Alter Everything

MaddieJ · ‎10-17-2022

What are the essential pieces of a data democratization strategy? Learn from Alteryx Chief Transformation Officer Steve Brodrick as he shares tips on building a road map, creating effective processes, and upskilling teams.

Panelists

Steve Brodrick - LinkedIn
Megan Dibble - @MeganDibble, LinkedIn, Twitter

Topics

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Transcript

Episode Transcription

MADDIE 00:02	Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen, and for today's episode, I pass the mic to our community data journalist, Megan Dibble. Megan first started using Alteryx as an analyst at Stanley Black & Decker, where she met Steve Brodrick, our guest for this episode. Steve also used to work at Stanley Black & Decker and is now the chief transformation officer at Alteryx. So in this episode, Meghan and Steve dive into data democratization, including what it is, why we should care, and how to figure out a data democratization strategy. From creating a roadmap and prioritization process, to establishing an enablement plan for analysts across the organization, Steve is full of practical tips and wisdom that will help you on your journey. So here's Megan to kick off the conversation.
MEGAN 00:55	Hi, Steve. Thank you so much for joining us here on the podcast. It's great to have you. I would love for you to just tell us a little bit about yourself.
STEVE 01:03	Sure. Good to speak with you, Megan. So my name is Steve Brodrick. I'm the chief transformation officer here at Alteryx. I have had experience most of my career inside finance, and have had a lot of involvement in the last, I'll say, 5 or 10 years in transformation analytics. When I'm not doing that, I am an avid outdoors person and like to kayak, and hike, and ski, and pretty much do anything outside.
MEGAN 01:30	That's awesome. I love to be outside as well, being in Colorado. Yeah. It's really fun to have you on here too, since we both used to work at Stanley Black & Decker. So that's a fun connection. So I'll just jump right into my first question for you. And that is, what does data democratization mean?
	I know that's a buzzword that we're hearing a lot in the industry. So what does it really mean to you? How would you break it down to someone?
STEVE 01:57	Yeah. I mean, the part I think about when I think about data democratization is just getting the data in the hands of people who are going to use it. In various situations I've been in or my teams have been in, there's varying degrees of having access, and being able to get out the data sources you need to try to drive decisions. So when I think about democratization, it's making that available to people who are trying to solution something or look for insight, etc.
MEGAN 02:22	Yeah. That's awesome. It's good to kind of really explain what that term means. And once we know that data democratization is getting data into the hands of the users, why should we care about that in our organizations? Or why would we want to get the data into the hands of everyone?
STEVE 02:43	Yeah. When I think about that question, Megan, a couple of things come to mind. I think one area that I would mention is there's just been an incredible amount of data that's available in our daily lives. So the ability for us to wake up in the morning, you check your phone for what the weather is, you hop in your car, and you put the Waze on and figure out where that app will take you to as far as distance to get to a location. Once you're there, you can go to your phone and grab different spots to go shop, or wherever you're looking to arrive in that particular day. So it's just the amount of data that is available at your fingertips is massive. And why should we care about it is, I think, if there's an ability for teams or individuals to harness the data that's around them, it just makes them be able to operate with a lot more speed and agility, and also become more effective as a result of having your hands on the data sets. When you're trying to solve a particular problem, it just allows you to gain some perspectives, and iterate, and hypothesize, and really work through it on your own. And I think that really lends itself toward more innovation, if you have access to data at your fingertips. So there's a lot of benefits to it. But I think those are some of the ones that come to top of mind, based on your question.
MEGAN 03:59	Yeah. Definitely. Along with that, when we're talking about getting data into the hands of your team, making them more agile, curious to hear about any experiences you have with that. Any examples you have from previous jobs about what that looked like, and if there were challenges you had to overcome.
