Innovation and Data Science Leadership

As you're digging through the data, you know, you find other things to go down the rabbit hole. And what's really cool about that is if you enable a subject matter expert to be capable to work with data themselves, they know where the goal is buried. They're the ones who are going to go find that goal for your organization. And I see a lot of folks become real heroes because they make a big difference.

welcome back to the data bites podcast today i am so honored to have a legend in the data and analytics space heather harris is the field chief data and analytics officer at alteryx but brings with her a wealth of experience having worked at previous organizations such as Alaska Airlines and USDA, one of our favorites who we ran a datathon for in Women in Data, along with being an executive coach and mentor for our friends over at Women in Analytics. So Heather, welcome to the Data Bytes podcast.

So happy to have you here today. Oh, thank you, Sadie. It's such a pleasure to be here. Speaking of legends, you're a legend yourself.

So it's so great that we can do this together. Yeah, I was so excited to chat with you because in your title, it's not just cheap data and analytics officer, but filled cheap data and analytics officer. And I was like, yes, this one, this is a conversation of somebody who's out there talking to different organizations, see what's happening in the environment and can really give us that ground scope of what is going on right now in the data and analytics space. And so I'm really just curious, particularly with everything this past year that's happened with AI.

Where do you actually see enterprises at in their analytics maturity? Are they starting to really move forward because of the push from AI? How does the business landscape look today? Well, I think definitely the hype around generative AI in particular is definitely bringing attention to data and analytics in general.

It's really helping organizations realize, wow, we have all this data we need to do more with. But it's interesting. What I call the analytic maturity curve, it really varies. And there was actually a study by International Institute of Analytics where they went out and surveyed over a thousand companies and asked them to assess where they are on a scale of one to five.

And remarkably, the average is only a 2.2 because organizations definitely still have a lot of data they haven't tapped into and a lot of talent they haven't tapped into to be able to access and get insights out of that data. So it's definitely a journey. And one other thing I see is even organizations that we think of as being leaders in AI, maybe have some of the most well-known products in the market, even those organizations internally, their back office workers are struggling, Excel, spreadsheet hell, even today, and struggling to get the most out of their data.

I'm very familiar with the Institute of Analytics, and they do some great reports. They were kind of my, I will say, Bible for a little while when And I was, you know, first a new hire and we were going and it was a large organization. We were going through a massive, you know, analytics transformation with new architecture, new roles, et cetera. And so we referred to them a lot in their reports.

And this was almost 10 years ago. And I remember the average was about the 2.5, you know, in between the two and the three tier. And so I'm so curious because, you know, now 10 years later, I'm like, how are we still as an industry at that, at the two to three level?

Is it that we're not progressing as organizations or is it that more organizations are coming in and starting their journey? So in a way, it's kind of lowering the average, if that makes sense. Yeah, I think what I see, and this is where the field part of my role comes in, is that I do work across Alteryx's more than 8,400 customers, including half of the global top 2,000. And so I get to see trends and what organizations are doing.

I think what organizations have done well is lean into hiring more advanced analytical talent to go after that hire in, but it still has left the rest of the organization desiring to get more out of their data. So that's where I see the gap still lies. And then the second place we see the gap is that department by department might be doing really great stuff with their own data, but there's still an opportunity for departments to come together for the operations side of the house, that data to influence what we do in marketing and sales, for example.

And so that breaking down the internal silos is still a big opportunity for organizations. I can see Alteryx helping a lot with this just because it really is a comprehensive platform. And one of the things that I love about it is it allows people to start to get more value from their data without even having coding skills. So I just love it when we have tools and products that make using data, playing with data, getting insights from it accessible.

How do you see this accessibility transforming the way organizations either handle data or or even do their analytics? Yeah, I think people who historically would tell you, hey, Sadie, I'm not a data person. These guys over here are data people are starting to realize, oh, wait, I can be a data person. The other thing that I'm seeing is more and more when you come into a meeting or a decision or an opinion, you're really expected to have data behind it.

