Honor Hey, Harper, really great to have you on MAD Data. How’s it going?
Harper Going well, hey Honor, pleasure to be here. Super excited to come and chat and see what we have to talk about today.
Honor Yeah, and you’re joining us as our new co-host, so maybe tell folks a little bit about yourself.
Harper Yeah, absolutely. Super excited to be joining my data here and being the co-host. My name is Michael Harper. You can all call me Harper, though there’s far too many Michaels in the world out there. I am a solution architect here at Databand. But my background is in data engineering. I’ve been working in the field for about five or six years now. My education was through mathematics and accounting and economics, and while all those things were super fascinating, I never had a passion for any of them. And then I got involved in data management. I started working in business intelligence and data modeling, and I realized like, Oh, I like those things because they’re all forms of data. And so then coming into this world and then just seeing the evolution that’s occurred over the last five or six years has been really exciting and just really enjoying talking with people about the problems and the challenges that they face in the data world and then trying to solve those problems because they’re they’re all their own unique puzzle and they are always a different type of jigsaw. They’re not very straightforward.
Honor Yeah. And I’m really glad you touched on the relevance of data. And that’s really very much based on a previous conversation that we’ve had as well, which is why we’re creating this episode about this topic of why data is everyone’s business. And before diving into that entire narrative, can you tell us a little bit about the history of of data? That’s a really broad question.
Harper Well, it all started back in the year 2000 B.C. No, it’s probably not too far off, right? I mean, data has been around forever. And the way that we describe things, the way that we take sensory input from the world around us, they’re all data inputs. We’ve only started thinking about those things in terms of a electronic computer since the 20th century. But in terms of like the data conversation that we’re referring to, you know, we really talk about that the data warehousing movement that really began in the eighties and nineties and companies wanted to come in and start doing analytics on those items. But what they realized is before then data was always viewed as the byproduct of the revenue generating activities in the company. So really, they were just a means to an end. We want to understand the accounting that’s occurred over the last few weeks; we want to understand the sales that have occurred, but they never really viewed it as a point of information that would be valuable to their company and executives in some of the more savvy companies in the late 80s and 90s realized, OK, if we bring this information together and we start slicing and dicing it along different lines, a number of sales per item sales for states because sales for store, we can really get some deeper insights into how we can drive our business forward and really start to compete at a different level than what we are currently doing. And so that really became the way that data warehousing started and you had a centralized place that people would bring all of their data into. Usually there’s a dedicated team that’s managing that data warehouse, and they still operated under the principle of least privilege, which we see in a lot of different software engineering fields. And that idea of least privilege got applied to data in the sense that, OK, we want to minimize the access that people have to this. And the biggest reason that came about is because executives were worried about security risks. You know, like at the end of the day, this data is sensitive. It’s our proprietary information. And if it were to leak, if it were to get out, I mean, we see all of these different articles over the last five or 10 years when these data data leaks occur, we know the damage that can have to a company and also the trade secrets are out there. And so by limiting the access there and storing in a single place, it really mitigated those security risks. But it also made it difficult to get more information spread throughout the rest of the organization. So eventually, these data warehouses became exclusively used by upper management senior management to make these decisions. And while that was valuable for them to understand those insights that were coming about, it didn’t really empower the people that were working with them under them on the individual teams and help them understand that. OK, if I build out this feature or if I release this new product line, how is it really driving the bottom line for a lot of individual contributors? And the companies still feel like they don’t have an impact on the company’s progress at the end of the day? And that’s really one thing that’s being faced by companies today is like, how do we find a way to engage everyone in organization to work towards this singular goal? And that’s really why I’m excited to talk about what I call the democratization of data. Yeah, big shout out to Brian Knight. He’s the first person that ever told me about this idea, and I just love the term. I love the idea of it. What do you think about when you hear democratize data? Like as someone that’s may not be as entrenched in the data world, what does it sound like to you?
