More Intelligent Tomorrow: a DataRobot Podcast

From Retrospective to Prospective Analytics - Ikechi Okoronkwo

February 10, 2022 More Intelligent Tomorrow Podcast Season 2 Episode 3
More Intelligent Tomorrow: a DataRobot Podcast
From Retrospective to Prospective Analytics - Ikechi Okoronkwo
Show Notes Transcript

Ari Kaplan sits down with Ikechi Okoronkwo of Mindshare for this episode to discuss using predictive analytics to make key future decisions.

Mindshare is a global media agency dedicated to forging competitive marketing advantage for global clients like Unilever, General Mills, and Ford. Ikechi Okoronkwo heads Mindshare’s Business Intelligence & Analytics team, which is lauded in the industry for their fresh approach to analytics.

His mission is to simplify and refine decision-making processes using data and analytics to drive good growth: growth that is ethical, unbiased, and beneficial. His team builds tools that shift the focus of analytics from retrospective to prospective–from looking backward to looking forward.

“People think of analytics as reporting or dashboards charting progress against a target ROI. But the best types of insights are those that influence an imminent decision, whether that's a budgeting decision, an optimization problem, or any question about how to move forward.”

Their analytics do, of course, have a quantitative aspect: using the scientific method to test and validate assumptions is critical. But Ikechi challenges his team to continually find ways to drive a balance between data and the creativity that empowers clients to push their boldest ideas.
“Our job is to provide our clients simple frameworks to help them make smarter data-driven decisions in everything from creative to content to strategy.”

They begin with qualitative consultative conversations, from which they build an outcomes framework and define successful key performance indicators (KPIs). Only at that point do they start to talk about the data that will be needed to serve as indicators of performance against their articulated outcomes.

They then apply machine learning technologies to collect relevant data, leveraging proprietary methodologies to determine which types of data should be fused into each aspect of a client’s outcomes framework.

While the industry has been focused on collecting as much data as possible on people's behaviors–what they're clicking on, which sites they're visiting–Ikechi’s team invests in AI and machine learning to understand what’s behind those rational signals. For example, neurological studies in a neurolab might validate the self-reporting in surveys. They might observe participants and their subconscious reactions to a specific format or moments of higher receptivity when ads are viewed.

At the same time, data privacy and ethics are paramount to their solutions. They firmly believe clients should own their own data, and they’ve led the industry in their work to recognize bias in data. Their ethics tools enable continuous monitoring of how data is being collected, from where, and how it is used. In the end, this helps clients make objective decisions.

“It's not a question of how much more data we can collect to be better at our job. It's really more about what outcomes our clients want to drive and how we can empower them as business partners.”

Learn more about Ikechi’s innovative approach to analytics in this episode of More Intelligent Tomorrow. We’ll cover:

  • How to define exactly what data is needed to make critical decisions and how best to protect it
  • How to identify bias and know when you’re collecting the wrong data
  • How to use data to test feedback in the creative process

Ikechi Okoronkwo (00:00):
The big vision that we have is moving people from retrospective analytics to prospective analytics. In other words, from looking backwards into looking forward, using predictive analytics to influence decisions that are coming up in the future. It's not a question of how much more data can we collect to be better at our job. It's really more about like, what outcomes are we looking to drive? And how can we be an extension of our clients as business partners?

Ikechi Okoronkwo (00:25):
Sometimes when people talk about AI machine learning, the way they talk about it is, "Oh, it's going to just do what humans do." In some situations, that could be somewhat correct. But I think the more interesting use of it is helping us just be better and helping us to fill in gaps where if I'm pressing a button, maybe I don't need to press that button, or maybe I don't need to go look at a chart and point. Maybe there's an algorithmic or automated way that can identify those different options, automatically test them.

Ikechi Okoronkwo (00:51):
I don't think it's like a Black Mirror type of thing where we're going to be in this dystopian future, where everything's automated. I think that if you're concerned about that, then help shape that, help build that.

Ari Kaplan (01:05):
Hi, Ikechi. So glad to have you on the podcast.

Ikechi Okoronkwo (01:08):
Thanks. Ari. Happy to be here and looking forward to the great conversation.

Ari Kaplan (01:12):
Great. Ikechi, you're head of BI and analytics at Mindshare. Why don't we start with how did you get to Mindshare?

Ikechi Okoronkwo (01:19):
That's a long story, but I'll give you the short version. I came to the United States for my undergrad education. I graduated from a high school in Kenya, and so I came here to a school called Manhattanville College, which is in Westchester, and did my undergrad studies there. Then worked for a bit, but then I decided to sort of go back and really reevaluate what I wanted to do as a career. I was thinking of going into finance or something in the quant field. I did my MBA at Pace University.

Ikechi Okoronkwo (01:49):
I had a concentration in marketing management. During that program, I met a professor who introduced me to a case competition at a company called GroupM, which you may know is the parent company of Mindshare. That's how I got introduced into the advertising space. She talked to me about field of analytics, that it's growing and they're looking for people like myself who are interested in a balance of creativity and data and how that comes together to really create value for clients. It was really compelling for me at the time.

Ikechi Okoronkwo (02:22):
At that point in time, I felt that I was in the right place. I think the aha a moment for me happened when during the case competition and during the process of preparing for it, it gave us two weeks to answer this question. And just the type of the experiential learning experience that I had really was the moment that I knew that I wanted to be in this space. I knew that being an analyst is something that was both motivating, interesting, and something that I was frankly good at and had some aptitude for.

Ikechi Okoronkwo (02:53):
That's where it started. I started off at GroupM as an analyst, and then I transitioned from there to Mindshare. GroupM is sort of the holding company and I was working within their data analytics services group. And then I made the transition to Mindshare to lead advanced analytics for Mindshare in the US. The team was very small back then and part of my challenge at the time was how do we take some of the good work that we're doing and scale it.

Ikechi Okoronkwo (03:19):
I spent the first couple of years at Mindshare scaling up the advanced analytics practice. We had a reorg and we felt that there were a lot of disparate practice areas, siloed practice areas that needed to really be one team. Within that reorg, that's how I was given the leadership role over business intelligence and analytics. That was merging the advanced analytics team, as well as our measurement and reporting teams as well.

Ikechi Okoronkwo (03:45):
That's where I am now and we've been growing and it's been a really great experience. We have a lot of great plans for the next couple of years.

Ari Kaplan (03:52):
That's great. And yeah, GroupM very familiar with. They're one of the most creative companies out there. People of listening in, if you haven't heard of GroupM, you look at the top a hundred brands or companies with brands, Unilever, Nestle, Ford, and others, and they help come up with a lot of the creative. I find it fascinating. On one hand, a company very creative. And then on the other hand, analytics people kind of view it as number crunching.