STEVE 04:21	Yeah. For sure. There have been many. And maybe one broad statement. I'll give you an example, but just the amount of complexity in our daily lives, or if you're in any position where you're trying to drive decisions. Today's environment is certainly more complex than 5, 10, 15, 20 years ago. So when I think about that question, I think about just how much is out there, as far as available for us to go in and drive solutions with as far as the data is concerned. And one example in our past experience, we were both at Stanley, Black & Decker, was when tariffs hit. The company, we had to quickly look at the data sources that housed all that information as far as our part numbers, our commodities, our SKUs, and understand where the biggest impacts were, so that we could go drive decisions around pricing and offsetting those costs. And you can imagine how difficult that is without visibility and transparency. So the advent of tools and processes that help us get our hands on that data, understand it in a deeper form, and then be able to drive decisions, which is so critical for anybody who's looking at trying to solve a problem, or engage around a challenge that involves data.
MEGAN 05:35	Yeah. Totally. I remember that time back at Stanley when everybody was talking about tariffs, that there was a lot of stress. [laughter] And I think that having that data available, that takes off just one piece of the stress around tough economic situations like that.
STEVE 05:53	Right. That was a good example to me, Megan, of the power of having the information, and the transparency to figure out how you drive decisions. So I totally agree with you.
MEGAN 06:02	Yeah. Going along with the topic of democratizing data could involve change. I was thinking about this topic, and I was thinking about there could be fears in the organization around data security, about, "If we let all our users or all of our employees have access to all of our data, there's going to be huge data security implications." And perhaps there are just some other fears around that. So what's your take on general fears about data security around the topic of democratizing, getting data to everyone?
STEVE 06:37	It's a very valid question I think a lot of teams are looking to solve for. And in today's environment, you're trying to empower local decision maker or analyst, and help give them access to the right information sets to drive decisions and make recommendations. At the same time, there's a need to make sure that it's done in a controlled way where those data sources are validated, the access to the data is getting in the hands of the right people. The way that I like to think about it, Megan, is there's probably a sliding scale in most companies, where you have all the way on one side of the spectrum full democratization and the ability to have everything at your fingertips. And all the way on the right side of the scale is a highly controlled environment where the users don't have really any access unless they go to somebody in the organization that provides it to them. In some cases, for example, in regulatory environments or banks, there's a need to have that high level of control on data and what those sources of data are. Those are, in some cases, systems of record, and it's really critical. However, there are also many examples of analysis that involve data sources where there's less need for regulatory compliance, and it's just more about empowering teams to drive decisions based on those datasets that became available to them. In most cases, I think it's probably somewhere in the middle where you need some level of control and governance and access, but at the same time, you can really empower users to have the ability to drive insights, and test their hypotheses with data, and start to iterate on solutions, etc. So to me, it's a sliding scale. And teams that will succeed in working through this are ones that involve both the business user plus the IT organization, the privacy organization, and they work together to understand what particular data set is being used, and what level of governance is appropriate based on the particular area of need.
MEGAN 08:30	Yeah. That's great. And it shows that it's not just a black-and-white issue. There's definitely a scale. And you mentioned that IT and analytics and other teams need to be collaborating or be in communication. When we talked about this earlier, you mentioned the Data Council at Stanley Black & Decker. So would love to hear about what that was, and how that allowed for communication across those channels.
STEVE 08:55	Yeah. I think there's an opportunity there, where we just had that discussion about various areas of data, and ensuring data quality and access and governance. But I think the thing that I've learned over the last several years, in particular, is the data touches a lot of people in the organization. And one of the things that was effective about that council you mentioned is we organized every couple of weeks around the key stakeholders. Some of those folks were requesting data to drive KPIs and reporting. Other people in that community were looking at various technologies to support getting access to the data. There was the IT organization who was looking at the applications and the data sources, and ensuring the quality of those data sources. So what we did is we created a community around the needs that were surfacing for insights and analytics. And rather than create an environment where everybody was operating in silos, we had a team meeting every couple of weeks working through what were the priorities that we were trying to solve for, and making sure that it was done in a controlled way, but also without sacrificing speed. And that usually is a cross-pollination of people who can make that happen as opposed to one individual function or business unit that established a view. So I think first couple of meetings we had, if I recall, we were evolving it over time. But it got to a point where there was a lot of great collaboration. And each area, I think, over time had a better understanding for what the data analysis needs were, and what they were being used for. And then it became a lot of collaboration to get to the outcomes we were looking to drive. So I think it had an impact on the team of really creating more of a team environment around this.