Honor I mean, that sounds like access, right? So going back to the point you made previously, that it came from the data really started in this place of centralization that it was stored in this place. It was for security reasons, almost kind of had like a moat built around it. So the less you access it, the lower the security risk it, the lower the chances are that it’ll get leaked and so totally understand the logic behind that. But of course, the other side of that argument is in the name of protection. It ends up becoming something that is highly exclusive rather than inclusive. And so. Curious to get a little bit more context, too. So, OK, it was centralized. What has really changed in the landscape right now that makes us say maybe centralization is a bad idea other than obviously there are a lot of, I would say, always moral reasons of why that isn’t necessarily the case. But from a business standpoint, what has changed about data that makes a centralized location no longer as logical as it used to be?
Harper I mean, really, what we’ve seen over the last 10 or 20 years is just an explosion of the amount of data that businesses are able to bring in and they’re able to track, you know, you’ve got the Internet of Things out there these days and there’s sensors on everything, and we can get data points from the number of people walking into our stores to the number of people that are accessing our website. And all that information is still being collected, usually in a transactional layer at this point. Most companies are loading that data into a data lake, and it’s really not being used by anybody or if it is that data lake is still grouped by function, right? All of the marketing data is in a particular place, all the finance data is in this particular place. But what happens if marketing and finance talked to each other? What happens if sales data starts talking to the marketing team and then we influence our our marketing leads to the sales information that we’re getting? So those are the questions that are starting to be asked now. And the issue that we’re seeing is it’s through the centralized methodology that came about over so long before we really got into like the cloud computing that we’ve got to at this point, we’re having these barriers that we have to get through and making people and giving people the ability to answer the questions for themselves. Is it really the next thing, the next phase of data management that companies need to implement? In my opinion, at least, that will help them move forward and help them grow and help them impact their business decisions to continue competing with the rest of the companies around them.
Honor I actually want to talk about really quickly what you touched on the differences in the split of functions, determining who has access to what type of data. And we are learning that basically, for a business to be competitive, you can’t really structure your data so early on in that way, giving different departments this access to other aspects of the business will actually allow greater decision making and basically empower employees across the company to actually contribute to the bottom line.
Harper Absolutely. I mean, whenever we start to have people start talking to each other and from all different verticals of the company, that’s only going to be good for the company. Right. Because now we can have a common language to communicate with each other and talk about how we’re going to move together towards the goal of improving our enterprise. Right? But in order to get there, we have to find ways that we can still a protect our data from, like a security standpoint and also make sure that we aren’t giving out personal information. We’ve seen a lot of concerns about personal identifiable information PII data. With the advent of the California laws and the GDPR. So how do we protect those things? But then we also increase the amount of access that we give to different key stakeholders within these verticals in our company. And the other aspect here that I find really interesting is. The question of does it make sense that only the key stakeholders of these companies are the ones that have access to this data? Is it actually the manager of the sales department that knows the best decision to be made based on the analyst analytics studies that are done? Or should we really be getting input from the sales reps and the people doing all these cold calls and having them validate whether the information we’re seeing or the story that’s being told by the data actually aligns with the experience that they’re having in the field? And that’s the exciting part for me is this sort of a little bit of a social movement that’s occurring here within days where we can have the ability to empower folks who normally aren’t at the decision making table to bring their insights and help drive those decision making. So again, not only are you expanding horizontally and you’re having all of these verticals talk to each other, but then we can also expand vertically as well. And having everyone who has access to this data and have the ability to answer the questions that they have and then provide that information from an analytics perspective to guide the decision making further up the vertical there. Have you ever worked in the company that really gave full access to, even if it’s just like a particular vertical of data to everyone in that organization? And I feel like their thoughts were on it.
Honor It’s not encouraged, right? And I feel like it’s it’s almost seen as there there is a level of unconscious bias and that in order to be efficient, we have to first be pre-selective in who has access. So you basically make the assumption that certain group or certain groups of employees do not possess the data literacy to actually make any sense out of what they’re looking at and therefore providing them that access might not necessarily yield anything beneficial. But we are in an age where we are recognizing that employees across a company, any organization have the ability and the power to make a huge impact on the businesses and the organization. I mean, I’m just thinking of even like social media, right? If someone at Starbucks is, they could they could turn an interaction into an amazing branding stunt or the polar opposite of that, depending on what that interaction is right and who captures it and where it gets shared and how many likes does it get? How many views is it? Get like we are in a place where that fundamental like that dynamic of how much impact a single individual can make on it in an organization, regardless of their place in their title. We’ve never seen this before.