Ari Kaplan (04:20):
How's that culture? You have the most brilliant minds, celebrity interfacing, and then you're number crunching. What's that like?

Ikechi Okoronkwo (04:29):
Yeah, that's a really great question. Because just to answer the question in a different way, I actually teach a course at Fordham University, which is marketing analytics insights. What I was telling the students is when you think about what an analyst is, sometimes people jump to, "Oh, that's somebody who deals with data, who's sort of building models." I challenge them to say, "Analysts, you can find them anywhere. An analyst is not necessarily a quant.

Ikechi Okoronkwo (04:54):
An analyst is somebody who's making observations and deconstructing a situation and the variables in the situation, putting back together in a way to answer some sort of question or drive some sort of insight to influence an action." Right? That is not necessarily quantitative. Now, to be a good analyst, it does... I think o be a great analyst, it does require having quantitative methods, using the scientific method to really test and validate the things that you're saying.

Ikechi Okoronkwo (05:20):
To your point, in a company like GroupM or even WPP, where we have creative agencies, we have data companies, we have tech companies, we recently acquired a AI company, then you have analysts like myself where it's not just about the number crunching, it's really about solving problems. That's why I love working within the GroupM family and specifically at Mindshare, one of the GroupM agencies, is we've really embraced that marriage between art and science. We don't just blindly look at the numbers to make decisions.

Ikechi Okoronkwo (05:50):
We're really thinking about how can we make sure that we're pushing creative ideas, bold ideas, to your point, some of the biggest companies who are really looking to be at the cutting edge of the industry. Our job, specifically my job is to help provide simple frameworks for them to understand what's effective, what's not effective, and then allow them to make smarter decisions using data. A lot of companies talk about being data driven. What does that really mean?

Ikechi Okoronkwo (06:18):
Well, in a simple way, it's making sure that data is bled into the decisions you're making. Data is not a shiny object that you tackle at the end to kind of justify what you're doing for a meeting. Data is a culture, the culture of testing and experimentation. It bleeds into everything, from creative to content, to strategy. I'm not just saying this just to kind of say good things about the company. We really live by that mantra. As an analyst, I'm so proud to work there.

Ikechi Okoronkwo (06:49):
I think that's one of the reasons why I've stuck around in that organization for so long is because they do respect the value proposition of analytics and data. There's a lot of exciting things that we're doing in that field.

Ari Kaplan (06:59):
Very well said. I definitely appreciate that. With the marketing analytics, I don't know if I mentioned, I think, in a prior conversation that I used to work at Nielsen and IRI. Respect Mindshare incredibly. You were seen as the leader, the broad thinker, the innovator, where a lot of the other companies were kind of like old school. When you say analytics and BI, it was just like a regression formula that was used 20 years ago and hasn't really changed much.

Ari Kaplan (07:31):
Today, what are some of the... You acquired an AI company. What are some of the more leading edge type of insights that you're providing?

Ikechi Okoronkwo (07:39):
Absolutely. Even speaking about the work that I'm doing, in advertising, usually when people think about analytics, they jump straight to reporting, right? They jump straight to build me a dashboard so I know how I'm delivering against my target or what my ROI is for whatever widget I'm selling. Now, what we've really tried to evolve is what I was talking about is the value proposition of analytics, right? What does analytics mean in terms of when you're putting those people on your staff plan, what are those people doing?

Ikechi Okoronkwo (08:10):
What is the value they're creating? The big kind of vision that we have is moving people from retrospective analytics to prospect analytics. In other words, from looking backwards into looking forwards, using predictive analytics to influence decisions that are coming up in the future. When people talk about insights, analytics insights, what does that really mean? The best types of insights are insights or something that's influencing an imminent decision, whether that's a budgeting decision, whether that's an optimization problem.

Ikechi Okoronkwo (08:42):
An insight has some sort of input into a decision-making process. The things that we're building are around making sure that analytics is part of the decision-making process specifically, not insights that are just informative, where, oh, we delivered against a target, then it's like, well, now what? What are we going to do next? In that vein, we're very ruthlessly focused on outcomes for our clients. The tools that we're building, the analytics that we're doing are around that.

Ikechi Okoronkwo (09:12):
There's a lot of cool stuff that I'll talk about in a second, but the point I want to make is we're not just doing it for analytics sake. We're not just building the next fancy model or cloud infrastructure just because we can. We're doing it based on an outcomes framework across the brand demand spectrum for clients. Let me give you some examples. You mentioned sort of like the legacy regression based approaches that are used, like marketing mix modeling.

Ikechi Okoronkwo (09:35):
Well, one way we've taken that further is when you build a marketing mix model, whether that's a Bayesian model or some sort of model that gives you a response curve looking historically at your media, non-media, and other factors, well, usually what happens is you get a presentation that sits on someone's desk with some ROIs wise and some due to charts and all that type of stuff.

Ikechi Okoronkwo (09:59):
What we've done is we've built a platform called Synapse, which is basically a scenario planning ecosystem where you can ingest those response curves into this tool, and then you can use that for forecasting and optimization purposes. Now, you can do that with a regular MMM. But where this really starts to shine is it can take on new inputs. As new data comes in, you're taking those response curves and you're basically forecasting to the future, and then you're validating how that forecast plays against actual sales.

Ikechi Okoronkwo (10:30):
And then if you have a disparity, if you have an error, now what we're doing is we're looking at the data on an ongoing basis to calibrate those coefficients or calibrate those curves. Now, that's a lot of work. That's deep analytics work. Now, the goal and where we are now is automating that process, right? That's just one thing that I can talk about. We have a lot of time, so I'll get into some other things.

Ari Kaplan (10:51):
Mm-hmm (affirmative).

Ikechi Okoronkwo (10:52):
What we're doing there is most clients in the space, especially in advertising, are doing sort of marketing mix modeling, and where we want to start is we want to become an extension of our clients as business partners. What we're saying is, okay, the first place we'll start or one of the things that we'll do is offer you things that enhance investments that they've already made, that our clients have already made. When they come to us, we're not saying, "Oh, we'll build you that model better and try and push out the vendor."

Ikechi Okoronkwo (11:18):
No. We're saying, "We'll take whatever you've got, whether you're doing it yourself, whether you're doing it with a third party, or whether you do it through us, it doesn't matter. We're here to operationalize analytics for you and make sure that it bleeds into the decision-making processes," like I mentioned earlier. That's one thing that we have, a scenario planning tool. Another thing that we're focused on, which I probably started with, is more on the data side.

Ikechi Okoronkwo (11:41):
When you think about AI, machine learning, really any type of data driven pursuit, you have to have clean data. It goes without saying, garbage in, garbage out. In the advertising space, in the ad tech and MarTech ecosystem, it's quite complex, as you're familiar with because you've worked in the space. We've built tools to help us with data governance and QA at scale in an automated fashion.

