Data Excellence: Strategies for Success with Zainulabedin Shah

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: Hey, everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest has transformed data and analytics practices into value creation centers of excellence by aligning global business strategy with data strategy.

with data strategy. with over 18 years experience in data strategy and analytics and over 12 years of experience in overall strategy. He has a master’s in business science from UT Austin. He has experience both at large companies and in the entrepreneurial space. [00:01:00] Please welcome former senior director of global analytics insights and data strategy at Herbalife, Zan Shah.

Welcome Zan.

Zainulabedin: Hi, Ryan. Thank you so much. Pleasure to be here.

Ryan: Thanks so much for coming on. I’m very excited to talk to you. So I’m going to start you off with the same question we start off everybody with, and that is what is one thing you wish more people knew about using data to make better decisions?

Zainulabedin: Sure. So I would say in my experience, it is thinking about data as a resource.

Ryan: Hmm.

Zainulabedin: Um, I think what you very often find, or what I’ve found in my career is people think of data in terms of its functionality, right? What can data do for me? Which makes sense because that’s, you know, typically people are in for, you know, how am I going to benefit from this thing?

And I equate it with, you know, water as a resource, right? Water is a resource. I think we all understand the value of it, right? As human beings in particular, we’re mostly made of water. [00:02:00] But typically when we think of water, we think of its functionality, right? I can use it to take a bath, to wash my car, for transport if I’m on a ship.

And I think that’s the jump. People are often struggling to make in their own mind in terms of what data is. Data is a resource which empowers organizations to do so much

and it enables so much functionality to create value for organizations and businesses. But I think that’s the thing I’ve not seen very often in

my career.

Ryan: Yeah, it, I think is this kind of transition from thinking about

it reactively to proactively, as opposed to how can this kind

of, you know, answer this one question that I

have to more of a, we have all

this information. How can it guide us to a better future?

Zainulabedin: Yeah, I totally agree. Right. It’s thinking about the

foundation of what you’re trying to achieve as an organization

and what’s the [00:03:00] role of data. And in today’s day and age, data is literally the lifeblood of everything any organization is

trying to do. Whether they’re already

kind of a

fully digital company or whether they’re a

traditional legacy company trying to transition into the digital

space.

Ryan: Hmm.

Zainulabedin: but I think to your point, what’s more common is we have a very specific and particular use

case. And so we need to bring data

into the org, uh, the ecosystem of

the organization. to facilitate that use case being delivered because the people making decisions about the business think that use case being executed is going to generate

business value.

Ryan: Yeah. You know, I I think as, as I hear you talk about

that, it, Kind of ring some bells with some conversations that I’ve had in the past around strategy And data strategy and kind of just using data. I’ve had a lot of conversations

with people, um, not so much on the podcast. Most of the folks on The podcast are pretty [00:04:00] turned on, you know, they’re leaders in data already.

Um, But I think one of the things that I find

is there’s this idea of, okay, well, We we know that moving into a more digital future is something

that’s going to benefit the business. There’s, there’s hints to that everywhere. And so there’s

this, I think, well intentioned drive to, okay, well, we’re going to build a data

strategy.

We’re going to figure out what does data mean to our organization and

we’re going to go do it and While I think that’s really well intentioned, one of the pitfalls that I see there is

sometimes Doing something data or

technically oriented really for its own sake, as opposed to thinking about what is the business’s strategy? What are the goals of this organization

and how can we use the resources, data, and the tools

that we use to interact with it as tools to help

us get there? So one of the things that I said in your introduction is that you’re pretty good at

figuring out how to [00:05:00] meld those two pieces of strategy. What is the

business’s overall strategy and then how is data strategy a part of that for someone who’s in that transition and

trying to kind of improve the maturity of how their organization uses and understands data?

What are some

recommendations for making sure that they’re aligned and they’re not spending time on projects

that are kind of cool and technical, but may not directly impact

their business goals?

Zainulabedin: Yeah, no, it’s funny. My kids ask me all the time, what do I actually do for a living? And this is it. This is the thing I actually do, right? So you’re right. Every

organization I worked for has had this gap. I’ll call it, um, between what are the business objectives strategically, what’s the business trying to accomplish.

And how do, You fish the data out to enable those initiatives? For me, I would say the, probably

the most important thing is creating prioritization at the top

of the organization. Right. Um, kind [00:06:00] of any endeavor, if there’s not buy in at the top, it’s going to be very hard to drive change throughout the organization. Right. So getting your, your

executives, your senior leaders on board with this idea of becoming a data informed organization, what’s the actual value of doing that? Right. And then starting down the path of prioritizing What are the businesses top one, two, three objectives for the year? I think that’s a very good way to start providing focus on now.

