Episode 92 · 7 months ago

#92 Democratizing Data in Large Enterprises


Democratizing data, and developing data culture in large enterprise organizations is an incredibly complex process that can seem overwhelming if you don’t know where to start. And today’s guest draws a clear path towards becoming data-driven.

Meenal Iyer, Sr. Director for Data Science and Experimentation at Tailored Brands, Inc., has over 20 years of experience as a Data and Analytics strategist. She has built several data and analytics platforms and drives the enterprises she works with to be insights-driven. Meenal has also led data teams at various retail organizations, and as a wide variety of specialties in Data Science, including data literacy programs, data monetization, machine learning, enterprise data governance, and more.

In this episode, Meenal shares her thorough, effective, and clear strategy for democratizing data successfully and how that helps create a successful data culture in large enterprises, and gives you the tools you need to do the same in your organization.

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You're listening to data framed, the podcast by data camp. In this show you'll hear all the latest trends and insights and data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hi Everyone, this is a dull data science educator and evangelist at data camp. If you've been listening to data frame for a while now, you promptly know that data de Morchatization is one of my favorite topics of discussed on the show. Every now and then I get to take a bird's eye view with guests and discuss the broad ways organizations can become data driven, and this is one of these episodes. Today's guest is meanlier Menal, is a data analytics strategists and transformational leader with over twenty two years of experience building datalytics platforms and driving enterprises to be insides driven. She has a wealth of knowledge and specialties, including the at literacy programs, data monetization, enterprise, the ATALYTIC strategies amongst others. She's led data teams at Veris, retail organizations such as macy's, tatlored brands and more, and she's one of the few data leaders I've spoken to that can really articulate a simple, coherent strategy for data democratization. Throughout the episode we talk about the components of data, the MOCHETIZATION, they at a culture and people, the importance of standardizing business metrics to achieve data the MOCHETIZATION, which we spoke about at length, had enlist data champions, is analytics leader and much more. If you've enjoyed this episode, make sure to rate, subscribe and comment, but only if you like to. Also, don't forget this week will be hosting Data Camp Ray at Aur our digital summit on June twenty three. During the summit, a variety of experts, including minal from different backgrounds, will be discussing everything related to the future of careers in data. Whether you're recruiting for their roles or looking to build a career in data, there's definitely something for you. Seats are limited and registration is free, so secure your spot today on events to data campcom slash radar. The link is in the description. Now on to today's episode. You now. It's great to have you on the show. Likewise, idle, thank you so much for the opportunity to speak in this show. So I'm excited to deep dive with you on your work leading data science and tailor brands, the importance of data, the morchetization and how data leaders can really create a vibrant data culture. But before can you give us a bit of a background about yourself? Absolutely so. Thanks again for letting me speak about data democratization. It is one of my favorite topics to speak about. So, as stones of the background, I've been the space for about twenty over twenty years, and have experience like the good, bad and ugly of the space. Will worked across multiple industries, which gave me also the experience to solve for unique data problems. I enjoy or enabling data enterprises to essentially become data driven, and you know that has kind of become my motto, to provide the right information to the right people at the right time. I'm currently working at tailored brands. I had data science and experimentation here in addition to data and analytics. That's really great. So you mentioned here, like day to the mortization is definitely the media for days conversation. I want, I said, the stage for today's chap by first umpacking what it means the morchtized data. You know, we've seen a lot of organizations investing in tons of resources and becoming there driven. So I love the first sort of the conversation to understand how you define data, the mortization. If you ask me, I think I found the best definition as something from what Bernard more basically said, and what he said is, and I quote, data democratization means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway... the data and the goal is to have anybody use data at any time to make decisions, with no barriers to access or understanding. And so, if I have to kind of rephrase this a little bit, I would just say data democratization is not just about data access, but actually comfortable data access. So you have a documented, ready to use platform with tools and a culture that actually provides data driven decisionmaking and data literacy. So, in addition to just telling everyone that or we have centralized all of our data access, you actually need to make that access very, very comfortable and convenient so end users can use it to become more self service. I love the concept of comfortable data access because I think a lot of people stop at just the data access component of it, which really hurts the ability for people to make data driven decisions at scale. So, as you mentioned, they are definitely multiple components to the marcktizing data effectively. This can range from scaling data access and their infrastructure, centralizing data, having like strong government data, as well culture and upskilling and a lot of different things that need to come together to be able to create a data driven organization. So I love it if you can walk is a stupid different ways organizations can accelerate. There at the organization, absolutely. I hope you have a pen and paper ready at Dell because it's a list of items. So I would first say that if the organization is just starting with data democratization, the first thing they need to have is an enterprise data strategy with a prioritized list of priorities as to what needs to be done in data itself. The reason why this is so key and so critical is the fact that this is a multier journey and without the executive or enterprise support it is going to be almost impossible to make this mission a success if we do not have the strategy in place. So this is very, very key and very important. So before anyone even embarks, you have to know what is it that you're setting out to do and have basically a long term vision set out. Once you have your data strategy set out, you essentially start building out or thinking about a more forward looking architecture. So why they say forward looking architecture is that you have to at least look out three to five years in terms of what your platform is going to be able to provide. So you have your near term priorities, you have your strategic objectives for the organization. Where do you see your organizations in terms of data over the next three to five years? And that's what your architecture is going to turn out to be, so that you are not continuously, you know, doing migrations or not, continuously trying to revisit this architecture and you're always in the process of building out and never at the point where the organization is fully self sufficient with their data neeys. The third is so again we talk about where you try to reduce data silos within the organization. So that's one key part of data demecratization is to have access to the data visin within the organization itself. Now there may be some limitations to certain data which you may will not want to bring into a central warehouse or a Central Data Lake, and that is okay, but for most part eighty five to ninety percent of the organization's data essentially would bring, would pipe into the system, and so you need a centralized data team who basically helps with the piping and who helps essentially with managing the access across this. The fourth is essentially data quality. Now this is very, very key and very important to everything that you do. You basically build data lineages and data quality as part of your process as you're piping data in. So ensuring data quality will also give credibility in terms of what is being reported out of your platform and also when you get to the point where this goes to executors or when you start going into doing more data science or building data models and machine learning, you know that your data is not skewed and or biased. You can always trust the quality of the data. So you trust the output of whatever is coming out of that data itself. The fifth one and my most favorite, is the semantic layer. So the...

