OBSTACLES TO BECOMING DATA-DRIVEN

In-Ear Insights: Obstacles to Becoming Data-Driven

In this week’s In-Ear Insights, Katie and Chris tackle a common challenge in organizations wanting to adapt a data-driven marketing methodology: stakeholders who don’t make data-driven decisions. Learn why this is the case and what you can do to help transition those stakeholders to more of a data-driven point of view.

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In-Ear Insights: Obstacles to Becoming Data-Driven

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about the attitudes and obstacles that we run into when trying to help people become more data driven.

One of the biggest data driven companies or theoretically data driven companies based on their academic publications, they’re open sourcing of certain types of software, their technical research is the company Netflix, which I think probably does not need a whole lot of explanation as to who they are.

And in some background reading I was doing I came across an article from then Chief Content Officer now CEO Ted Serrano’s back in 2018 when they were talking about how they use data to make decisions about for example, which shows to keep which shows to get rid of, and surrender said it a statement that baffled me.

He said, while Dean is a tool for them, their various projection models, the cost analyses do not dictate their decisions.

You said it’s 70%, gut and 30% data Saranda said most of the time it’s enforced informed hunches and intuition data either either reinforces your worst notion, or just supports what you want to do either way.

And the first thing that popped into my mind when I read this quote was, that’s not how that works.

Data is not there to justify the decision you already made that is supposed to help you make the decision in the first place.

Okay? Well, we’re talking about change management, and helping and helping organizations make better decisions.

This attitude is not unique to Netflix, it’s shocking, that comes from a company that is ostensibly so data driven, but this is not unique to them.

How do we encourage people past this point of view of data reinforces the decisions I’ve already made two, let’s use data to actually make the decision.

Katie Robbert 1:52

Well, there’s well, so there’s a couple of things, you’re right, this is not a unique point of view.

And I think more often than not, data is used to back up something that somebody already wants to do, or they look at the data and ignore it, because it doesn’t align with what they want to do.

And so what we’re talking about is being proactive versus being reactive.

And so a data driven organization is proactive, versus what Netflix is using their data for in this instance.

And that’s reactive.

And so by that, I mean, you put the data first, or you put the data last.

And so this person has already made their decisions.

And so then they’re just sort of like, looking at the data go, did I make the right decision, it doesn’t matter, I’ve already made my decision versus let me go ahead and start with the data before I make any decisions.

And that’s the, that’s going to be the hardest thing to change in terms of a culture like, so you have one individual person who might struggle with, well, I know the weather says it’s going to be a blizzard, and it’s going to rain, but I really want to go out today.

So I’m gonna go out anyway, regardless of what the data is telling me, or I’ve, you know, I’m working really hard to, you know, maintain my health.

And it looks like I’ve hit my calorie quota for the day.

But you know what, I really want that cookie.

So I’m gonna do it anyway.

And so we as humans, would need to overhaul our thinking in terms of what the data represents to us.

Is it just something that’s there? And annoying? Or is it something we actually use.

And so you’re talking about a couple of different types of people, when you bring that into a larger scenario where you have not just one person, but a whole organization of people, that you’re trying to get them to put the data first and not just make decisions, and then use the data later.

Like, that’s a huge cultural shift.

And so this is just me sort of outlining the nuts and bolts of why it’s so difficult because we as humans are not necessarily hardwired to put the data first, we work on instinct and wants and needs, and that we might check the data later, or the data didn’t exist for us.

And now we’re just sort of catching up to the data that exists around these things that we’re trying to make decisions on.

So all of that being said, in this instance, where we’re talking about trying to get a company, a person, a team, to put the data first.

We need to help them understand why they need to be doing that.

So like what’s in it for me, as an individual, if I put the data first, how does that make my life easier, make my life harder? You know, what do I get out of it, the sort of the selfish part of it.

And so, you know, helping them understand why putting the data first is a better option than not, you know, and it’s going to, it’s going to come with some uphill battles of well, it’s going to take longer for You’d make a decision, but it will be a better decision.

And that’s where it’s going to be the crux of the conversation is, right, but you just said, it’s going to take longer, and I can make decision without the data.

Christopher Penn 5:12

So I guess, your your two basic Levers is, here’s how this will improve your profitability or whatever.

And I think the other one that perhaps folks who are in the trenches from a data, you know, you know, data profession, don’t think about as much but could be used for building alliances within an organization is risk reduction.

Real simple example, CFC employees, employee a, employee B, they both perform the same job, they both have the same KPIs and their KPIs are comparable.

If you like employee a more than employee b, a b, employee a as a man, maybe employee B is a woman, and you pay employee a 11%.