How do you start cultivating a business strategy, which aligns with the data strategy that you can execute. Right. Rather than having them in, you know, two siloed pieces, like all the technical people are talking about, well, here’s the architecture we can build based on the systems and based on the ingestion and the ETL and the blah, blah, blah.

And it’s like, that’s amazing technical work, but if you can’t drive it to the business piece and vice versa, the business wants to achieve an [00:07:00] objective. And they think moving into the digital age and driving data forward into the organization is going to help. But they may not have the data they need to actually answer the business questions they’re trying to answer to move forward.

So that’s the gap, right? It’s really about educating the senior business leaders and getting them on the same page in terms of what business priorities are. Then I think now you’re able to go back and start strategizing at a data level in terms of what work can you prioritize and execute.

Ryan: Yeah. I, I love that. I mean, I think that, you know, obviously for, for data professionals, it can feel, you know, like I’ve spent my whole career in data and As much as I still love to think of myself as fresh out of college, I, I guess, you know, I’m, I’m getting older every day, just as all of us do. But, you know, I, to me, it feels like, you know, data is this kind of large, expansive, longstanding field.

But if we compare it to, you know, something like, you know, finance and [00:08:00] accounting, right? Like that, you know, there are so many fields of, of business pieces of

the business puzzle that have, been around so much longer than and so I think that you’re absolutely right with this kind of Making sure that everybody on the team knows what types of capabilities are out there and then

connecting those to the business goals. you know, I liken it to something

like headcount, right? People on the team are a part of the puzzle, just the same as, you know,

a tool or some data infrastructure. And, Sometimes there’s this drive to be like, Hey, we’re going to shoot the moon, go like build

this massive data warehouse or do some sort of,

you know, unbelievably large data undertaking.

and if I liken that to something like headcount, it’s like, okay, well, you know, if you quadrupled the size of your business,

you’d probably be able to do all sorts of, cool things with all those people. But is that the best decision for your business? And people are

like, Oh my [00:09:00] gosh, like quadrupling our head count.

Like that’s not the best, you know? So it’s kind of the same thing, like one thing at a

time, incremental return. One of the things that I’ve also

found is because this is, you know, no matter what we’re talking about, there’s some

element that’s new. So if we’re talking about increasing an

organization’s data maturity or undertaking some sort of, you know, new initiative to try And help meet a business

goal, there’s going to be change management involved in that. And for me, some of the people elements have shown to be the ones that require the most care and attention. So when you talk about, okay, so we’ve

decided now. on what one of these objectives is going to be for the year. What advice can you give people for kind of managing the change, especially the change on the people

side of getting a data initiative done?

Zainulabedin: Yeah, that is a great question. I mean, to me, that’s the key to the [00:10:00] whole thing.

Right. For change management in general, but for data in particular, trying to inject data and analytics into the way an organization behaves.

Um, I would say there’s no real magic bullet. It just requires

time and patience and communication.

Um, so the way I think of it

is if a data

and analytics organization is sort of the kitchen of a

restaurant,

Ryan: Yeah.

Zainulabedin: most organizations are very adept

at kind of cooking up boilerplate. Right?

like short order stuff, foods going out, translate that into reports or your, your analysts are

the servers in the restaurant. They’re delivering that food. Your

stakeholders are the diners, the food’s good enough and they’re

getting full, so they think that these are the analytics we need,

but what they don’t understand is being

able to actually cultivate a menu. And pare down maybe the volume of food.

[00:11:00] So lower in quantity, higher in

quality, generates more value, right?

And so I think how that manifests itself

in a practical way is really

education. So probably the best example I could give you from my own

career was, um, we went through a transformation, right? I know it’s very, uh, it’s a very overused phrase, but that’s what it was, right? We

went through a business transformation that was really rooted in thinking more analytically about our

decisions.

Not to wipe out experience, right? But now you’re bringing the data analytics as another tool in

addition to business experience. And we spent probably six months with the senior people, the decision makers in the sales organization, to educate them on how to consume the new infrastructure and framework we were developing.

Right, so all new BI, all new reports, all new analysis that my team was developing, but we didn’t want to just throw it over the fence and let them figure it out. It was about really holding their hand and getting them to be able to be as [00:12:00] conversant in those analytics as my team was. Once we felt like they were there, then we felt like, okay, now we can push these out to the field.

Now you, as senior vice presidents can start holding your teams accountable, your

directors, your managers, all the way down to

sales producers, because you all understand what these

analytics charts, visualizations, reports, whatever you want to call them, are intended to drive in

terms of business behavior, right?

