Alter Everything

A podcast about data science and analytics culture.
MaddieJ
Alteryx Community Team
Alteryx Community Team

On this episode, we hear exciting Alteryx user stories from Melissa Burroughs and David Sweenor, co-authors of the new ebook, "Automating Analytics: A Human Centered Approach to Transformative Business Outcomes."

 


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Melissa and David headshot.png

 


Transcript

 

Episode Transcription

MELISSA 00:00

So I've been thinking a lot about digital transformation lately. So I happen to have a statistic in my head. According to the Boston Consulting Group, something like 70% of digital transformation initiatives fall short of reaching their objectives. And I think there are a handful of pretty common reasons why this happens to organizations.

MADDIE 00:25

Welcome to Alter Everything, a podcast about data science and analytics culture. You just heard Melissa Burroughs, one of our guests for this episode and co-author of Automating Analytics, A Human Centered Approach to Transformative Business Outcomes, an e-book she wrote with Alteryx colleague David Sweenor.

DAVID 00:44

This topic of change management and how to build an analytic or data driven culture I think is hugely important. And keep coming back to this human centered approach, but you have to make the humans part of the solution.

MADDIE 01:01

Our hosts for this episode is Mel Erbes. You might recognize her name from the Alteryx Input blog. She's passionate about storytelling, and as David and Melissa just wrote a book, she invites Melissa and David to tell different stories of digital transformation and the importance of automating analytics. And we'll mention this again at the end, but you can find the Automating Analytics e-book and our show notes at community.alteryx.com/podcast. Here's your host Mel Erbes.

MEL 01:32

I'm your guest host Mel Erbes, senior manager, content strategy and engagement here at Alteryx. And with me today, I had Melissa Burroughs and David Sweenor.

MELISSA 01:42

Hi everyone, I'm Melissa Burroughs and I'm the director of technology solutions marketing here at Alteryx. Which means I think a lot about automating analytics in the context of cloud ecosystems. And I'm honored to be joined by my colleague and friend, David Sweenor. David, you want to say hi?

DAVID 01:59

Thank you, Melissa. My name is David Sweenor, senior director of product marketing here. I tend to specialize in the data science and machine learning space, but I've been in analytics my entire career. And I am dialing in from the Green Mountain State today.

MEL 02:14

You're also the authors of the recently released O'Reilly publication, Automating Insights, A Human Centered Approach to Transformative Business Outcomes. Perhaps you could provide a brief synopsis of the book.

DAVID 02:26

Sure. I'll give it a whirl here. I think people are a little bit fearful of analytics and AI and things like that. So what we wanted the key themes in this book were really-- we wanted to discuss really taking a human centered approach to analytics. Robots are not going to take your job or rule the world. We wanted to discuss why should you automate analytics, why continuous answers are important, and really how to democratize access to insights across your organization.

MEL 02:57

Wonderful. So since we're talking stories today, in addition to automating analytics, I've also been reading the seven basic plots why we tell stories. And according to journalist Christopher Booker, there are essentially seven basic story plots ranging from comedy to tragedy. It's a fascinating read, much like you're automating analytics, of course. So with this in mind, I'd like to play a game with you that both combines storytelling and analytics since they go hand in hand. I'll name a plot and you share a relevant analytics storyline. Sound good?

MELISSA 03:31

Yep, let's do it.

DAVID 03:33

This sounds fun. I'm excited [laughter].

MEL 03:35

All right. Here we go. So let's start with comedy. It sounds like a bad joke, two Alteryx [inaudible] with physics backgrounds walk into a bar. But seriously, how did you end up here? What's your story?

MELISSA 03:51

David, do you want to go first or do you want me to go first?

DAVID 03:53

Oh, go ahead, Melissa.