More from a data perspective, they perform the same job of creating the same output, and therefore they should have the same compensation from a human emotion input.

You like employ a more maybe, you know, you’re it’s there’s a bias that you have in your judgment.

From a risk reduction perspective, I think you could make the case that yeah, the data suggests that you have to pay these two employees identically for identical work.

And if you don’t do that, you are at risk of lawsuits and all sorts of other things.

And at that point, it’s not just the data person being able to say, Hey, this is a problem, but it can work with HR and compliance and other departments within company and legal to say, we have a problem, and we need to become data driven to avert this problem.

If we don’t, these are the consequences.

Katie Robbert 6:49

But what if, what if those, what if you don’t know what those consequences would be, so let’s say I’m a, you know, marketing specialist, marketing analysts, whatever you want to call me, and I’m being asked to set up a social media campaign.

You know, instead of checking, you know, where my audience is, or how much I should be spending, I just go ahead and do it, I put it on the wrong channel, and it doesn’t perform, you know, it’s in in my head.

worst possible outcome is like, I’ve wasted my time, maybe a little bit of money.

So that to me is not a big enough risk, to say, let me go ahead and put the data first, I’m just gonna keep doing things until something works, how do you counteract that.

Christopher Penn 7:40

And the it, and that particular instance, you need some comparable supporting data, so you have your account.

And then you have, say, three other competitor accounts, peer competitors, and say, like, Look, your numbers are not even close to the competitors, your competitors are clocking to 7% engagement rate, you’re clocking a 1% engagement, you are objectively doing a worse job with with the tactics with the tools, the processes that you are using right now, you’re objectively just doing a worse job.

And we need you to get to parity with our competitors.

It’s so what’s your plan to get to parity with our competitors? And yeah, you can let that person flounder for a while, say, Hey, how about let’s try some data.

Katie Robbert 8:34

But it’s interesting, because you’re talking about it from the perspective of the manager talking to the team member.

And, you know, so if you’re telling me like, you know, we’re not doing as well as our competitors, what’s your plan to get there? You know, you’re still not convincing me, the person who needs to change their attitude, that I need to be using data, like you’re just yelling at me, basically, you’re just telling me I’m not doing a great job.

And so the likelihood is that I’m just going to quit and go find someone who’s not gonna yell at me.

So that’s not helping me change my attitude about putting the data first.

Christopher Penn 9:10

Right, but we got rid of you out of the organization.

So that was when

Katie Robbert 9:14

this is why you don’t manage the people.

Exactly.

Yeah, no, I mean, I think honestly, like that.

And I think that that’s a very common scenario, when people are trying to, quote unquote, encourage their team members to look at the data is they’re approaching it from a place of frustration.

They’re approaching it from a place of underperformance.

And so that’s not very motivating.

And so, you know, to sort of put it into a context that a lot of people can understand, like, let’s say, you know, you have certain fitness goals and you’ve been working with a trainer, and you show up to your training session and the trainer says You didn’t hit the mark, I have six other clients this week who have put in the work and you have not put in the work this week.

So you’re not going to, you know, reach your goals.

So what are you going to do about it? Well, I’m going to sit on the floor and cry, because you just yelled at me.

That’s not very motivating.

And I’m probably going to fire you as a trainer, you know? And so you have to think about the opposite like, Okay, what’s going on that you didn’t look at the plan that I set out for you what’s going on? That you didn’t check your stats every single day? And so it’s not like a, for lack of a better term, it’s both carrot and stick? It is yeah, it is carrot and stick because you need when you’re working with people who need to put data first, and they’re not putting data first, you need to understand why.

And so are they impulsive? Are they you know, just want to get things done? Do they have a fear of like not getting things done, or slowed down or like, whatever the thing is, so you have to dig into a little bit of psychology.

And so that then becomes the carrot, once you understand the thing that’s going to motivate them, you know, well, you know, if you can hit these numbers, then you’re likely to hit your own personal goals of getting a promotion within the next 12 months, whatever the thing is, and so you need to understand that piece of it.

And then from the organization standpoint, they need to understand where they fit in, you know, to the whole puzzle.

And so, if you do your job, this over here does their job, and this team does their job, and then we all get a big ass bonus at the end of the year.

And so helping them understand where they fit into the Jenga tower.

Christopher Penn 11:42

Okay, so in the example, we started with one of the and this requires a lot more background, or anybody one of the personality characteristics that prevents that individual from being data driven appears to be flat out arrogance, like I know better.

Hey, I’m the CEO, I know better.