And I think that’s how we really became

more acculturated as an organization into becoming data informed.

Well, again, it started with the education piece. That just takes time, right? Even if people are willing to go through

the change, and it’s easy to influence those stakeholders to go through that

journey, it still takes time, right?

For anything new,

anyone going through any change, it requires time to kind of crawl, walk, run, and become proficient in the new skills that

you’ve learned.

Ryan: Yeah, I, I absolutely love that. I think that one of [00:13:00] the

struggles that I’ve experienced being a consultant is that

there’s kind of a very

specific way that consultants generally get used, right? There’s. Yeah. As compared to someone who is an internal resource at an organization.

and lots of times things on either end, right?

Like

whether that’s like gathering requirements or figuring, like having a

perfect picture of stakeholders or on the tail end, you know, how are we documenting? How are we

doing change management? How are we finding

people who are going to be great champions of this new infrastructure that’s been built and then investing

the time in them to make sure that they have

everything that they need. Lots of times the highest value to bring a consultant in is to do like the really hard technical part. And so I’m kind of jealous when I get to talk to people who get to do some of that stuff on either end, because it is, as you mentioned, just so impactful. You know, if we build something incredible, [00:14:00] but then somebody, Who’s consuming that report doesn’t intuitively understand what its intention was, or, you know, what’s the mental model that they need to use in order to approach, you know, whether it’s a report or a database or something like that to get the most value out of it, you know, the, the technical incredibleness of whatever it is that we built doesn’t get fully felt.

So. You know, you started the answer by saying, you know, I really think that that’s the most important piece. I couldn’t agree more. I think making sure that everybody has had the proper amount of, you know, knowledge and education transfer and change management is really, really critical in order to see the results and the better decisions that come out of whatever it is that you’ve built.

Zainulabedin: Yeah, I mean, that’s the key to the whole thing, right? I

I love a beautifully architected data infrastructure. If, if the people who for whom it’s intended to generate value aren’t extracting value from that architecture, then it’s a really

fancy tool no one can use. [00:15:00] And I don’t think that that benefits anybody.

Ryan: yeah, one thing that I love

to do with all of the guests that we have on here is

there’s obviously the common thread

of you know, technical competence and understanding the purpose of data and all of that But what’s really neat is how each

person that I’ve gotten to talk to gets to use those skills to impact

different areas of the business.

And so what I, I think the listeners get a lot of benefit out of

is, is kind of hearing some of those specific

use cases. And I know that one of the things that you’ve had experience with over your career is generating reports, um, particularly in a sales arena. So I’ve, I’ve also, uh, generated a lot of reports for sales.

My, my first job before I went into consulting involved a lot of that. And then obviously I’ve gotten to do quite a few consulting projects in that area. And I, I personally found that there were,

you know, Very, very, um, specific and unique, both [00:16:00] challenges and solution types that worked for sales. And I was wondering if you could share a little bit of your experience about how we can think about metrics and how we can think about providing the best and most useful information to drive better sales performance.

Zainulabedin: Yeah, absolutely. Um, so I would say first of all, it has to do with alignment, right? So, um, Whatever goals the CEO has, ideally, those are trickling down to the organization to connect directly to what a sales producer’s piece of that pie is, right? So, um, you know, in the last sales organization I was a part of where this really worked well.

The CEO had overall goals across product lines and a couple of other things. Um, then the chief sales officer, he had goals very much tied to sales performance. Then his regional SVPs had same goals tied to their region. And then that trickled down [00:17:00] by channel, by location, and then down to individual. So now it enables this conversation with the sales producer to, it makes it easier for them to understand that your production actually ties back to the company’s overall performance.

And you and I can be in the same office, Ryan, and we’re getting the same opportunity because I have the data, which shows. The same number of people are walking in or we’re generating the same number of quotes or whatever, but you’re killing it because your conversion rate higher, mine’s not. Now we can tie our numbers back to, again, the highest level of all of the organization.

So it enables an easier conversation. I think that’s the first part. The other part, which is very specific to sales is making sure that the incentive for the salespeople is actually driving the right behavior because you can have the right metrics in place, the quote unquote, right metrics. Thanks. But if it’s not aligned with the behavior, which is getting them paid, they’re going to figure out how to get paid, right?

Salespeople [00:18:00] are very adept, especially highly effective salespeople at knowing how to kill what they can eat, right? To put it bluntly, if the incentive and the compensation plan is not aligned with the organizational goals, it’s very difficult to have that sort of synergy from top to bottom across your organization.

