Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
June 10, 2021

Data Science VS. IT Department

Data Science VS. IT Department

Data Science VS. IT Department

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BY: TARTLE

Data Science vs IT

Believe it or not, there is often more than one department at a company that does things with computers. Even more shocking, they do different things. Unfortunately not everyone knows or appreciates that, even within the same company. 

For example, as companies become more and more data driven they are looking to their data science/analytics departments for solutions. That certainly makes sense on the surface. Unfortunately, the corporate executives tend to forget that the analysts can’t institute new systems on their own. Often, implementing new analytics will require outside support. Unfortunately, IT is largely a maintenance department that occasionally finds new solutions to problems. Within a major company, IT’s job is largely to keep the computers working, both in the hardware and the software departments. At best, they can identify and install new software for gathering data, but they typically won’t do the gathering.

Conversely, the data science division might identify the software but most likely would not be handing the install. Therefore, when a business is trying to get more value from its data it can cause unintended conflict between data science and IT, usually with data science blaming lack of IT support for not being able to get enough quality data to get the job done. However, all data science needs to do first and foremost is to get data and analyze it. It doesn’t much matter how they get that data. So, one great way they can do that is to sign up with TARTLE and buy their data from us. Or, more accurately, from you.

One of the great advantages is that we offer access to source data in real time. We can connect you with our members willing to sell access to their medical data. This is not only past data, but current since it is possible to get connected to various health tracking apps as well as IOT devices like Fitbits. If there is a need for more specific data about behaviors you can simply ask our members directly. Since they are all here with the goal of being able to get rewarded for sharing their information the engagement rate is going to be a lot higher than a blanket survey sent out to the general public. 

This is all in stark contrast to the way data analysts typically get their info. They usually get it from third party aggregators or from social media companies that sell it in large blocks. There is little opportunity for buyers to customize what they are getting, meaning they have to spend a lot of time and money sifting through data that is probably irrelevant. It’s also old. Data acquired through second, third, or even fourth parties is likely to be weeks or even months old, meaning that you are trying to make solid business decisions based on old information. In essence, you’re guessing. Yes, it’s an educated guess, but a guess nonetheless. Real time data allows for much more accurate projections as it minimizes the time between observed behavior and the response to it, whether that be a new product, marketing plan, or company policy. 

Another benefit of working directly with TARTLE’s users is that you only pay for the data you need when you need it. We’ve already touched on the fact that through current means of data collection, you will likely spend a lot of time sifting data you don’t need. You might also have to spend money to get it in regular batches, usually coming at times when it isn’t needed. With TARTLE you can customize the type of data you need, how much, and when, which in the end will save time and money.

If you’re working in a data science department, you can sign up as a buyer at TARTLE.co today and get started. We’ll get you connected with members eager to help you develop better solutions for your business. 

What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

For those who are hard of hearing – the episode transcript can be read below:

TRANSCRIPT

Speaker 1 (00:07):

Welcome to TARTLEcast with your hosts, Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.

Jason Rigby (00:25):

Data science verse IT.

Alexander McCaig (00:26):

Yup.

Jason Rigby (00:27):

It shouldn't be a battle.

Alexander McCaig (00:29):

It shouldn't be a battle.

Jason Rigby (00:29):

It's local article that we're reading here.

Alexander McCaig (00:32):

It is a battle like what's going on inside these businesses, Jason?

Jason Rigby (00:37):

Well, data scientists, according to the article at cio.com, data scientists are increasingly being asked to deliver business value and they're being held accountable for the results. Given this reality, data science teams can't wait weeks for IT administrators to come up with the resources they need develop proof of concepts and train artificial intelligence models. Yet too often, this is exactly what happens leaving data scientists at odds with their IT departments.

Alexander McCaig (01:00):

Right. So the boss is coming in, the boss is like, "I need an answer to this right now. You guys are the ones that analyze data." And then the data science guys are like, "Well, where do you want us to get the data from? Our IT guys aren't buying it. Where's that collective of information coming to us." There's a disparity.

Jason Rigby (01:19):

Right.