MELISSA 03:54

Thank you so much. I love that you know that about us, Mel. As far as comedy goes, I would probably describe my career as a sort of comedy of errors. I always say to people who look at my CV, not all those who wander are lost. Which I think is a token quote. Well, basically for me professionally, my whole career has been spent basically discovering what Kevin Costner said in field of dreams was a lie. If you build it, they don't come. It's not enough to just build, right? So I went from as you said Mel, started in physics like David. I went from from science into engineering, into product management, and then ultimately what I do today, product marketing. And for me, chasing that value, right? The if you build it, they will come, I don't think that people show up because you built it. Why people show up is whether they see and appreciate value in what you've done. And supporting that value exchange is what really lights me up. So if you build it, they will come, it's incomplete. That's the beginning and the end. And it skips all that really important stuff that has to happen in the middle after you build it to get the people to come. And I feel like our audience of data analysts also likely sympathize with this experience because if you build it, they will come is also false for them.

MELISSA 05:31

The parallel here would be if you have the data, the business will improve. And the data is just the beginning, right? You have to prepare it and cleanse it and blend it and analyze it and model it, visualize it, communicate it and incorporate feedback before the business can improve. So that's my angle on comedy as far as my professional [laughter] experience has gone.

DAVID 05:56

I love it. I love it. Well, I think I went into physics because I didn't know what I wanted to be when I grow up. I guess I still don't know what I want to be when I grow up. But what physics taught me was, how do you solve problems? And you have to be fairly okay at math to get into physics. But you can solve fun problems like if you drilled a hole through the center of Earth and in a frictionless environment and jumped into it. How long would it take until you to get to the other side? Do you know the answer to this one Melissa?

MELISSA 06:28

You wouldn't.

DAVID 06:29

Well, besides being melted by the center-- the ball of fire in the middle but 42 minutes in this frictionless environment, synthetic world. In physics, you could play with liquid nitrogen. So that was fun. But it really got me into math and I did a co-op and I started getting into neural networks. So I learned-- I was predicting survival, giving a bunch of biometric data, think like heart rates and pulses and things like that. I was predicting the failure of helicopter rotor torques and things like that. I think analytics as to quote The Big Lebowski, it really ties the room together. And so my professional career after physics was spanned high tech manufacturing. So I was doing data science work there. I've done data warehousing, was an analytic COE and now I'm on the dark side of the world of marketing. And I like helping people solve problems. And so that's why I'm here.

MEL 07:21

Well, we're glad you're on our side.

DAVID 07:23

Me too.

MEL 07:24

Yes. So the next plotline we'll explore is overcoming the monster. You often hear about automation, and the fear of change. is AI scary?

MELISSA 07:37

Oh, I love this. David, why don't you go first?

DAVID 07:39

Oh, well I think of AI it can be a little bit scary because I think it's misunderstood by a lot of people. So the brief history of AI, if I was to think about it, it started out with Stanley Kubrick's 2001 A Space Odyssey. Then we moved into The Terminator with Arnold Schwarzenegger, and then we moved into Westworld. But I think AI in general, it's much more pedestrian. I think of it very simply. It's analytics plus automation. You fuse these things together and you have artificial intelligence. A lot of the customers that we speak with they're using Computer Vision to extract text data from receipts and invoices and PDF documents. And the world would classify that as a form of AI. But they don't call it that when they're doing that. So I think it's pretty misunderstood and I think it's a bit more pedestrian and more approachable than many people think.

