Fake Data: The Rise of False Analytics


Data is the new oil and it pretty much feed all aspects of our lives including personal, financial, business and health. We use more data than the all previous generations combined. However, we don’t realize the implications of the massive data age. The media today covers fake news ignoring the fact the fake news are created by fake data. In this show, we drill down in to the specifics of fake data. We discuss the following:

What is fake data?
How is fake data generated?
Why fake data is a bigger issue than we believe it is?
How fake data could have almost impacted lives of heart patients in Japan?
Finally, how to overcome the challenges presented by fake data.

Fake Data: The Rise of False Analytics

Show Notes

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Thank you for joining today's podcast series focused on big data and analytics and the latest trends on digital world. I am your cohost Jeremy Roberts and with me always is Sameer Khan.
Finally getting cold and heat is going away. I'm excited. I need to go into the Tupperware you know, like all the jackets, the big puffy jackets, you know? Pretty exciting. Yeah, they were hiding somewhere in the in the closet, right? For a long time. Yeah. And what's crazy is as we all know, the midterm elections just recently happened. But you know, with all that surrounding all this, no matter what side you are in politics, know, everybody knows this term that the President has coined as fake news, right?

Fake News is a big thing. And, and our interpretation of fake news is just either stories or content.

That's literally fake. It's made up, somebody has decided that it has value, right.

So, so one of the things we want to talk about today is something that most companies don't think about, you know, a lot of people talk about fake news, or here in the marketing analytics world in the digital world. What about fake data? One of the I mean, that's what about the idea that fake data is creeping into our lives and what we do and how is that affecting us? Yeah, I don't know if you've seen some trends, but I've seen some trends. Yeah, absolutely. Yeah. I think there's a lot of trends when it comes to fake data. And you know, I'm glad that you brought that up and you know, stating like, start from

Fake News. As a matter of fact, majority of the fake news are based off the fake data. And people. The news is kind of the front end. And the data is the back end. So, I'm glad that this is the topic of the day. That's what we're going to talk about. So fake data, the rise of false analytics.

I like that. So fake data the rise or fall same, like sounds like a movie represents we're going to happen, right?
Right? So okay, so let's start this off. So, what is state did and we have a definition here, let me read this to be exact. Fake data is incorrect data created by either deliberate or non-deliberate models. For example, someone changing certain records for personal gain, versus a calculation error leading to incorrect data. So yeah, we can all calculate things incorrectly. But

Somebody's purposely changing or purposely feeding you fake information or in putting fake information or to sway the results. That's a little scary. Yeah, it is very scary. And when we talk about this, we're going to share a lot of examples. But like you said, the definition that we have, and this, this is,

you know, this is something that we came up with on our own. And we wanted to be so when we started looking into fake data, but Jeremy and I, we, we looked at many definition, they're very stereotypical. They were very like Wikipedia definitions. And we thought, like, what's the simplest way to communicate? So that's where we came up with, like fake data is incorrect, or inaccurate data created by either deliberate or non-deliberate motives, plain and simple. So, it's straightforward. Like Jeremy was saying it's either someone could deliberately change the data for personal gains, or it could be some type of non-deliberate attempt, like maybe something happened during the collection of data, it exchanged hands. And somehow part of the data was dropped or there was a calculation error in it. So there, you know, a fake data is not necessarily created just because someone wanted to create it. But it could also happen, because the way the setup or the acquisition of data was,