Ikechi Okoronkwo (12:07):
What that allows us to do is spend less time fixing problems that happen after the fact and making sure that before we even get to the point of activating using data, we put as many protocols in place to make sure that our taxonomies are correct, to make sure that the dashboards that people are seeing are as updated as possible. And then this is not necessarily a technical thing, but making sure that we have strong protocols from managing expectations with clients around when they can see certain types of data sets in our system.

Ikechi Okoronkwo (12:38):
We have a really strong practice around that from a data operations standpoint. Our chief data strategy officer, our global chief data strategy officer Shane McAndrew, that was one of the things that he pushed a lot when we did our reorg is making sure that that layer, that foundation of data was really strong. So that way, every type of application we built on top of that is going to be more scalable, reliable, and something that helps our internal teams do their jobs better.

Ikechi Okoronkwo (13:03):
That's another thing that we've spent a lot of time on. The last example I want to give is in the machine learning space, which overlaps with the relationship that we have with DataRobot. We've been doing a lot of different things. We've built a lot of homegrown tools in the machine learning space to answer different questions around helping to prioritize KPIs, around building models and forecasting in ways that are not linear regression, that are not one dimensional.

Ikechi Okoronkwo (13:27):
The whole point about that is really scaling what we can do from a media effectiveness standpoint to help our teams work better. When you think about analytics, remember when I mentioned people just jump to, "Oh, these are the people who do reporting." You can see based on everything that I'm talking about, all the things that we're doing and not just about sitting at the back end of the process. We're all the way even in the beginning.

Ikechi Okoronkwo (13:49):
When we get the brief to when we execute media, to when we're doing measurement, to when we're looking at the strategy afterwards, and the resulting feedback loop, we're involved in that entire value chain. That's really been ever since I came to MIndshare, as I started off in advanced analytics.

Ikechi Okoronkwo (14:05):
And once I started to take on that larger role, the first thing that I kind of said to myself what I need to do is make sure that, number one, reset the value proposition of analytics, but we make sure we embed ourselves in the organization a lot more, instead of kind of complaining and saying, "Oh, they don't listen to us analysts. We give them our reports. Do they really listen?" No. Well, what you need to do is really add value. Come to them and tell them, "Okay, what are you doing? How can data make this task better?

Ikechi Okoronkwo (14:32):
Or how can data make this conversation better?" We've been on a campaign to do that and that's where all these tools have really grown and all these services have really grown and are embedded into what we do a lot better because of that focus on the operating model. It's not about the ego of the analyst or of the data practitioner. It is about the service that we provide to the organization, which ultimately leads to client needs and client growth.

Ari Kaplan (15:10):
Yeah, that's a great background and overview. I think one thing perhaps our audience may want to hear more of is the vast amount of data that's being collected on consumers. There's like panel based data, digital data, client data, point of sale data. What type of data is being collected? What's kind of on the edge now? What are you hoping to collect that you're not yet?

Ikechi Okoronkwo (15:33):
Yeah. To kind of answer that question, that's a hot topic in the space right now, data, data privacy, what are we collecting and not collecting. I'm very proud of GroupM because they've led in this space specifically around who should own the data. We strongly believe that clients should own their own data. We're not in the business of aggregating as much data as possible, because that in itself is a futile pursuit if you don't have a resulting outcome that you're looking to drive.

Ikechi Okoronkwo (16:04):
For us, it's not collect as much as we can, and then sort of just like do some analysis and hope for the best. No. It really starts with those qualitative conversations, those consultative conversations with clients about why they're talking to us. In our industry, most everything starts with a brief, right? The client either wants you to come in and help them with a specific comms objective, or they're just looking for you to manage their overall comms across different channels.

Ikechi Okoronkwo (16:31):
What we're doing is we're talking to them and we're basically saying, "All right, let's create something like an outcomes framework across the different strategic levers that influence your business." And then from there, what we're doing is we're saying, "Okay, so within that, what are the success KPIs that we're going to be looking at?" Right?

Ikechi Okoronkwo (16:48):
And then from there, now we can start to talk about what type of data we're going to need to serve as either fast moving or slow moving indicators to help us track performance against those outcomes, against those strategic levers. And at that point, that's when the conversation around what data we need to collect starts, right? Obviously we have infrastructure in place to collect different types of data. We have methodologies and how certain types of data are fused into the decision-making process.

Ikechi Okoronkwo (17:17):
But I just kind of really wanted to highlight that point. It's not a question of how much more data can we collect to be better at our job. It's really more about like, what outcomes are we looking to drive, and how can we be an extension of our clients as business partners?

Ikechi Okoronkwo (17:31):
One thing I wanted to kind of highlight is when you think about the different types of data you can collect, for example, you mentioned panel data, we have media exhaust from the different platforms where we're buying, placing media, et cetera. In the industry, especially over the last couple of years, it's focused on collecting as much signal in terms of people's behaviors, right? What they're clicking on, different sites that they're going to. But there's more to that behavior than some of those what we would call rational signals, right?

Ikechi Okoronkwo (18:02):
That's why we're investing a lot of technology and time and really building platforms to look at different things, to look at attitudinal pieces of data. For example, one type of data is survey-based data, but then that is self-reported. What we're looking are some of those unconscious or subconscious reactions to either creative or to different types of formats, or even just moments of receptivity when people are viewing our ads. We're delving into things such as neurological studies.

Ikechi Okoronkwo (18:34):
We have a neuro lab within Mindshare where we actually hook people up to different type of nodes, and we're looking at their emotional valence. We're looking at different types of reactions in terms of different things that we're looking to test. And that allows us to kind of build this framework where we're looking at emotions, as well as accuracy, right? One of the things that we call that is the precisely human framework.

Ikechi Okoronkwo (18:57):
Obviously the rational triggers are important because that allows us to build models in a scalable way to drive decision-making around optimization and budget setting and all that good stuff and activation. But we want to make sure that we are not being biased in the types of things that we're looking at. To your point, there's a lot of other data that we can collect, but it's not just about kind of like opening up the vacuum cleaner and hosing it in. It really needs to be fueled through a very well thought out outcomes framework that is developed with the client.

Ikechi Okoronkwo (19:30):
So that way we're pulling in the right pieces of data. And for the things that we don't need, we don't even talk about.

Ari Kaplan (19:34):
It's super fascinating. I think it was about five or six years ago when I was at Nielsen, they acquired a company that had a neuro lab. I'm sure it's progressed a lot since then. Sometimes it didn't really work well. Other times it did. It was really like combining that data with other others and understanding when it worked and what questions you're trying to answer. But how do you view the neuroscience as like an additional data feed? Does it work well in your perspective?

