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Can we be honest about something?
This industry is a little obsessed with being data -driven.
No, that's not the honest part.
You already knew that.
The honest part is that not all of us are actually all that great with data.
And if you are the kind of person who's pretty handy with an right kind of insights to show the whole picture.
My guest today is Mo Halaba, CEO of Data Wisp, which if you're not familiar is an AI -powered data visualization tool.
Given his position, Mo has a unique perspective on the disconnect between the amount of data most organizations are collecting and how effectively they're actually using it.
Plus, I've got to say, I rarely speak to people who are so smart they're able to take highly complex matters and make them accessible to anyone.
But lucky for you and for me.
That's what this talk is all about.
Let's jump in. Welcome back to the Product Manager podcast.
Mo, thank you so much for making the time to join us today.
Thanks for having me.
I'm excited to be here and talk about AI and data and all these other buzzwords as well.
We're looking forward to it as well.
Can you tell us a little bit about your background and tell us how did you get to where you are today at Data Wisp?
Absolutely. It's kind of long, but I guess bear with me.
I started my career in finance, in equity research, and just telling people what stocks to buy and stuff.
That involves a lot of research and manual data work that is an absolute pain in the butt.
I ended up moving on from that and doing corporate M &A for a while.
That was kind of more of the same, but just from a different perspective.
Instead of telling people what to buy, we were doing acquisitions.
For these acquisitions, we were doing a lot of our own research as well and digging through mountains of data.
Eventually, I got tired of that and I decided, hey, I want to go out and do something on my own.
I want to start a startup.
I've always been really passionate about video games, so I thought I would do something in that field.
I started this gaming startup that absolutely failed miserably, but in the process met some people that were working on something really cool, which is an e -sports team.
They were buying players from unknown regions and stuff, but they were using software to train them using data and analytics.
I found that really interesting, so it was like money ball for video games.
Then from there, that kind of grew into its own things because some of the other teams wanted it and some of the other tournament organizers wanted to put it on the screen and show stats and stuff.
That's kind of how I went from working in finance to working at a data analytics company.
Eventually, we decided to grow outside of gaming.
Now we're full -blown analytics.
Awesome. We'll dive deeply into that in a moment.
Actually, it's a perfect segue.
Today, we're going to be talking about how to really use data to inform your decision -making.
To start us off, I'm curious, what do you think a lot of orgs get wrong about so -called data -driven decision -making?
I know that term is a little bit of a hot -button one.
Yeah, I think there's a lot of folks out there who expect data to make decisions for them, like data -driven decision -makings.
Yeah, it's sure some fields are highly technical and you have data scientists and you have decisions that are highly driven by data, but that's not what we're here for.
That's not what DataWisp is for.
We're much more about data -informed decision -making and it's taking decisions that you were going to make without data and simply just giving you access to some data to look at so that when you make that decision, you're more informed.
An example of that might be, hey, where should we put this button in our user interface?
Should we put it up here?
Should we put it down there?
Should we move it at all?
If you're seeing that your conversion rates are like people coming into your website and then they're getting lost on your main page, you might want to move the button.
But if you're finding that 90 % of people make it onto your page and click the button, you don't need to worry about that.
It's just little things like that where instead of guessing, you are now educated by some data, so data -informed.
Yeah, I also prefer data -informed.
My Canadian is showing.
Can you tell us a little bit about the ways that data visualization can help us to democratize the understanding and usage of data within organizations?
Absolutely. I think not everyone is a numbers person, especially when you go to non -technical people, when you look at more creative fields or product managers or whatever.
Some people just understand data better when they see it visualized in picture form in front of them.
It's one thing to say this pattern is changing over time.
It's another thing to just show someone a graph where they can see how the KPI is trending over time.
Then if you have a main branch and a new branch and they're moving together and then at what point the data forks in a direction, it's so easy to just look at the graph and be like, what happened in July?
As opposed to looking at a table or quarterly statements or something like that.
Visualizing it, especially for non -technical people, makes it so much easier to know what questions to ask next, in my opinion.
As a self -professed non -numbers person, I very much endorse visualization of data.
What are some of the ways that orgs can safeguard against incorrect interpretation of data, now speaking of which, so that decision makers have a clear understanding of the data points?