Harper Well, it’s interesting because it almost makes it sound like dealers in the business of virality, right? Sure, we can have these marketing experiences that occur on social media. You know, I’ve seen a couple of different brands and have a really good social media engagement on Twitter, and they get into these fun, little like Twitter fights back and forth on the good and good nature, right? But like you said, if you have a data team that’s in reading that and understand this clicks and the shares and the speed at which it moves and who and the demographics that are going, they’re like, OK, great. Like, now we have all this new information that can be used to define a new campaign, but also we can also share that information with the social media managers or the salespeople that at the lower levels, like the individual contributors and say like, it doesn’t mean anything to you. Like What is this? What does it sound like to you? And I guess in my opinion, at the end of the day, that’s really what we’re seeing in society as a whole. But if we apply this to companies in general, like empowerment of everybody is good and ultimately good for the bottom line.
Honor It’s good for the business.
Harper Absolutely. And it’s it’s really fun to be in the business world at this point because I think that’s a very dramatic shift that’s changing from, you know, the late 20th century to really the early 21st century here of how organizations view everyone in the organization, not just the upper echelon. Because at the end of the day, everyone in your organization has a different background. They have a different perspective, and they have unexpected insights within their mind that you just aren’t going to know about unless you ask them about it.
Honor And it’s the diversity right? The diversity of experience and background. And I think the other piece that probably wasn’t valued the way it is today is that authenticity is that connection, is the closeness. It’s that degree of even you never really saw emotional intelligence touted the way it is today, but the closeness or the ability for a business to quickly react to information they are learning directly from customers that makes or breaks how well you’re doing, how it really makes or breaks your your bottom line. So. I feel that in general. The argument for democratization of data is won, right? There is no reason why we would want to not allow this level of access. So and then of course, in the space, we always hear the term data mesh or at least we’ve been hearing it. And it sounds amazing from a philosophical standpoint. I think the question that I hear a lot in the community is, how is it even how is it actionable? Like, how do we make this a reality? What does it take to actually start living the data mesh?
Harper Absolutely. It’s it’s funny because you see the terms like data motion and data fabric is even out there as well. And I’ve had a lot of conversations in other channels or forums or Reddit posts. And, you know, people are like, they like the idea. But how does this actually work in practice? But I can tell you right now that I’ve seen job postings for companies like IBM looking for a senior data fabric as if it’s fabric developer roles at this point. So this is very much like a movement that’s real and it’s moving forward. But again, it comes down to how are we going to do this? And the first thing that you really have to do is that you have to embrace it wholeheartedly and you have to start looking at, OK, what are the steps that we’re going to take that are going to empower everyone in our organization to work with this data? We have to weigh that against the security concerns that you have. And so this really ties hand in hand with a solid data governance strategy. And by coming in and saying, OK, we’re going to start at the data governance level and we’re going to talk about how do we want our data to be stored? How do we want our data to be treated when it’s moving between systems, when it’s moving between storage locations? And how do we want it to be treated whenever it’s being accessed by individuals? And so the way that you start here is to identify the major concerns, the major risks. And at the end of the day, security is going to be the biggest concern with anybody when it comes to people accessing data and people moving data. And not only comes to the proprietary data for the business, but again that personal identifiable information. So the best practices that you can start here is just talking about all of the areas that you’re storing your data and how are you protecting it? Encryption is going to be the default way that people are going to look at this. We’re going to want to encrypt items whenever they’re at rest. We’re going to want to encrypt it when it’s in motion. Whenever it comes to data at rest, you can do both server side and client side encryption when you have laptops that are being given out to all of your different employees. Most laptops and operating systems have the ability to encrypt the hard drive that’s on the laptop these days. So that’s something that you can consider making part of just your standard practice for everyone’s computer inside of your organization. The other the other thing here is to say, OK. We took the principle of least privilege, and we applied that to data access in the past, and it really created this separation of the ability to view and understand how this data correlates. But let’s take it back here. Let’s take back here and say, OK, how can we better apply this? So that way it benefits us now. And going through that process, going through this democratization process, I think companies are going to be surprised at the unintended gatekeeping that has kind of occurred because of that principle of this privilege. And they probably came from very well-intentioned data policies. Right. But the question that I like to ask when it comes to this privilege is, OK, what can I abstract? So it’s great to have your data at rest and your data in motion encrypted whenever it so that way, the people that access it, only those who have the information to unencrypted can access it. But do I necessarily need to move every piece of data from storage location A to location B? Is there a way that I can abstract this information so that way, even if it were intercepted in motion and even if it were unencrypted after that interception, what if it only carries like a UUID? What if it only carries the identifier for a particular store location in Hungary? Budapest But at the end