What does it mean to be a data-driven organization and how do we get there?
We're live in Brooklyn, New York, at the site of the Formula E Race with Tiger Tyagarajan,
who is the CEO of Genpact.
Hey, Tiger.
How are you?
Michael, great to see you.
Thank you.
Tiger, what does it mean to a data-driven organization?
What is the outcome you're trying to deliver?
I think the Envision Virgin Racing outcome is, we want to win the race.
You work backward from the outcome.
Then you say, in order to deliver that outcome, what are the various decisions that are being
taken every second?
Let me take that to the business world.
If you are lending to a business and I'm a bank, then I want to make sure that I lend
to the best customers at the best prices and I win continuously versus competitors and
I delight the customer.
Which means, I want to do it fast.
I want to do it quick.
I want to take the least risk.
I want to reduce losses.
I want to get the maximum price.
You keep working backward.
That's built on a lot of decisions that are being taken, which is a combination of prediction
and judgment.
More and more, machines are getting very good at prediction, which means you have to have
the data platform aligned.
Where is it all going to come from?
They all come in different formats.
How do you bring that together?
Some of it is real-time.
I'll give you one example in the commercial lending space.
Typically, when people lend, they look at FICO scores, credit scores of all of us.
Once you finish the lending and, a year later, you are still a customer and you repay whatever
you borrowed, and you enter a 30-day default.
You have not paid your bill.
You get a call from someone in the bank.
They look at the profile of the customer and they say, "Oh, the person's credit rating
is X." Guess what that data is.
It's a year-old data, which is completely useless.
In reality, businesses should be finding a way to get credit data every day about a person
and then decide, in the call with me, what to do with me based on my credit in today's
situation, not yesterday's situation.
What are the largest challenges that you see your clients facing when it comes to setting
up the right problems to be solved with data?
The real problem is identifying the big problems to solve, so the three or four situations
that they end up in.
One, a lot of them want to boil the ocean.
I want to solve everything.
That doesn't work.
The second is, I want to solve the easiest thing.
It doesn't generate much value, in which case, what's the point?
Companies lose attention.
People lose traction.
The best opportunity is to really look at a set of real problems that are worth solving,
identify the cost versus the benefit, and then pick very few—the lesser the better
to begin with—because then you can drive change.
You can bring the data together.
You can get traction at the organization.
When you declare victory, you actually have delivered enough impact for everyone to feel
excited and get to the next journey.
There's an organizational maturity dimension to this as well.
Absolutely.
Yes.
A company needs to gain experience with these types of problems.
Yes.
Yes, and that experience also helps in one other thing, which becomes a big obstacle
in all of these, which is, let's go back to the decision-making framework that I laid
out.
We humans love making decisions.
I'm the expert, so I'm the guy who makes the decisions.
You're going to take that away and take parts of it, not all of it, parts of it to a machine?
No.
I'm going to resist that change until my last day at work.
How you drive me to change my behavior to actually allow me to give up that machine-driven
prediction in order for my job to actually become better because now I can use all my
time and judgment.
Let's go back to the risks.
The engineering team is feeding, "Hey, driver, you should do this.
Sam, do this."
Ultimately, the decision is taken by Sam.
Sam decides what he wants to do on the racetrack.
That never goes away from the driver.
The same here.
That shouldn't go away from the human.
As we finish up, what advice do you have for organizations to make this transition to becoming
data-driven organizations with all of the change issues that you were just describing?
Yeah, I'll start by saying a belief that change has to come from the top.
A believe that we have to use data to drive prediction in order to get better at serving
our customers, serving our employees, and serving our shareholders, stakeholders, and
investors.
Once that gets driven down, then identifying the initial waves of, call them, projects
and initiatives.
All that is top-down.
The second thing is, how do you get the troops and excitement in the troops because everyone
has to be engaged.
Let them percolate ideas.
The third is, they all need to get engaged with retraining and reskilling.
Understand the domain and context.
Understand the data.
They must be able to speak the same language, which is a continuous reskilling program.
Tiger, we spoke earlier about why it's important for companies to become a data-driven organization.
What are some of the challenges that companies face?
I'll start by saying that you have the traditional way businesses have run, and they run very
successfully.
That is the single biggest challenge that you start with because the moment you say,
"We're going to use data to take decisions," you are going against what has been successful
so far.
That's one.
The second is, what's going to happen to me?
I am the person who used to take these decisions.
I am the expert.
People come to me.
You're saying it's some data that's going to do it.
What is that?
If I have power, then I prevent that from happening.
I challenge that.
I prevent change from happening.
That's the biggest thing or biggest challenge.
Okay.
When companies interfere with what I think is actually the natural lifecycle of technology
adoption, which is, technology moves forward, the implications are, they will be disrupted.
Yes.
What is the antidote to that?
What should companies do to not fall into that trap?
It starts with leadership that builds a vision that creates the excitement as well as the—I
want to call it—fear of being left behind but the excitement of going ahead, of leveraging
data, building, using it for decisions and science and so on, and cascading that across
a core group in the company so that people start.
The second thing is making it very positive.
If the discussion is, it's all negative, that it's all going to be about your job, my job,
then it becomes a negative.
It has to be a positive saying, "I'm going to capture more market.
I'm going to become bigger.
You're going to become bigger.
There are going to be more of you," then that creates a positive momentum.
You need to establish some set of positive goals but, at the same time, identify a core
group of people that you can take on board with you.
Right.
Right, as any change initiative in large enterprises, boiling the ocean doesn't get you anywhere.