MELISSA 08:38

I couldn't agree more, David. Absolutely. And we often will see this refrain that people are getting automated out of their jobs, right? Half the time it's, "Will this AI automate my role away?" And really, from the perspective of analytics, that automation, as you said, it's really much more pedestrian than folks think, and it's ultimately about automation. Well, automation is a benefit, right? It clears the way that cruft of repetitive manual tasks. It basically acts like a jetpack that you can strap onto your back. For example, there's a story that I know about a manager of a holding tax team, part of a I think it's a construction material trading company based in Turkey. And the team was receiving data in a ludicrous number of formats. We're talking your usual suspects CSV and Excel and text files, but also PDFs and web files and SQL Server data and SAP data and net log and net SAS and all sorts of things that even I'm not familiar with. And this made that group's pre-processing tasks really time consuming, really open to human error, and it resulted in some serious tax penalties for the business. And so you could say, "Well, that's job security because I'm busy crunching all these different formats all day." But really, they needed to deliver these regulatory reports for many companies at once in a more consolidated manner. They were missing deadlines. They had to provide that error free financial reporting that every organization relies on and blending and comparing all those various data sets. It's essential when it comes to different tax periods across different jurisdictions. It's a complex process.

MELISSA 10:25

And you can imagine a single reporting process could take between a day and a week just of data preparation time, not to mention needing verification across all those sets of eyes. If you are an analyst on that team sure, that never ending cascade of tasks could make you feel safe. But the reality for those team members is that it was a nightmare because it was the same thing all the time. It was boring and scary, which is sort of the worst kind of film in my opinion. Now David, you know me well. Would you care to hazard a guess as to what the answer was for this holding tax group in this nightmare scenario?

DAVID 11:11

42.

MELISSA 11:12

42 [laughter]. I think that's the answer to life, the universe and everything, right?

DAVID 11:18

That's right. It is [laughter].

MELISSA 11:20

I promise this has a tie back. So back to the book that we wrote analytic automation, right? That was really what made the difference for this organization. That repetitive, heavy lifting can now be done lights out for them. Human error, thing of the past. The team can support holding tax and reporting processes now over more than 50 countries, more than 150 different locations all around the world, and still log off in time for dinner every day. And so that's when we talk about overcoming the monster as a theme, really recognizing that sometimes the monster is your friend [laughter].Re-contextualized monster.

DAVID 12:04

Yeah, interesting perspective, Melissa. I love that story. And one of the things that just really resonated with me when I started here at Alteryx, and I worked for a lot of different analytics companies and have been in the business-- well, I have no hair, so you can probably tell how long I have been in the business. I had a customer. I will never forget this. The first week they said, "David, Alteryx has changed my life." And that was very transformative to me. I'm like, "What do you mean? This is software. How is it changing your life?" And like, "No, listen. It's really changed my life." And to get back to your analogy, Melissa it was this analytics jetpack that you referred to. They were riding this jetpack because they were doing the same thing day in and day out. It was Monday and it was copying and pasting things from system A to system B, and they were working long nights and weekends, and it really was soul crushing. It killed their soul. And through analytics automation they were able to spend more time with their family. And so I really like this human centered approach that we discussed in the book because you hear the word automation and analytics that we mentioned this earlier, that's going to take your job. But analytics are at the core of just about every decision that are made. And so we want them to be innovative. We want them to be productive for companies, but we want them to have a life too. And so I really like this human is at the center of really this analytics revolution that's out there. To tie it back to [inaudible], I don't know, but it exceeded my great expectations to use a bad pun [laughter].

MELISSA 13:48

Yay, [inaudible].

MEL 13:50

Well, that's great. And the idea of time as a reward is wonderful. And additionally, other types of ROI from like bottom line and top line growth, and perhaps we'll move into the next one, which is rags to riches. I love a wonderful Cinderella story. Perhaps you could share your favorite customer stories.

MELISSA 14:13

Oh, I can hop in. I know I just shared a story about that construction organization. Now Mel if you'll if you'll clarify for me. So rags to riches is in essence, where your protagonist, they acquire some new power, some value that they lose it all but they gain it back and then they grow from the experience. Is that right? It's sophisticated, right?

MEL 14:39

That's exactly right.