So the pressure is on to have a data informed opinion or a decision. And historically, a personnel in the line of business would turn to some of this more analytical talent, the data scientists, to help get that insight. And the problem is there just too much for that more historical side you know traditional data science analytic talents to to do on their own So tools that are code and Alteryx has been in this space more than 25 years making data accessible and not just data accessible, insights accessible to folks who really are subject matter experts and they actually know what they're looking for in the data.

So you don't have to have that translation back and forth between the line of business expert, the subject matter expert, and the data scientist. So it's breaking down those barriers. Now, having said that and being a data scientist myself, there is still a great need for data scientists and a great need for analytical talent. We just want them working on our hardest, toughest problems that are going to make huge business impact.

because I think that's the great thing about this space is there is more than enough work to go around for any of us all of us and more people to come into the space particularly because we've all been we've all had it happen to us you know we start on a project and we dig into the data and we maybe uncover three other problems or you know three other insights that need to go down a rabbit hole and trail off and tune further. And so that's what I love so much about this space is there's so much opportunity for everyone.

And I'd see a lot of companies today talking about not just getting insights from data, but also automating workflows. How is automation, do you see, kind of changing the analytics and data landscape? And are there any implemented solutions that you found really useful in this area. Yeah, I'll talk about two things.

I want to talk about that rabbit hole thought, you know, that as you're digging through the data, you know, you find other things to go down the rabbit hole. And what's really cool about that is if you enable a subject matter expert to be capable to work with data themselves, they know where the goal is buried. They're the ones who are going to go find that goal for your organization. And I see a lot of folks become real heroes because they make a big difference.

Yeah. In regards to just the automation of workflows, are you starting to see that take shape in the analytics and data space? Yeah, actually, I wouldn't say it's starting to take shape. It's been taking shape for years and years since I've been, and especially working with Alteryx.

So let me give you some examples of how it takes shape. So for example, I was with an HR leader last week. She was talking about how, you know, Alteryx has allowed her to bring together various sources of people data. And this is an organization with close to about 35,000 employees.

And, you know, they're looking at trends in people who are working more than 100 hours a week, which is very common in the healthcare industry, but not really super great that these people are working long, long hours. So she's able, she talked about how Alteryx is able to help her get all that data together so she can get those findings. And then they're being stood up in a dashboard for people leaders to go access. The challenge she has is those people leaders may or may not do that extra work to go click and get those insights.

So the solution is, oh, I already have the data. And with automation, I can actually generate an email to people leaders who I'm finding have employees at risk of burnout or at risk of doing too many hundred hour stints that are not really helpful in the healthcare space to have people, you know, working that even if they don't feel the burnout themselves, we know from an organizational point of view, or perhaps a patient experience or patient outcome point of view, that may not be the right thing.

So she realized like, oh, I can automate this. I can have those findings sent specifically to the manager, the person who might be at risk or the people on their team, and then they can take action. So we think about personalization in the retail or consumer space. Well, it's personalization in our insights as well and automating the delivery of those, requiring less work of our business folks to go get them themselves.

So that's just one example. Another example would be in financial processes. Let's say accounting. One of the most common problems our customers are working on is reducing their time to monthly close up their books.

And so by automating, bringing that data together, actually putting journal entries in or creating the reports or creating commentary they need to. So I was with a customer last week that, you know, the comments they have to put are all auto-generated with alterings. So it's not just about the insights, but it's about the creation of the output that you need. And this is, I think, such a game changer even for analysts too, because all of us have been there where it's like a month of really rapport or a quarterly report of life.

It just feels like you're doing the same thing over and over. But if you can automate some of this workflow and have triggers for where it provides that insight to the decision maker, I mean, that saves up again, just so much time to go off into those rabbit trails of the other things that we want to investigate, the other things that we want to look into, the models that we want to build. And so I think that's just a game changer from all levels of the organization in terms of capabilities.

You've led a lot of data initiatives ranging from, you know, working in the airline industry to semiconductors. Having spanned different industries, are there leadership strategies that you found are universal regardless of the industry that you're in? And what are some of strategies? Well, as a people leader myself, I think, you know, at the end of the day, it's people.