Right? Because salespeople are going to focus on how do they make their paycheck bigger. And those incentives need to be tied back to the metrics that are driving overall organizational success. And it’s challenging, right? Because salespeople know their business better than anybody, obviously. And so to come in without an informed perspective and sitting down and talking with them and listening to them on what are actually the ways to measure your success consistently and effectively.

Which would tie back to measuring your performance and getting you paid. That is a gross error, right? It’s very key. I think to solicit input of salespeople doesn’t mean you’re going to do everything that they say, but to at least solicit their input and get [00:19:00] their perspective before carving out the incentive plan, then building metrics, which tie the two together.

Ryan: Yeah. I, I love that. I, I, I, you hit on all of the layers that, that I have found in my own experience. I think that one of the things that. is most unique about sales functions within at least most organizations, kind of the average across is that they tend to be one of the departments that has the highest percentage of variable compensation.

So, you know, obviously there are lots of different companies and lots of different comp strategies and there, there’s all sorts of stuff out there. But I think as a standard, there’s a whole lot of, you know, Limitless ceiling in the sales department where there may not be the same in other places. And so you talked, uh, you know, perfectly about what I think the, the key thing there is, and that is having this set of hierarchical goals.

And I [00:20:00] think That sales is actually a great lesson for the rest of the organization on this front. So there’s been a lot of research that’s come out. I won’t quote percentages cause I’ll get it wrong and I don’t want to misspeak, but the research is super clear that people feel materially more engaged in their job when they can directly connect the impact of their work to the organization’s goals.

You know, I, I’m, I’m sure that there are people all throughout the world who are, you know, Hey, I’m just here for a paycheck or whatever. But my experience has been that, that most people pick where they want to be and they’re there because they want to do their job and they want to see their company and their team succeed.

So the more that I can connect the work that I’m doing to the success of the organization, the more motivated and the more excited I am about doing that. And obviously [00:21:00] ensuring that someone’s pay is tied to that is important, but also very much ensuring that the reporting is tied to that is extremely important.

So one of the struggles that I think organizations have is how to go about Okay, hopefully the organization has some like high level goals. All right. We got like a one, three, five, you know, 10 year plan. Like we, we have a vision of where we’re going to go. What is some advice that you might give to an organization that’s trying to take that and then break that down?

You know, sales is one of those areas where it’s like, okay, well, if we need a million dollars and then we got two people, each of them get half. And then, you know, we, underneath them, there’s four people, they get, you know, a quarter. Other types of goals. What are some recommendations that you can provide for making sure that everyone’s swimming in the same direction and you have that nice clean roll up so that everyone can kind of feel their impact?

Zainulabedin: Yeah, I would say it [00:22:00] starts with, um, a shared accountability. Right. So, um, when I moved earlier in my career, I moved from analytics into more of an operations role as a data driven operator, right, but more of an operations role. And the team I inherited was very splintered because they had goals tied to their own individual functions.

Right. So meaning the finance manager was tied to finance goals. The marketing manager was tied to marketing goals. And as a team, we weren’t really tied to the goals of driving the product forward. Right. So the way that I like to operate is I’m an agile operator, right? That’s my leadership style. Um, I want to push the information to the people who have pushed the authority to the people who have the information.

And so listening to the teams to tell me what could they actually commit to executing. And then how having the conversations, okay, that aligns or doesn’t align with what we’re trying to achieve [00:23:00] organizationally.

Again, that takes time, right? It’s not something you just flip a switch and it happens magically.

Um, when I was kind of trained on agile, I was told that it’s going to be an 18 to 24 month journey. And, uh, that’s probably about right, right? You start it, you practice it, then you start getting good at it. But I think that’s the way it started was creating a shared accountability for everyone across functions to understand that if one person on the team achieves their goals, that’s not sufficient for the team to actually achieve its goals.

It might be better if you’re running fastest and I can’t keep up with you. Maybe it behooves you to run a little bit slower and grab my hand and run with me until I can get up to speed with you. Now we’re running at the same speed. Now that actually benefits the team, right? And I think that starts with creating this environment of shared accountability where everybody feels across functions that they’re in it together.

And those measures of success, they all celebrate together when they win. And they [00:24:00] all go back to the drawing board to figure out what opportunities are there for growth when the goals aren’t being achieved.

Ryan: Yeah, I, I absolutely love that. I mean, obviously there’s this kind of balance that has to be struck between, you know, personal accountability and the team, but I couldn’t agree with you more, like any, you know, chain is only as strong as its weakest link. So making sure that the team succeeds together is absolutely critical.

Uh, I’m glad that you brought up agile. I think that. I, I tend to lean much more, uh, in that direction as well. And one of the things that. I have found with it is that agile as a word means slightly different things to most people who say and hear it. And so I think there, there are obviously a couple, you know, paradigms around agile that are pretty standard.