MELISSA 14:41

Okay. So there's like a bit of a rollercoaster trajectory. I personally can't say that I know many analytics professionals who've admitted to gaining and then losing incredible data powers. Borrowing as memory wipe of some sort. But there is a story I know that this reminds me of. And I'm going to steal it from my colleague David. David, I feel like you mentioned this to me recently. Now, I can't recall the details of which organization this was. But you had heard of an analyst who had been using Alteryx for a number of years and took a job at a new company fairly recently. But when he arrived at this new business, he discovered they weren't using Alteryx. He got them to trial it. But ultimately they just wouldn't buy Alteryx for this fellow. They insisted this analysts use other tools the company had installed. And I understand that that analysts decided to quit that new job as a result and joined a new company. And one of the conditions of his joining was that they would buy him Alteryx.

DAVID 15:48

Yeah. No, that's a great. It is actually one of my favorite stories. Go with Alteryx or go home. That's how I sort of summarized that one. But that's exactly what happened. And then they had it and it was taken away. He had to leave.

MELISSA 15:59

Exactly. But he stood his ground. What do you think or in what ways do you believe this analyst must have grown as a person through this experience?

DAVID 16:11

Wow. That's a question for me, Melissa?

MELISSA 16:13

Yeah. Well, I mean, we can extrapolate if you know this person better. But the whole point of rags to riches is that for the journey that character has developed. Imagine what you've learned if you walked out of a new job because you didn't get the tools you needed.

DAVID 16:32

Yeah. I think I would probably say having the power and having it taken away and they were probably back to copying and pasting things from system A to system B. And he realized having been on the other side of the coin, that there is a better way to doing things. There is a smarter way of doing things. This ability to automate analytics. That it gives people their their life back. I keep coming back to that. This human that's at the center of everything. But yeah, I wouldn't want someone taking that power away from me.

MELISSA 17:03

Yeah, absolutely. I imagine that the confidence that that must have instilled to have overcome that obstacle and to have regained that power, that would be such a shot in the arm in terms of one's self-esteem. Really recognizing your value as a professional, you need to have the things that work for you in order to do your best work.

DAVID 17:24

Absolutely. Absolutely. Alteryx is like the-- a lot of people refer to it as the Swiss Army knife of analytics because there's just so much capability and there's something really for everybody in there. Whether you want to just prepare data for reporting or do more sophisticated things with automated machine learning or forecasting or geospatial, there's just a wealth of capability in there that gives people this power and it makes them hugely important and impactful on their organizations that they work for.

MEL 17:56

Well, speaking of power and impact, our next plot line is the quest. And perhaps you could walk us through some tales of digital transformation.

DAVID 18:08

So the quest, are we thinking like it's just like Monty Python or Indiana Jones or something along the way?

MEL 18:17

Yes. Raiders of the Lost Ark.

DAVID 18:20

Okay. Yeah. This wasn't in Alteryx story per se. But when I was working in an analytics center of excellence earlier in my career, being at center of excellence we interacted with all the business units around this previous company. And I would have people come up to me, I was a solutions consultant at the time, and they said, "Dave, I want to be smarter." And I'm like, "That's great. Me too. What do you want to do?" But they had heard the term analytics or data science. I don't think that data science term existed then. We used the term data mining. But they wanted to do something with analytics. They wanted to do something with data mining. They knew it could help their functional area, but they didn't quite know how to apply it to their specific business problems. So they went on a quest and really I had them step back and say, "Hey, you know what it's the biggest pain in your butt that you're dealing with today." And articulate these use cases. And then they would use analytics to solve their particular problems. So I think that's a quest where they were in that Raiders of the Lost Ark or what have you. They're in this dungeon, they want to be smarter, they know there's there's gold in this tunnel or what have you, but they didn't quite know how to get to it. And that was the role of-- my role at the time was to help them find the hidden treasure.

MELISSA 19:48

I love that. That's such a good tie end. I can hear the theme in my head now.

DAVID 19:54

I hope it doesn't stick with you all day. Sometimes these things just sit in your head.

MELISSA 19:59

I think it's a wonderful march to have in your head. I feel inspired.