of the day, no one knows what that actually means, right? It’s just the number 15. So that’s really, I think, the best way that you can really start working on making sure that your data governance is protecting your data and also making it easy to move those things around. And once you’ve established those best practices, really invest in a data platform and a data ops team and really take the time to create the data tools that your engineers need to be able to adopt these standards and really lower that barrier of entry. If you have a utility package that takes care of all of the encryption for you, that takes care of all the abstraction for you, that allows your engineers to only think about solving that jigsaw puzzle, and therefore all of the security risks are already taken care of for them. They’re going to work more quickly, and they’re going to solve these problems faster as well. And that’s where you start, because once everyone has the tools to do that, you can start opening up access at that point. And once you start opening up access to the data lake, for example, they can then each individual team can bring in the information that they feel is valuable to an individual data mart. And so that creates then that starts to build that empowerment even further. And so you have your key stakeholders of these teams that are bringing information in from the data lake using the tools that you’ve created. And then they talk to the individual contributors on the team and say, Hey, we have this data market, which is just small data warehouse that you’re able to go into and do the aggregation and be able to do the the analytics that you’re looking to do. And then that information can be. Really thrown back up the ladder, so that way, people in the management positions have more information to make the critical decisions that they have to make. Hmm. So that’s that’s that’s the first step is really embracing the idea of empowerment and making sure that that first step of embracing is addressing the security concerns that are going to ultimately be the make or break decision comes implementing this decentralized data storage.
Honor That’s the main rebuttal. Right against democratization will always be security risks, which is a perfectly valid reason for why we are where we are today. So my next question is educating the stakeholders who are involved in making this a reality. So what we often hear is a data engineers end up having to exercise a certain degree of heroics on their team. Maybe they’re the ones in the field, they’re the ones closest to the issues. So then they have that. It’s almost like the onus is on them to then almost like manage up and and educate mothers on why this is important. What kind of catching up do you think needs to happen in, say, like business schools like it? Is this at this tie in like formal education? Right now
Harper we’re starting. We’re starting to see more of it at like the MBA level and even in some of the business classes. When I was in school 10 years ago, I had a very basic IT management class that taught me a little bit of visual basic. But at the end of the day, it made me appreciate the skill level that it took to do this type of development and to do this type of analysis and data management. But it didn’t really teach me anything about the concerns that people had when it came to data security. I’ve only really learned that in the field. And so that education aspect is what’s going to help us as a whole as a society, as a business, as enterprise, move closer to being able to have everyone work on the same page. And ultimately, at the end of the day, this just comes down to data literacy, right? And for me, data literacy is the ability to read, write and communicate data in context. So the data sources understand the constructs, understand the analytical methods, techniques applied. But at the end of the at the end of the day, I can say that someone is a data literate person when they have the ability to describe the use case of using this data and then apply their skills to that use to that data and ultimately provide you with the value that they’re going to get from extracting that information from the data. And I think that we’re going to see data literacy be a more common skill set in the job market as more and more people graduate from from college as we get more people into the job market. The difficulty here is really it’s a managing up situation like you mentioned, right? It’s it’s not that leadership doesn’t understand why this needs care, and it’s not that leadership is necessarily opposed to making these changes, but it’s it’s scary to go into a domain that you don’t feel you have the skills or you aren’t properly equipped to really work within that domain. And so we have to create a culture that is psychologically safe and we have to create, I guess, tools, business skills and soft tools and educational seminars that not only teach people coming into our organizations about what it means to be data literate and how you can be data literate using the data tools that were created in our previous point, but also working with executives. So that way, they can feel confident that the data is secure and that they can go in here and they can start to read and they can search right this data and even eventually communicate this data up and down the vertical so that everyone’s using that same language, right? That’s that’s really the key point. Like build those build build of those fundamental governance tactics as best practices, those data tools and then take those and start building out an educational module, the educational track that works for everyone. You know, I think in business, usually you see trainings get set up for people that are coming in during the onboarding process or they’re someone scaling up to the next level or for the next promotion. But we need to make sure that we’re working from a top-down and a Bottom-Up perspective here. Mm-Hmm. And it really comes back to the next thing that I’d be interested to hear how you’ve worked with this. But like culture is so important not only from in society today, but also within an organization. We see, you know, those damn millennials out there, like the culture is so important to them and like the way that you work with people in the way that you treat people. Into how do we find a way to make a culture aligned with this idea of democratizing and decentralizing data that both meets those security concerns and also helps people learn how to use those things as well?