I agree. And what we'll do today is we're going to do a few things we're going to, first we're discussing why it's a problem, right? there we'll do is we'll go talk about some different industries, and that way everybody can relate to that. And then the last thing is, we'll come back and talk about how you can come back, take data, but I mean, when you think about it, you know, I like that fake data is Yeah, fake data is bad because you're, as a marketer, a lot of times your trusted with a lot of money, your trusted with the lifeblood, financial source of the company, and if they're giving you a million dollars to spend, and they're even looking for, let's say a 1.3 return on the investment, you know, and you're using fake data to be able to make decisions on million dollar spend the

that's a big problem. Yeah. It's scary. So, I mean, so I know you brought up this thing you want to kind of quickly talk about this extensor technical vision 2018. And I know you were talking about that earlier. Yeah, absolutely. So, you know, we talked about like, what is big data and let's say why it is a problem, right? So and there are lots of different companies and organizations that I've done some research and study around it. One of them is Accenture so Accenture's technology vision survey they did in 2018, when the survey found that 79% of organizations today are basing their most critical decisions on data, which is good, we want them to base it on data. But the problem is majority of those 79% of the organizations they have not invested in capabilities to verify the validity of the data. So you take that example in Madrid

These organizations are using data to make decisions. And a large majority of those have not spent time to verify the validity of the data. What if all the data that they're collecting, and eventually this data is driving your IoT devices, your

your performance marketing devices, your finance devices, all of this, if you have malicious data, and then it's going to throw off your business decisions out of the water, you create eventually will also create, at some point physical harm to someone. And we're going to share some specific example how that would look like. But that's the reason why it's a problem is it's coming from all sources. And it's in certain cases malicious. It could harm people in a lot of different ways. And it will have a much larger even at some degree catastrophe impact if it's not kept in check.

I completely agree.

And when it comes down to it, you know, at the end of the day, who's responsible for the data?

If you're the audience that we have, and you're in positions like us, it's your responsibility. You're the one who's turning around and saying, you know, what, I, I read this data, I interpreted this data, I did not see it as an anomaly. I don't see it as, as just common seasonality, you know, where we have changes and performance. It's something that's changing the way we look and look at and target our customers. It's, it changes the way that we do business. And it's your fault. It's your fault. So you need to own it, right? You must own it. And that's the only way that you're going to be able to fix it. I agree on that. So let's put out a disclaimer here. What we're trying not to do here is to scare people we're trying to do is make you aware. We're trying to make you aware that you

Know what just

take one extra step and what you do I know you already have 1000 things to look at. The next one is just look at the validity of the data. And we're going to go through them and how to combat it. So let's go through the examples for the industries. And let's then come back and be very diligent about how to come up with a deal. Yeah, and one thing I was going to say before you start with that is we have to specific tools, where someone who is like really interested about the whole fake data concept we're going to at the end of the podcast, so keep on listening. We're going to give it to specific tools that you can use today are completely free, where you can get your hands and test fake data. So let's go with that.

It's like a free prize and you get a donut to Right, exactly.

okay for me to free prizes. Every time I drive by a Krispy Kreme. I told my kids Oh my god.

Yeah, I love it. Oh, but you know, the orange light, right? No, it's like when you drive by, you see the orange light like kids know so I'll stop and go all the way across the highway to exit right away and you get free donuts and the thing is, you feel obligated to have to buy another donut but you don't have to. Now you can just take the free donut and leaves maybe after a cup of water to come on man analytics have a tip of the day the free donuts

Okay, fantastic.

Let's get done a serious Okay, first one healthcare. Let me let me read these to make sure where exactly so in the healthcare industry, so cardiac stem cell trials were halted by the US National Heart, Lung and Blood Institute the end HL BI. So, US National lung. Let's see that. They had concerns about fake data. Those recently announced that a years long investigate

Discovered falsified or fabricated data and 31 papers from there and versus laboratory. And the Washington Post reported and examination by the Harvard Medical School has verified that these 31 scientist’s distributions from the research center contain fraudulent information. Yeah, so, this is this is this is where it is, you know, this is where it could be could do a physical harm. So, if you think about it, all these scientists, these research agencies, they are spending a lot of time and they're requesting grants for the research. And if that research essentially has some type of introduction or interpolation of fake data into that research, then all the research is just false. And these are these are very sensitive topic because if you could imagine what if that research was essentially is trying to help people overcome their cardiac diseases. And there are subjects that are being put test with these researches initially before it's rolled out to the common public and approved by the FDA and all those good stuff. If this gets rolled out, and if it's tested on individuals with this type of fake data, it could be catastrophe, people could lose their lives. So that is the reason why I wanted we wanted to talk to start with topic and healthcare, like how significant the impact would be. And this is a great example with this, the stem cell research that was about to get launched and tested on many real individuals was halted by these governing authority once they found out that a significant portion of the research was based on fake data, and this was done so that they can get the grants for that research, which is pretty sad. pretty sad to know.