Maybe set the vision at the broad level, create the excitement at the broad level, and then
pick the one or two places where you start driving that.
The way you pick those are maximum impact.
Leaders are brought in, leaders of our champions, or leaders who are influencers of other people.
Oh, John got into this and John is driving this?
I must do something because I know John is kind of the favorite in the company.
He's a rock star in the company.
What final advice do you have on making all this happen?
I'll start by saying that it has to ultimately start with, what are the outcomes they're
trying to deliver?
I guess the outcome has to be around an outcome to the shareholders, an outcome to clients,
an outcome to employees, an outcome to society, and making sure that you start from there
rather than starting from, where can we use this technology?
The hammer searching for the nail is a bad way to go about it.
Start from the outcome.
Then figure out the best place to really create a big impact that actually other people can
look at and say, "I want to follow that.
I want to be as big," et cetera.
How do you choose the right project?
So many companies say, "Oh, we'll do an AI project," or a data science project.
It's not going to change their business.
Yeah.
Yeah, so, Michael, we are learning as we have done this with a large number of customers.
There is a new learning that we are beginning to develop.
That learning is going after the big game-changing move.
While it's ultimately the most interesting and right thing to do, it may not be the fight
first thing to do.
If you want quick wins, and you want people to say, "Wow, this is pretty cool and not
that difficult," then it may be better to grab smaller things that drive.
Small improvements that actually don't take away my job and start that.
Then, layer on top of that.
The good news is that all of these technologies, the second time is a shot to drive improvements.
You can watch the improvement deliver results pretty quickly.
The cost involved is not that much.
By the way, you'll have failures.
If it fails, pull the plug and move on to the next one.
You have a portfolio of these.
Start with the easy ones, small ones.
Create wins and go to the big ones.
What's the purpose of starting with small ones?
Quick wins.
Show success fast.
People lose patience and fatigue.
The faster you show success, people say, "Yeah, this works."
The bets are smaller, to begin with.
You're not trying to boil the ocean.
There's another big one.
AI, machine learning, et cetera are still new use cases and new sciences.
There is a lot of experimentation.
When you experiment, by definition, there's going to be failures.
Big visions that then lead to big failures, unfortunately, will lead to, "I'm not going
to try this anymore," which is tragic.
Starting small, having some wins that then ensures future success is, I think, a better
way to go.
Eventually, we all are reading about the hype that AI is going to change our company.
Yes.
How do we get to that point or is it just hype?
No, no, it's not hype at all.
It is not hype at all.
We've got enough examples in the pharma industry with some of the patient safety areas, some
of the areas around safety in engines, in preventative maintenance, in lending and the
speed of lending and so on.
Enough examples of complete game-changing moves.
All I would say, Michael, is to create the vision for the game-changing move and then
say, okay, we all bought into the vision but we will start here and prove the case.
Let's put all our energies on this, which is smaller, more contained, and can deliver
results faster.
Then layer on top of that.
Layering and what I call version one, version two, version three, version four.
Ten versions in a year is not a bad idea.
You need to have the clarity of having the vision of where we're going and what's possible.
Yes.
The north star is important.
Why are we doing this, in the long run, is important.
The size of the prize being so big is important.
The fact that this is game-changing is important.
All I'm saying is, having agreed on that, I think jumping wholeheartedly into the big
bang may not be the right one.
CDs or small banks may be better.
What can companies learn from your experience here with Envision Virgin Racing?
I would say three things that I would point out.
Number one, getting talent that is diverse in the way they think, in the skills they
bring, in the language they initially use, which is very different.
In our instance, often it'll be domain, process, and data versus algorithms, software, and
so on.
Then, finding a way to make those teams work well together, which means it's leadership.
That's one.
The second would be, how do you create an ecosystem which is not just the team itself
but the team connects to other teams outside—some inside the company, some outside the company—and
driving that ecosystem?
The third is, how do you get the vision to be big enough to change the game and really
go after changing the game?
But do it by first saying, I'm going to play faster, better at the current game and show
that it works.
You're operating on both these levels.
On the one hand, you need this very broad, expansive vision.
But at the same time, you need a game plan that is incremental here, incremental there,
small changes.
Yeah.
Yeah, and high-frequency change.
The high-frequency change is a key.
Yeah.
These are not large ERP, SAP projects that takes two years and, after two years, you
realize, "Oh, how do we make this work."
These are, okay, 90-day sprint, the next 60-day sprint, another 90-day sprint….
Finally, as we finish up, what about the notion of culture change for organizations to adopt
this willingness to experiment?
Oh, no, Michael.
You've just nailed it with that question itself and the word "culture."
In the end, if you were to force me to says, what's the one thing that is the ultimate
secret sauce, it's culture.
How do you get the culture to embrace this?
How do you get the culture to actually become multilingual, bilingual?
How do you get the teams to work together?
How do you get the AI expert to be listened to by the risk expert rather than the risk
expert saying, "But you don't know underwriting.
I've done it for 20 years.
Don't tell me how to do this," and vice versa?
How do you get the AI expert to say, "Move out.
My algorithm is going to work."
No, your algorithm is not going to work in my business.
Understand my business.
How do you create that culture?
How do you create the culture of, failure is okay; let's learn and move on?
How do you create the culture of, the technology is not the big thing here?
It's actually, what outcome are we trying to deliver?
Sometimes, cool technology guys are searching for the next place to use their next new toy,
so it's all about culture so, therefore, it all starts from the top.
Tiger, thank you so much.
Thank you so much, Michael.
Thank you.
A pleasure talking to you.
Likewise.