But I think one thing that I have [00:25:00] Struggled with myself and seeing other people struggle with is kind of striking the right balance between like, how agile do we really want to be? You know, like there are some situations where things change so fast that it becomes very difficult to like actually make material progress on those things.

And, you know, so particularly within data, talk to me a little bit about what is the right balance to strike with. Producing data products in an agile fashion while still making sure that you have enough runway to get the things you need completed, completed before the goals move.

Zainulabedin: Yeah, great question. Um, so, you know, like a lot of my answers kind of start the same way, right? Because I like to start from the back in terms of thinking and then building out a foundation and then executing going forward. So I would say firstly, it is really incumbent on stakeholders to set those priorities.

Right. So translating into agile, having a sufficient backlog, right. And having that backlog with direction and [00:26:00] focus, that’s actually aligned with what the stakeholders need to drive the business going forward. Then in my experience, what we’ve seen is what we were able to deliver, say like in a sprint is.

How much can the team actually absorb? Not my team doing the development work, but the team for whom the work is intended. How quickly can they absorb that work? Right? Um, and what I mean is, I’ll just give a very simple example. A new dashboard’s been built. To the team that’s building it, because they’re data and analytics folks, it’s very easy to interpret.

It’s artistic, like, because it’s a visualization. It’s very simple to convey the story that the picture’s telling. But to a non data person, it might not be so easy. And so in our backlog, we’re like, okay, great, these stories are done. We’ve checked these off, the definition of done, check, check, check. Now we go to present a sprint review to the stakeholders, and they’re struggling to absorb, right?

They just, the sugar tastes great as [00:27:00] that teaspoon’s going in. They’re just having a hard time swallowing it, right? So it might take one more sprint of additional handholding and education and enablement to get them to really understand the value of the work. So it doesn’t make sense for us to roll out a new feature when they’re still having trouble absorbing the current feature, right?

That’s kind of what our experience has been is you can work as fast as you like. But if your customers or internal clients aren’t able to take that work and run with it, then you have to slow your work down to the pace or vice versa, speed it up, right? Because sometimes they’re hungry for more. They intuitively understand what you’ve developed and now their priorities are changing within the month or the quarter to say, okay, this is great.

And we’ve implemented this and we’re seeing value at the sales producer level or at the individual level. And we’re measuring our results all the way up to the region or the top of the channel. Now we need more in depth reporting or insights into the space. Can you reprioritize your work to [00:28:00] develop that?

I think that’s part of it as well, right? But ultimately it comes down to your audience, your stakeholders, how are they absorbing the work you’re doing? And that dictates the pace of development and what you’re delivering.

Ryan: Yeah. I, I, I love that. I mean, that’s, that’s I think the greatest piece of advice in, in all of business, no matter what piece of the puzzle it is, which is just always listen into the best of your ability, communicate with your market. You know, whether that’s a, you know, a product that you’re moving out, you know, to clients or customers, or whether it is, you know, somebody internal, like always listening to them, like, do you like what we made?

Do you, do you want it? Um, that’s, that’s really, really good advice. So one of the things that I think would be really interesting to hear from you is to talk a little bit about the balance between the actual preparation that goes into understanding and [00:29:00] building data infrastructure versus the portion, which is like pure analysis and insights.

You know, one of the things that. I have found is very different between data and analytics folks and the types of people that value data and analytics, but don’t create it for a living is there’s a bit of a disparity between like, okay, well, what goes into the actual, you know, making of the sausage. So I remember when I first started out, I’ve mentioned this a couple of times on the podcast, like, Okay, sweet.

I’m going to be a data analyst. That means that I’ll be analyzing data. So I’ll spend almost all of my time digging in and finding cool trends and, you know, Hey, look at this impact, or, Hey, we should stop selling this, or we should make this new thing. And, you know, all of the, the kind of like fun, exciting, really profitable part of data analysis.

And when I got into it, I recognized that there’s the reality of you have data in lots of different disparate [00:30:00] locations that need to get. integrated, whether that’s manually or on a larger scale. And, you know, then I have to, you know, Oh, okay, well, do we have to take taxes out of this or do we have to add on these other fees?

Or there’s all of this business logic and integration that needs to happen in order to get to the happy analysis portion. And that’s one of the things that I really like to talk to different data professionals about. So how do you understand the balance between that process and what are some of the steps that you can recommend someone take?

To maybe cut down is not the right word, but at least optimize that integration data logic portion so that as much analysis can come out the back end as possible.