MEGAN 10:28	That's a really cool thing that we had at Stanley Black & Decker, and the community of people at Stanley, Black & Decker that were using data. So how did you engage with that community?
STEVE 10:41	First of all, we took an inventory of the folks inside the entire company who spent the preponderance of their time in either data elements and master data, or analytics, or artificial intelligence, etc. Folks who were really working with data and analytics a significant portion of their time. That was the first step, is look at who that community is. And then over time, we set up what we called an ambassador network where we logged the skills of that community. And people were able to, essentially, do a proficiency and skills test that helped them understand where they were in a spectrum of data and analytics capabilities. And then we provided that team with access to training and other coursework that they could take on their own time, but that allowed them to continue to gain more skills and proficiencies with data analytics work that they were doing. I think that was an important step. Because everybody has different goals with regards to their skills that they're building. And I think, in this case, we were able to have a meeting with some frequency, I believe it was monthly, where that team got together. They talked about the coursework that they all were evaluating, and whether it was helpful or not, or to what extent it was helpful. And also, they were communicating what things they were working on that were driving outcomes or insights with data and analytics. That way there was a learning back and forth between the team members. And it became, I think, an area where people could learn from what others were doing. And that was a key part of helping them grow with their skills, as well. So I think that was something that was positive and engaged teams more widely around the topics of data analytics and democratization and automation. So I think that's something that helped, really, people at the point of impact grow their skill set in the area, and was a positive aspect for helping the community grow with their skills, as well.
MEGAN 12:35	Yeah. I think I remember that program. And it was cool to see how people were using certain tools, whether it's Alteryx or Power BI, and then get ideas for how to use them for your own projects through communicating those successes, or showing off those use cases internally. That was something that I remembered from my time there.
STEVE 12:55	Yeah. And I think, to build on what you just said there, in most cases those teams were either trying to organize the data, or they were trying to visualize it and look for anomalies or areas of focus. Or lastly, create value with the data to a point where it could drive decision-making that created value, reduced inventory, improved efficiency, drove potential for growth, things of that nature. So there's such a wide arena when you think about data and analytics, that it was good to get that group together to help understand what things were they seeing value in, and what were they building skill sets around to get the outcomes they were driving. So I think it was positive for that team to go through and learn together.
MEGAN 13:40	I would love to hear what are the most important steps for a company to take towards data democratization, really getting into the practical takeaways.
STEVE 13:51	Yeah. When I think about data democratization, a few steps that I think are critical are number one, it's not just the access to the data, but it's also the quality of the data that's important. So companies continuously evaluating their enterprise data, and the data foundation that it sits on, and their tech stack. I think that needs to continue to evolve in most companies over a multi-year, multi-decade roadmap. As companies acquire or become more complex, it's really important to have a continued emphasis on the data foundation. So I think that's a critical part I wanted to make sure I mentioned today, that I think is a key part of democratization. Another one is just trying to align around priorities. It can become very easy in today's world to just try to solve for all data. And in some cases, small data or some sets of data can be really useful to drive certain outcomes without a broad-based multi-year effort to [tie out?] data 100% within a large company. So I think that requires teamwork to understand what are the outcomes we're looking to drive with insights and analytics and data. And there are various ways to enable a prioritization process around teams that kind of funnels the work effort into the right areas that drive either significant efficiencies, or help with driving effectiveness in an organization.
STEVE 15:11	The third one, I would say, is probably more related to the people side. But just because you have access, or the data has become available, does not necessarily mean that you're able to organize it, use it, and create decisions off of it. It's a learning curve that I think any individual, including myself, goes through. So I think having a very deliberate effort around taking an inventory of the skill sets that people have in analytics, and coming up with individualized roadmaps that help analysts along the journey of how do they organize around data, how do they visualize it, how do they create value with it, is something that does not happen overnight. So I think companies that pay close attention to that, and really empower their teams with training, and also there's a key element of digital literacy and data fluency that I think is becoming more critical in many organizations, if not all organizations, based on the more complex environment, and the need for speed that I've mentioned so far today here. So I think that third party is very critical. If there's one more I would mention that is an important element of democratization, it's that it can be a force multiplier to have leadership who understand the importance of people driving skills within analysis and data, and sponsoring training, or acknowledging wins that occur in the spaces of organizing, wrangling data, and driving analysis that proves to be valuable to a company, whether that's driving efficiencies or pointing to growth that may lie ahead, etc. So I think as leaders, it's an important step to encourage people to try to work in an environment that democratizes data. And also work up the learning curve on how they grow as analysts using the data, visualizing it, then creating value.