Honor I mean, going back to the definition of the one that you laid out for data literacy, which I think is great, the ability to read, write and communicate data and context that to me, really stands out as a like a universal goal. Right. It’s like when we’re talking about any kind of literacy to not see this as an exclusive specialty skill. So that’s it’s like financial literacy or the ability to do this should not be restricted to do you have a degree in this and have you worked in this space for 20 years? It’s it’s a fundamental skill that I think it is needed in all disciplines, in all industries, regardless of where you are at in the organization. So I think that with this with just the direction of where data is moving and how fast it’s traveling and how essential it is to every business, basically, we can make the argument to say that no matter what you sell a service, a product, really, you are a data company. Right. Because without data, you will have no insights to make your best decisions that will impact your business. And also, some of your decisions are not even up to management, it’s up to the employees who are out in the field. So if we were to really look at this more from like a blue sky, like we say, we can achieve this. Like what do we think would be like outcomes for just society in general? Like what are the benefits of this? I mean, I have my answers, but I want to ask you.
Harper Sure, sure. Just put me on the spot while you’re at it, OK? I know I think there’s a. I think the important thing here. I’m sorry, there’s something stuck in my head that I really want to get out there. All right, the deal with the risk aspect before it, before I move to the outcome. I think the thing that’s most, I think what we’re going to see in terms of data literacy over the next 10, 20 years, it’s it’s going to be something. Not only is it going to be a universal skill, but it’s going to be something that’s taught right alongside math and English and science at the middle school and high school level. We’re just moving to such a large, data driven world and in the advent of the internet age that people have to know how to do these things and because they just there will be they’ll be there, they’ll be left behind like it’s an essential skill for anybody coming into the business at this point in time. So I’m excited to see how that grows, not only the lower levels of education, but at the high levels of education. And then when you when we were talking, you were talking about culture. So and when you were talking about culture, honor, you mentioned the idea that hold on my brains everywhere. Give me a second time this you. I you sense what did you say? I saw it, I mean, I know. I’m actually really fine. I just there’s literally I’m literally stuck and I need to like, just let it go. But you said something that I thought was really, really good, and I wanted to comment on it around data. And so you’re saying you when
Honor every business is a data company?
Harper Yes. Yes. Yes. Yes. Yes. All right. Recentering. Yeah. You said something really powerful when you were talking about culture, your honor, and that’s the idea that every company is a data company at this point. And I think that’s really the first step in changing the way organizations view themselves. And once they start analyzing how they view themselves, they can start really addressing the culture that they’re putting out there, not only for people to perceive that they’re going to consume, but also internally for how their people are working with it. Because as you write at the end of the day, data is no longer that byproduct of those product generating revenue at the same. Because at the end of the day, it is no longer a byproduct of those product. God damn it.
