of 2020 Australia seemed like it was entirely on fire? Record wildfires spread throughout much of the continent, turning the skies red. In fact, at some point, it even rained fire. Then, back over in the United States, there were massive wildfires in Oregon, California, and Colorado. These also turned the skies red and I even saw video of a fire tornado. Yes, a fire tornado. It looked like something out of a movie. It also put enough smoke into the atmosphere that affected the color of the sunrise and sunset as far away as Michigan for a week. I don’t care who you are, that’s impressive.
Fortunately, there are some ways to bring at least a sliver of good out of all those fires. Researchers took advantage of the situation and studied how the smoke affected the light reaching the earth. Naturally, it was less. What was more surprising though was that the smoke didn’t so much absorb the light and thus keep the heat in the Earth’s ecosystem as much as it scattered the light. That allows the heat to dissipate, with some of it even going off into space. The net result of all those wildfires is therefore actually a net drop in temperature.
One of the really interesting things that the researchers found was that fires in different areas of the world created different kinds of smoke and therefore different scattering effects. For example, the fires in Oregon created darker smoke that scattered more light than the fires in Australia which was mostly burning dry brush.
This data is important in that it helps us refine our climate models. The net cooling effect was something that was unexpected, meaning that our climate models were off and needed to be refined based on the new data.
That discovery points to one of the more important philosophical underpinnings of science in general – the concept that we should question what we think we know, that we should always be searching for better data to improve our understanding. Perhaps that is nowhere more important than in the realm of climate since we are basing policy off of our climate models. Given the importance of government regulation to the environment and to the economy it is imperative that they continue to question what they think they know. In doing so, more research happens and people are able to find unexpected things, both good and bad, leading to models that more accurately reflect reality.
In other news on improving our data sets, a different group of researchers were having a problem tracking smelt populations. As scientists tend to do when they want to track animal movements and populations they tag the animals. The problem was, when they would check up on their tagged tiny fish, they found there were far more of them gone than they had predicted, without a corresponding population decrease. They were puzzled until they looked in the belly of a heron and found some of their tagged smelt. It turns out herons love the little fish and don’t discriminate based on whether or not the critters have a tag. The discovery solved the mystery of the missing tagged fish and helped the scientists better understand the local ecosystem.
What does all of this have to do with TARTLE? What is something that both anecdotes have in common? They both involve researchers solving a problem by getting as close to the source of the information as possible. That’s exactly what we advocate here at TARTLE. We want people to be able to get to the data’s source, you. That way, they can correct whatever assumptions and biases they might be starting out with and in the end make decisions that will actually make things better for everyone.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Credit scores are a big deal in the modern world, especially in America. Here, banks, car dealerships, and others will literally judge you as a person based on your credit score. If you have a low score, then you are obviously bad, regardless of your situation. If you have a high one, then you are obviously good, regardless of your current habits. But it is actually far crazier than that. If you actually are a responsible person and pay off your debts, your score can actually go down. Yes, you read that right. Paying off your debts will actually make your credit score go down. This actually leads to the concept of the ‘credit building loan’. In this perverse concept, a person actually goes in debt to build a better credit score. Yes, this is completely nuts. It also keeps people in what amounts to financial slavery.
Think about it. If you want to buy a car, you first have to go into debt for something else in order to get your score high enough to buy the car. Then the debt for the car helps you get your score high enough to go into even more debt for a house. If you want to go to college, or help your kids to go, you have to have a certain amount of debt in order to get any sort assistance. If you’ve been responsible and paid them off, you’ll be expected to shell out for college in cash. To put it simply, our current financial system encourages and rewards people for being in debt. Which, again, is insane. If the above isn’t enough to convince you, the word ‘mortgage’ comes from the Latin for ‘death grip’. Let that one sink in.