Zainulabedin: Yeah, so I’ll go back to my restaurant analogy. So what you’re describing, I would say, like, we’ll call it data engineering, just to put it simply. It’s now you’re sourcing the ingredients for the kitchen, right? And you’re organizing those ingredients to make sure that they’re easy for the people who are actually doing the cooking to, you know, to cook at an [00:31:00] optimal level.

For the people eating, honestly, they don’t care about that stuff. It’s not that it’s not important, but they just don’t care about it. They just want to know that when they order something, their food’s going to come out hot and delicious. So what I find to be helpful is being able to generate small bits of incremental value through analysis and insights as that heavy lifting is being done on the backend, right?

And so what I mean by that, going back to Agile is not taking six months to say, we’re going to build this amazing thing for you. It’s like, okay, the thing you actually are asking for might take six months. But I can deliver some incremental value now, right? So in the next two weeks or four weeks, that usually starts establishing credibility in terms of the work the team is doing.

And with that credibility, now you have some, uh, rope, I guess I would say, that’s getting a little longer [00:32:00] in order to start spending time to build more of the backend data infrastructure. I think the key to the whole thing though is establishing credibility. up front with just quick wins, right? Those quick wins are,

uh, they’re invaluable in terms of getting the permission to, to do the right work on the backend infrastructure side.

Ryan: I loved what you were saying there. And so for me, one of the things that has been kind of a big point. As you know, a client comes to me when they hire a consultant. Lots of times, they’re not just looking for work to get executed.

They’re also looking for some direction and recommendation based on the experience that we’ve had as consultants working with lots of different people. And one of the biggest paradigms is exactly what you were just talking about, which is essentially thinking about what is the next most valuable thing that I can do to show return and focusing on that [00:33:00] win.

It gives, as you mentioned, so much value in terms of building trust between whoever the consumer is and the centralized reporting function. One of the other things that I’ve found is it kind of causes the quality of the questions and the clarity and the direction that they have around what they need

to improve. So if I go and I do something for six months and then I come back to them, You inevitably find that either, either the landscape has changed and they need something new or additional, or, you know, the, their requirement wasn’t 100 percent perfect.

So going with this kind of very iterative approach, I’ve found not only gets them incremental value along the way, but ensures that wherever you end up is actually

where you want it to end up.

Cause there have been

so

many checkpoints along the

way. So.

Obviously there’s this

drive to do big and [00:34:00] incredible

things.

What are some recommendations that you have as far as like managing that process

effectively and getting somebody on board with doing

something a little bit more

incrementally?

Zainulabedin: Yeah, um, this I think actually comes back to what’s the impact that the team on the analytic side, what’s the impact they’re creating, right? Um, so I worked for an organization that was implementing a new telephony system. They just opened a call center and they wanted the same telephony system which existed at the call center to go to all the retail locations.

So now from a consumer experience, you pick up the phone and call. The idea was someone would answer your phone somewhere, some employee of the company. So the challenge organization faced was nobody kind of owned that operationally. Technically there was an infrastructure

team to get the phones set up [00:35:00] and working.

And you have a line, I have a line, and you can go through all the call center stuff of skilling and all the rest, but nobody was actually accountable for making sure that the system was working as it was supposed to.

Ryan: Mm.

Zainulabedin: So we were asked to do reporting on it. So we did. And the first level of questions were all, are you sure the data is correct?

And I think this is very standard for a lot of people kind of in our space, right? Because often business, uh, you know, practitioners are surprised when they see numbers that aren’t necessarily aligned with their own kind of anecdotal gut feel. And so what the team, my team was able to do was not only provide high level numbers.

So the high level number was answer call percentage. Right? What percentage of calls coming in were actually being answered? And that was material at the executive level because we’re paying money for those calls to, for the phones to ring, right? We’re spending money on marketing. The marketing drives consumer behavior.

The consumers drive phone calls into the [00:36:00] call center, into the branches. We want those people to, you know, have someone answer the phone.

And as that level was so low, the team was able to dive into the details of those calls. And start explaining why that answer to call percentage was so low. And where it was so low and where it was so high and give kind of a roadmap to management of who you could go talk to to understand what behavior was the team executing that was actually what you want and what you expect and go talk to this other person not to go you know wag your finger at them but to understand if they’re telling you they’re answering the phone whenever it rings And the data is showing something different.

Now you have at least enough information to go have an informed conversation with them to understand Maybe they’re not operating in this new environment the way they were supposed to because they weren’t coached properly Right, but it was really it was incumbent on the [00:37:00] team to kind of flesh that out Once that was exposed the behavior changed almost overnight, right?