It really doesn't matter what industry you're in. The end game of a good leader is to help their people do their best work, achieve, you know, in the best way that they can, you know, for the organization, but also for their career. I would say I'm very much a leader who I want people to be able to do more for themselves than they thought they were capable of and not in a pushing them and driving them sense more For example I known a number of individuals who wouldn have said they were data people who didn have the confidence or belief in themselves that they could do data work.

And so helping people see that they are more than capable and that they can succeed and do more things. And even in my role now, as I'm working with other organizations. It's seeing a barista for one of our large brands see that their organization is a customer and has this technology and saying, I know I'm a barista today, but I see myself as being able to go embrace this technology. And this was someone who told their story on stage or when I was at the airline, having someone who was in an administrative assistant role say, hey, I know I'm not a data person, but I see you're doing this upskilling across the organization.

Do you think I could sit in and then totally transforming and changing her career? I think it's about helping people see what they can't see in themselves, their own capabilities, their beliefs. I actually, I'll even share, I see this as a parent. I see kids who are able to do amazing things with technologies that I would say even their own parents or maybe teachers don't recognize that a five-year-old can actually go build a workflow.

I know five-year-olds who can code. So I think we do a disservice. And I see this with leaders sometimes in believing who is capable. And I think that's their own self-limiting beliefs.

But that's really my passion is helping people achieve more than they thought they were capable of. I love that because it's, I think, such a good challenge in a way for ourselves and for others that we may lead. And I love that you also brought it back to yourself. Like a lot of times we may be imposing that on others just because it's a belief that we have about ourselves.

And so, you know, what I find in leadership so often is I just have to work on myself and that it normally translates to everybody else. But I'm Curious what advice you give to those who are looking to become a people leader, right? Whether that's a supervisor, manager, director, VP, whatever leader that space is for them, how do they get that experience leading people when maybe you're in an individual contributor role? Any advice for those individuals?

Oh, 100%. I love seeing this happen. So, for example, in the data space in particular, since we're about data, the opportunities there are, let's say your organization is using a technology like altering self-service technology. There is an opportunity in organizations.

And one of the keys to analytic maturity is to bring people together across the organization to cross-pollinate, to share the work they're doing. And so we see these often in the form of an internal user group. So I'll give an example. When I was at Alaska Airlines, I wanted to have an internal alter user group and also for some of our other self-service technologies.

But I didn't have to be able to run quarterly, which was our goal was to do a quarterly meeting. So I knew I had some folks doing really great things in different parts of the business, supply chain, operations, revenue management, different areas that were HR, for example, that were not connected typically. And instead of putting out a blast email, hey, I'm starting a user group, I want four people to help co-lead. That was not going to get the answer I want.

Most people will not respond to a mass call. So what I did was I went individually to folks and I said, hey, I see you're doing this really great thing. I see you also have leadership capabilities and leadership aspiration. How would you like to co-lead our internal Alteryx user group as an opportunity to demonstrate your leadership capabilities?

and all you have to do is you're going to have three other co-leaders. So you only have to lead one meeting a year, come up with the content for one meeting, hour and a half, or however long you want to do it. And then the four of you can also collaborate for other opportunities, but something bite-sized, but something where there is something in it for them. Now, did I have the ambition of having a user because I knew it would increase analytic maturity for the airline?

Yes, I did have So, you know, it wasn't purely altruistic, but it was, you know, to use an overused expression, win-win. It was a way to help grow people, demonstrate their leadership while also getting the needs of the organization. And those are the kinds of opportunities I see, whether it be helping stand up an internal user group. Go speak at your, you know, a community user group in a market.

I was just in Tulsa speaking at their user group. They take turns bringing different customers in. You know, they all meet together and talk about the work they're doing. Speak at a conference.

You and I were talking earlier about women in analytics, right? They have great speaking opportunities. Our conference, the Inspire Conference, gives people a chance to be on stage. Write articles on LinkedIn.

There's so many opportunities where you can take the data stuff you're doing, day to day and turn it into an opportunity to demonstrate, hey, I'm a leader. And when I would bring people up into leadership, what we would do in their performance planning for the year is we'd say, okay, you want to become a manager. Let's demonstrate this year that you're doing management capability work. Let's find opportunities so that when it comes time to promote you, you're already doing what a manager would do.