So one of the things that's coming and I will put

a disclaimer here to another one is that we're not saying there's a percentage, we're not saying it was 30% of data, and we're not saying it was 60% of data, or any percentage, what we're claiming here is a based on our initial, you know, get research and gathering. To figure this out, it was a significant and amount of data that was able to persuade the study in a wrong way. It was unreal from there, and, and depending on what the industry is, depending on what the use cases, it could just be a very bad week, let's say for marketing, let's bring it back to market. It could have been a very bad week worth of data that just shocked people, you know, or it could have been some malware or some kind of, I call it click on they came in and just did a whole bunch of bad things on your website just to throw your data off. You know, so when it comes back to this healthcare thing, we don't know exactly

How much data was false, but it was enough to where the scientists can create falsified papers to be able to do this. And like I said, we don't know the motivation of it. But it's, it's an example to be worried about. It's scary. You know, I'm glad somebody found out about it. Yeah, absolutely. And I think, you know, like you said, Jeremy, it is. We're not trying to pull down any organization. We're not trying to point fingers at people. The idea here is to let people know the intensity and how the fake data could impact their lives in all different ways, including healthcare.

Does that make sense? Cool.

How about you do the next one, the finance one? Sure. So finance. So another example in the banking world so another study and survey done by Accenture's banking technology vision survey, what that informed us that today you know, we believe like bank Okay, you know, there are banking system

They got to have the most accurate data sets ever. Now, when it comes to the customers and the end users, they do have a lot of accurate data. Although the problem happens when the banks start infusing and upending that data with external data sources. Here's an example. So let's say if I'm trying to apply for a loan, my bank already has my information, it has my credit history, it has, you know, how much money do I have in the bank and all the other stuff, but let's say if they want to know some more details about my lifestyle, they want to know how many cars do I have, they want to know what company do I work or what kind of other engagements and in participating like what do i do and leisure Do I go vacation somewhere, and this data will come from different sources, right, you know, probably it's going to come from some type of for transportation, data aggregation, it might come from some type of authority that collects the data on my vehicles.

So if banks try to take this data and try to incorporate into their main data set, which is more accurate than the problem is when you do this, and when you don't verify the source and validity of this upended data, and you start to make decisions on it, that could have financial implications beyond anyone can think about. And that's where Accenture study found is like a lot of banking institution, they are basing the results on the data. And a lot of data comes from external sources, in some cases, that could be unverified and lead to major financial implications. So it almost seems like it there's some great movies, I think it's called like The Big Chill or the big. I'm butchering this, but it was it was a movie about the housing crisis back in the 90s. Right. And what happened was, is that the financial

markets and the housing markets. They didn't use any data. They just basically had these loan officers say, you look like a great guy, Samir. Let's give you a house. Yeah. And you're like, well, I can afford a $400,000. Home is like, no, Samir, you need an $800,000 home, you're like, Well, I'm not really wanting that, and I'm not ready to put up. No, it's okay, Samir, you seem like a great guy just sign you up, and that they use no data. And so I think they swapped over to the other end of the spectrum, and now they're abusing the overuse of data. Yeah. And that that also led to the financial crisis of, you know, with 2008, where there are certain pre-indicators that were already telling people that this is going to happen, and the companies started giving those balloon loans and eventually it impacted the economy and collapse the entire economy. scary scary.