Credit scores as they have been traditionally calculated rely on limited data, focused only on past behaviors. Now though, more data than ever is available and it is being used in new ways to calculate your ability to pay off a loan. Rather than just aggregating how often you pay off revolving debt, credit companies can now look at how fast you drive, how much money gets spent how often and where. They can even see where and when you take vacations. All of that behavior can be analyzed for risk in near real time.
This marriage between big data and big credit can be beneficial in a number of ways. First, since the data is more recent, it is easier to track changes in behavior that indicate more or less risky behavior. If someone who has been at risk in the past suddenly finds a new job and starts paying an electric bill, while also cancelling their cable bill the chances of getting a desired loan go up. Rather than it taking a year to build up a credit score based on the changed behaviors, the increase in responsible behavior can be noticed right away. Similarly, if a person who has been responsibly paying off cards and loans for years suddenly starts going to the casino, the dealership has some reason to hold back on that new car loan.
It becomes easier to take extenuating circumstances into account as well. If someone suddenly has trouble making a payment because of a recent job loss or divorce, that can be weighed against other data to see if it reflects a temporary circumstance or a real shift towards irresponsible behavior. That kind of information may not matter in regards to a new loan but can still apply when trying to rent a new apartment.
Isn’t it a little scary though that the credit score companies can access all that data about you? A little, though in this case it’s easy to see how it can actually be used to benefit the company and you. However, if you are still uneasy about that, we don’t blame you. In that case, we recommend that you sign up with TARTLE and synch your various accounts. That will allow you to take back control of your data and only let even the credit score companies see it as you see fit. And when you do choose to share it, you can actually get paid for it.
What’s your data worth?
I don’t care which party you belong to or if you belong to no party, I think it’s pretty clear our political modeling is very broken. The recent Georgia runoffs have illustrated that yet again. Many who have typically championed the use of polling in elections are finally beginning to realize that there are problems, even admitting that election modeling is broken. Yet, they are still charging ahead in the belief that big data all by itself is the way of the future.
This is silly in a couple of different ways. One is simply that on one hand, they are saying that their techniques are fundamentally broken and on the other that they know exactly how to fix it using the exact same kind of information they currently don’t know how to use. Another is that they seem to be relying on the idea that they just need more data, as though merely having more data will solve problems. As stated, that is a rather silly notion. If all you have is bad data, it doesn’t matter how much you have, it’s still bad and will lead you to bad conclusions. It also helps if you know what to do with it. Otherwise, piles of good data don’t help you anymore than piles of bad data.
How so? Often polling sizes are extremely small. A sample of one or two thousand is taken to represent a population of millions. This is totally ridiculous. There is no way that kind of sample can be truly representative. For one, it is very tempting to do your survey in a limited area. And while no area is truly monolithic in its voting patterns, it’s well known that cities tend to vote one way and everywhere else another. If you over sample a particular geographic region then you are going to overrepresent one point of view while underrepresenting the other. Even if you try to sample over a large area, it’s still hard to consider such a poll accurate unless the sample size is much more significant. There is also the fact that different political inclinations will affect whether or not a person will even talk to the pollsters. Conservatives in general are much less likely to even talk to most pollsters, which creates a massive deviation in the polls.
What about knowing what to do with certain data? One of the things that polling agencies have been doing is looking at local shopping habits to predict how a given area will vote. The idea would seem to be that if lots of people are in the line at the local Starbucks, then they will be voting for the Democrat and all the people at Cabela’s will be voting Republican. Certainly that fits into stereotypes. Yet, if you are trying to be accurate in your polling then you need more than stereotypes. After all, republicans also drink coffee (even from Starbucks) and Democrats have even been known to go hunting and fishing on occasion. That person ordering the tofu wrap with the long hair may be walking out to a pickup with a couple cattle panels in the back. The neighbor who just loaded half a dozen guns into the back of a minivan for a day at the shooting range may be more at home sitting in the corner of a coffee shop eating a scone while reading Voltaire. Individuals are much more complicated than what one or two shopping preferences could ever entail. You simply can’t make accurate predictions based on these kinds of models. To really develop an accurate election model, you need to be able to get down to the individual level. What motivates a person to vote a certain way, or at all? If a person has voted for one party the last fifteen years but suddenly switches, the why is incredibly important.