A lot of people look at AI and they're like, AI is going to replace people.
We're not about that.
We're all about removing some of the busy work and stuff that bottlenecks data scientists.
A great way to safeguard against that is to have a data scientist on your team or to hire someone who actually knows what they're doing.
The difference is that you can get away with one or two data scientists instead of 10 if you have good software that allows people to...
Basically, what you want to eliminate is the need for technical skill to be able to analyze data.
I shouldn't have to write SQL to be able to get a chart, but that doesn't mean that I should just make decisions off of that chart without understanding what the data means.
When you remove that technical barrier, you allow people to spend more time with data and they can become more educated about what are the right ways to use data.
They have more time to spend talking to a data scientist to understand what that data shows and doesn't show.
Just as an example, there might be variables that are correlated with each other because causality or whatever, but basically keeping a person in the loop is the best way to avoid that.
Whether that's a data scientist or your best engineer, I still think that if your choices are do nothing or do something, you should still go with do something.
What's your playbook for slicing data into contextual chunks?
Can you share an anecdote for what that looks like in practice?
I can. The best way to think about it, in my opinion, is you want to look at what your business goals are.
It's not just about working with data in a vacuum.
The reason that we do all this stuff with data is because we're trying to understand something about our business that we didn't know before we looked at the data.
Usually, it depends on the business question, but is our tutorial useful?
If we're trying to answer, how good is our tutorial?
Well, one way to slice the data might be people who have used our tutorial and people who haven't completed our tutorial, and then looking at churn rates or dividing the whole dataset by people who've done the tutorial and people who haven't done the tutorial and seeing how they differ.
Maybe there's no difference.
Then we say, okay, well, either the tutorial is not effective or the app is really easy to use and they don't need the tutorial.
But if you see that people who complete the tutorial, on average, use the product way more and are way more effective with it and are retained for longer, then you can go, oh, well, maybe there's something to this tutorial thing.
It depends on what you're trying to solve for in the first place.
That's a really clear example.
I really appreciate that.
You've spoken before about the difference between actionable and non -actionable data.
Can you just talk a little bit about what you mean by each of those things?
What's actionable data?
What is non -actionable data?
How do we identify and leverage both of those types effectively?
Yeah. It's more about how you are able to act based on the insights that you get.
It's not that the data itself is different in any way.
It's just more like, what are your options?
I'll try to give you an example.
If you are looking at, let's say, for DataWisp.
We have a funnel. We do marketing.
We try to attract people.
They come in. They use the app.
They like it or they don't like it.
Then they turn or they don't.
The higher you go up the funnel, the more there is that you can do.
If no one is booking a demo, you go, well, maybe the button's not big enough, or maybe the landing page is not compelling, or maybe we need a new graphic.
If somebody's logging in, booking a demo, talking to us, trying the product, asking a bunch of questions, and then churning, it's like, well, maybe they didn't like it, or maybe it didn't solve their need.
That's way less actionable than some of the stuff towards the top.
It's like, what can you do based on this information?
Certain things are more clear than others.
If it's like a funnel where you're just going from step one to step two, well, if you see a 50 % drop off in step three, for example, you go, okay, you can make a list of things that what effects drop off from here to here, and then you can get after them.
Whereas, when you get all the way to the end and they're just using your product and all that stuff, it becomes a lot harder to go, okay, well, they churned because X.
Because at that point, who knows?
The way to do that then is to try to do more customer interviews and watch people using your product, and there's other tools for that.
What are some of the tools that product teams can use to do a better job of collecting and parsing some of their user data to inform the decisions they make?
Yeah, so it depends.
There's a lot of different software that you can use, and it really depends on what kind of product you're building.
If you're building a game, it's very different than if you're building a website or an e -commerce platform or whatever, but you need something that collects events or that tracks when things happen and stores them in a place that makes it easy for analytics.
Ideally, if you're building a game, there's Unity plugins that can take events that happen in game and record them and store them in Snowflake.
If you can get something like that, that makes it way easier for you to start actually analyzing that data.
That's the basic requirement.
For e -commerce and stuff, there's this ton of vertical -specific solutions that do stuff like that, so I would say it just depends on what you're building.