Ikechi Okoronkwo (20:02):
Yeah. I mean, there's kind of two ways to answer that question. The first way I would answer that question is the pursuit of looking at different things and testing to see how the work is a worthwhile pursuit, right? For example, to your point of how things work well or not, there's a process of experimental design that happens or experimental test and learn where we're collecting this information and we're trying different things to see how it influences decision-making and how correct it is.

Ikechi Okoronkwo (20:30):
But the second way I would answer that question is absolutely it's very predictive. We've actually been doing a lot of projects. I don't necessarily run the neural lab, but I partner with them to look at the data and build insights. We're thinking of ways of scaling that and embedding that more into things that we're doing on the brand side of measurement, on the commerce side with advanced analytics. All of that is kind of in the workshop at the moment. What we're seeing is those things are very highly predictive.

Ikechi Okoronkwo (20:55):
Once merged with other things, this is where machine learning comes in, we're not just sort of guessing, or we're not just saying, "Oh, I think that's how it works." We're using the numbers to lead us in the right direction, right? And then we're doing this thing where, as you may know, like holdout tests, right? We build something. We develop some sort of mathematical representation of what we believe based on what we measured. And then we test it against the data. And if it holds true, then at least that's an aha moment.

Ikechi Okoronkwo (21:22):
But we don't just run with that and say, "We found it. We found the holy grail. Everyone go home and we'll just got to keep cranking the machine." No. It's a contact sport. Sometimes I feel like I tend to speak in cliches, but bear with me. But analytics is a contact sport. Data is something that needs to be engaged and sort of massaged and revisited. There are things that are going to work. There are things that we're going to find that we think are true that aren't, but that's the game.

Ikechi Okoronkwo (21:48):
That's what makes it fun, and that's what makes it a worthwhile an important part of the strategy of any organization. If you're just using your gut and your experience, the old noggin, that can only get you so far. That's very important, right? Sort of an institutional understanding of a space is invaluable, right? But the data is a great equalizer, because what that allows you to do is allows you to challenge your own beliefs.

Ikechi Okoronkwo (22:16):
And in our world that is becoming increasingly platform-based, increasingly digital, there's more pieces of information available to us to understand what's happening, rather than sort of just kind of relying on benchmarks, right, or what was done before, kind of copy and paste from last year when things were going up. And then if things are going down, we're like, "Well, somebody must have screwed something up." No. Maybe consumer behavior shifted. Maybe your audience has gotten older. Whatever that may be.

Ikechi Okoronkwo (22:42):
Maybe their emotional states due to exogenous factors are now changing. That's where, for example, neuro data will help you to be able to find something. Find something and say, "Oh, wait, all of these rational triggers are heading in this direction. But when we're looking at this, it's pointing in a different direction. Maybe that's the source of the delta that we're seeing or the difference that we're seeing in what we expected." It is fun, and it's also, I think, it's scary, right?

Ikechi Okoronkwo (23:10):
Because for example, when things aren't working, everyone sort of gets into a frenzy, but that's why it's important to have these infrastructures in place to allow you to investigate in a more rational, in a much more standardized way, rather than the days of old where we're like, "All right, let's get the consultants in here to kind of like figure it out," right. So yeah, that's what I would say.

Ari Kaplan (23:32):
As part of this process, you ever see a clear way to tell when it's the data that's wrong versus like a shift in the ecosystem?

Ikechi Okoronkwo (23:42):
What I would answer that, I would say it's not always clear. I would answer the question by saying that the frameworks that you built up front to understand the levers that you can pull and the resulting data allow you to diagnose those things a lot faster and in a much more methodical way. Sometimes the answer is clear. Like for example, I'll give you an example, somebody was talking to me about, for example, their CPMs and why are they going up.

Ikechi Okoronkwo (24:10):
Well, could it be because they're overcharging us, or it's maybe because we need to bring more people into the funnel. Well, maybe you're conquesting because due to the life cycle of your product, you're conquesting your competitive brands and that's naturally just going to be more expensive. Very quickly, I can just look at a chart and all of a sudden put my finger on it. But then that's where bias comes in, because now I can do that. Now I've developed hypothesis. Now what I do is I go to the data to either disprove or prove what I think is happening.

Ikechi Okoronkwo (24:43):
And that standardized process allows me to get to that point quicker. I'm not making sort of that gut reaction. I could be right. And then to my point, someone who's experienced in this space can just point out what you think would be happening. But I think having those frameworks in place, those measurement frameworks in place, allows you to be a little bit better with that. Now, this is where more technological approaches has come in. Because from the description I just gave you, you can see that it's a very human driven process, right?

Ikechi Okoronkwo (25:12):
There's an analyst. There's a strategist who's making those decisions. The more that we can implement tools that allow human beings to do less work in that deductive process, the better we will be, the faster we will be, and the more accurate we will be, right? That's where we're trying to invest in tools and processes and platforms to not necessarily replace humans from the process. Sometimes when people talk about AI and machine learning, the way they talk about it is, oh, it's going to just do what humans do.

Ikechi Okoronkwo (25:47):
And in some situations, that could be somewhat correct. But I think the more interesting use of it is helping us just be better and helping us to fill in gaps where if I'm pressing a button, maybe I don't need to press that button, or maybe I don't need to go look at a chart and point. Maybe there's an algorithmic or automated way that can identify those different options, automatically test them, and then I can look at that, right?

Ikechi Okoronkwo (26:10):
I'm already going two to three steps into the process, rather than before I would have to go in and look at a dashboard, and then develop the hypothesis. That's time. And as we all know, time is money. And especially even in our space, time to values is really important. Because when you launch a campaign immediately, every one's like, where are my insights? Come on. Where are my insights?

Ikechi Okoronkwo (26:32):
The more that we can do things to help to shorten that gap and, to your point, make better decisions and be more sure of those decisions of you using a data-driven approach, you can put protocols in place to validate what the data's telling you over time. Like I mentioned, doing holdout periods and all that type of stuff to retrain your model and all that type of stuff. The more we can do that the more we can do that at scale, it's just going to make our industry that much more fun, that much more efficient, that much quicker with doing what we do.

Ikechi Okoronkwo (27:02):
And I think everyone can agree that we would like that. We're not trying to take ourselves out of business. We're trying to up our game.

Ari Kaplan (27:18):
One of the big trends I see and we see at DataRobot is going from what you said, where you have humans, you have artificial intelligence, and it's not necessarily replacing everything. It's replacing the boring, repetitive aspects of humanity. And then everyone ends up collaborating. Augmented intelligence, in your case, is the partnership with a creative folks and your group. A couple things. How do you win over creatives saying adding in, slipping in data driven approaches will help everything?

Ari Kaplan (27:51):
And then once they start getting involved, how does the testing feedback that you do get weaved into that whole creative process so everyone gets better?