You've already demonstrated. So that's what I say. Look for the opportunities to demonstrate your leadership capabilities. That's fantastic advice.

And that's one of the main reasons why I love our chapter system within Women in Data, because it's kind of like user groups, but for women in data. And so it just, for me, I learned so much through that experience and having the opportunity to have led a chapter. And I love it now today because it's about, there's so much more opportunity to lead beyond just that title, right? Beyond that official manager position, VP position.

And we meet amazing leaders in our communities And so I just love the advice and particularly the really practical advice of like don send out the mass email go to people one I went to the person I sat next to and was like, hey, I'm doing this. You got marketing skills. Can you come help me? And she couldn't run away because she sat next to me.

So that's right. I think, honestly, a lot of folks in data tend to be naturally introverted. And so sometimes they just need to be asked and acknowledge. They may not be the first to raise their hand, but when you see something in them, and I think that's what leaders are supposed to do is help find and draw out those strengths of the people that we work with or that are under us to help them lean into what their strengths are, or to help them develop those strengths.

But you're absolutely right. Leading a women in data chapter, it shows not only that you can organize, but that you can collaborate. There's so much about leadership. It is about collaboration.

Can I bring people together to achieve the common good? As you look back on your career, if there's one lesson you wish somebody would have told you sooner or earlier, what would that be? Well, I think a lot of us struggle with articulating our own value, that dance between feeling like we're sounding boastful or bragging and actually making sure our organizations know what our value is. And I think, you know, leaning into that earlier in my career may have served me more.

I don't know, but I do know that that's one of the things that I'm passionate about now, not just for myself, but for others, is understanding your worth, understanding the value of your contribution. And in data in particular, the frontline workers, the knowledge workers doing the data work, prepping, the blending, the analytics, the machine learning models, sometimes they see the win in front of them. Like I was able to build this, you know, great model, all the statistics about the model made it like the most perfect.

Well, I mean, we don't want a perfect, but we know it would be overfitting, but, but they, that they did something good technically. The next step there is to be curious about what is this report or this analytics or the fact that I could bring this data together? What is it doing for my organization? And continue to be curious and ask the why.

A great story around that is I was at another great source of healthcare organizations. this analyst was able to bring together multiple sources of data to make sure he had a really good curated list of emails for their physicians. And that was the win. And he was so excited about like that he had this really good list of emails because no one had been able to actually achieve that before, which is too much data.

And I said, well, what are these emails used for? And he's like, well, we have to email our physicians some metrics every month. And I was like, oh, well what metrics are you emailing them and he said oh it's their cesarean section rates and i was like why would you email them those oh well we have an initiative to bring down c-section rates okay so i keep asking why right well why would you want to do that oh well you know we see that patient outcomes are better it costs the hospital less money and the physician experience is better when we send these to them directly.

And I was like, okay, so wait a minute. What you're saying you really did was save the hospital money, improve patient outcomes and improve physician experience. So I hear you, right? And the aha moment.

And I think I encourage folks who are listening that are in data work, find where you're impactful because I have yet to not find it for someone when I have this conversation with them. So I encourage you to find it and be willing to articulate it because I can tell you, your leaders will be super excited to know you're doing this. Yes. I'd love that you're using kind of the five whys, not just to like dice out a problem, but to really get to your own value, right?

Of, you know, not just collecting emails. Yes. That's the very top level, but when you dive deep down into it, the impact that you're having and being able to communicate that is so key. And anyone working in this space typically has a really curious mind.

So we're used to asking the five whys, but now we just need to do that back to ourselves. And I think that is fantastic advice that each of us can take and implement this week. So thank you for that. And most importantly, I just want to say a big thank you for all your work and most importantly, your mentorship in this space.

It's just, I can tell just from this conversation that you are rich with information and really do care about the people that you connect with and lead. So thank you for your continual leadership in this space. And most importantly, your mentorship of others. This has been a pleasure.

Well, thank you so much for having me, Sadie. It's been a real joy to be with you today. Right. And to all of our listeners, remember to stay curious and keep learning.

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