MEL 20:03

Great. So along the same lines, our next one is of a similar theme and that is voyage and return. So in that respect, perhaps you could speak to the role of upskilling.

MELISSA 20:18

Gotcha.

DAVID 20:18

sure. And what's the difference between sort of the quest and the voyage and return, Mel? I'm curious.

MEL 20:24

That's a good question. And I don't know if there is that much. But along the way you return with that experience and those lessons learned along the way. I don't know if Indiana Jones really learned anything as opposed to just receiving a treasure.

MELISSA 20:45

It's sort of like The Hobbit, right? The Hobbit is a voyage and return story, isn't it?

MEL 20:50

Right?

DAVID 20:51

Over three very long movies. We did learn that Indiana Jones doesn't like snakes though [laughter]. So we got that piece covered. So upskilling. There's a story that I really enjoyed. There was one of our customers out of the UK and they're an accountancy and one of their things that they needed to do was they would process timesheets. So contingent workers and there was going to be a regulatory change in the UK. And if this change went through they would have to hire quite a few people to process these timesheets that they were getting. And so what do you do as a business? You think about this-- now, we don't know if the regulation is going to go through. So do I hire 50 people to do this? Or do I consider an alternative approach? In the background of the individual that started this is, he's not in the data science. I think it was like maybe a sports background or something like that. But so [inaudible] approachable solution. But what they did is they decided to automate the processing of these timesheets. And they saved hundreds of thousands of dollars. They had better accuracy through the analytics automation. They had better customer service because they actually hooked up the output of the analytics workflow to WhatsApp. And so people were getting automated notifications that they got the money or they're waiting on the payments from the vendors and things like that. And that's just really a story that's really stuck with me because you don't have to be an expert.

DAVID 22:37

You don't have to have a background in mathematics or science or whatever. This is one of the most approachable solutions in the world and anybody can use this. And that's really core to our company's ethos. We want to empower every single person in the world to use data and analytics to solve problems. So that's my story on upskilling. Now, I don't know what movie that ties to, but I love the story.

MELISSA 23:05

That's beautiful. It's almost like a reinvention of his identity as a result, as well as the organization benefiting at the same time. When I think about upskilling, right? I tend to take it to a very personal place, right? This is an almost a career development angle. And and there's an analyst that I'm aware of at a major US airline who was working on cruise scheduling and had an experience through analytic automation that I think really did take him on this sort of voyage and return. Although, I think he returned back to something better, at a higher level. And so basically this analyst was responsible for the reserve crew assignment process. And it had been totally manual. It relied on individual schedulers intuition to make the daily decisions of who went where. This was, of course, unsurprisingly, very slow and crew assignments could only be made so far in advance as a result, not very far. And so this analyst in discovering automated analytics through Alteryx began to learn how to create code free predictive models, right? As David was saying, this is meant to be accessible, it's meant for normal humans become superheroes, right? And so with this new analytic automation under his his wings, this analyst was able to generate crew reserve forecasting models that could schedule both for pilots and for flight attendants across 10 different air bases. Now, naturally, this wiped out human error because of all that copy pasting that no longer needed to happen, and it dramatically increased the efficiency of crew scheduling. And it freed the rest of the analytics group to solve increasingly valuable new problems for the airline.

MELISSA 24:59

And this particular analyst, as I mentioned here, there's a real personal angle to the story having learned hands on about machine learning through the Alteryx environment, right? Really learning by doing. He was invited to join the data science team as a result of this project. He has become a full fledged data scientist in the organization, and he continues to work deeply on data science solutions to this day. So he went out, tried something different, was able to improve his organization and himself as a result.

MEL 25:36

Sounds like a true hero. Yeah. Wonderful story.