As far as AI and the role of AI in assisting with some of this, the parsing of data, you'd mentioned having a data scientist on staff is your best option or several.
Is there a place for AI in terms of helping to make some of the data make more sense to folks, especially non -technical folks?
Absolutely. That's our whole business at Data Whisper.
In terms of what the AI can help with is the technical skill that is required to be able to parse data, to be able to transform and shape it how you want to get the answers that the AI is really good at that.
This is something that only data scientists could do before, but now anyone like me who's completely non -technical can use one of these AI tools, and the AI just takes care of the code part of it, and you can just ask questions in plain English.
So anybody with a bit of curiosity and a basic knowledge of the business or the product can start digging into data.
I think that's cool because it creates this cool flywheel effect where the more people care about data, the more the company is going to invest resources into having nice clean data and making it available to people and all that stuff.
Then they benefit more from having that.
Let's talk a little bit about leveraging data for iteration.
Once you've applied your data and applied learnings to a specific feature, for example, how do you create a feedback loop to inform future iterations of that feature?
Absolutely. I'll give you an example of something we did at DataWisp, actually.
With Wispy, our AI, we care very much about the number of people who are using it, and more importantly, how long they're willing to wait for an answer.
We had the option early on of having a really fast Wispy that would return okay answers and having a much slower Wispy that would return way better answers.
Not always way better, but sometimes way better.
Quickly find out by looking at data how long people are willing to wait because you see after a certain point that people just drop off.
They're not willing to wait more than 30 seconds to a minute.
Our smart version of Wispy would take sometimes two minutes, but it would return stuff that was really cool and nobody would sit around and wait for that.
We decided we wanted to focus on the speed one, and so we tried to make DataWisp as fast as possible.
You can see the metrics go up.
At first, it was the slow one, and then it was a 50 -50 split where we gave half one and half the other.
Then we just went to the fast one, and you see the numbers go up as we go through this iteration.
This is the sort of thing where Moritz and I, Moritz RCTO, were on a call with each other.
We were designing these little messages that come up as Wispy is working on the answer.
If it takes 30 seconds, you can't just spin for 30 seconds.
It had these messages, hey, I'm still working on it, or data science is hard, or whatever.
We were trying to figure out how long the timing should be between these messages, and then we were like, wait, we could just look at data.
We just looked at how long people were willing to wait, and we just spaced it out according to that, and we rolled the feature out.
That's like a concrete example of how you iterate on that stuff using data.
That's really cool, and I kind of wonder, because we often talk about training AI models and what that looks like, is there an element of training users?
When you say that there's an element of impatience when it comes to users waiting for a higher quality answer, that seems like it's a trainable behavior.
How do you approach that decision?
You're holding it wrong.
That's not how you do it.
You remember that one?
Was it Steve Jobs? It was like, you're holding the phone wrong.
It was a famous thing.
People like the new iPhone came out, and it wasn't getting good reception, because they put the antenna somewhere where your hand was, and they just came out and said, hey, you're holding it wrong.
You should hold your phone like this.
That's an example of not the right way to do it.
That's the absolute worst way to do it.
In general, it's about expectation setting.
It's a mix of education and also expectation setting.
They kind of go hand in hand, but at the end of the day, the user does have an idea of what they want.
It ends up being a compromise.
We understand that what DataWisp is doing is for non -technical people, and the existing behavior is, I'm going to email Joe, and Joe is going to get me the answers.
These are already people who don't have to put in an awful lot of work to get something.
Now, it sucks for Joe, but that's the reality of this person.
We can't ask them to do a whole lot of work.
We can ask them to do a little bit of work, but if it's more than that, at the same time, it is some marketing and some expectation setting.
Do you want a good answer, or do you want to allow the answer?
How long does Joe take to get back to a week?
Is it worth waiting a minute to get an answer from DataWisp?
30 seconds would be nice, but a minute is still shorter than a week.
You drop little hints, and you need a good copywriter for this.
On your website, setting expectations right from the beginning, hey, get your answer in minutes, not weeks.
You don't put seconds, because if you put seconds, they expect it like this.
If you say, get an answer in seconds, that causes an expectation mismatch.
It's funny, I say all that, and then our website probably still says seconds.
But I think in sales calls, you just have to be really upfront with the customer and say, look, if this is something that takes you how long now?