Honor Data is no longer a byproduct that drives
Harper of the revenue the revenue generated. So, yeah, because at. At the end of the day, data is no longer a byproduct of the revenue generating activities of an organization. Data is the revenue generating revenue of the organization because that’s really what’s getting people to understand what they need to do to move forward. So if you take the time to implement this data governance, that’s going to empower all of your employees to really work with the data and answer the questions that they have and then provide these analytics both up and down the vertical with them. And then you take the time to educate everybody on how to do that, and you really take the time to build out a culture that people want to be a part of. That’s data driven. I mean, ultimately, you’re you’re going to get trust, right? Like, once you start doing that and you empower everyone to participate in that conversation and participate in that activity, it takes trust for everyone to work on that together. And with trust comes transparency. You get to see what everyone is working on from day to day basis. You get to see what everyone’s thinking about, you going to see further into the research that they’re doing in the way that they think about these problems. And with that visibility, you start to get into the observability aspect of it as well. At the end of the day, that’s why we’re seeing an explosion in the data observability space, because what people need at this point is that they need to trust their data and they need to trust the processes that are generating this data. And the only way to do that is to pull back the veil and look at what’s going on in our storage space. Let’s look at how our data is changing over time when we load more data. And let’s look at the pipelines that are going that are running on a daily basis and understand how we can ensure that they are providing us with the data that we expect in the data that we need
Honor and actually one of the looping, looping back really quickly to something you said earlier that I thought was really important relating to trust, transparency, visibility of the observability is that sense of safety. I think the psychological safety is important at all levels and then with safety comes confidence. And then with that confidence comes that the ability to really look at data make smart decisions and trust that now you’re if your organization is, it’s almost a cyclical thing, right? Like in the organization is data literate and you trust that every person is able to read and interpret data in context and make good decisions. It just becomes this compounding effect where everybody’s decisions than now because they are trusted decisions. They also then inspire trust in others. A, I just felt that I had to say,
Harper No, no, no, I love it. I love it. You’re on the exact same wavelength that I am, too. And I think the the key. The other aspect of this, too, is that like, you know, we all know that to get trust, you have to give trust, right? And that’s that’s the most difficult part. And that’s that’s why observability is becoming important, because how do we how do we how do we trust something objective that will give us trust into this data? So that way, we can then give more trust to people that are subjective because humans are subjective by nature, right? So how can we create objective trust to then give us the psychological safety to give subjective trust to the people that are working with this data? And once you establish that trust through observability, you, you start to get to a point where you’re having an inclusive environment right at inclusion of everyone around you. It allows you to more rapidly understand any level of unintended gatekeeping that I referred to previously, right? And if we’re talking about everyone about, OK, here’s the data. Here’s how we want to allow you to use the data. How can we help you use the data? In what ways do you want to use the data? Well, those perspectives in those conversations and even those barriers these people are facing help you understand what needs to change about the way that your data policy is written in the 20th century and bring it into the 21st century. Right. Because trust and inclusion, I mean, I don’t know anybody that feels like getting more trust and getting more inclusion is a bad thing for their psychological safety. I don’t know about you, but
Honor on the same page as you more, the more the better. Absolutely, absolutely. And so the two or two main things just to because we were I want to recap, so we were talking about the outcomes of this movement being trust, the transparency, the visibility, observability, the inclusion. And then so putting it back into to think really strictly in terms of like bottom line profit like. What does that really mean for businesses?
Harper I mean, if you have more people participating in finding answers to the questions they have as to why something isn’t working or why will this new future benefit our product or will this new line benefit our story? The more people that are offering perspective on that, the faster you’re going to get to like some insightful information. And with that, insightful information comes better decisions. And ultimately, that just leads to growth, right? Like the more people you can involve to get a sound objective decision that guides the company forward, you’re going to start growing not only people within your organization to be better professionals, you’re going to start growing the bottom line. You know, if you understand why this feature is going to sell better. Great. Let’s make that feature. And then suddenly you start seeing people come in for more demos this and people come in for more trials. You start seeing existing companies, companies wanting to expand their contract because they want access to this new feature that you’ve developed. That’s what’s ultimately going to reward the executives who are making the decision. And hopefully that’s going to reward the reward, the individual contributors that are helping find this information as well. That’s another conversation, but hopefully this group trickles all the way down, right?
Honor Yeah. So you’ve got the business growth leading to people growth where, I mean, it’s lost that cycle, right? So you’re so we’re thinking in terms of like like what types of growth we’re talking really both customers retention as well as talent attraction and retention, and it becomes this cycle of just perpetual improvement. And it’s and it’s also really healthy kind of ecosystem to be fostering.