Ikechi Okoronkwo (28:01):
Yeah, it's a good question. When anyone figures that out, the please call me because that's something that we're navigating every single day. Well, from my experience, and again, this is my opinion and my experience, other people may have something that works better, but I've always found that the way to build congruence in any situation, and this is not even just talking about creatives or financial industry or any industry, I've found that ways to build congruence is to make sure that you establish shared values across all stakeholders in whatever decision is being made.

Ikechi Okoronkwo (28:34):
Let me explain what I mean by that. If I'm trying to make a decision with you and do you think we should use method A, I think we should use method C, we're going to have that conversation. We're going to have conflict, right? We're going to go back and forth. That's where, for example, a creative in our business would say, "Well, it's all about the creative. All this fancy analytics and algorithms and incrementality testing, it's not about that. It's a compelling ad that gets people emotional and they go and buy widget A." Right?

Ikechi Okoronkwo (29:04):
But then the data person will say, "Well, you, you may be right, but we can quantify our physical universe. We've been doing it for the last I don't know how many years, and we know that it works. And that's a more rational sort of look at it." I'm not saying that creatives aren't rational. My point is people lead with different parts of their brain or different parts of the experience, and that's totally fine. That diversity of thought breeds excellence. However, if we are focusing on the method and not the goal, well, that's where you have issues.

Ikechi Okoronkwo (29:33):
But if we align on our shared values or shared goals and going back to, for example, what I mentioned, the outcomes framework, we ask the creatives, "Okay, what are you being KPI'd on? What are the KPIs that you're being evaluated on?" Well, guess what? If we do X, Y, and Z, we can help you to not only understand attribution and incrementality against those KPIs to the best of our knowledge, while being very candid about the margins of error, right?

Ikechi Okoronkwo (30:00):
I think one of the biggest issues that we have with analytics, at least that I've noticed that is a big pet peeve of mine, is when analysts have this air of superiority and they come in and say, "Oh yeah, we have all these fancy tools that's going to solve everything. AI is going to save everybody. Just everyone sit down. Let's take it from here." No. That's wrong, right? If you come in, you just say, "This is how I can help you do your job better. And in turn, help you deliver better outcomes for your client. Would you like that?"

Ikechi Okoronkwo (30:26):
Yeah, of course. All right, great. This is where in the value chain of how we're going to talk to the client, this is what... You do what you do. I do what I do. And when we have a culture of testing and experimentation, if I come back to you with something that says, for example, the creative isn't where working as well or something like that, that's not bad. Don't get mad at me. That's just a way for you to understand what... It's a trigger for you to investigate. What do you mean it's not working? What part of it's not working?

Ikechi Okoronkwo (30:53):
Is it the creative, or is it the way we're delivering the message, right? Is it the right ad format? Are we working with the right partners? And that there is a way for us to create stickiness with the client. That transparency in how we do that is what's going to make the client trust you more. They don't feel that you're just coming in to defend your awesome creative or your awesome strategy. That conversation, as you can imagine, we haven't done any math yet. We haven't done any models yet. We haven't even talked about that.

Ikechi Okoronkwo (31:18):
It's just that upfront qualitative sort of strategic conversations that are invaluable, and that's how we're able to bridge the gap. That's how we're able to coexist so well. Because people would think, oh, you have the artists over here, then you have the egghead over here. Yeah, they're not going to get along. But it's like, no, we're all trying to do the same thing. And if that happens, in my opinion, in my experience, I've seen that great things happen.

Ikechi Okoronkwo (31:43):
Going back to the outcomes framework, another analogy I would use is playing off the same sheet of music. The whole orchestra, all of us have the notes. This person's playing that instrument. This person's playing that instrument. That person's playing a different instrument, but we're all playing the same song, singing the same song. The client is also involved in that. There isn't a gap between what we're trying to do and how we deliver it to the client. In that scenario, for different conversations... And I've seen it play out really well, right?

Ikechi Okoronkwo (32:13):
And sometimes it can almost be like theater when you're going and presenting to a client. Not because you're putting on an act, but it's like a dance, right? You're talking about, okay, this campaign that we did, the creatives come and talk about the strategy, and they talk about the data that they used to inform the strategy. I always beam, I'm smiling like a child when that stuff happens in meetings, because it goes back to the point I made earlier in the podcast, right? An analyst is not just a data person.

Ikechi Okoronkwo (32:39):
An analyst can be anywhere. When creatives and planners and strategists and investment folks are talking like analysts, you've hit the holy grail, right? It's not like, oh, you do this and I do that. No. You kind of play off of each other. And then once it gets into the weeds, the little beads of sweats start to come up, then jump in and start to talk about some of the data driven stuff, which you don't lead with, right? Because no one really cares about some of that detail. They just care about how you're using it.

Ikechi Okoronkwo (33:06):
Again, insights to drive actions. That's what they care about. That was my very long-winded way of answering your question. To be honest with you, and I know I joked about it, that's the job at hand, but not everyone has had the experiences at least that I've had, and I've had great mentors and people who I've worked with who've taught me some of these things. When I build my teams up, one of the first things that I talk to them about is put your egos to the side, put your methodologies and what you know to the side, and you're talking to people.

Ikechi Okoronkwo (33:38):
When you're talking to other folks within the agency, make sure that you're understanding what they need and have several conversations before you even start to talk about what you're going to do to make that process better. And then that way, you're building that congruence against the shared goal. When everyone breaks and goes and does what they do and they come back together, it's done in harmony, rather than with friction. Friction's good. I mean, conflict is good. I love it when people are like, "Oh yeah, your model is wrong."

Ikechi Okoronkwo (34:03):
And I'm like, "Well, let's see." Or I'm like, "Oh, your creative is bad," and you're like, "No, it's not. I'll tell you." It's a playful, valuable conversation, which breeds better outcomes for our clients.

Ari Kaplan (34:15):
Yeah. Ikechi, your analogies are music to my ears.

Ikechi Okoronkwo (34:20):
Considering the analogy.

Ari Kaplan (34:24):
You had brought up shared values and bias, which is really fascinating in this mold. I understand you have a tool called the Data Ethics Compass and also like to weave bias, ethics, transparency. Can you talk the importance of this and how ethics bias play into your decisions?

Ikechi Okoronkwo (34:44):
Absolutely. That is an initiative that I would say is at the WPP, GroupM level. It's not necessarily a Mindshare thing. But this is where folks from within the agencies are involved in this project to make sure that... Remember I talked about clients should own their own data. We're only taking what we need. But the reality is also, we need to make sure that we're doing two things. And again, I may not do justice to the Data Ethics Compass.

Ikechi Okoronkwo (35:09):
There are people who come in for pitches and talk about this at length, but I'm aware of it and it bleeds into the work that I do. I'll do my best to kind of describe it. But when you think of whatever data that we're using to make decisions, what we're basically saying is bias... And this is, again, my opinion, which is also informed by the way we talk about it. Bias in itself is not necessarily a bad thing, right? Bias is just a natural phenomenon and a natural phenomenon that humans experience, as well as systems, because systems are built by humans.