MEGAN 17:01	Yeah. Those were a ton of awesome, practical steps and takeaways. And it's cool to hear that, knowing that I was an analyst, I didn't always get to see those high-level conversations. So it's cool to break that down in the podcast. And I can attest that I enjoyed being a part of the training. And there were lots of trainings on data literacy and different tech stacks. And so being able to develop those technical skills was fun for me. And now I know it was part of a really solid strategy of data democratization, so that's awesome.
STEVE 17:34	That's great. That's great to hear.
MEGAN 17:36	Yeah. So then, switching gears a bit, but kind of in line with the topic of having senior-level support and buy-in, you've talked in the past about how it's important for companies to align on data organization and process. And so from that side of things, looking at the organization and processes around data, what are some practical ways companies can align?
STEVE 18:03	Well, you mentioned it there. I feel like data, and democratization of that data, is really important. I also feel that once you've got your hands on data and you're organizing it in a way where you can start to structure a path ahead for whatever you're trying to solve, there's an important element of process that is critical, as well. So data that's presented in isolation is maybe a value, but if you create an environment where your business processes change and shift, I think that's often the goal of an analyst and somebody who's working through a challenge to solve. And you really have a difficult time doing that if you're not assessing your current process, looking at the steps of decision-making and how to automate, but also create a better path for the future process that you're engaged around. So for example, if you're in a supply chain organization, and you're faced with how do you optimize inventory levels, it would be advisable to work through how do you connect your demand sources, but also the supply sources to create an optimal level of inventory that supports the customer, but with the appropriate level of safety stocks, etc. So the data is an important part of that, but also how do you take decisions differently based on that? And the whole spectrum of demand through supply, and ultimately your suppliers, is a pretty vast arena. Right? So I think it's important, as we think about data democratization, to also think about what ways are there to automate process, in addition to the data itself? And also visualize that, so that it becomes more standardized as a go forward process. Maybe the third element that I think is critical is mistake proofing. So if you have an environment where you've got your hands on the data, you have done a value stream map and you figure out what the path is ahead to create a new future process leveraging better data and insights, it's also keeping an eye toward what's flexing for your inputs and your outputs. And looking at how you're driving outcomes with the data sets and analysis that you're doing. And continually evolving that over time.
	So it's never a dull moment when you're working in an analytics and democratization environment. But I think those are some of the key steps that help companies be more agile and sustainable.
MEGAN 20:14	Yeah. That's great. A lot of great points there. Sounds to me like data democratization involves not only having the data, having the tools, but also establishing the processes, and investing in the people, as a way to summarize. So--
STEVE 20:28	That's a great way to summarize it. I couldn't have said it better.
MEGAN 20:31	Awesome. Well, I guess I summarized it well because you explained everything so well. We'll just go with that. [laughter] But yeah, this is--
STEVE 20:40	We're a good team, Megan.
MEGAN 20:41	Yes. This has been an awesome conversation. So thanks so much for being willing to be on the podcast and share your insights from experience with us. And really enjoyed it.
STEVE 20:54	I've enjoyed it too. And I'm a learner in this space. It's an area of high interest to me. And it's always exciting to think about the space of data analytics, and how you can really empower teams with insights, and get to more agile decision-making that I think everybody's after. So I'll continue to learn in this area. And it's been great to share at least some of my experiences here with you today.
MEGAN 21:17	Great. Thanks, Steve.
STEVE 21:19	Thank you.