DAVID 25:40

Yeah. Just another comment on upskilling. I was just thinking about it. What makes this Alteryx different is I think about the role our community plays in this. And people ask me, "Why did you come to Alteryx?" And the community I think it's up to like 270,000 members, and so I like to say a quarter of a million people can't be wrong. I don't want to underestimate the power that the community has because in the end, people want to learn from like minded individuals that have very similar problems to them. And the community really provides that path to peer to peer networking and helps them upskill. So I think the community is hugely important to other users and our company. But that 270,000 members is quite impressive.

MEL 26:40

Without a doubt, for sure. And of all those members, many are on their way through the digital transformation process. And perhaps you could speak to some tales of failure along the way.

MELISSA 26:56

I like that, Mel. All right. We'll take it to a dark place. David, you cool if I kick this off?

DAVID 27:02

You go ahead, Melissa.

MELISSA 27:04

Awesome. Thank you. Well, I understand. So I've been thinking a lot about digital transformation lately. So I happen to have a statistic in my head. According to the Boston Consulting Group, something like 70% of digital transformation initiatives fall short of reaching their objectives. And I think there are a handful of pretty common reasons why this happens to organizations. First and foremost, wrong mindset, right? People are naturally averse to change, I think, in general. So you need leadership to set the right tone early. Also, organizations miss the opportunity to embrace IT as a transformation accelerator. Not just a necessary evil, but an enabler. So that's number one, wrong mindset. Number two, culture. So cultural change that is the core of a successful digital transformation. It's all about getting different departments and different groups within departments to work together better. And collaboration really needs to be baked in as well as valuing data. Then there is number three, I'd say talent. You need the right people to drive change forward, right? If you have who you've always had and you do what you've always done, David, what are you going to get?

DAVID 28:31

Well, you're not going to get very far [laughter]. It's like me walking on the treadmill all day in and day out, just doing the same thing, but I'm not really going anywhere.

MELISSA 28:42

Bingo. Exactly. So digital transformation, right? It's in the word, transformation. Transform, change. And that does mean cultural change. Sometimes personnel change. And then the last item, what would that be for? This is my fourth common reason why, is technologies. So we talk a lot about people and processes in digital transformation, but it also requires good data and data technologies to succeed. And doing that well requires really looking at a full stack of technology, right? New information requires new systems, as well as new security and new compliance infrastructure. All those groups should be working together and involved early. If you make them an afterthought, well, you get tragedy. I mean in a big picture perspective. It can't just be one point solution.

DAVID 29:39

Nice, nice. Along similar lines I think you said 70% of these digital transformation initiatives fall short. I read another survey at the believers from New Vantage Partners, but they said 93% of projects fail. Not due to technology, but due to people and process issues. So this topic of change management and how to build an analytic or data driven culture, I think is hugely important. And keep coming back to this human centered approach, but you have to make the humans part of the solution, right? We can go right back to Carter and talk about change management, but we want people whose way of working is going to be disrupted or changed or different. You have to involve them in the process along every step of the journey. So I think there's a huge thing to be learned about change management. How do you do it well in companies? I think there's a lot of steps that companies need to think about when they are going to reimagine or innovate their business processes. And it goes all the way from the people, the process and the technology. And one of the things that winning companies do, there's an article from McKinsey, it was called The Drumbeat of Digital or something like that. But they constantly iterate. So the days of these long, drawn out waterfall processes where a year from now we're going to have nirvana and then it's always takes five years, it seems. But doing these things incrementally and celebrating the small wins and keep iterating and refining that approach. Don't wait for this big bang thing to happen.

DAVID 31:25

And I think to do that, you need both a tops down approach as well as a bottoms up approach. Because you have to have the people. They have to be excited to want to participate in the process. Because you don't have the users and their employees at mind when you're doing this, you're going to fail. So involve them early and often because they're part of the solution.

MEL 31:43

That's great advice, David. And then to wrap this up, we'll go with our final plot, which is rebirth. And Melissa, you had mentioned the idea of shifting mindset. How does one go from this is how we've always done it to that more proactive level of thinking?