Alexander McCaig (01:19):

Between these two interoffice groups. There's no efficiency between that. So an easy solve would be just have the data science team just go buy the information.

Jason Rigby (01:31):

Yes.

Alexander McCaig (01:32):

They just act as a buyer on the TARTLE marketplace, get the information they need, screw the IT department. You know, let's not wait on them. Let's be responsible for ourselves and we'll get exactly what we need right now, do that analysis, and in a 24 hour turnaround time, give it back to the boss.

Jason Rigby (01:46):

Yeah. We talked about this before, but I love the words business value.

Alexander McCaig (01:51):

Yeah.

Jason Rigby (01:51):

They're being pressured for business value. That's a nice terminology for-

Alexander McCaig (01:56):

They're data scientists.

Jason Rigby (01:57):

Yes.

Alexander McCaig (01:58):

Their job is to analyze data, put it into a picture or drive some sort of answer.

Jason Rigby (02:03):

Right.

Alexander McCaig (02:04):

It's not for them to create the business value. They're there to process, analyze, and give you an output. The IT department needs to deliver on whatever's going on with the IT. They're two different job sets.

Jason Rigby (02:17):

Yes. Yes.

Alexander McCaig (02:17):

But now people are trying to blend them. They see them as like the same thing. And that if the data scientists aren't producing some sort of answer, well it's like, "Well, what the heck?" Because now all these companies are becoming data-driven first.

Jason Rigby (02:29):

Yes. Yeah, that's a big pool now.

Alexander McCaig (02:30):

And now the data science job is increasing, right? The IT guy's-

Jason Rigby (02:34):

Responsibilities.

Alexander McCaig (02:35):

... are worried about security and what technology we're using. And the data science guy's like, "Well, you know, we're analyzing stuff, trying to drive these answers for the business." It's totally changed. It's it's almost like the power spectrum has changed, right?

Jason Rigby (02:46):

It's almost the sales versus marketing. Remember in the old day in the old school companies?

Alexander McCaig (02:51):

Yeah.

Jason Rigby (02:51):

There was always, like, I don't ... And I think this can kind of be the ... Because we've talked about silos in business before and I think leadership plays a big role in this and understanding this.

Jason Rigby (03:01):

Marketing get sales and sales help with the marketing.

Alexander McCaig (03:05):

Yeah.

Jason Rigby (03:05):

You don't have a company if you don't have sales. You don't have a company if you don't have marketing. You don't have a company if you don't have data scientists nowadays.

Alexander McCaig (03:13):

You have to have them.

Jason Rigby (03:13):

And you have to have an IT department because all the data that these data scientists are creating, if it gets uploaded to the cloud, what happens then?

Alexander McCaig (03:21):

Then what?

Jason Rigby (03:21):

Yes.

Alexander McCaig (03:22):

The IT guy's like, "Well, what about security," all types of stuff. Models are becoming more hybrid. Workplaces are becoming more decentralized. Jobs, they're blending more. People have to be more dynamic. They can't be cut and dry like black and white.

Jason Rigby (03:36):

No.

Alexander McCaig (03:38):

You have bleed over. The scientists, now, are working on the business aspects and vice versa.

Jason Rigby (03:44):

I think that's why, as a leader, especially if you're a leader out there and you're looking at, you know, maybe you're a data scientist and you're in the IT department or CIO or something like that, having the leadership ability to be able to pull these people in a room. And I think Socrates had such a great way in teaching. When he would lead, he would do less talking and ask more questions and then allow those in the room to be able to come up with the answer. Because if you put those heads of each of those departments and you put them in a room together and you say, "Here's the problem," on a whiteboard, and then you ask questions for that, you're going to come up with the right answers.

Alexander McCaig (04:20):

Yeah.

Jason Rigby (04:20):

And they will begin to bridge each other. And like you said, it will create a hybrid model. And maybe there needs to be a liaison between the two. To me this is a leadership problem.

Alexander McCaig (04:30):

That CIO is that liaison.

Jason Rigby (04:31):

Right.

Alexander McCaig (04:31):

He should be listening, translating-

Jason Rigby (04:33):

Yes.

Alexander McCaig (04:33):

... figuring out what's going on. And then he should be leading off of that.

