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.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
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.
Speaker 3 (00:07):
Welcome to TARTLEcast, with your hosts Alexander McCaig, and Jason Ridley. Where humanity steps into the future, and source data defines the path.
Alexander McCaig (00:26):
Jason, do you... In ancient Egypt, as you were going into the afterlife, you would have to meet Osiris. And Osiris, there was a couple of steps that would actually happen, but the heart was actually taken out before someone was mummified, and that was put into a canopic jar. I can't believe I remember that, a canopic jar. I think that's what it's called. And so, as that transitions in the afterlife, your heart was actually weighed, they were trying to find a balance. And it was a balance of how you actually lived your life. So the data that they were measuring, was the weight of the heart against some sort of feather, I forget what the feather was, to see if your life was in balance. And they're like, is that data in balance? And if it was, if it was a light heart, you'd be onto the next step, where you go in front of these 60 whatever, judges or whatever, and you'd be judged on all the things you did in your life, if you were a good person.
Alexander McCaig (01:20):
But what we find is that even in the Egyptian book of the dead, we are doing this same thing now, albeit slowly and a very poor job on a government level, for looking at how equitable we are with measuring data across different demographics. And what we want to touch on is, are people represented properly with the information that is currently available? So, there's massive amounts of data, especially in very urban populations. And are we looking at that data, and affording change, or projects or funding into certain things that should be in the city, that require them more, than say, just restoring a park that's already in a highly wealthy area? So, I want to touch on this article here, what's the title of the article?
Jason Rigby (02:14):
It's called, Is Your Data Equitable? A New Tool to Measure Representation in Data. This just came out yesterday.
Alexander McCaig (02:21):
And was it Harvard, published this?
Jason Rigby (02:22):
Yes.
Alexander McCaig (02:23):
So, what we are focusing on is, how is it that we can represent specific demographics in urban populations? And if they are underrepresented, how do we find value data to show that? But one of the initial issues before we can even get into that step, is that the government entity is doing a very poor job of taking publicly available data, analyzing it, and putting it into their decision-making. So what they're getting is, whether it's a function of corruption or perverse incentives, whatever it might be, will draw attention away from data that's actually showing them where they should put time and resources. And they're putting it into things that actually shouldn't deserve it.
Jason Rigby (03:06):
Our old school mentality, because the average age keeps growing with city councils, and mayors.
Alexander McCaig (03:12):
Yeah, that keeps growing, so you have people that are from the, queens of the stone age are still making rules for people that are trying to develop much quicker. And if you're looking at economic disparity within urban populations, we can analyze that data. Where a grocery store sits on, it's not called a... A bodega, where a bodega might sit, and the prices are much higher, and it's actually keeping people beneath the poverty level, because they have to continually pay more for those resources, because it's hard for them to get access to these, maybe like a local Walmart, or some other big box thing, where they can actually get a larger amount of groceries more efficiently. And comparing that data is all possible, it's just been ignored. So, the issue here is that they're ignoring the data that needs to have light shed on it, even though it's publicly available data.
Jason Rigby (04:04):
Well, and I know even companies have come in and offered to help cities to, I know here in Albuquerque, I talked with a data CEO and he's like, "We came to help the city police department," he said, "Our data can find out in these certain areas, at this park, or this place or whatever, that why is there crime happening there from two to 3:00 AM, three nights a week? What if we put a light pole up, shined a light there because it's really dark, or what if we had an officer go there from two to three?"
Alexander McCaig (04:37):
Yeah, correct.
Jason Rigby (04:38):
"Just an officer sitting there in his car from two to three. And then now we've eliminated crime there, and then we find another spot and we can be real strategic with data." But instead, what was happening? They don't trust, that's what they were saying, because it's 65 and older, "I don't trust that," it's like, how could you not?
Alexander McCaig (05:00):
What do you mean you don't trust it? We're trying to decrease crime.
Jason Rigby (05:05):
How can you not trust the data? Because where does the source of the data come from? Your officers that had to fill out a report of a past action.
Alexander McCaig (05:15):
So, do you want, so CIA World Factbook, plus your officers, they're all lying? Let's consider something here. So, when you look at certain demographics, some demographics produce more data than others, so poor demographics don't produce that much data, they have less devices, they're less connected. They're not using as many tools, because they don't have the availability to purchase those tools. So, naturally you're going to have an asymmetry on what's being analyzed. So, the focus is obviously going to be on the wealthier demographic areas, especially in urban populations, so they're the ones getting all the wifi hotspots.
Alexander McCaig (05:53):
But if you start looking at it like, okay, if we put hotspots using the public data we have available in the poor demographics, we can actually increase an availability of opportunity and education in that area, maybe even economic growth. But that sort of disparity, finding the equitability in the data, it lacks balance right now, because they also lack the connectivity and the resources that people in a wealthier demographic naturally have. So, if a city is trying to strike that balance, they need to look at the data, and look to see are they being equitable with how they're actually analyzing it, from that sort of perspective?