Cool. Okay, so it's next one social media.

Who Okay, so this one is a recent lawsuit that was filed against Facebook for 14 fake ad watch data. And so in that report they talked about

that basically they this ad watching data was saved by over by, like 900%. So let's first define that What do you mean by ad watch data is just an impression? Or is this just really metrics that they used to sell ads? Yeah. So let's take an example. Let's say if we were to take this podcast and we were to publish this on Facebook, and then we do advertisement, right so let people watch this podcast and Facebook. Yep. And what Facebook at least according to this lawsuit filed against them what Facebook was doing is it was reporting an inflated viewership. So let's say if we only get about like 1000 viewers on this person

cast in Facebook at Facebook would report like we got like 10,000 viewers. So it made people excited, okay, where I'm getting a lot of views, a lot of people are watching my videos. And eventually, you know, that may lead more people investing their dollars into Facebook advertising. And what's interesting is at least according the lawsuit, Facebook not only inflated the ad views, but they also kept quiet even though they knew for a long period of time. So that's a very clear example of how this could impact the marketing advertising world with bit you know, the controls so you may have be and we'll keep the secrets behind the doors and not be reporting to the end users who things like okay, I'm spending the money for a cause and I'm already seeing the return of in reality, and that's what's happening. Yeah, and I'm curious how we will look us up and I'm really curious about how companies like to ID you know what their standards put

checks and balances in place with this. I mean, think about it. You know, this could be happening with any of these other, you know, advisors a Google Google know what they're doing Google Yeah, I mean that's what that's a very interesting point like it's a mystery although as much as we want to trust these companies to be able to deliver what they originally promise you know this type of lawsuit minutes surfaces it just rattles all of us up because as you were saying, originally, marketing, all the organizations are spending so much money in marketing. And if we come across a feast, a fake data example like this, where the company that we're spending money with, they change the data for their own gains is scary.

Scary because people start losing jobs company start going downhill and everything so yeah, exactly cool. Was this being actually this is an interesting one, this goes back to a guy who been in the news a lot named paul manafort who was the ex-campaign chair for Trump in the 2016 campaign so talked about him using fake data to seek a $5 million personal loan

so that's a so basically, he gave them a safe document right for his political consulting and they talked about him you know getting money from that and then he testified that it was actually fords and all that stuff but he's basically saying it was $4.4 million really close to 400,000 right so yeah significant difference yeah this one this one's a little bit more political this one is based on you know personal finance and stuff but the thing is it's when it comes down to is it's a that idea that this is happening everywhere in every industry i mean

people are still doing this rambling but it is and you know I’m glad that he explained the backup details of as well which i personally didn't use so that's thank you for that it was like you said it is it's fascinating and it's amazing how the fake data is not just limited at an organizational level is not just limited at a particular entity or industry it spans across everywhere and i feel like the personal finance component of fake data is one of the oldest one right we have heard a lot of stories where people change their documents to get maybe a loan from the bank which again is a fake data you're providing fake information to bypass the system that has been put in place and it could create havoc in a lot of ways so this guy and you know instead of showing the actual income which is 400,000 he showed it was 4.4 million and ended up getting a $5 million loan that's really


Yeah, he's got a lot of other financial problems. And he's, he's already in jail. So

you know, yeah, almost. I'm going to stop them. So sucks for him. So

yeah, cool. Let's move on to the next one. This one is a government one. This is interesting. So there's actually two different examples here that we pulled up. First one is in Japan,

looking at government entities, let's say about 80% of those government entities inflated the number, people within their staff that had disabilities showing that they hired for special needs because basically, in Japan, there's a legal requirement to hire people with special needs with certain disabilities. And it seemed like 80% of these companies are these government entities in Japan fake that they fake that data, which didn't really exist? Yeah.