MADDIE 21:21	Thanks for listening. For more data democratization resources, check out our show notes at community.alteryx.com/podcast. Catch you next time. [music]

MADDIE 00:02 Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen, and for today's episode, I pass the mic to our community data journalist, Megan Dibble. Megan first started using Alteryx as an analyst at Stanley Black & Decker, where she met Steve Brodrick, our guest for this episode. Steve also used to work at Stanley Black & Decker and is now the chief transformation officer at Alteryx. So in this episode, Meghan and Steve dive into data democratization, including what it is, why we should care, and how to figure out a data democratization strategy. From creating a roadmap and prioritization process, to establishing an enablement plan for analysts across the organization, Steve is full of practical tips and wisdom that will help you on your journey. So here's Megan to kick off the conversation. MEGAN 00:55 Hi, Steve. Thank you so much for joining us here on the podcast. It's great to have you. I would love for you to just tell us a little bit about yourself. STEVE 01:03 Sure. Good to speak with you, Megan. So my name is Steve Brodrick. I'm the chief transformation officer here at Alteryx. I have had experience most of my career inside finance, and have had a lot of involvement in the last, I'll say, 5 or 10 years in transformation analytics. When I'm not doing that, I am an avid outdoors person and like to kayak, and hike, and ski, and pretty much do anything outside. MEGAN 01:30 That's awesome. I love to be outside as well, being in Colorado. Yeah. It's really fun to have you on here too, since we both used to work at Stanley Black & Decker. So that's a fun connection. So I'll just jump right into my first question for you. And that is, what does data democratization mean? I know that's a buzzword that we're hearing a lot in the industry. So what does it really mean to you? How would you break it down to someone? STEVE 01:57 Yeah. I mean, the part I think about when I think about data democratization is just getting the data in the hands of people who are going to use it. In various situations I've been in or my teams have been in, there's varying degrees of having access, and being able to get out the data sources you need to try to drive decisions. So when I think about democratization, it's making that available to people who are trying to solution something or look for insight, etc. MEGAN 02:22 Yeah. That's awesome. It's good to kind of really explain what that term means. And once we know that data democratization is getting data into the hands of the users, why should we care about that in our organizations? Or why would we want to get the data into the hands of everyone? STEVE 02:43 Yeah. When I think about that question, Megan, a couple of things come to mind. I think one area that I would mention is there's just been an incredible amount of data that's available in our daily lives. So the ability for us to wake up in the morning, you check your phone for what the weather is, you hop in your car, and you put the Waze on and figure out where that app will take you to as far as distance to get to a location. Once you're there, you can go to your phone and grab different spots to go shop, or wherever you're looking to arrive in that particular day. So it's just the amount of data that is available at your fingertips is massive. And why should we care about it is, I think, if there's an ability for teams or individuals to harness the data that's around them, it just makes them be able to operate with a lot more speed and agility, and also become more effective as a result of having your hands on the data sets. When you're trying to solve a particular problem, it just allows you to gain some perspectives, and iterate, and hypothesize, and really work through it on your own. And I think that really lends itself toward more innovation, if you have access to data at your fingertips. So there's a lot of benefits to it. But I think those are some of the ones that come to top of mind, based on your question. MEGAN 03:59 Yeah. Definitely. Along with that, when we're talking about getting data into the hands of your team, making them more agile, curious to hear about any experiences you have with that. Any examples you have from previous jobs about what that looked like, and if there were challenges you had to overcome. STEVE 04:21 Yeah. For sure. There have been many. And maybe one broad statement. I'll give you an example, but just the amount of complexity in our daily lives, or if you're in any position where you're trying to drive decisions. Today's environment is certainly more complex than 5, 10, 15, 20 years ago. So when I think about that question, I think about just how much is out there, as far as available for us to go in and drive solutions with as far as the data is concerned. And one example in our past experience, we were both at Stanley, Black & Decker, was when tariffs hit. The company, we had to quickly look at the data sources that housed all that information as far as our part numbers, our commodities, our SKUs, and understand where the biggest impacts were, so that we could go drive decisions around pricing and offsetting those costs. And you can imagine how difficult that is without visibility and transparency. So the advent of tools and processes that help us get our hands on that data, understand it in a deeper form, and then be able to drive decisions, which is so critical for anybody who's looking at trying to solve a problem, or engage around a challenge that