Jason Rigby (04:36):

Right.

Alexander McCaig (04:36):

But separating these groups and then putting the IT department at odds with data scientists and then asking data scientists to step out of the realm-

Jason Rigby (04:43):

Right.

Alexander McCaig (04:43):

... and start working on business value. Understand this is what people do. And if there's going to be a hybrid approach, you need to respect that properly.

Jason Rigby (04:49):

Right.

Alexander McCaig (04:50):

Rather than having these departments combat one another.

Jason Rigby (04:53):

It's just the blame game.

Alexander McCaig (04:54):

That's all it is. The article is just talking about one big inter-office blame game.

Jason Rigby (04:57):

Right. Well, we can't do this. We're having to wait a week or two to do this. So we're ineffective and we've got this and we're waiting on, "Well, why haven't you produced anything?" "Because they said," the big word was accountable.

Alexander McCaig (05:08):

Yeah.

Jason Rigby (05:08):

So they're holding these data scientists accountable. So now they're turning around and blaming the IT department.

Alexander McCaig (05:13):

Yeah. Everyone's always trying to shift responsibility, right?

Jason Rigby (05:16):

Yes. Once they become accountable.

Alexander McCaig (05:17):

You know what we say? If you're a company, start being responsible, go buy your data from TARTLE.

Jason Rigby (05:22):

Yes.

Alexander McCaig (05:22):

Be responsible in your data purchasing, analyze it responsibly, and then responsibly get your answers to the people that need them.

Jason Rigby (05:29):

If you want to purchase data from TARTLE.

Alexander McCaig (05:31):

Yeah.

Jason Rigby (05:31):

How would you do that?

Alexander McCaig (05:32):

You go to tartle.co, T-A-R-T-L-E dot co. You're going to get started, you're going to sign up as a buyer. And that's going to bring you to a totally different web platform. It's a lot like buying stocks.

Jason Rigby (05:43):

Right.

Alexander McCaig (05:43):

And then you can choose specifically whatever it is you want to buy. You can create those data packets for that specific information just for you and that data analytics department.

Jason Rigby (05:51):

I mean, TARTLE's new and we have these early adopters coming in.

Alexander McCaig (05:55):

Mm-hmm (affirmative).

Jason Rigby (05:55):

So if they're wanting to purchase data and they're an early adopter, their company, they may be watching this like, "Yeah, this is something I'm interested in." How is the early adopting phase? How is all that going for TARTLE right now with them when they log in.

Alexander McCaig (06:12):

It's going pretty well. As a buyer, right?

Jason Rigby (06:15):

Right.

Alexander McCaig (06:16):

You're the one that's coming into the market and you're telling the sellers, "Hey, we're looking to buy information." Okay? And the creation of those data packs, of those assets, that the sellers of info are filling out, you've got to tell them what to fill out, right?

Jason Rigby (06:32):

Right.

Alexander McCaig (06:32):

You've got to say, "Hey, we're coming in, we've got a new data packet. We want to do research on heart disease. And we're looking to buy from 10,000 people here in the United States, age 55 plus, this sex, this gender in this zip code." And you have the ability to do that on the TARTLE marketplace. And what you find is that when you come in as a buyer and you create that data packet and you give people time to fill it out, the volume of those numbers are just going to increase and it gives you a bigger pool of people to purchase from.

Jason Rigby (06:58):

Plus, I think the beautiful part about it is you're not just purchasing lag data. You're purchasing data, that's-

Alexander McCaig (07:03):

You're purchasing data that's real time.

Jason Rigby (07:05):

Yes.

Alexander McCaig (07:05):

It's always updated. It's always completely fresh. It's never historical data that you project forward to assume that's going to happen in the future.

Jason Rigby (07:13):

Right.

Alexander McCaig (07:13):

You remove the assumptions. You remove 70% of your hunch decision making that you're doing in the office, right? Especially between departments. You start to move closer to perfect information. And with perfect information, you can make perfect decisions, relatively.

Jason Rigby (07:27):

Yeah. I think that's the big key. And that's why I wanted people to understand, especially those that are out there in these departments, one, work on your leadership.