Terrible. It is it is very sad again, if you think about it 80% as an astronomical number, and especially, you know, the government, we trust our governments, right? We believe them to do the good work. And if you think about the, this, these entities what they were mandated, they were mandated to hire a certain amount of people with special needs. And in they didn't necessarily want to hire them for whatever reason. And what they did is they changed the data. And they showed like, Oh, yeah, we have a completed a quota for a special need. And we're all set. And apparently, as you were saying, like 80% of them are not. So that's very sad.

Yeah, and then, here's another one. everybody complains about the price of plane tickets, right? You complain about a plane tickets prices are going up. I remember when I used to fly from San Antonio just to Dallas, even though you're only in the air for

Really in the air without your seatbelt on for 15 minutes and it will give you a little tiny breakfast, right? You have a little like croissant or something no this is before the days of other gods you they gave you a little tiny breakfast It was like we're going to trade with the with fruit even add fresh fruit. And you know why they don't do that anymore because companies like United Airlines made the mistake of using predictions based you know, predictions for what the revenues are. And they use fake data to make those predictions and it costs them over a billion dollars in this revenue. Wow. Wow.

There goes your croissants and fruits.

I want my career I fly so much for work and I'm so sad that I have to bring my own snacks now hang out here

because when you travel internationally like specially in your Europe or Asia you actually still get breakfast he actually still get food on the airline even the local lights you do yeah which is which is amazing i mean they're also operating flights and they're still servicing their customers are still giving you the food but you know here we go like a data miss job mishap and then you know $1 billion losses amazing
you know what's going to happen next they're going to have you swipe your credit card to get into the toilet next time there's going to be like those little spark

hilarious if you go in and you put in like five minutes or something like that there's a timer on there and you swipe it so when you're in there but no longer waiting for a claim for napkins

it's like when you go to the spray washing your car and there's a lightning says four minutes 22 seconds and it's counting down and like oh crap i better hurry up

are the doors open

That will be even worse.

He checks you out, right?

Okay, so let's get back into how we're going to finish this up. How do we combat fake data? So, one thing I was missing in

the combat part, I think one of the one of the important thing is

you know why the fake data is on the rise, right? There are multiple things happening simultaneously. We talked about like, why it's a problem and how big of a problem it is right. Now. Let's take it in a little bit before we get on to the combating steps, like mitigating it. One is there has been a lot of big name breaches like hacks in bigger organizations like Facebook, Uber, Target, you name it. And because of those issues, where every

Few weeks ago, like all my data is public because of some company got breached. Right? The trust in these major corporations is at all-time low. And what's happening is because of the low trust people are providing wrong information to the companies, instead of filling people's fault.

Actual especially it is it's not the fault. It is the it is the result, right? If you look at the, you know, the causation and the result, this is basically the reaction to what's causing because the companies are being careless, they're not paying attention to their infrastructure security as much as they should. They're not spending the money to go with companies that can protect our environment. They're getting hacked, and the information is getting leaked, and people are getting disappointed. And what as a RSA is another security company that commissioned a survey and research what they found that 41% of the consumers are admittedly providing false information to the organization because they don't trust that organization. And so that's one source. The other sources. Also because of people breathing, you know, some malicious people hacking these big organizations, they themselves are introducing malicious data on the organization when they're hacking, right, so that's another source of fake data. And the third source is for personal gains, people within those organizations for personal gains are making changes to the records for whatever they want to achieve. So those are typical three cases. One is individuals not providing the correct information because they lost trust. Second is hackers introducing malicious information. Third, is people changing the data for personal gain and forth I would say, which also happens it's mistakes, right, you know, calculation mistakes, know, some type of data mistakes that happened. That led to

Creation of fake data. So those are four areas where that is causing the rise and fake data.