Ikechi Okoronkwo (35:43):
Based on the type of data that you pull into an ecosystem, it will bias the results and it may not necessarily be intentional. But I think where the problem comes around is if you are aware of this problem and you do nothing about it, or you intentionally see some of the decisions that are made and choose to turn a blind eye. That's where things go awry.

Ikechi Okoronkwo (36:04):
The Data Ethics Compass are basically a set of tools or an overarching tool where basically we're able to score data and be able to look at the different things that we're pulling in and be able to, number one, test, okay, what is the source, where it's coming it from? What is it being used for? And then looking at the resulting decisions that are being made and looking for triggers that allow us to be able to say, "You know what? The way this was collected is a little bit sketchy.

Ikechi Okoronkwo (36:28):
The data that we're getting from here when we're looking at the results that we're seeing from this data being used, it leads us in a certain direction." It's one of two things. Let's investigate further. Let's have some conversations. Let's make sure that we understand that we're not jumping to conclusions. But if we get to a point where we believe that something is not worthwhile for a client use case, then we have to come to the client and basically say, "Look, based on the data and based on the investigations that we've done, we advise you in one direction or the other."

Ikechi Okoronkwo (36:56):
Right? This is compliment to our toolkit to help us to make better decisions. To build on that, and this is more specific to Mindshare, but within the GroupM Network, we really all have pretty strong, shared values, but Mindshare specifically, we talk about good growth, right? And good growth can mean a lot of things, but I'll give you my take on it.

Ikechi Okoronkwo (37:17):
And that's something that I love within Mindshare, because if you talk to different leaders, they'll give you a slightly different description, but it all is saying the same thing, where we're looking to drive outcomes for clients. We're looking to make more money for them. We're looking to expand. But the reality is, we have a responsibility to our clients, as well as to culture and our larger social connection, social kind of network to make sure that that growth that we're building is not having unintended consequences, especially ones that are negative.

Ikechi Okoronkwo (37:50):
This is not about saying one client is good and one client is bad. That's not what we're talking about. What we're talking about is when we do what we do on behalf of clients, how are we hold holding ourselves accountable and also helping clients hold themselves accountable? For example, we have a tool that my team and a bunch of other teams within Mindshare have worked on where we have a carbon calculator. We're looking at the carbon impact of the campaigns that we're doing and some of the tools that we're using to support those campaigns.

Ikechi Okoronkwo (38:18):
We go back to the outcomes framework, right? Let's just talk a little bit from a math perspective. Let's say you have different KPIs that you're looking to drive, with the carbon impact being one of them. Then what you can say is, "Listen, if we do scenario one, this is the resulting impact on sales and your brand lifts. If we do two, et cetera, three, et cetera. But then when we also look at the different mixes, here's the carbon impact." It gives the clients more options to make decisions. We're not going to sort of beat people over the head with it.

Ikechi Okoronkwo (38:47):
We're not trying to say we're better than anyone. All we're seeing is we are self-aware and we will do everything within our power to use our tools, use the great people within our organization to make better decisions to drive sustainable growth that we can repeat and feel good about. If you talk to anyone who's really trying to build a good business, yes, short-term ROI is great, but long, sustainable ROI is better. We all want to be able to build things, but also be proud of ourselves in the process.

Ikechi Okoronkwo (39:18):
In today's world, there's so many issues from a variety of different perspectives that now we can't claim ignorance about. When we think about the Data Ethics Compass, when we think about good growth and impact on the ecosystem and thinking of climate change and all that stuff, is we can't claim ignorance and we want to lead. We're actually gleeful in doing that. We want to be at the edge of having these conversations, and we don't want to just follow. We want to lead, and we want to influence that.

Ikechi Okoronkwo (39:46):
Because again, we're a company and we want to show the DNA of the people that we work with. These are the things that come up. We're not passive in that regard. That behavioral and those personalities that exist, it comes to bearing the tools. As you've done your research and you know about the Data Ethics Compass, the type of people we hire, the type of mentality that we promote translates into the tools that we build. I think that's great.

Ari Kaplan (40:20):
Some of the things you've been saying that I'm really drawn to are when you say it's okay to have friction and it's okay to have bias. Some of the topics I should also mention, congratulations! You won Adweek's Media All-Star 2020. I think I can see why now one. But one of the topics I've seen you write about is provocation on purpose. I love that title, but can you elaborate on that?

Ikechi Okoronkwo (40:45):
Absolutely. One of the things that I think is really important, I've learned this over time, especially just in my life and in my professional career, if you have intentionality, you usually tend to have better outcomes. When I say better outcomes, I don't necessarily mean everything's going to go fine. What I mean is you'll just be able to be more at peace with your surroundings and also be able to achieve the things that you want to achieve in a much better way, right?

Ikechi Okoronkwo (41:16):
You may not always win, but then each loss is... When you have an intentional framework around what you're doing, even a loss is a teaching moment and an ability for you to say, "Oh yeah, yeah, I should not have done it that way. I'm really upset in this moment. But now I have more information to come back to bat and do it better next time." And that goes in when we talk about even from an analytical standpoint, that's the scientific method, right? Test, observe. Test, observe. Just keep going. It never stops.

Ikechi Okoronkwo (41:42):
When we think about provocation with purpose, it's really taking that intentionality one step further. When we think about what we're doing in... Let's say we have a task with a client, or we have a tool that we want to build. I don't believe, and I work around other people who some are like-minded, some think differently, but I don't believe that you can have anything great without some sort of conflict. Sometimes these words like competition, conflict, they have negative connotations, but they're not negative things in themselves.

Ikechi Okoronkwo (42:10):
What I mean by that is when you provoke, when you stimulate something and you have an intentional sort of goal that you're getting at, if that goal is positive in nature, then that provocation is not negative. For example, as I mentioned the example I was giving you before, you have one way to do something, I have one way to do something. If we were both like, "Oh no, you go. No, no, no, no, you go. No, you do it," we'll never get anywhere.

Ikechi Okoronkwo (42:36):
But if I'm like, "All right. Well, we're trying to get to point X. I believe that my way will work because of X, Y, and Z." And then you're like, "Oh, I think my way will work because of A, B and C." And then it's like, "All right, let's show our work. Let's kind of dig into it and let's challenge each other." That is magic and that's where the music is made. That's where the magic is made, and that's how you get to good places. How does that come to life in a practical way? For example, when a client gives you a brief, right?