Um, we used to have a meeting every week on sales performance You The first week we fleshed this out, everybody kind of got the, you guys can do better from the Chief Sales Officer, right? We gave them the ingredients to the recipe. The next week, one of the regional leads, his call percentage, his answer call percentage spiked up like crazy because he did the thing that we asked him to do, right?

Like, we have the data. You have to put it to use. Go talk to the people in your region to understand what they’re doing or not doing. And figure out how to bottle up what’s working and spread that out consistently. And then within, I think, a month or two, the whole region was performing at a super high level.

Then all the other regional levels are going to that regional head to say, what did you do? What was the secret? Well, the secret was we kind of did what the analytics team told us to do. [00:38:00] Right. Um, cause they had the insight in the data. It was just up to us to then go take that information one level further, push it to the people who are actually executing the behavior and align the information with the behavior.

Ryan: Yeah, I love that. I mean, that’s just kind of like a slam dunk use case of analytics done well. I mean, I think there is sometimes this mentality of data on its own will fix problems and it is really just a picture. into what has happened. Uh, now obviously, you know, sure. There are like predictive models that can, you know, attempt with varying degrees of efficiency to talk about the future.

Um, but in, in general, when we talk about reporting and analytics, I think like the main focus is on descriptive analytics right now. Um, and. With that, it’s like, okay, cool. So we have a clear picture of what’s happened. There has to be that like decision and action phase that happens after that in order to, to [00:39:00] fully get the value out of it.

Um, and I, I absolutely love that because, you know, at least for me, one of the things that I find is again,

you know, I’ve, I’ve mentioned this a little bit, but, you know, data and analytics feel like this very technical,

computer oriented, you know, And what it really is, is hopefully driving interpersonal connections and driving human behavior and human decisions, human connections and actions.

And it’s really just a picture

into what’s going on. Uh, I absolutely love

that. The things

that I also like to make sure that I talk to our guests about is to hear about

some of the things that have been more

challenging.

What are some of the pieces of the data puzzle that you’ve found most challenging over your career?

And what are some of, you know, the solutions

that you’ve been able to find

that?

Zainulabedin: Oh, that is a fantastic question. Um, on the data [00:40:00] side, I would say

sometimes you don’t have, and this is becoming,

I think, less relevant with time because now the ability to ingest

data from widespread sources is becoming more common.

But back in the old days, right, you might not have had that ability.

And

so now

you’re being asked business questions, which are

perfectly good questions, but you don’t have a

source to generate data to answer that question in the way

you want that question to be answered,

right?

And so again, I’ll

give you an example. Um, in an, in an omni channel business environment,

you have phone calls and you have internet leads and you have people walking in. And ideally as a decision maker, you want to understand all that data kind of, um, in an integrated way to understand your opportunities coming in.

But on the back end, that requires all of the systems to be configured correctly. [00:41:00] And then that more importantly requires the behavior of the employees interacting with those systems. to be consistent with how these systems are in, you know, it’s supposed to be enabled. And that’s often the tricky part, right?

Um, I just, so many times I couldn’t tell you, Ryan, we would get questions when we would drill down into it. And now you’re in like a little lab sitting with the developers on the backend to understand, is this a systems configuration thing? And they would pull up develop, they would pull up examples where it’s like, so what we’re seeing in the data is the salesperson is taking shortcuts.

in order to make the sale quickly. And the shortcuts they’re taking, while it makes sense from the customer experience perspective, because the customer has to go pick up their daughter in 15 minutes, or they’ve got a dentist appointment and they just want to get in and out, it’s transactional. It’s creating a disruption in the data flow because now the shortcut they’re taking in the system is mucking up the data to an extent that you [00:42:00] no longer have clear insight as to what’s actually happening operationally, right?

I think that’s becoming less of, an issue over time, though. Now, as the tools have become more sophisticated, as systems are easier to configure, as you don’t necessarily need to be, you know, an engineer in order to actually work with data anymore, because the tools are so good.

Ryan: yeah.

Zainulabedin: Other side I would say is with the people, um, people who are resistant to change, and

that can again, just take time.

Right? Um, like I saw a cartoon recently which sort of summed this up very, very beautifully. There were two windows and one said, who wants change? And the line to that window was sort of infinitely long. And the other window was who wants to change? And there was nobody standing at that window, right? And I think often people, especially when you’ve been in an organization for, you know, decades and you’re very familiar with the existing processes and you’ve got a history of [00:43:00] success that you can stand on to say, this is the foundation of what we do here.

This is the way we’re always going to do it. It can be challenging to get people in that?

mindset, to adopt a growth mindset, to understand that data as a tool can enable a different way to work, not materially just throw all of your experience away, but enhance your experience, enable to work faster. And that can be a challenge as well.