We're going to end this with a happy note at the end because this is that, you know,

yeah, let's talk about the counter then what do we do Jeremy, come on, help yourself. Okay, number one,

how to combat so you got 16 years of one leverage social identity data collection methods uses dig into Google identifying tools, Facebook and others. So example good yet was Giga, Giga allows you to do social Simon, right. So when you're in there, it's basically an authentication tool. So when you go in there and you sign in, rather than in putting your own email address and coming up with your own password and putting in your own information, which then could be fake because of those 41%. It says, Oh, I want to use a secondary source which is somewhat verified because we can't say a Facebook

profile is real. Right? Right. It could be trolls or box or could be anything or it could be like your avatar of who you wish you were in life to shape your Facebook profile. But what it is it's using a secondary,

secondary tool to import that data in. So when you're

when you're doing things that use secondary tools to ensure that your data is correct, that's one way to combat it. Yeah. And I think, you know, if you talked a little bit about that, if you think about it, generally speaking, if someone were to ask, you know, like, for me, for example, me if I were Do I really like a PDF file that I want to download is like a white paper of some sort. And I am not going to have so much time to enter every single information on those 15 different fields that asking me to feel

instead of that, if they had allowed me to use my Google profile, or my Facebook profile or my LinkedIn profile to download it, what it would do is, I would

Just click a button, approve it, and then download it and later stage I can disconnect the profiles from that company’s information. But it will, in a way provide that company, my information. And a lot of cases

people will have correct information, at least in their social networks. You know, in this is a complete extreme example, that there may be some people who have fake profiles, which is a different story. But what we're talking about it will reduce the fake data to a greater degree when we use social identity platform like Google identity tools.

Exactly. And with those, your profile is what input in input into those tools and then verified, you know, through means that, you know, make you some legit. Rain will just keep it up. Yeah, cool. So number two. Number two, we're talking about this idea or that there's human error, right? When you're managing big databases

Lot of times I remember when we were looking to old databases trying to do data cleansing when we're doing lead gen back in our old roles.

There's human error. Right? There's tons and tons of stuff, those human error. So one of the tricks there is

manage that with AI technology. Right?

Absolutely. it out. Yes. It is important to introduce technology where we're applicable to bypass some of the human’s ability to touching the data, where does it needs to be so any kind of data processing, any kind of large scale data management, there are new technologies that are popping in market that allows you to manage the data more and more effectively? In a lot of cases, you know, that is driven by AI. One example that comes to my mind is Watson Analytics. That's a platform that allows you to use AI to drive decisions off data

over flip

is another example of a content management platform driven by AI. So try to use these modern technologies to help you bypass some of the human error and human component. Now, having that said that, it doesn't mean that you're always going to get 100% results, there is a greater chance that if a fake data is introduced early on, and then you introduce your AI technology that AI technology going to learn from the fake data, which is going to be even worse. So you have to use that technology by caution. But it is a good way to mitigate the impact of fake data.

Agree, Okay, next one.

So using technology integrations to manage your flows between data, your it in your business system, so really, this kind of comes back to synchronize mantra about connected technologies, right. Just ensure that you have connected technologies that are passing profile data on the back end, and a lot of what that

Does not just profile data of our customer data on the back end, using a common back end could be,

you know, a data lake of sorts, it could be any kind of data management platform a DMP. But those API integrations allow for just a clean load data. That's a big thing. Yeah, that's, you know, I don't want to add anything to that very well said, Okay. Cool. Number four, implementation of GDPR or similar data law, data integrity laws worldwide, to improve confidence

to organizations data management kid goes, Yeah, this is basically just best practices of using GDPR. There's nothing really what you can say about that. Here it is, implement GDPR Yeah, and implement GDP ours and similar data integrity laws. It is extremely important. Like I think what GDPR has done and you know what I like about it is, it is

Even though it puts a lot of stringent requirements in the organization, if implemented correctly, and followed correctly, he can shape the wave of the new generation of data where we can see much more controlled fake data obviously isn't fake data is not going to disappear suddenly, but it will be a much more controlled

mechanic ism to prevent the fake data from spreading too far out and causing backdrop.