And that’s a situation where there isn’t, if you are you don’t have any supervisor, you have a you have no role authority, you may have relationship with RTB, you have absolutely no role power to change that person’s behavior, because literally everybody reports to so in that situation, what do you do?

Katie Robbert 12:26

Well, you wait for the company to go under.

Christopher Penn 12:29

So update your LinkedIn profile, our new free Trust Insights, LinkedIn course at trust insights.ai/linkedin course,

Katie Robbert 12:37

which is a real thing.

So go check.

I really know.

But in all honesty, I mean, that’s where, you know, you’re playing the longer game.

And so let’s say, you know, Chris, I keep making impulsive decisions, because I think I know best based on some random article I read that aligns with what I think is my point of view.

And so therefore, I’m like, Oh, well, this article says, I don’t need to, you know, lead with data, I just need to lead with my really great gut instinct.

So I’m just gonna go ahead and do that.

Because I’m the CEO.

And I can I guarantee people who are listening to this have heard this conversation before in their own personal experience.

And so your job, Chris, is not to keep telling me.

blatantly, Hey, Katie, you’re wrong.

Because you already know, I’m not going to hear it.

I’m not going to hear the words, you’re wrong.

I’m going to hear, okay, you’re just being a problem.

So Chris, I need to figure out how to get rid of you.

The approach there is then to keep presenting information be like, Hey, I found this, you know, information that shows if we did it this way, you know, we would get better results.

And you you, you know, narcissistic, arrogant Jackass would look so much better.

There’s so much psychology involved with all of this.

And I think that that’s the hard part for people who are purely data driven, or want to be data driven, is understanding the human psychological side of it is that in order to get the basic black and white, you know, the data says do X, the amount of psychology that goes into it, to get more than just yourself on board is a big effort.

Christopher Penn 14:23

And psychology is not something that data driven folks are generally given a whole lot of training on, if any, I mean, psychologists tell us something people in general and

Katie Robbert 14:31

I’m just gonna say like, in general, we’re not trained unless that’s, you know, what you go to school for?

Christopher Penn 14:35

Exactly.

So, for someone, maybe someone like me, who’s a numbers nerd, where do we start to learn that? How do we start to learn that particularly if we ourselves are not all that self aware?

Katie Robbert 14:51

That’s where having a really good associate a partner, you know, community to You know, maybe lean on for your deficits.

And so Chris, this is one of the reasons why you and I work so well together is, I understand and can do the psychology aspect of it.

But I also respect the data aspect aspect of it.

And you can respect that there needs to be a psychology piece of it.

And you can present the data.

And so bringing those two pieces together.

And so this is where, you know, when we talk about goal setting, or we talk about, you know, communities, or really anything like, there is rarely an instance, in your personal professional life, where you do or where you are doing something solo, you are leaning on other people to fill in the gaps that you yourself, you know, need support with.

And so, in your example, you know, what do you do with someone who’s more data than psychology, find someone who can fill in those gaps of the psychology, you know, a trusted person and associate.

So maybe you’re working on a marketing team, maybe you’re working on a data analytics team and epidemiology, tt, ITT, whatever.

Maybe look outside your discipline, and say, hey, you know, I recognize that you’re really good at talking to customers and understanding what they need.

Do you think maybe you could help me? I’m working on this project, and I need to convince the CEO, you know, that we need to be putting the data first, what advice would you give me to convince them pretend they’re a customer, pretend they’re this like, work with someone who has those strengths, instead of just trying to like muddle through and figure it out on your own, and it might be outside of your team.

It might even be outside of your organization.

Christopher Penn 16:44

That reminds me of a, a life hack that I heard a long time ago, from Sue jaunty was often the VC space.

She said, if you ask for what you want, you’re gonna get advice.

If you ask for advice, you’re gonna get advice.

But you’re also probably going to get what you want.

Which is because you essentially, you make that person an ally, by helping them feel like they’re an expert, or they’re in a position of power dominance.

And as such, it’s a way to sort of sneak in, like we were talking about Katie, sneak in helping a person think is their idea, like, oh, yeah, you know, where you want them to go, like, I need you to make this decision.

But hey, how, what’s your advice about getting to this, and you know, the person does that.

And then suddenly, they’re like, oh, and you should do the thing that we really want them to do to begin with?

Katie Robbert 17:39

Well, and I want to be clear, when we talk about, you know, the psychology of it, I’m not talking about manipulating people, you know, like, don’t just like, Don’t straight up, start manipulating people for to you to get what you want.

That’s self serving, it’s really more of just understanding what motivates them.

So that you can meet them where they’re at.

And so if I’m someone who needs to feel like I’m right all the time, your job isn’t to unpack that with me and like, course, correct and get a whatever your job is to give me what I need to do my job.