Alexander McCaig (07:35):

Yeah. One, work on leadership. And two, there are tools out there-

Jason Rigby (07:38):

Yes.

Alexander McCaig (07:39):

... great tools, that give you access to information where you guys can really get some beautiful answers. And if you're a data scientist, TARTLE is like a gold mine for you.

Jason Rigby (07:48):

Yes.

Alexander McCaig (07:49):

Because now you can analyze qualitative and quantitative data on human beings. Or even if a business wants to come in and sell their information. I mean, this is the most perfect place for you to come in and be like, "Oh my gosh, I didn't know this type of granular data existed." And there's no other place in the world to get it. And you can sign it for free. It doesn't cost your business anything. You can create your data packets for free. You only spend the money and pay for it when you want to buy it.

Jason Rigby (08:16):

Yeah. That's crazy. That's amazing to me. How does TARTLE, and we'll close on this, how does TARTLE help businesses?

Alexander McCaig (08:27):

How does it help a business?

Jason Rigby (08:28):

Yeah.

Alexander McCaig (08:29):

Like I was trying to say before, and maybe I talked about it too quick, businesses operate on a hunch most of the time.

Jason Rigby (08:35):

Yes.

Alexander McCaig (08:35):

It's a lot of guesswork. Or if they are an analytics first business, they use a lot of historical information and try and project it forward to make their decisions. So if you go to TARTLE and you're using quality, right down to a second real-time information about behaviors, quantitative or qualitative-

Jason Rigby (08:52):

Right.

Alexander McCaig (08:53):

... You have the perfect information for saying, "This is our business problem. Or this is our business opportunity. What's our solve." You're going to know exactly what you need to do. TARTLE removes all of that guesswork that you've been doing that you're so used to just operating on.

Jason Rigby (09:10):

Well, everybody's projection reports are wrong anyway. I mean, if you did one in 2019 or 2020, you're all wrong because COVID just threw a wrench, it created a whole different dynamic.

Alexander McCaig (09:18):

Yeah.

Jason Rigby (09:18):

It creates a whole different subset of data. Now you have an issue of ... I mean, just look at online shopping.

Alexander McCaig (09:23):

Think about the obvious. Listen to this. In 2019, we had reports saying this was going to happen in the next 6 years. What happened in 2020? They wrote new reports.

Jason Rigby (09:32):

Mm-hmm (affirmative).

Alexander McCaig (09:32):

Why? Because the old ones are outdated and they weren't truthful. And they're always reporting and re-reporting because they're not getting good information.

Alexander McCaig (09:41):

So if you, as a business, want good information, go get that good, sourceful, truthful, perfect information.

Jason Rigby (09:48):

And 2021's going to be a whole different dataset once it gets opened back up again.

Alexander McCaig (09:52):

Yeah. Hold on a second. Five seconds, a new dataset.

Jason Rigby (09:55):

Yeah.

Alexander McCaig (09:56):

Hold on a second. Oh wait, there's another new dataset.

Jason Rigby (09:59):

Oh, that's another one. Yeah. Yeah.

Alexander McCaig (10:00):

Yeah. That's what we're talking about.

Jason Rigby (10:01):

People change. You have to understand humanity in the way that how many variables ... You could drive a Prius and still hunt.

Alexander McCaig (10:08):

Yeah.

Jason Rigby (10:09):

People aren't boxes.

Alexander McCaig (10:12):

You can't put things into a box. You and your business need to be flexible.

Jason Rigby (10:14):

Yes, exactly.

Alexander McCaig (10:14):

Your mindset has to be flexible. Your operations have to be flexible. And as a leader, you have to be flexible.

Jason Rigby (10:19):

Have to. Especially nowadays.

Alexander McCaig (10:21):

No man steps in the same river twice.

Jason Rigby (10:22):

No.

Alexander McCaig (10:23):

Somebody leave a comment. Tell me who said that. Thanks everybody.

Jason Rigby (10:27):

[inaudible 00:10:27] in the fire. I was like, "Oh, wow."

Speaker 1 (10:34):

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?