Ikechi Okoronkwo (43:06):
Most people would think, "All right, everyone. Let's just go down the list of the 10 things the client said and just check the box." Right? That's the way we think about it and that's not the way I think about it. Challenge the brief. "Hey, client, you said you want to do this, but does it make sense based on this other thing that you said, or does it even make sense based on where culture is going?" That's what we're there for. The client's hiring you to help them see things in 3D, in 4D, maybe not in the 2D way that they were looking at it.

Ikechi Okoronkwo (43:35):
If you just give them 2D answers, you're not helping them. Provocation with purpose, if I have to summarize, is just the intentional manifestation of making sure that everything we do has a specific goal in place. We're establishing that. And then we're creating that conflict and we're creating that sort of diversity of thought, that intentional way of sort of pulling out different techniques.

Ikechi Okoronkwo (43:59):
Even thinking from an analytical standpoint, don't build one model, build three models that potentially could give you three different answers with different types of data. And guess what? The answer is somewhere in between there, right? And that's fine. Somebody would say, "I looked at this report and I saw X. And I looked at this report and I saw Y. So which one's wrong?" It's like, no, that's not the way to look at the world, right? There's many different ways you can approach a problem. Provocation with purpose bleeds into.

Ikechi Okoronkwo (44:26):
It can be a professional pursuit in the way we just do our work. It can be a mantra with which to have interpersonal communication and understand that conflict and debate is an intellectual pursuit. It's good in nature. Because kind of the last point I want to make is people who are really leaders, whether they're leading people in an organization, whether it's in like a sports field, or whether it's whatever you're having in life, if you're not bumping up against the walls and testing and challenging yourself, you're not really growing.

Ikechi Okoronkwo (44:56):
We have the terminology growing pains. Well, that's the point. You can't grow without some sort of conflict. That provocation with purposes, it's a cliche term, but it's a mantra to live by that really has some universal truths that I think are really meaningful.

Ari Kaplan (45:11):
Very fascinating and very well said. One of your other interests is experiential learning. I wanted to hear your thoughts on that and your views on mentorship and the team that you're growing or have grown at Mindshare and how you elevate all of them.

Ikechi Okoronkwo (45:26):
Yeah. When I think about what I love my most about the work I do, yes, I'm an analyst by trade. I'm a quant. I like numbers. I like to solve problems. But I think the thing that has given me the most job satisfaction and value is being able to watch other people grow in terms of coming in and having let's say their skillsets at 70%, and then watching them get to 80, 90, 100%, 110, and further, right? That for me is something that has given me a lot of job satisfaction. I started off with a small team.

Ikechi Okoronkwo (46:05):
I used to be an independent contributor, building my own models, working by myself, and I realized that, number one, I enjoyed my job better when I started working with other people and sort of became that multiplier effect of teaching rather than just doing. And I also realized that it gave me positive outcomes with regards to my professional growth in terms of trajectory. That's something that I just dove head first into.

Ikechi Okoronkwo (46:28):
When I think about experiential learning, mentorship, these are all things that I didn't have figured out before, and I've just seen how they've worked in different parts of my career. Let's talk about experiential learning to start right? Thinking about my team, I can do a training where I sit in front of some folks and tell them, "All right, this is how I did what I did," and sort of give them keys to success or whatever, or I could just throw them in, right? I could throw them in, and then I could give them some psychological safety to say, "Listen, mistakes are fine.

Ikechi Okoronkwo (47:01):
Jump in. Figure it out. I'm here." I could take one of two approaches. I could be a mentor, or I could be like a coach, right? If I'm a coach, I'm sort of asking them questions, wait, you sure you want to take that turn? Why are you taking that a turn? Does it make sense? Asking them to interrogate their work, whatever, whatever. And then a mentor is a little bit more instructive where you're sort of like, okay, in my experience, go this way or try that, right? I wouldn't say one is wrong.

Ikechi Okoronkwo (47:27):
I think it's a balance that you strike when you have a team and you're trying to build a team, because some people respond to a coaching method or some people respond to a mentor method or another method, right? It's that continuous sort of evaluation of not just your approach, but also the people around you. That's experiential learning. It's something that you benefit from and something that other people benefit from.

Ikechi Okoronkwo (47:51):
But then there's another way to look at experiential learning, which is when you think of something... When you're thinking of something like when you're trying to recruit people into an organization, you can do an interview where you ask them questions, or you could throw them into a situation which mirrors things that they will be doing in your organization. And that will pull things out of them that will give you much more information maybe in the same period of time than like a normal interview or a conversation would.

Ikechi Okoronkwo (48:23):
That's one thing that I've observed. It has implications in the professional realm, as well as for academics. Kind of going back to the way I got into GroupM, it was through a case competition. I would call that experiential learning, right? Somebody said this to me before, "Well, it wasn't really part of your academic experience." And I would argue, I'm like, no, it was probably the biggest part of my academic experience, because what happened there never happened in the classroom, right?

Ikechi Okoronkwo (48:52):
The reason why it didn't happen in the classroom was for a variety of reasons, right? Number one, the stakes were higher. It was a situation where there were jobs on the line. It was time bound. I had a short period of time to come up with a team obviously, but I took a leadership role in that process. But with a team, come up with the right solution and go and present, the beads of sweat were dripping my forehead, but go and present in a scenario that I was just not used to. That is experiential learning, right?

Ikechi Okoronkwo (49:16):
I can go into a whole thing about different types of experiential learning, but it manifests itself in so many different places from an academic standpoint and even in the workplace. Because sometimes, you always hear this, oh, I'm not putting you in that situation because you don't have the experience doing it. It's like, what? How am I going to learn? How am I going to learn? I am constantly throwing my team curve balls, but again, with intentionality. I'm not doing it just because it's expedient. I'm not doing it to like play games with them.

Ikechi Okoronkwo (49:44):
I'm doing it because I personally believe in that philosophy of, I'm going to throw you in, and you might screw it up, but you know what? When you come back, I'll laugh. I'll be like, "All right, how was that? It was tough, wasn't it? All right, let's talk about what you did really well, and let's talk about the things that you didn't do really well." That's experiential learning. That methodology is something that I've tried to use as I've scaled up my team, talking through, when I first started at Mindshare, probably had a team of like four or five.

Ikechi Okoronkwo (50:12):
Now I have a team of 150 across different cities in the US, in different countries around the world. People sometimes ask me, how do you manage all of that? I'm like, I don't. My team manages it, right? And they're like, well, how did they... Well, no, because I intentionally tried to put them in the situations. It was very tensed sometimes. But I think that without doing that, you can't really grow. You can't stretch. Kind of going back to what I love most about what I do, I'm so proud of where my team has come, and I don't see it as my success.

Ikechi Okoronkwo (50:46):
I see it as a success of other people and that's my success. I had a friend ask me, "Would you want to become an independent contributor, do some R$D, build some tools? I'm like, I mean, yeah, I miss that. I miss being hands-on. Not more than I love what I'm doing now in terms of scaling out the team that I'm working with. Kind of thinking about what my vision is for the next couple of years is I want to build a really world class data science organization, an analytics organization globally.