How does a pollster develop this kind of connection and collect this kind of data? Through TARTLE of course. A pollster can sign up with us and connect with people willing to share their political preferences and go into why they think one policy might be better than another. Such information could be particularly useful to pollsters or campaign managers, both of whom are interested in determining what messages resonate with which voters. The kind of connection that TARTLE offers could also be a great help to those interested in forming a different party, one looking to deal with the concerns of most people, rather than trying to get people to care about the things the party is most concerned with.
What’s your data worth?
Have you ever noticed how you stop and listen almost every time a bird sings? Even if you don’t stop, you still listen as you keep going. In fact, it might seem like you can’t help it. The truth is, you can’t. We’re literally genetically hardwired to pick up on the sound of birds. That and a rising or setting sun are things we can’t help but notice whenever we encounter them. Why might that be?
In the case of birds, we can hazard a guess or two. Birds of prey and scavengers let us know that there is likely food in the area. The birds themselves of course would have been a source of food back in the early days of humanity. Not to mention, the presence of a lot of birds means there aren’t a significant amount of predators around, meaning the area is safe. The sound of a bird also lets you know there is probably water nearby. Our ancestors may have also learned to associate the presence of many different kinds of birds with good farmland. A diversity of bird species means there is a diversity of other sorts of life in the area. Plants of different kinds provide homes as well as insects and small mammals to provide food for the birds. And lots of insects and plants means the soil is likely fertile. Those are just educated guesses though. One thing we do know for sure in the modern day is that the presence of birds correlates with human happiness.
How so? Well, on one level, it could be the birds themselves. Who doesn’t enjoy listening to the sound of a robin or nightingale? Or even just watching a hummingbird hovering outside the window? It certainly brings a little smile to my face. Or maybe it’s the fact that where there are birds, there are natural areas. If there are birds around there are trees, streams, flowers, and tons of other beautiful and relaxing things. About the only bird you hear in the city is a pigeon. And even there, people often sit on benches and throw bits of bread to attract the birds. We are literally happier wherever there are birds (except maybe seagulls, nobody likes seagulls). As for whether or not the correlation between happiness and birds is because of the birds themselves or because of the natural areas they tend to be in – it doesn’t matter much. The birds and the natural areas are almost always a package deal anyway.
We tend to create those natural areas wherever we can. For people with discretionary income, one of the things they like to do is get out into nature. Whether it’s camping, rock climbing or backpacking people will spend a lot of money just to go outside and enjoy nature. Even in the cities, wherever there is enough money to do so, parks are a feature, the most famous of them being Central Park which is practically a forest in New York City. We crave to renew our connection with nature, even if only for a little while.
One of the interesting side effects of spending time with the birds and the kinds of places that they like to live in is that it makes us more productive for the rest of our lives. Why? Simply because we are more relaxed, more at peace. After all, we are part of nature too, a fact we often forget. Getting out in it every now and then reminds us of that and recharges us in a way that no amount of modern entertainment can.
Just as data shows us that there is a correlation between happiness and birds, so too can looking at data from birds warn us of problems in the natural world. Even just superficially, birds tend to circle an area where another animal has recently died or is about to. They are amongst the first to flee from a forest fire. And of course, a drop in the diversity of bird species could be an advanced warning for the introduction of a plant disease or a new invasive species. Collecting and analyzing data on bird behavior could help devise better ways of managing and caring for the environment that we both share.
What’s your data worth?