involves data. MEGAN 05:35 Yeah. Totally. I remember that time back at Stanley when everybody was talking about tariffs, that there was a lot of stress. [laughter] And I think that having that data available, that takes off just one piece of the stress around tough economic situations like that. STEVE 05:53 Right. That was a good example to me, Megan, of the power of having the information, and the transparency to figure out how you drive decisions. So I totally agree with you. MEGAN 06:02 Yeah. Going along with the topic of democratizing data could involve change. I was thinking about this topic, and I was thinking about there could be fears in the organization around data security, about, "If we let all our users or all of our employees have access to all of our data, there's going to be huge data security implications." And perhaps there are just some other fears around that. So what's your take on general fears about data security around the topic of democratizing, getting data to everyone? STEVE 06:37 It's a very valid question I think a lot of teams are looking to solve for. And in today's environment, you're trying to empower local decision maker or analyst, and help give them access to the right information sets to drive decisions and make recommendations. At the same time, there's a need to make sure that it's done in a controlled way where those data sources are validated, the access to the data is getting in the hands of the right people. The way that I like to think about it, Megan, is there's probably a sliding scale in most companies, where you have all the way on one side of the spectrum full democratization and the ability to have everything at your fingertips. And all the way on the right side of the scale is a highly controlled environment where the users don't have really any access unless they go to somebody in the organization that provides it to them. In some cases, for example, in regulatory environments or banks, there's a need to have that high level of control on data and what those sources of data are. Those are, in some cases, systems of record, and it's really critical. However, there are also many examples of analysis that involve data sources where there's less need for regulatory compliance, and it's just more about empowering teams to drive decisions based on those datasets that became available to them. In most cases, I think it's probably somewhere in the middle where you need some level of control and governance and access, but at the same time, you can really empower users to have the ability to drive insights, and test their hypotheses with data, and start to iterate on solutions, etc. So to me, it's a sliding scale. And teams that will succeed in working through this are ones that involve both the business user plus the IT organization, the privacy organization, and they work together to understand what particular data set is being used, and what level of governance is appropriate based on the particular area of need. MEGAN 08:30 Yeah. That's great. And it shows that it's not just a black-and-white issue. There's definitely a scale. And you mentioned that IT and analytics and other teams need to be collaborating or be in communication. When we talked about this earlier, you mentioned the Data Council at Stanley Black & Decker. So would love to hear about what that was, and how that allowed for communication across those channels. STEVE 08:55 Yeah. I think there's an opportunity there, where we just had that discussion about various areas of data, and ensuring data quality and access and governance. But I think the thing that I've learned over the last several years, in particular, is the data touches a lot of people in the organization. And one of the things that was effective about that council you mentioned is we organized every couple of weeks around the key stakeholders. Some of those folks were requesting data to drive KPIs and reporting. Other people in that community were looking at various technologies to support getting access to the data. There was the IT organization who was looking at the applications and the data sources, and ensuring the quality of those data sources. So what we did is we created a community around the needs that were surfacing for insights and analytics. And rather than create an environment where everybody was operating in silos, we had a team meeting every couple of weeks working through what were the priorities that we were trying to solve for, and making sure that it was done in a controlled way, but also without sacrificing speed. And that usually is a cross-pollination of people who can make that happen as opposed to one individual function or business unit that established a view. So I think first couple of meetings we had, if I recall, we were evolving it over time. But it got to a point where there was a lot of great collaboration. And each area, I think, over time had a better understanding for what the data analysis needs were, and what they were being used for. And then it became a lot of collaboration to get to the outcomes we were looking to drive. So I think it had an impact on the team of really creating more of a team environment around this. MEGAN 10:28 That's a really cool thing that we had at Stanley Black & Decker, and the community of people at Stanley, Black & Decker that were using data. So how did you engage with that community? STEVE 10:41 First of all, we took an inventory of the folks inside the entire company who spent the preponderance of their time in either data elements and master data, or analytics, or artificial intelligence, etc. Folks who