June 10, 2021

Data Science VS. IT Department

Data Science VS. IT Department

Data Science VS. IT Department

SHARE: 
BY: TARTLE

Data Science vs IT

Believe it or not, there is often more than one department at a company that does things with computers. Even more shocking, they do different things. Unfortunately not everyone knows or appreciates that, even within the same company. 

For example, as companies become more and more data driven they are looking to their data science/analytics departments for solutions. That certainly makes sense on the surface. Unfortunately, the corporate executives tend to forget that the analysts can’t institute new systems on their own. Often, implementing new analytics will require outside support. Unfortunately, IT is largely a maintenance department that occasionally finds new solutions to problems. Within a major company, IT’s job is largely to keep the computers working, both in the hardware and the software departments. At best, they can identify and install new software for gathering data, but they typically won’t do the gathering.

Conversely, the data science division might identify the software but most likely would not be handing the install. Therefore, when a business is trying to get more value from its data it can cause unintended conflict between data science and IT, usually with data science blaming lack of IT support for not being able to get enough quality data to get the job done. However, all data science needs to do first and foremost is to get data and analyze it. It doesn’t much matter how they get that data. So, one great way they can do that is to sign up with TARTLE and buy their data from us. Or, more accurately, from you.

One of the great advantages is that we offer access to source data in real time. We can connect you with our members willing to sell access to their medical data. This is not only past data, but current since it is possible to get connected to various health tracking apps as well as IOT devices like Fitbits. If there is a need for more specific data about behaviors you can simply ask our members directly. Since they are all here with the goal of being able to get rewarded for sharing their information the engagement rate is going to be a lot higher than a blanket survey sent out to the general public. 

This is all in stark contrast to the way data analysts typically get their info. They usually get it from third party aggregators or from social media companies that sell it in large blocks. There is little opportunity for buyers to customize what they are getting, meaning they have to spend a lot of time and money sifting through data that is probably irrelevant. It’s also old. Data acquired through second, third, or even fourth parties is likely to be weeks or even months old, meaning that you are trying to make solid business decisions based on old information. In essence, you’re guessing. Yes, it’s an educated guess, but a guess nonetheless. Real time data allows for much more accurate projections as it minimizes the time between observed behavior and the response to it, whether that be a new product, marketing plan, or company policy. 

Another benefit of working directly with TARTLE’s users is that you only pay for the data you need when you need it. We’ve already touched on the fact that through current means of data collection, you will likely spend a lot of time sifting data you don’t need. You might also have to spend money to get it in regular batches, usually coming at times when it isn’t needed. With TARTLE you can customize the type of data you need, how much, and when, which in the end will save time and money.

If you’re working in a data science department, you can sign up as a buyer at TARTLE.co today and get started. We’ll get you connected with members eager to help you develop better solutions for your business. 

What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.

Feature Image Credit: Envato Elements
FOLLOW @TARTLE_OFFICIAL

For those who are hard of hearing – the episode transcript can be read below:

TRANSCRIPT

Speaker 1 (00:07):

Welcome to TARTLEcast with your hosts, Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.

Jason Rigby (00:25):

Data science verse IT.

Alexander McCaig (00:26):

Yup.

Jason Rigby (00:27):

It shouldn't be a battle.

Alexander McCaig (00:29):

It shouldn't be a battle.

Jason Rigby (00:29):

It's local article that we're reading here.

Alexander McCaig (00:32):

It is a battle like what's going on inside these businesses, Jason?

Jason Rigby (00:37):

Well, data scientists, according to the article at cio.com, data scientists are increasingly being asked to deliver business value and they're being held accountable for the results. Given this reality, data science teams can't wait weeks for IT administrators to come up with the resources they need develop proof of concepts and train artificial intelligence models. Yet too often, this is exactly what happens leaving data scientists at odds with their IT departments.

Alexander McCaig (01:00):

Right. So the boss is coming in, the boss is like, "I need an answer to this right now. You guys are the ones that analyze data." And then the data science guys are like, "Well, where do you want us to get the data from? Our IT guys aren't buying it. Where's that collective of information coming to us." There's a disparity.

Jason Rigby (01:19):

Right.