Ryan: Yeah, I, I absolutely love that. I’ve definitely

experienced those challenges and I, and hopefully the

listeners get some benefit out of some of the

solutions that you brought there. One of the things that I think is, you

know, really evident from getting to talk to you is how important

the human element is even to our technical field. so in light of that, I’d like to

make sure that

everybody, you know, gets to know.

Okay, well, well, sure. Like Zan is established and smart and knows what he’s

talking

about, [00:44:00] but give them a chance to get to know you as a

person a little bit better

and to connect with you. So tell us a little bit more about you outside of analytics.

What makes you tick? What do you

like to do for fun or

outside of work?

Zainulabedin: Yeah, so, uh, I am very devoted to my family. Uh, that’s really the center of my life, right? My faith and my family. So if you think about it in concentric circles, my faith is my anchor. And then how that kind of manifests itself in my daily life is my, my family and then my community, right? So outside of

work, um, I spend time volunteering.

So I’m the the president of my business, uh, sorry, middle school PTO. Um, my youngest daughter’s in seventh grade, so I’m in the middle of a three year commitment doing that. And then also I am the head of logistics of a local nonprofit that delivers food to homeless people. So in, in Northern California, we have a bit of an issue in some cities with, you know, homeless encampments.

And, um, the issue is like how they’re being managed, right? Like people need a place to live. I think [00:45:00] that’s, you know, to make it apolitical, we’re all human beings, right? Uh, everybody needs to eat. Everybody needs shelter. So just doing our small part to make sure that.

we can deliver food to these people as they need it.

Right. Um, and then kind of more sort of on the fun side, I’m a huge sports fan. So typically a lot of my analogies would dovetail back to sports. And, um, Yeah.

you know, I, I try to find balance in everything I do, right? Uh, like I want to be good at what I do and I want to create value in what I do. And I want to have an impact in the, you know, with the people that I interact with, and I think that’s one reason a lot of my answers are coming back to people, because I really believe like, that’s kind of my value is, you know, my purpose in life is to impact people in a positive way and to serve others.

Ryan: Yeah. I, I absolutely love that answer. There’s a, uh, a lot of similarity there. I’m also, I’m a, a huge Red Sox fan, Boston sports in general. Um, actually I

was, uh, I was going to save this for, uh,

a few minutes down the road, but this is as good a segue as any. [00:46:00] I, you know, This past weekend road in the pan mass challenge,

which is 186 mile bike ride across most of the state of Massachusetts to, yeah, to raise money for,

um, cancer research at Dana Farber.

So we’re going to put a link in the, in the show notes for today. And if there’s anybody that would like to donate a hundred percent of that donation

goes. To Dana Farber Cancer Institute to support new research and families that are experiencing, you know, member, Uh, battling

cancer. So if you, if you’d like to check that out and learn more,

volunteer, donate, that would be greatly appreciated. Uh, no,

Zan, if somebody loves what you’ve had to say today and wants to

connect and reach out and learn a little bit more, pick your brain a little bit,

what’s the best way to get in

contact with you?

Zainulabedin: Uh, I would say LinkedIn. Um, so my full first name, I’m sure it’ll be spelled out, but it’s Zainul Abidin Shah. Um, Google search me. I show up at the top, which is kind of remarkable because I’m not doing anything actively to

make that happen. But yeah, I’m, I’m happy to [00:47:00] interact with people, you know, mentoring, having conversation.

I love mentoring people. I think that’s one of my great, um, opportunities in life is to kind of pay forward my own experience

to make sure that people don’t have to step in the same, you know, uh, holes that I did as I was kind of ascending through my career.

Right. Um, but yeah, LinkedIn is the best way, right?

I’d be happy to talk about data, data strategy, you?

know,

the career opportunities, whatever anyone wants to

talk about. Like, I love

interacting with people.

Ryan: I love that. Yeah. Thanks for, thanks for sharing that. Zan, This has been an

incredible conversation. Uh, the, the human element and the amount of experience that you’ve accumulated and have shared with everybody today is just so obvious and so appreciated. Thank you so much for taking the time to be on the

show and to

share yourself with us today.

Zainulabedin: Yeah. Thank you so much for having me, Ryan. This was delightful. It was really fun. Thanks so

much.

Ryan: Awesome. I also want to make sure to thank the listeners, uh, especially if you’ve listened this far. [00:48:00] So if you learn something or you laughed, please make sure to tell a friend, like, subscribe, share your favorite podcast, uh, really helps to keep the engine moving, Zan. Thank you again so much for being here.

And this has been another exciting episode of the Making Better Decisions Podcast. Thanks for listening.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.