And so it’s, you know, you’re not truly playing psychologist, you’re just understanding the psychology.

And so I just want to put that disclaimer on there that your job is not to start to try to, like, you know, dig into people’s heads and like, fix their childhood traumas, like you’re trying to get a job done.

And so by understanding the motivations of individuals that you need to get decisions from, you can then start to speak their language and meet them where they’re at, because that’s what they’re going to hear and respond to, versus you saying, we need to use the data, we just need to use the data.

That’s not going to get you very far.

And it’s a waste of everybody’s time.

And so, you know, you’re absolutely right.

So, you know, by putting people in that position of, well, you’re the subject matter expert, you’re really just playing into their ego.

It’s not a manipulation, because you’re not trying to ultimately change who they are.

You’re not trying you don’t have you shouldn’t have, you know, a nefarious agenda, you know, just like change the course of the whole business.

Yeah, again, Chris not why it’s why you’re not allowed to do this.

Christopher Penn 19:27

No, that makes sense.

And figuring out those motivations, in some ways.

It’s, it’s, it’s the two different kinds of data we’ve been talking about forever, right? You have quantitative data, which is the numbers and the numbers that use to set goals and know that you’ve achieved things and it’s the qualitative data, which is the the emotions, the the intangibles.

And if you’re, if you’re someone who’s more like me, who focuses very heavily on the quantitative data, we do forget that qualitative data exists.

We do forget that we need to align the quantitative data with the qualitative things that are going to make people feel better or motivate them to, to take action in directions that we know the business needs to go from so who,

Katie Robbert 20:24

so let’s say I wasn’t, you know, in the picture, Chris, and you were tasked with, you know, getting someone to put the data first, after this conversation, you know, what, what plan which you start to put together?

Christopher Penn 20:40

The first thing would be at, and this is, you know, the whole volunteer movement thing that I’ve been doing on the side is very much about that, like, what is the motivation of the stakeholder? What, what is it that motivates them? What are they held accountable for? And what is the what are the underlying factors that go into it? So, in a publicly traded company, yeah, the earnings per share is a key metric, right? But the earnings per share number is not super motivating, right? It just basically means you’re making a whole bunch of other people rich.

So what are the personality traits of those stakeholders? Are they arrogant? Like, do they need fame? Are they greedy? Do they need money? You know, they motivate money? Are they had an aesthetic? Are they? Are they motivated by sensuality and things like that? Are they fear driven? I know they do.

Are they motivated by by loss aversion.

And depending on the personality type, that can better help I put together a plan, say, Okay, here’s what we here’s the decision that we need them to make.

Here are the things that are inherent to them in a qualitative perspective that are blocking that, you know, this person is an arrogant person.

And so the data is not going to matter to them, you can say whatever you want in data language, and they will not care.

What are they motivated by a little bit motivated by status, they want to be seen as, as experts in the industry, they want to be seen as leaders in their industry.

Okay.

How do we get to that? Well, if they were able to showcase just how, how smart their, their machine learning infrastructure is, and how it’s better than their peers, then they get to make those bold claims.

Okay, well, if that’s the case, then how do we illustrate to them? Okay, well, to do that, to get to that fame, you need to use the data that machine learning infrastructure is spitting out.

And so there’s sort of a chain of events of, here’s the motivation, he has the underlying factors in that, here’s what we have available to us.

And then can we structure the story that we’re telling them because data, data is despite what people say data doesn’t tell a story, people tell stories to other people with data.

And so we have to figure out okay, well, how do we take this information conveyed in a story that resonates back in a wild closed does reading on Neuro Linguistic Programming, because some of these manipulation techniques, frankly, but one of the things I liked about what they said was everyone’s mind is like a doorway, and all the doors, different shapes, different angles, and stuff like that.

And the information you have to convey is in the form of a mattress, the mattress is literally going to go through the doorway a certain way, and everyone’s door is different.

So if you just fling the mattress, it’s only going to get through like a few doorways and most door is going to bounce it off, you have to take the time to say, okay, how can I turn the mattress to fit that specific doorway, and get the information in based on how that person is ready to receive it? And that’s essentially the plan that I would take if I didn’t have, you know, trusted advisors, and people like you to ask, what’s the approach for this first, how do we deal with this person.

Katie Robbert 23:54

And so it’s interesting, because, you know, we are working under the assumption that the person, the stakeholder is, you know, arrogant and out of touch, and, you know, all of those things, you know, I’m human, and I can certainly be those things.

But I would like to believe that that’s not my core personality.