At risk of sounding like a broken record, COVID has dramatically changed the way we do a lot of things. One of the single biggest changes to how we live now has been in our buying habits and the retail world has had to adapt accordingly. This is of course obvious, but in any industry there is a demand for data that is more granular than just whatever is obvious on the surface. For that, you need a way to track purchases. Fortunately, this kind of system has already been in place for years in the form of bar codes. Most of those little rectangles of black lines on every product that you purchase was first issued by a company called GS1 US. Because this company issues most of the bar codes out there, it is one of if not the biggest aggregators of purchasing data in the world. It is their data that has shown through cold hard analysis the massive shift to online shopping.
How massive is that shift? So massive that in the first month of COVID lockdowns online shopping grew as much as it would have in eight years of normal growth. We are now approaching the one year mark since the first lockdowns were initiated in the US. During that time, online shopping has only grown, driven by continued restrictions, some businesses going under, and people who would just rather not deal with masks or other issues that arise when going out to the store.
That is only part of the story though. While some businesses have been destroyed by COVID restrictions, others have sprung into existence in the last year while others that were only niche businesses in 2019 are now mainstream. Take curbside pickup. There were a few restaurants and grocery stores that were already exploring these options. Walmart in particular – being highly data driven – had already identified that many preferred to not deal with going in the store. So when the lockdowns started to roll out, they already had the infrastructure in place for something that has now become a major part of their business. Not that these things are always flawless. If you aren’t careful, you can get a lot of interesting substitutions in your order. Fortunately, data analysis can help identify if there is a systemic issue that needs solving or if such things are merely anomalies.
One of the most interesting businesses that is well suited for the COVID world is Carvana. This business not only lets you buy vehicles online, it will deliver them to your door. They include several high resolution photos so you can get all the information you need on the car before making a purchase. That definitely helps if you are someone who would rather not deal with car salesmen and driving around to different car lots, sometimes taking days before you find one you like.
In many ways, COVID has merely accelerated trends that were already in motion. Even before 2020, businesses like DoorDash, GrubHub, UberEats and others were gaining steam. Shipt, Shopify, and others have shared similar explosive growth. It isn’t only relatively new companies or places like Walmart that have been moving in this direction though. Even established businesses with high end items like jewelry stores in New York City have gotten in on the game. I have it on good authority that it is possible to by jewelry for your fiancé from NYC while never leaving your desk in New Mexico. If you ask us, that’s a little wild, but a little awesome too.
The only thing we would like to add is that too many of these businesses are still reacting, operating on old data. TARTLE, through our data marketplace we can connect businesses to individuals directly, allowing them to identify trends just as they are getting started, if not before. In that way, we can help shape the future in a way that is better for everyone.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Breaking news! Digital transformation needs data! So sayeth the sages at Forbes. In other news, water is wet and the sky's still blue. Now, that you’ve had a moment to recover from that shock, let’s actually spend some time thinking about this.
The first thing to note is that digital transformation is something that’s been going on for decades at this point, just not by that name. Digital transformation is just another buzzword that the corporate world likes to invent and throw around to sound smarter than they are. So, what is it? When we strip away the corporate sheik and the buzzwords, what is this process that has been going for so long and how is data involved?
The process is simply that of moving from an analog to a digital world. The first calculators were a part of the transformation, emailing another, as well as the move to HDTV. One of the most impressive part of society’s ongoing digital transformation is the rise of the humble mp3. Due to the digitization of music it has become available virtually anywhere streaming over your phone. A whole generation has already grown up never having heard and maybe never having seen an analog cassette tape. About the only analog music you can find in the stores now is an old turntable.
Now, that all seems fairly mundane. But it only seems that way. Every one of those innovations reveals another or was made possible by another. Streaming mp3s are only possible due to the internet and massive servers storing music files, which represents a significant shift from physical to digital media.
There are also a variety of corporations still working on making better use of their digital assets, using sensors throughout their facilities to track the production and movement of products and make the process more efficient. Naturally, every single sensor is putting out data that is stored in a server and then analyzed. The process of analysis has also undergone a digital transformation. Once, it was all done by people. Now, there are complex algorithms that can handle the simpler kinds of statistical analysis.