They tell you, and you go, okay, well, we can do it in a fraction of that time.
You set expectations a bit.
Yeah, it's interesting.
I feel like there's an existing behavior from folks who are so used to using search engines that answers should come instantaneously when the technology is very different.
Also, chat GPT has spoiled people, because people just think AI is magic.
They don't understand how it works at all.
And also, they've seen how fast it can produce a lot of text, for example.
So if I say, hey, chat GPT, write me a travel itinerary for a five -day trip to Milan, it'll just go, boom, done.
So regular people just assume this is magic and assume that it can do anything quickly.
But there's a difference.
I mean, when you're connecting to a database, you've got a billion rows of data in that database.
The AI is not even the part that takes a while.
You have to run that query, and that query may take some time to process, and there's absolutely nothing we can do about that.
But because someone saw chat GPT be really fast, they go, well, this should take two seconds.
And so one of the things that Wispy does now that it didn't used to do is when it's spinning, it actually tells you what state it's in now.
So now it goes, I'm thinking.
And then when it's done thinking, and it's just waiting for your database, it goes waiting for your database.
So it communicates what's happening at all times to set expectations throughout the process.
I can see that being really helpful.
I think that also users are really trained to when something is spinning for what they perceive to be too long.
The assumption is that it's broken, or you need to resend the query.
Interesting. So we looked at a lot of data of this.
We looked at how long and first of all, track everything.
So like, as soon as you have a live product, start tracking things.
So we tracked everything like every button that you can press in data with we track every time anyone presses it.
And so we have a really good idea of how all this stuff works.
And so we also did user tests.
And yeah, we noticed that like, if something was spinning for a while, people just refresh the F5, you know, the so you like you really the product has to communicate what it's doing, if it's making people wait for more than five seconds.
That makes a lot of sense.
And yeah, I think that track everything could be the tagline of this episode.
Track everything that your users do in your product, because it will be useful at some point.
So I know you already shared an example of data informing a decision.
I think that was a really good concrete example.
I'm curious what the most triumphant example of data informing a decision, whether it was, you know, it's a customer, whether it's your own team.
This isn't at data wisp.
But the most triumphant I told you I worked in eSports before the most triumphant is we literally won a tournament based on this stuff based on data.
So the way that this software worked was actually really cool.
And I can give you a data wisp example as well, the way the software works is really cool.
It allowed, like you said, to filter data based on certain different conditions or whatever.
And so the most kind of tools would give you heat maps.
So this is the whole map.
And this is like where the other players on the other team usually hang up.
Right. And if you look at a heat map over an one hour long game, it just kind of looks like green everywhere.
But that's not helpful.
Like if I'm in a game and the whole map is green, that doesn't tell me where they are.
But what was really cool is when you started breaking that down by situation.
So on the first round of the game, where are they when they have this much money?
Where are they when they don't have money?
Where are they when they have these guns?
Like where do they stand versus when they have different guns?
And so you can start to create these scenarios where all of a sudden the heat map is like five red dots.
And now you know exactly where your opponent is.
And so you can use that stuff in competition.
And they were using it and winning with the lowest salary budget of any team in the competition.
So it was incredible.
That's awesome. I'm sure that there's some esports folks salivating over that right now.
The best data wisp example I'll give you is we helped a game basically triple their user base in a very short period of time.
And the way they did that was they segmented the users based on what acquisition source they were using.
And then they looked for specific in -game behaviors that they valued more than others.
And so they were able to find when we acquire users through this channel, they behave in -game the way that we like.
And so they were able to focus their efforts on certain acquisition channels that they preferred.
The company is called Honeyland.
We did a case study with them.
It's on our website.
So that was really cool.
Yeah, that's really interesting.
I'd like to check that out.
Well, Mo, thank you so much for joining us today.
Where can people follow you online?
So if you're on X, it's Electronic Mo.
Otherwise, you can follow me on LinkedIn.
It's just my name. Data Wisp is the same.
So you can follow Data Wisp on X and on LinkedIn as well.
Awesome. Well, thank you so much for joining us.
And yeah, for the Crash Course, it was very accessible for non -data folks like myself.
I try to make it simple.
Thanks so much for having me, Anna.
Thanks for listening in.
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