Jason Rigby (06:28):
And I didn't want to just be negative, and that's what I was typing up on here, guys. I don't know if you heard about the Rockford, Illinois study with a plan to end homelessness. And so, they got together and said, "It's a 150,000 person city, it's 90 miles Northwest of Chicago," and they said, "By the end of the year, we want to have zero homeless." So, there were 700 homeless people. That's small, it's 150,000, small city, but you can scale this out. So you decide to say, what can we do to get this to zero? So, what they found out was, well, let's collect data. Let's go out and have the officers and people, let's put it all on one, let's get it in an Excel sheet or whatever they were using at the time, I think it was pretty rudimentary, just an Excel sheet. But let's go find these people's names, let's have conversations with them. Let's get all 700 names.
Alexander McCaig (07:25):
Primary source data. How many times I got to say, you have to go to the person.
Jason Rigby (07:30):
So, this is what the mayor did, this is an awesome study, and you guys, there's a Fast Company article on it, you guys should read it because it's really cool. Because homelessness is rampant here in the United States. So, they took all 700 of the homeless people in the city, put all the names, put what issues they were facing when they talked to them. Is it mental illness? Is it just down on their luck, they lost their job? Whatever it may be. Then they got all 700, they got the data, they took all the resources, whether it was the police department, whether it was the homeless shelters, anybody that had community outreach, and he brought them all in a room together.
Jason Rigby (08:05):
And he said, "We're going to sit here and go over 700 names, and we're going to do this." I don't know if he did it weekly or monthly or whatever, "And we're going to talk about it." Well, what he found out was, "Oh, I talked to him, and he stayed in my homeless shelter the other day. And he told me," this, this. "Oh, I talked to her and she was," this, this, "And we can put them in this house." Oh, well I can help them with this." And so, then all these community outreach, begin to, instead of being as governments do, instead of being silos and worried about their own little... They began to work together, almost at zero homelessness now in one year.
Alexander McCaig (08:38):
That's amazing. And that collectiveness, they were equitable with how they were analyzing the data. Because they looked to people that were producing zero data, and they had to go to them directly. And the only way you can be equitable with your analysis, and pushing out resources, is if you actually include everyone. And so, you need tools that properly include those people, and afford them opportunity at the same time. And if we can strike that balance, you're doing something special as a government. And that way you'll put the wifi hotspot in the right place, you'll put a local... What is it? The emergency care center, there's a bunch of them.
Jason Rigby (09:12):
Or you know the subway is busy at these certain times, but it's not efficient to have it run at that time, so it's not efficient for the city with their budget. But so now you have to make people wait an extra 20, 30 minutes to get on, when you should be running.
Alexander McCaig (09:28):
In that 20 to 30 minutes, let me tell you what happens. I've seen this. I've seen this in Philadelphia. The working parent, mom or dad, doesn't matter, they've lost now an hour commute, both ways. They don't have time to go home and cook now. They've been working 12, 14 hours. What are they going to do? They're going to go right over to the McDonald's and get their kids a meal that lacks a massive amount of nutrition, and everybody's going to get fed on that for a buck 50 a piece. And they're not going to go to the bodega, because it's too expensive. They're not going to cook, and there's no local grocery store on site.
Alexander McCaig (10:03):
So there's this weird food scarcity, and because an action that happened, a systemic action of the government saying, "Okay, this is not cost effective for us, to be running this, we're going to cut it back an extra 30 minutes." It's not about you being cost-effective, you're here for the benefit of the public, not yourself. Figure that out. That's why you screwed up so much with your data, because your perspective has been about you, how can we analyze data so we can get a bigger value for ourselves?
Jason Rigby (10:34):
And it's that way with the companies that are collecting data too, like you said on an earlier podcast that you guys will have to go and watch.
Alexander McCaig (10:40):
How do I get a bigger annuity stream for me? Not, how do I give the power, the choice and the tools back to people, and give the annuity stream to them? A government has resources, they can always ask for more resources. And if your citizens are doing well, you will naturally do well. It's not if you do well, the citizens do well, wrong. You have to uplift people first, they are the foundation of who you are. So, if you want to be equitable, be equitable with the data and how you analyze it, and how you actually look at human beings for being human beings.
Jason Rigby (11:10):
Yes. And I think you're seeing the shift, and I think COVID was a good thing. I was listening to a podcast earlier, and they were talking, the CEO was talking about having people only come to work twice a week, or they could come five days a week, and then they get a whole week off. Now, all of a sudden people are like, "Well, if I'm only going to the office a few times a week, or I'm only going one or two weeks out of the month, I could probably live an hour and a half away."
Alexander McCaig (11:38):
Well, that's cool. And now people that were otherwise excluded, because of their social status. Or, not social status, demographic, their socioeconomic status, that's what I meant. If they were excluded because they don't have a car to go to where that businesses is, now opportunity has opened up for people that didn't have opportunity, and that's the beauty about remote work. But you also have to get those people online, it's a function of connectivity at the same time. So, remote works great, as long as you have the ability to get on the internet. But now there's a great opportunity for more people to join up, more people to find prosperity for themselves, by taking responsibility of saying, "Oh, this is now available, I got to go get that."
Jason Rigby (12:22):
I love that, that's perfect.
Alexander McCaig (12:23):
Yeah, I think that's pretty neat.
Jason Rigby (12:24):
I think we should end it.
Alexander McCaig (12:25):
All right, cool.
Jason Rigby (12:26):
This is cool, thanks guys.
Speaker 3 (12:35):
Thank you for listening to TARTLEcast, with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future, and source data defines the path. What's your data worth?