The digital world is taking over in others ways as well. There are already automated semi trucks on the road, bringing loads of goods and material to different locations in the country. Some of the simpler articles online are now written by a computer rather than a reporter. In some places, even retail stores are getting automated. Amazon for example has set up retail stores in New York where you never have to go through a checkout line. The store tracks what you get off the shelf and bills your account accordingly. In Japan, convenience stores are moving in the same direction. And even in small towns in the US department stores are letting people scan their purchases with their phones and heading out of the store.
To bring things back to data, all these moves were data driven, data that at least suggested that products could be delivered more efficiently by moving further into the digital world. An unfortunate aspect of the transformation is that it is largely driven by a desire to increase profits. Of course, there are still numerous benefits that derive from that motivation, but what if the desire to help people was the real driver? What if we gathered analyzed data and then applied it with profit as the secondary goal and creating a better world first? Is it even possible?
The answer to that is ‘yes’. As someone wise once said “put first things first and the secondary things will follow.” This is what TARTLE is trying to accomplish, to harness our continual digital transformation not to merely drive the bottom line but to help people and give them the freedom to develop themselves and reach their full potential. That is why we want to give you control of your data. By being in control again, you are also an active part of the process with the freedom to decide where and how much you even want to participate in the digital transformation. You get to decide how much you and your data will be involved, not a faceless government or corporations.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
Literal mountains of data are generated every day. Especially in urban areas where there are more people and more connectivity to the internet than in a more rural farming community. Availability of data unfortunately doesn’t mean that data is being used well or that the data is accurately representing the people generating it. How can that be? If it’s your data, isn’t it naturally going to represent you accurately? While that seems intuitive there is often hidden context that the data doesn’t pick up.
Let’s look at a typical, urban example. It’s often lamented that people in the inner city aren’t getting enough nutrition, they eat far too much fast food and their health suffers as a result. It’s often assumed that this is due to poor education or that nutritious food isn’t available, that the inner cities are “food deserts”. This is at best only half the story.
Many times, good food is available. The problem is that they are at small bodegas that charge a lot more than the Wal-Mart a few miles down the road. So, when the parents of a struggling family get home from work, they have a few choices; go to the bodega to get some high-priced but fairly fresh food they can barely afford, head to the big box store for food they can afford (but will still need to be prepared) or pick up a few cheeseburgers at the fast food joint that is both affordable and requires no preparation. After a hard eight hours or more on my feet, I know which one tempts me.
Even that added context ignores other points. Why does food cost so much more at the bodega? Even for the same brands? The two main reasons are their size and their location. The fact they are small means they can’t get the kind of bulk discounts or negotiated prices that are available to the big box stores. Also, the location means it’s just physically more difficult to get product to the store. It’s a lot of time and fuel to burn just to drop off a few boxes. Since the bodega still has to make a profit, they have to raise prices accordingly. All of that information won’t get picked up if you’re just looking for data on people’s eating habits.
It actually is possible to get good data that has all the context and representation you could ask for. Rockford, IL decided they were actually going to do something about homelessness instead of just continually throwing money at it like most do year after year. Their first step was a novel one, they realized they needed data and to get it, they actually went on the ground and talked to people. Yes, a government entity actually thought of putting in the effort to get primary source data. What’s more, they actually did something with it. They didn’t just talk to people about the problem, they talked to them about what potential solutions might be. As a result, they got different people in different organizations working together to solve the problem instead of jealously protecting their turf. Within one year, they almost completely solved homelessness.
Their on the ground approach meant they were getting data from people who typically produce very little and they got context for that data as well. Rather than just staying back and collecting data from second and third parties, they went to the source and got better, context rich data that allowed them to get the right tools to solve the problem.
If this sounds familiar, it’s because this is exactly the kind of approach that TARTLE has been preaching. By going to the individual you get solid data and the reasons that data exists. Armed with context-rich source data better solutions are possible, solutions that are cheaper and more beneficial for everyone.
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