Harper Yeah. Well, and I think another side effect that you see here is that your data engineers no longer have to be heroes right now. I’m not sitting here saying that you’re going to build out this decentralized data management platform and you no longer need data. Engineers like know just that everyone with their data literacy is going to be able to do more data engineering work. And then your data engineers can focus on more advanced use cases or they’re working on building those tools that are going to make it easier for everyone to adopt this governance. But, you know, long term, I think that. I won’t say that data professionals are going to go bowling. I think that everyone is going to be a data professional. Right. And I think that those of us who are going to specialize in data, we no longer become gatekeepers to data. We no longer become the ones who are fixing all of the data pipelines and fixing all of the data problems and readjusting the dashboard. So that way, the decimal is only two precision points. Behind is that instead of three points behind, I think those of us that focus in on data, we become this like interdisciplinary professionals who understand how data best bridges the gaps between each of these verticals in the enterprise, as opposed to being, you know, the data guy. Because ultimately, or the data woman, excuse me. But at the end of the day, we all need to be a data person and with strong interdisciplinary minds working as you are data professionals that’s going to help your company continue with that growth. That includes everybody and builds trust and the outcomes that you’re providing.
Honor I love that. Yeah, I’m with you on that. And I think definitely the interdisciplinary nature of business will only become more and more felt as all these different departments are starting to recognize that they have so much cross learning that they that they need to be driving. So to wrap up our conversation, we’re thinking in terms of action steps. What would be like a top three next steps that folks can take to start advancing this movement within their own organization.
Harper Oh, OK. Three action steps. I mean, you’re right. Theory is great, but unless we put some walk behind all that conversation, it doesn’t really get it, doesn’t have us moving, get moving in that direction. And actually, we’re right. There is where it starts to think like, I think I think the first thing that you need to do as someone interested in democratizing data in your organization is to simply start the conversation. I mean, at the end of the day, data is about storytelling. Data is about communicating. Data is the greatest storyteller is what I like to tell people. And so I would encourage anybody listening that wants to go on this route that once they start working within their organization to engage the stakeholders on their team or in their company, and just open the conversation about how your company views data and also bring to that table, bring to that conversation how democratizing data can empower everyone through ownership of their role and their future and their product. So that way, they can feel like they are contributing to the bottom line and they are contributing to the company’s growth because by having that ownership, that’s what’s going to make people excited to work. Well, maybe not excited to come to work because people always want to be on vacation, but it’s going to make them excited to do the work that they’re being asked to do their investments through that conversation. Yeah, absolutely. Absolutely. And invest in an engaged employee is the most anybody can ask for. Right? And through that conversation, hopefully the stakeholders and individual contributors and and the managers here can all find the steps that are needed to align both the current company culture and the ideas of democratizing data into a unified vision for a data driven company. And if you don’t have access to those stakeholders, what you can do just within your own team is just start talking to each other about how you would want to use data like how they think about data. Like is data useful to your team? Hint. Hint? It is. But once you start having that conversation, talk about the questions that you want to answer, right? Like if we had this data, what would it? What would I answer? How would I? How would I understand to better build, to better do my job, what data, what I need and how that would question what I want to answer with that data? You know, you can even go as far as to start establishing a local data strategy. You know, say you had access to the data leak. What would you do with it? Well, I’d like to bring it into a data warehouse or data mart of some sort, and we segment that data and this in that particular fashion, we would govern the data by implementing these security measures to make sure that it’s secure. And if you come up with that plan, maybe those are the steps that make you feel empowered enough to talk to those stakeholders so you don’t currently have access to. And another thing too is like just to get that conversation started, like be willing to put yourself out there like you can champion a data community for everybody. Data is not just for engineers and analysts. And if you start to have that conversation among your peers, it’s going to grow from there because I promise you everyone’s excited to answer questions that they’re curious about. So, so, so yeah. So my first I’d like to start the conversation is just start communicating. And once you started that conversation, I think they’re really the best way to go from there is just or even, well, we’re not going to. Once you start that conversation, one way that can help drive that conversation forward or even show people that you are the type of once you start that conversation. I think one thing that goes really well with that is being able to demonstrate your data literacy like this will help people understand what you want to do with the