Alexander McCaig (01:19):

Between these two interoffice groups. There's no efficiency between that. So an easy solve would be just have the data science team just go buy the information.

Jason Rigby (01:31):

Yes.

Alexander McCaig (01:32):

They just act as a buyer on the TARTLE marketplace, get the information they need, screw the IT department. You know, let's not wait on them. Let's be responsible for ourselves and we'll get exactly what we need right now, do that analysis, and in a 24 hour turnaround time, give it back to the boss.

Jason Rigby (01:46):

Yeah. We talked about this before, but I love the words business value.

Alexander McCaig (01:51):

Yeah.

Jason Rigby (01:51):

They're being pressured for business value. That's a nice terminology for-

Alexander McCaig (01:56):

They're data scientists.

Jason Rigby (01:57):

Yes.

Alexander McCaig (01:58):

Their job is to analyze data, put it into a picture or drive some sort of answer.

Jason Rigby (02:03):

Right.

Alexander McCaig (02:04):

It's not for them to create the business value. They're there to process, analyze, and give you an output. The IT department needs to deliver on whatever's going on with the IT. They're two different job sets.

Jason Rigby (02:17):

Yes. Yes.

Alexander McCaig (02:17):

But now people are trying to blend them. They see them as like the same thing. And that if the data scientists aren't producing some sort of answer, well it's like, "Well, what the heck?" Because now all these companies are becoming data-driven first.

Jason Rigby (02:29):

Yes. Yeah, that's a big pool now.

Alexander McCaig (02:30):

And now the data science job is increasing, right? The IT guy's-

Jason Rigby (02:34):

Responsibilities.

Alexander McCaig (02:35):

... are worried about security and what technology we're using. And the data science guy's like, "Well, you know, we're analyzing stuff, trying to drive these answers for the business." It's totally changed. It's it's almost like the power spectrum has changed, right?

Jason Rigby (02:46):

It's almost the sales versus marketing. Remember in the old day in the old school companies?

Alexander McCaig (02:51):

Yeah.

Jason Rigby (02:51):

There was always, like, I don't ... And I think this can kind of be the ... Because we've talked about silos in business before and I think leadership plays a big role in this and understanding this.

Jason Rigby (03:01):

Marketing get sales and sales help with the marketing.

Alexander McCaig (03:05):

Yeah.

Jason Rigby (03:05):

You don't have a company if you don't have sales. You don't have a company if you don't have marketing. You don't have a company if you don't have data scientists nowadays.

Alexander McCaig (03:13):

You have to have them.

Jason Rigby (03:13):

And you have to have an IT department because all the data that these data scientists are creating, if it gets uploaded to the cloud, what happens then?

Alexander McCaig (03:21):

Then what?

Jason Rigby (03:21):

Yes.

Alexander McCaig (03:22):

The IT guy's like, "Well, what about security," all types of stuff. Models are becoming more hybrid. Workplaces are becoming more decentralized. Jobs, they're blending more. People have to be more dynamic. They can't be cut and dry like black and white.

Jason Rigby (03:36):

No.

Alexander McCaig (03:38):

You have bleed over. The scientists, now, are working on the business aspects and vice versa.

Jason Rigby (03:44):

I think that's why, as a leader, especially if you're a leader out there and you're looking at, you know, maybe you're a data scientist and you're in the IT department or CIO or something like that, having the leadership ability to be able to pull these people in a room. And I think Socrates had such a great way in teaching. When he would lead, he would do less talking and ask more questions and then allow those in the room to be able to come up with the answer. Because if you put those heads of each of those departments and you put them in a room together and you say, "Here's the problem," on a whiteboard, and then you ask questions for that, you're going to come up with the right answers.

Alexander McCaig (04:20):

Yeah.

Jason Rigby (04:20):

And they will begin to bridge each other. And like you said, it will create a hybrid model. And maybe there needs to be a liaison between the two. To me this is a leadership problem.

Alexander McCaig (04:30):

That CIO is that liaison.

Jason Rigby (04:31):

Right.

Alexander McCaig (04:31):

He should be listening, translating-

Jason Rigby (04:33):

Yes.