MELISSA 32:01

That's really nicely phrased, Mel. Well, I think that it's easy to step back and be kind of a Monday morning quarterback, right? And armchair philosopher, like, "Well, you just kind of get past it." I think that seeing examples of success in other organizations can really help you identify where it is that you need to catalyze change in your own. And get past, doing it the way we've always done it. And so within that context, I'd love to share a story that I know about that was having this exact kind of struggle of needing to reinvent but being very entrenched in the way things were. They are a 3D printer manufacturer and they were struggling with a few things. First, they needed to support ever more data sources than before, with a limited team. Had to do with some organizational growth, and management was relying on several disparate and disconnected platforms, right? They were using straightforward things like Excel spreadsheets, as well as common systems like Salesforce, CRM and Oracle to try to get a complete view of the business. And that had been working comfortably when they were fairly small. But as they were growing and it was a problem of success, which is great. Their file sizes started growing too. Growing out of control, right? And the data formats, again, they were just not compatible with each other. And it became impossible for management to recognize things like pre-sales and post-sales data. And the leadership was being blinded to the true performance of the organization because they couldn't reconcile their data at scale. There were change agents present at this organization in the analytics team, but leadership had gotten comfortable, right? They were hesitant to let go of doing things in spreadsheets, right?

MELISSA 34:00

Even though the business had clearly outgrown the old way, the psychology in management was just not ready for that kind of a disruption. And so the analytics team recognizing this was a specific type of challenge they had to address, they started doing bake offs. Basically the old way and a new way, right? Bringing in analytic automation to directly compete with the standard kind of comfortable old procedures. And management got to see for themselves that they got the same analytic results that they wanted just faster, cleaner, more auditable and without errors when they used analytic automation. So finally, and this does take patience to get people to kind of become reborn and see the world in a different way. After months of the analytics team basically doing the books twice, leadership was willing to relinquish the old processes that were clearly holding the business back from growth. And as a result, the whole organization got to accelerate, right? They're saving time. They're having regular and more effective conversations internally and with their partners. And it opened the door to clear process mapping, improvement of documentation and unprecedented unexpected improvements like a true 360 degree customer reporting system and cross departmental collaboration that really empowered individual creativity within the org. And so as a result of analytic automation, the whole business's mindset was able to change from reactive to the market to becoming proactive and really having a hand in shaping the future of 3D printing.

DAVID 35:50

Wow. I don't think I can follow that, Melissa [laughter]. That was a really impactful story. Just doing things the way we've always done them. I'm going to just share a quick story about when I started my career it was in the high tech manufacturing space. And I wanted to, maybe I was trying to show off. Maybe I was just too young and didn't know what I was doing, but I wanted to-- we're trying to improve manufacturing quality. So you have all this data like I'm going to do technic called principal component analysis. And for those of you who are not familiar with that, it's just really a data reduction technique when you have lots and lots of variables that could be influencing your process. So I do this analysis and I was really proud of it. And I showed it to one of the managers that owned a particular part of the manufacturing production process. And he's like, "What's this?" "Well, that's a principal component analysis. This is how it works." She's like, "I don't trust it. I don't understand it. So we're going to go with our gut." I learned early on that communication is actually really, really key in some of these. And there's this organizational inertia that people have done things a certain way. It can be quite challenging to overcome.

MEL 37:07

Great. Well, I'd like to thank you both for being here with me today. Melissa, David, it's been a pleasure. And we'll catch you next time.

MELISSA 37:16

Thank you so much, Mel.

DAVID 37:17

All right. Thank you so much. It's been a pleasure.

MEL 37:19

Thank you.

MADDIE 37:23

Thanks for listening. You can grab your download of Melissa and David's book, Automating Analytics at community.alteryx.com/podcast and click on this episode's page. Catch you next time.

 


This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).
Special thanks to @andyuttley for the theme music track, and @TaraM for our album artwork.