were really working with data and analytics a significant portion of their time. That was the first step, is look at who that community is. And then over time, we set up what we called an ambassador network where we logged the skills of that community. And people were able to, essentially, do a proficiency and skills test that helped them understand where they were in a spectrum of data and analytics capabilities. And then we provided that team with access to training and other coursework that they could take on their own time, but that allowed them to continue to gain more skills and proficiencies with data analytics work that they were doing. I think that was an important step. Because everybody has different goals with regards to their skills that they're building. And I think, in this case, we were able to have a meeting with some frequency, I believe it was monthly, where that team got together. They talked about the coursework that they all were evaluating, and whether it was helpful or not, or to what extent it was helpful. And also, they were communicating what things they were working on that were driving outcomes or insights with data and analytics. That way there was a learning back and forth between the team members. And it became, I think, an area where people could learn from what others were doing. And that was a key part of helping them grow with their skills, as well. So I think that was something that was positive and engaged teams more widely around the topics of data analytics and democratization and automation. So I think that's something that helped, really, people at the point of impact grow their skill set in the area, and was a positive aspect for helping the community grow with their skills, as well. MEGAN 12:35 Yeah. I think I remember that program. And it was cool to see how people were using certain tools, whether it's Alteryx or Power BI, and then get ideas for how to use them for your own projects through communicating those successes, or showing off those use cases internally. That was something that I remembered from my time there. STEVE 12:55 Yeah. And I think, to build on what you just said there, in most cases those teams were either trying to organize the data, or they were trying to visualize it and look for anomalies or areas of focus. Or lastly, create value with the data to a point where it could drive decision-making that created value, reduced inventory, improved efficiency, drove potential for growth, things of that nature. So there's such a wide arena when you think about data and analytics, that it was good to get that group together to help understand what things were they seeing value in, and what were they building skill sets around to get the outcomes they were driving. So I think it was positive for that team to go through and learn together. MEGAN 13:40 I would love to hear what are the most important steps for a company to take towards data democratization, really getting into the practical takeaways. STEVE 13:51 Yeah. When I think about data democratization, a few steps that I think are critical are number one, it's not just the access to the data, but it's also the quality of the data that's important. So companies continuously evaluating their enterprise data, and the data foundation that it sits on, and their tech stack. I think that needs to continue to evolve in most companies over a multi-year, multi-decade roadmap. As companies acquire or become more complex, it's really important to have a continued emphasis on the data foundation. So I think that's a critical part I wanted to make sure I mentioned today, that I think is a key part of democratization. Another one is just trying to align around priorities. It can become very easy in today's world to just try to solve for all data. And in some cases, small data or some sets of data can be really useful to drive certain outcomes without a broad-based multi-year effort to [tie out?] data 100% within a large company. So I think that requires teamwork to understand what are the outcomes we're looking to drive with insights and analytics and data. And there are various ways to enable a prioritization process around teams that kind of funnels the work effort into the right areas that drive either significant efficiencies, or help with driving effectiveness in an organization. STEVE 15:11 The third one, I would say, is probably more related to the people side. But just because you have access, or the data has become available, does not necessarily mean that you're able to organize it, use it, and create decisions off of it. It's a learning curve that I think any individual, including myself, goes through. So I think having a very deliberate effort around taking an inventory of the skill sets that people have in analytics, and coming up with individualized roadmaps that help analysts along the journey of how do they organize around data, how do they visualize it, how do they create value with it, is something that does not happen overnight. So I think companies that pay close attention to that, and really empower their teams with training, and also there's a key element of digital literacy and data fluency that I think is becoming more critical in many organizations, if not all organizations, based on the more complex environment, and the need for speed that I've mentioned so far today here. So I think that third party is very critical. If there's one more I would mention that is an important element of democratization, it's that it can be a force multiplier to have leadership who understand the importance of people driving skills within analysis and