Alexander McCaig (04:33):

... figuring out what's going on. And then he should be leading off of that.

Jason Rigby (04:36):

Right.

Alexander McCaig (04:36):

But separating these groups and then putting the IT department at odds with data scientists and then asking data scientists to step out of the realm-

Jason Rigby (04:43):

Right.

Alexander McCaig (04:43):

... and start working on business value. Understand this is what people do. And if there's going to be a hybrid approach, you need to respect that properly.

Jason Rigby (04:49):

Right.

Alexander McCaig (04:50):

Rather than having these departments combat one another.

Jason Rigby (04:53):

It's just the blame game.

Alexander McCaig (04:54):

That's all it is. The article is just talking about one big inter-office blame game.

Jason Rigby (04:57):

Right. Well, we can't do this. We're having to wait a week or two to do this. So we're ineffective and we've got this and we're waiting on, "Well, why haven't you produced anything?" "Because they said," the big word was accountable.

Alexander McCaig (05:08):

Yeah.

Jason Rigby (05:08):

So they're holding these data scientists accountable. So now they're turning around and blaming the IT department.

Alexander McCaig (05:13):

Yeah. Everyone's always trying to shift responsibility, right?

Jason Rigby (05:16):

Yes. Once they become accountable.

Alexander McCaig (05:17):

You know what we say? If you're a company, start being responsible, go buy your data from TARTLE.

Jason Rigby (05:22):

Yes.

Alexander McCaig (05:22):

Be responsible in your data purchasing, analyze it responsibly, and then responsibly get your answers to the people that need them.

Jason Rigby (05:29):

If you want to purchase data from TARTLE.

Alexander McCaig (05:31):

Yeah.

Jason Rigby (05:31):

How would you do that?

Alexander McCaig (05:32):

You go to tartle.co, T-A-R-T-L-E dot co. You're going to get started, you're going to sign up as a buyer. And that's going to bring you to a totally different web platform. It's a lot like buying stocks.

Jason Rigby (05:43):

Right.

Alexander McCaig (05:43):

And then you can choose specifically whatever it is you want to buy. You can create those data packets for that specific information just for you and that data analytics department.

Jason Rigby (05:51):

I mean, TARTLE's new and we have these early adopters coming in.

Alexander McCaig (05:55):

Mm-hmm (affirmative).

Jason Rigby (05:55):

So if they're wanting to purchase data and they're an early adopter, their company, they may be watching this like, "Yeah, this is something I'm interested in." How is the early adopting phase? How is all that going for TARTLE right now with them when they log in.

Alexander McCaig (06:12):

It's going pretty well. As a buyer, right?

Jason Rigby (06:15):

Right.

Alexander McCaig (06:16):

You're the one that's coming into the market and you're telling the sellers, "Hey, we're looking to buy information." Okay? And the creation of those data packs, of those assets, that the sellers of info are filling out, you've got to tell them what to fill out, right?

Jason Rigby (06:32):

Right.

Alexander McCaig (06:32):

You've got to say, "Hey, we're coming in, we've got a new data packet. We want to do research on heart disease. And we're looking to buy from 10,000 people here in the United States, age 55 plus, this sex, this gender in this zip code." And you have the ability to do that on the TARTLE marketplace. And what you find is that when you come in as a buyer and you create that data packet and you give people time to fill it out, the volume of those numbers are just going to increase and it gives you a bigger pool of people to purchase from.

Jason Rigby (06:58):

Plus, I think the beautiful part about it is you're not just purchasing lag data. You're purchasing data, that's-

Alexander McCaig (07:03):

You're purchasing data that's real time.

Jason Rigby (07:05):

Yes.

Alexander McCaig (07:05):

It's always updated. It's always completely fresh. It's never historical data that you project forward to assume that's going to happen in the future.

Jason Rigby (07:13):

Right.

Alexander McCaig (07:13):

You remove the assumptions. You remove 70% of your hunch decision making that you're doing in the office, right? Especially between departments. You start to move closer to perfect information. And with perfect information, you can make perfect decisions, relatively.

Jason Rigby (07:27):

Yeah. I think that's the big key. And that's why I wanted people to understand, especially those that are out there in these departments, one, work on your leadership.