data, even if they don’t know what the conversations about when you first started. Not everyone’s thinking about these things the same way, right? So if you take your ability to read and write and communicate data in the context of your team, in context of the peers around you, that can help people understand the power that occurs through democratization of data. You know, I would encourage anybody listening to set up some sort of informal talk and you look at like a coffee table like coffee chat just to talk to about how you went through the data and answered a question that you had and present at the end of the day, some objective metrics and some objective findings that either supports or denies an assumption that you had or a question that you had. Again, user data literacy to tell a story, you know, like by demonstrating that data literacy and telling that story, it’s going to make people interested. It’s going to make people intrigued. It’s going to make people start asking questions. And ultimately, you’re going to need to empower your team by showing them that this is possible. And that may be how you start that conversation right from there. If you’ve once you’ve started the conversation, you’ve got people on the same page. I think the real key here is education, right? If you’re serious about democratizing data and if you’re serious about this decentralized data management platform, work with in your organization to educate the benefit about the benefits of data literacy. Teach your team data literacy skills if they don’t have it right. That can be as simple as, like, excel and pivot. Yes, Excel is still widely used. I don’t. I don’t want it as a data engineer, but people like it, you know? But there’s nothing wrong with that because any step towards data literacy is a step in the right direction. And as you start to empower people to use those skills, you’re going to start to open their minds to the benefits of democratized. Once you have people start using those data literacy skills, that knowledge is going to open their minds to the benefits of a democratized data culture. And ultimately, that’s what we want to have here, right? Like, we just want people interested in having the conversation about democratizing data. Because once that occurs and everyone’s empowered to do that, then everyone’s going to be able to contribute to that bottom line. It’s going to help an organization grow. It’s going to make people feel included. It’s going to have people engaged. And that’s just it sounds like it’s a wonderful work. Culture to me started like super excited Americans that would love to work there, right?
Honor But that’s great. I mean, because OK, so just to recap the three steps. So we’re going to start the conversation and have just get it doesn’t have to be anything like a major endeavor, even just starting a conversation with your peers on your team. And then as you become more comfortable and put yourself out there to demonstrate data literacy. And so that’s step two. So that’s anything from you can set up like an informal coffee chat. And I also encourage folks to check out meetups in general. Just there is so much interest, I think, in getting together to talk about data. And I’ll add to the data literacy piece is that there is almost this I’ll call it imposter syndrome around data because it’s it’s always been. Packaged as such a specialized discipline. But it’s it’s actually a universal skill. And so supporting and encouraging folks to just go out there and start engaging. I love that. And three, educating your own organization, your enterprise about the benefits of this. So it’s just start talking. Demonstrate data literacy and education. I mean, basically. Very actionable three steps, I mean, regardless of where you’re at. If you’re a manager or if you’re not a manager, you, this is a good menu to choose from.
Harper Yeah. Honestly, I talked about a lot of this from the perspective of like the individual contributor. But for that, for the managers out there that are lost in this conversation, like you don’t have to be the one to do the talk, you don’t have to be the one to tell the story with data like, you can set up the talk and you can empower your team to start showing their data literacy skills that can help you understand what sort of education needs to occur, and that can help you understand what sort of conversations need to occur. Like there’s there’s so many different ways to go about this, right? You don’t have to be the one doing it all. Just someone has to step up and champion that cause, right? Because I guess. Whether whether you start with demonstrating your data literacy, whether you start with that conversation, whether you start with building on an educational platform to help people build those data literacy skills, I would say the one thing that I encourage everyone to walk away from here with is that. You have to be intentional about your data culture because your data can’t advocate for itself. At the end of the day, your data is only going to do what you ask it to do and you’re only going to get the information from it that you seek. And so until everybody has the ability to do that, it’s going to remain something that is packaged and inaccessible. And and, you know, it’s intimidating for people that don’t work with them on a daily basis, like you said on it.
Honor This is great. Harper, I found it really useful, and I think it’s really inspiring as well because this touches on so many aspects of what’s relevant to us is not just in a business context, it’s even in our personal lives that this degree of embracing of data is important for the present as well as the future. So Harper, I want to thank you again for coming on and doing our first episode as our co-host. And we are excited to do the next one, so I’ll see you around.
Harper Bye. Absolutely. It’s been a great time. Thanks so much. Looking forward to the next one. Okay.
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