data, and sponsoring training, or acknowledging wins that occur in the spaces of organizing, wrangling data, and driving analysis that proves to be valuable to a company, whether that's driving efficiencies or pointing to growth that may lie ahead, etc. So I think as leaders, it's an important step to encourage people to try to work in an environment that democratizes data. And also work up the learning curve on how they grow as analysts using the data, visualizing it, then creating value. MEGAN 17:01 Yeah. Those were a ton of awesome, practical steps and takeaways. And it's cool to hear that, knowing that I was an analyst, I didn't always get to see those high-level conversations. So it's cool to break that down in the podcast. And I can attest that I enjoyed being a part of the training. And there were lots of trainings on data literacy and different tech stacks. And so being able to develop those technical skills was fun for me. And now I know it was part of a really solid strategy of data democratization, so that's awesome. STEVE 17:34 That's great. That's great to hear. MEGAN 17:36 Yeah. So then, switching gears a bit, but kind of in line with the topic of having senior-level support and buy-in, you've talked in the past about how it's important for companies to align on data organization and process. And so from that side of things, looking at the organization and processes around data, what are some practical ways companies can align? STEVE 18:03 Well, you mentioned it there. I feel like data, and democratization of that data, is really important. I also feel that once you've got your hands on data and you're organizing it in a way where you can start to structure a path ahead for whatever you're trying to solve, there's an important element of process that is critical, as well. So data that's presented in isolation is maybe a value, but if you create an environment where your business processes change and shift, I think that's often the goal of an analyst and somebody who's working through a challenge to solve. And you really have a difficult time doing that if you're not assessing your current process, looking at the steps of decision-making and how to automate, but also create a better path for the future process that you're engaged around. So for example, if you're in a supply chain organization, and you're faced with how do you optimize inventory levels, it would be advisable to work through how do you connect your demand sources, but also the supply sources to create an optimal level of inventory that supports the customer, but with the appropriate level of safety stocks, etc. So the data is an important part of that, but also how do you take decisions differently based on that? And the whole spectrum of demand through supply, and ultimately your suppliers, is a pretty vast arena. Right? So I think it's important, as we think about data democratization, to also think about what ways are there to automate process, in addition to the data itself? And also visualize that, so that it becomes more standardized as a go forward process. Maybe the third element that I think is critical is mistake proofing. So if you have an environment where you've got your hands on the data, you have done a value stream map and you figure out what the path is ahead to create a new future process leveraging better data and insights, it's also keeping an eye toward what's flexing for your inputs and your outputs. And looking at how you're driving outcomes with the data sets and analysis that you're doing. And continually evolving that over time. So it's never a dull moment when you're working in an analytics and democratization environment. But I think those are some of the key steps that help companies be more agile and sustainable. MEGAN 20:14 Yeah. That's great. A lot of great points there. Sounds to me like data democratization involves not only having the data, having the tools, but also establishing the processes, and investing in the people, as a way to summarize. So-- STEVE 20:28 That's a great way to summarize it. I couldn't have said it better. MEGAN 20:31 Awesome. Well, I guess I summarized it well because you explained everything so well. We'll just go with that. [laughter] But yeah, this is-- STEVE 20:40 We're a good team, Megan. MEGAN 20:41 Yes. This has been an awesome conversation. So thanks so much for being willing to be on the podcast and share your insights from experience with us. And really enjoyed it. STEVE 20:54 I've enjoyed it too. And I'm a learner in this space. It's an area of high interest to me. And it's always exciting to think about the space of data analytics, and how you can really empower teams with insights, and get to more agile decision-making that I think everybody's after. So I'll continue to learn in this area. And it's been great to share at least some of my experiences here with you today. MEGAN 21:17 Great. Thanks, Steve. STEVE 21:19 Thank you. MADDIE 21:21 Thanks for listening. For more data democratization resources, check out our show notes at community.alteryx.com/podcast. Catch you next time. [music]

This episode was produced by Maddie Johannsen (@MaddieJ), and Mike Cusic (@mikecusic). Special thanks to @andyuttley for the theme music track, and @mikecusic for our album artwork.

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117: Building your data democratization strategy

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This episode was produced by Maddie Johannsen (@MaddieJ), and Mike Cusic (@mikecusic). Special thanks to @andyuttley for the theme music track, and @mikecusic for our album artwork.