And so, I would imagine, Chris, that the approach that you would take with me, would be different than the approach that you would take with someone who, you know, is more egotistical, for example, you know, and so, you know, it would just like, gonna, like 62nd sentence, like, let’s say, I wasn’t using the data and you needed to convince me, like, just give me a quick elevator pitch of an approach that you would give to someone like me versus like, you know, someone who believes that you know, data is an afterthought.

Christopher Penn 24:48

So I arrogance is definitely not one of your core personality traits.

In in, in the case of someone like you, it’s almost You take it almost opposite approach of providing a sense of reassurance and a sense of comfort, a consensus security, like, Hey, I know that sometimes you question whether you’re making the right decision or not, right? Here’s some things that we’re not manipulating the data to, to fit a view of the world, we’re saying we’ve used data to validate free of bias, free of our own emotional investment, that, in fact, the decision you’re trying to make is the correct decision, or these are the three paths.

And here are the at a few of the results of what will happen with you, you have data that says this, this is the problem, welcome.

Your data says this, this is the problem outcome, the idea, it says this, this is the probable outcome and someone who wants to see the big picture, and have all the facts on the table before you make a decision, you could look at that go? Well, I know I don’t want that, because that’s a flaming dumpster.

That’s okay, that one looks reasonable.

So I’m gonna take the one that looks like reasonable balance of risk and reward.

Again, for somebody who wants that reassurance of making good decisions, and is more cautious by nature.

That’s the approach you take, you don’t, you know, ask us, it doesn’t matter, because it won’t resonate with that person, like, I, you’re not telling me anything, I don’t know.

Instead, say, Here’s the menu of three options, only three options, you know, good, better, best, however you want to phrase it.

And then let that person choose from the the the options, knowing that, from a data perspective, you’ve done the work to say, here’s the likely outcomes.

Katie Robbert 26:38

And so I bring that up, because there are different approaches that need to be taken.

And so you’ve just described two very different approaches to trying to convince someone to use the data to make the decision you have the person that is more risk averse, more cautious, really just needs that reassurance, that, you know, things aren’t gonna go sideways, if I make this decision, and then you have the person who’s like, my idea is the best idea ever, you know, I don’t need the data.

But really, they need the data.

And then there’s all the different versions of that in between.

And so you could have 100 different people, and 100 different approaches to how you need to help them and support them to use the data to make better decisions.

And so, you know, this is why, at least from where I sit, there is no one answer other than understanding the person that you’re trying to work with even just trying to meet them halfway.

And what’s really

Christopher Penn 27:39

uncomfortable for people who are very quantitative people who like me, is that they really just the two examples we’ve just outlined, you’re not using the data yet leading with the data, you are leading with a story that you constructed from the data, but it is your obligation as a data person to transform data into a story that someone else can digest.

And I hate that about human beings.

It’s a lot easier for me to work with machines, like here’s the data of machines, like got it.

This is one of the reason why we like things like shad GPT.

Here’s a bunch of data, turn it into a story.

Right? That’s when you think about like generative AI, that’s really what it’s doing.

And so it’s one of the things that’s so compelling to us, because human beings communicate primarily through stories we have ever since we, you know, crawled out of caves or dropped out of the trees or whatever, you know, figured out fire.

We’ve been telling stories around the campfire for millennia.

And so as data driven folks, we have to remember that, that at the end of the day, we are telling a story.

It’s just a data store we’ve constructed from data.

Katie Robbert 28:48

Whereas I am definitely the person who wants to sit around the campfire for hours telling stories.

Christopher Penn 28:54

And that’s a skill that data folks have to rediscover.

Katie Robbert 29:00

Oh, yeah, there’s definitely give and take on both sides of it.

You know, I need to, you know, it’s not that I don’t put the data first, but I like the story.

I like the action.

I like the insights.

And he like, the data.

And so that’s, again, why you and I work so well together, because it’s a nice complement of skills.

And so I guess the takeaway for people who are listening to this, as they are trying to figure out which side of the fence they sit on with this, it’s it’s okay to be one or the other.

Very rarely can people do both? Well, people can do both, but not as an expert level on both sides of the fence.

And so my advice would be to find someone you trust someone you can partner with, who complements those skills.

So if you’re the data person, find the storyteller.

If you’re the storyteller, find the data person.

And then that way, those two things together Are become an unstoppable force because then you can do all things.

You can convince people, Hey, here’s the data because you’re meeting your stakeholders, your customers, your decision makers, where they are with the story and the data.

Christopher Penn 30:13

Exactly right.

If you’ve got stories that you would like to share about data, pop on over to our free slack group, go to trust insights.ai/analytics.

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Thanks for tuning in, and we’ll talk to you next time.


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