Alexander McCaig (07:35):

Yeah. One, work on leadership. And two, there are tools out there-

Jason Rigby (07:38):

Yes.

Alexander McCaig (07:39):

... great tools, that give you access to information where you guys can really get some beautiful answers. And if you're a data scientist, TARTLE is like a gold mine for you.

Jason Rigby (07:48):

Yes.

Alexander McCaig (07:49):

Because now you can analyze qualitative and quantitative data on human beings. Or even if a business wants to come in and sell their information. I mean, this is the most perfect place for you to come in and be like, "Oh my gosh, I didn't know this type of granular data existed." And there's no other place in the world to get it. And you can sign it for free. It doesn't cost your business anything. You can create your data packets for free. You only spend the money and pay for it when you want to buy it.

Jason Rigby (08:16):

Yeah. That's crazy. That's amazing to me. How does TARTLE, and we'll close on this, how does TARTLE help businesses?

Alexander McCaig (08:27):

How does it help a business?

Jason Rigby (08:28):

Yeah.

Alexander McCaig (08:29):

Like I was trying to say before, and maybe I talked about it too quick, businesses operate on a hunch most of the time.

Jason Rigby (08:35):

Yes.

Alexander McCaig (08:35):

It's a lot of guesswork. Or if they are an analytics first business, they use a lot of historical information and try and project it forward to make their decisions. So if you go to TARTLE and you're using quality, right down to a second real-time information about behaviors, quantitative or qualitative-

Jason Rigby (08:52):

Right.

Alexander McCaig (08:53):

... You have the perfect information for saying, "This is our business problem. Or this is our business opportunity. What's our solve." You're going to know exactly what you need to do. TARTLE removes all of that guesswork that you've been doing that you're so used to just operating on.

Jason Rigby (09:10):

Well, everybody's projection reports are wrong anyway. I mean, if you did one in 2019 or 2020, you're all wrong because COVID just threw a wrench, it created a whole different dynamic.

Alexander McCaig (09:18):

Yeah.

Jason Rigby (09:18):

It creates a whole different subset of data. Now you have an issue of ... I mean, just look at online shopping.

Alexander McCaig (09:23):

Think about the obvious. Listen to this. In 2019, we had reports saying this was going to happen in the next 6 years. What happened in 2020? They wrote new reports.

Jason Rigby (09:32):

Mm-hmm (affirmative).

Alexander McCaig (09:32):

Why? Because the old ones are outdated and they weren't truthful. And they're always reporting and re-reporting because they're not getting good information.

Alexander McCaig (09:41):

So if you, as a business, want good information, go get that good, sourceful, truthful, perfect information.

Jason Rigby (09:48):

And 2021's going to be a whole different dataset once it gets opened back up again.

Alexander McCaig (09:52):

Yeah. Hold on a second. Five seconds, a new dataset.

Jason Rigby (09:55):

Yeah.

Alexander McCaig (09:56):

Hold on a second. Oh wait, there's another new dataset.

Jason Rigby (09:59):

Oh, that's another one. Yeah. Yeah.

Alexander McCaig (10:00):

Yeah. That's what we're talking about.

Jason Rigby (10:01):

People change. You have to understand humanity in the way that how many variables ... You could drive a Prius and still hunt.

Alexander McCaig (10:08):

Yeah.

Jason Rigby (10:09):

People aren't boxes.

Alexander McCaig (10:12):

You can't put things into a box. You and your business need to be flexible.

Jason Rigby (10:14):

Yes, exactly.

Alexander McCaig (10:14):

Your mindset has to be flexible. Your operations have to be flexible. And as a leader, you have to be flexible.

Jason Rigby (10:19):

Have to. Especially nowadays.

Alexander McCaig (10:21):

No man steps in the same river twice.

Jason Rigby (10:22):

No.

Alexander McCaig (10:23):

Somebody leave a comment. Tell me who said that. Thanks everybody.

Jason Rigby (10:27):

[inaudible 00:10:27] in the fire. I was like, "Oh, wow."

Speaker 1 (10:34):

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?

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