I agree. So number five, we got two more laughs And number five is it's this idea that introducing capabilities to manage exponential data growth. So when you have organic data growth, great, you know, you're able to bring that data in, you're able to cleanse it properly, organize it put in the segmentation and audiences whatever you want to that data. But a lot of times when you have large influxes of data that either could be fake or not. You need to have some type of governance or you need to have some type of flow and system ready to be able to ingest on

data, not just say, I'm going to ingest this data blindly shove it into my systems and use this as real data, you need to have a way to justify the truth of that data.

Yeah, absolutely correct. And I think it is important that with today, we are producing data in zero bytes and every day in its enormous amount of data that's being produced. But that doesn't mean that the company should get lazy and say, Oh, you know, there's just so much data. I don't know what to do. But they should put resources and infrastructure around it to make sure that the, the creation of fake data is protected in somewhere the other way.

Hundred percent agree. Cool. So last one here is used data in the correct context. And I like this one that I'll put in quotes and who came up with this one, drink data responsibly.

Exactly right. It's almost like it's almost like an alcohol thing. Drinking


yeah, so so as you said, Jeremy, the putting data and correct context, what it means is when you originally acquired the data for a specific purpose, let's say if you acquire your customer information, so you can communicate to them on a regular basis about their account, use it for that purpose. Don't take your customer information and sell it to a third-party organization. So you're just making money off of it. Like don't get into those practices. And that's why the notion of exactly don't get into those things that you do didn't necessarily have the context for originally when you're requiring it. And that's what we're saying, like drink data responsibly, be responsible for the data you currently have.

Yes, and I and I, we both promise you that if you can do these things to come back fake data, share these practices with other people, be that data against data police within your organization and share these best practices.

You will have your job longer and your company, will you be more successful in the long run, just because I know you're ensuring you're ensuring smart revenue, you know, you're ensuring that there's going to be legitimacy that what you're doing. And then when you're sitting there at the table, and you're making those decisions based on millions of dollars, or even hundreds of thousands or even thousands of dollars, and you're looking at the data, you cannot sweat, you don't have to sit there sweating thinking,

should I do this or not? Are we going to pull the trigger on this? Do I believe what I see? Yes, you can truly believe what you see because you implemented best practices. Fantastic. Nowhere Very well said. So as promised, we mentioned earlier in the podcast that we are going to provide you two different tools to test things on your own. The first one is a Google Web Store plugin that you can connect your Google Chrome browser, and it's called it is actually called fake data.

It's a form filler. So let's say and you know, we're not, we're not asking you to go create fake data. But this is a tool that you can use to fill in the forums, which you don't want to put off your information.

It's called fake data a form filler you want hate. The other example is called Data fake generator calm. Now, and a lot of cases, you want to run a specific analytics exercise. And you just want to have some data in your hand to start running the test. You don't have access to the data or you don't want to use your actual organizational data. So what do you do is you go to a data feed generator calm, and it will generate a lot of different data sets for you. These are all not real data set. So you know, don't even try to use this data and just try to log into someone's account that's not going to happen. What it is going to do is going to give you things like you know, identity generator is going to give you

some sort of a username and password combination or variety of different data set that you can play with, you can plug into yourself.

system to make, you know, let's say if you want to test your password strength, you can use some of the fake data from there and try to test the password strength. That's just an example. So those are two tools as we promised that you can go test will put the links in the show notes.

Fantastic, and this has been a great show.

As always, we'll do our same the same advice here. Please send us your topics anybody you want us to interview. Questions will do live on air. We love it. Keep on giving us some great feedback and as always, you know, leave us reviews, leave us comments, you know, go to iTunes, go to Stitcher go to SoundCloud put in a review. Tell us what you think. You know, we're happy to accept anything so

absolutely. Very good. Yep. Other website as Jeremy said, analytics podcast and he lives today podcast.com

Yep, thank you guys and thank you for being here for fake data, the rise of false analytics and we'll see you next time.

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