Ikechi Okoronkwo (51:12):
I want to scale what we're doing to as many corners of the globe as possible. And again, what we're doing with our team here, I'm not saying that we are better or our approach is better than everyone else's. My whole point is, like I said, it's a learning experience. I want to go out there, find new problems, but scale the good things that we've seen, the best practices that we've seen, and learn more in that pursuit as well.

Ikechi Okoronkwo (51:36):
Because I feel like if you're not learning, if you're not scared, if you're not a little bit confused every now and then, then you're probably in the wrong place, right? That was a long-winded way of talking through I'm very passionate about experiential learning, very passionate about mentorship, very passionate about the concept of leadership theory. That's part of how I paid for grad school. I actually taught a leadership theory class at Pace University, and we also started something called the Setters Leadership Program.

Ikechi Okoronkwo (51:59):
A lot of the things that you hear me talk about, I sometimes sound like a pastor, like a preacher or something, throwing out these universal truths, but that experience really taught me about the intention of evaluating everything that you're doing. When I'm managing folks, I'm not just like their manager. I see that as almost like a very sacred task, because that's someone's life and that's someone's profession. I really want to help them to be the best that they can be.

Ikechi Okoronkwo (52:25):
It's not completely altruistic, because I've seen that the more I do that, the more it helps me, the more I grow. Everybody wins, right? We can talk for hours about that and I can go in many different directions, but it is something I'm very passionate about. I think it's important that I'm a data professional. We can talk about the tech space. We can talk about that for hours. But I think that if you don't master this, then all that to fall short and sometimes can take you into wrong turns, as we've seen from different things in the zeitgeist, right?

Ikechi Okoronkwo (52:57):
I think that there's a responsibility to understand that we're working with people, our clients are people, the consumers are people. Once you have that mentality with the way you grow your teams, with the way you deliver your work, with the way you or whatever, I do think that you'll have better outcomes if that intentionality has some sort of positive intent in there.

Ari Kaplan (53:25):
This is More Intelligent Tomorrow, where our listeners are fascinated on what our guests think about where is humanity going to be in five or 10 or even more years. Do you have any predictions or things you'd like to see?

Ikechi Okoronkwo (53:41):
I can maybe give a prediction, which is maybe part hope when we think of AI and machine learning. Going back to what I mentioned earlier, when people think of AI, it's this foreboding sort of subject where we're inching towards a singularity where we're going to create digital or artificial consciousness that's smarter than humans. And sometimes I wonder, I'm like, well, we're the one's building it. The more that we can have more ethical and thoughtful approaches to building out that future, I think that we might be okay.

Ikechi Okoronkwo (54:15):
I mean, I don't know, but that's something that I think that will proliferate more. I do think it's going to proliferate more, because when I say that will proliferate more, I think AI and machine learning. As you probably know, people have thrown around the term AI a lot. The way I look at it, the way I personally am looking at AI is around the workforce, how we can do better work as organizations, as organisms when we think... An organization is somewhat an organism.

Ikechi Okoronkwo (54:41):
Even with the partnership we have a DataRobot, it wasn't just about, oh, how do we get the next best thing? No. It was more about, how can we be better at what we are doing? What can we do to get better at completing task A, B, and C? And then how can we be better at linking task A, B, and C? And then can we even just not do task A, B, and C and put it off to the side and start working on X, Y, and Z, which needs more of a human touch? That's something that I see will grow more. I mean, the world's becoming increasingly platform-based, increasingly digital.

Ikechi Okoronkwo (55:12):
When you think about some of the platforms that exist today they're bigger than countries, right? It can be scary. I think it's an opportunity. I think it's an opportunity for us to interact with each other. Technology's not going away. I'm not sure which Industrial Revolution this is, but I think it's the fourth one. I think it's really exciting. You think back and you read through the history books of the things that we've been able to do since we discovered some of these technologies. I think we're at that precipice right now and I want to be part of that.

Ikechi Okoronkwo (55:40):
In terms of predicting or thinking about where things will go, I think that the use of these tools and the respect for what they can do for organizations will grow for sure. When I think of having a competitive edge, I don't think companies will be able to sit on the sidelines anymore. You can't do that anymore when the world is increasingly platform-based and digital, and this data is available to people. Human beings are not going to be as fast to do some of the things that we're going to need to do to operate and compete in that universe, right?

Ikechi Okoronkwo (56:13):
And again, I want to implore people, and again, this could be my optimism, I don't think t's like a Black Mirror type of thing where we're going to be in this dystopian future where everything is automated. I think that if you're concerned about that, then help shape that, help build that. For me, personally, I've jumped in. I'm all in. Thinking about Mindshare from a professional standpoint, that's how I'm approaching things. I'm thinking of how can we use these technologies to make humans lives better, to make our work better.

Ikechi Okoronkwo (56:42):
One of the things that we preach a lot within our organization, and I say preach in a good way, we mean it, is work-life balance, providing our teams with the right tools. Well, one of the ways to do that is not just giving people more vacation days, make their job experience better, right? If they're completing a certain task, how can that task be set up in a way where the person's experience at work is more pleasurable, is at a higher bar?

Ikechi Okoronkwo (57:07):
That way they have better job satisfaction from the time that they're putting in to the stress that they're having, reducing that, and then also giving them work that they can be really proud of, doing really cool stuff that they weren't able to do yesterday. I remember when I started modeling back when I was an analyst, I was coding in SaaS, right? And like writing code and running models for 20 minutes. That's not the situation that we're in today. Three to five to 10 years from now, it's going to be vastly different.

Ikechi Okoronkwo (57:35):
I want to be part of shaping that. I think that if I can predict where we're going, that is going to play a much bigger role in the companies of tomorrow, for people who embrace this and embrace it in a meaningful way, right? Not just like sign on just because everyone else is doing it, but embed into the DNA of the organization and resource it, invest in it, and then use it in an ethical way. I think that I can see that growing. I can see it having a lot of positive effects on humanity.

Ikechi Okoronkwo (58:05):
Just speaking about Mindshare, this is just going to have positive effects on our team and the work that we can do for clients. That's a win for me and that's a win for us. That's a win for our clients. I'm really excited about the space. I'm really excited about what we can do here. I think DataRobot is a leader in that space. I have my eyes on you guys, and I'm watching what you're doing. I'm really happy to have this partnership with you and sort of continue to build from here.

Ari Kaplan (58:30):
Well, Ikechi, this has been great. Thank you so much for all your excitement and incredible insights and embracing all of the topics that have come up. Appreciate having you on.

Ikechi Okoronkwo (58:40):
This was fun. Thank you for having me, Ari.