...semantic layer is very, very key just for the reason that that's the layer or the view that the end user has access to and this is the view that they use to essentially consume the data that exists within your ecosystem. So when we talk about comfortable data access, the semantic layer is the key to that comfortable data access because it tells your business users that, okay, you can basically query me without having to write very complicated sequel or doing very complicated joints. You have predefined metrics, you have predefined KPI, is defined within that semantic layer, so there is no reason for the organization to have confusion in terms of what is it that they're looking at or what is it that they're pulling out. Sixth is so once you have this platform in place, you meet regularly with your stakeholders and you tell them okay, and at every point in the journey, actually you have to meet with your stakeholders and continue to update them on the status of what's going on. And at this point what you should do is you have to be ready for some maybe reprioritization, because these are organizational goals that you're trying to meet as part of the build out of this platform itself. So you may expect some shift in priorities here or some medicinal priorities which you have to account for, but the meeting with the stakeholders also ensures their continued support for this journey that you are on. Then you focus on basically the tools for the end users to use. So these would be a standardized set of tools that essentially meet, again, the needs of almost eighty five to ninety five percent of your end user base. There are going to be some end users who are going to have very specific use cases and you have to think about how you want to address those. But for the bulk of the organization, essentially you have to think about what are the standard tools that we have to provide to them so that they are able to get the most value out of this platform. And then you have data stewards essentially identified for all of the data that is coming into this platform. For each source of data that you're bringing into this platform, you have data stewards identified, so subject matter experts who essentially are able to help define or dictate a say as to what the rules of the data going from the system, essentially coming is now these people also become your data stewards for all your governance processes and you know, privacy processes and everything as well. Then we talk about data privacy specifically to support CCPA and GDP are, for example, customer data is very sensitive. You have heaper rules in terms of healthcare data. All of this is very, very key. So the data steward essentially you work very closely with them to identify what sensitive versus not, what's not sensitive and who can get access to what. And then you work on data literacy. Your platform is set up and you basically want to improve and increase the adoption of it across the organization self. So data literacy programs where you provide continuous training. That there. It ensures that adoption and it ensures the continued usage of the platform so that it continues to stay effective. And you have to continuously evaluate and run statistics on the platform and understand if there are certain areas of your platform that are not being you so you've added functionality but then no one is actually using it and try to understand that. How did this become a priority and not get used? That is an exercise of the leader that you have to continually keep doing. And if you do these, and this is like a small list of components essentially, that lead to data democratization, then you know you are, I wouldn't say guaranteed, but you're bound to move towards the successful implementation. I love the clarity by which you approach the discussion here and how you position each element so concisely. Now, of course, out of all of these lovers, there's so many things that we can unpack, from governance, infrastructure upscaling, out of these different levers. What do you think is the most challenging and important levers scale and why? This is the why is that the case? So I think the most important one from my standpoint, and what has been the most challenging,... the data culture, and that comes to use, adoption and literacy itself. It's human nature, essentially. You know you're very you're very comfortable with what you work with and you're comfortable with what you have access to. It provides you so much security that you're the one who kind of knows this stuff and all of a sudden you come up with something new which is going to basically automate and then you're like, Oh my God, now seventy percent of my time that I was spending on doing my stuff has now been reduced a five percent. What do I do? And that's, I think, the biggest challenge that organization's face, and it's more so with legacy organizations, organizations where, you know, which have been in place much before, you know, Technology came into play. So affecting data culture is, from my perspective, the most challenging and the most important level to scale. That adoption is very, very key if you truly want to enable your enterprise to be data driven. So we have to make sure that the organization conforms to the guiding principles of the platform regarding governance, privacy, the consistency in key metrics that are being reported, and I'm we probably we can talk about enterprise key enterprise metrics being consistent across the organization the later point. But you know, all of those are very, very key in them having to accept it just so that you can truly create a culture of where the decisions that are made by data again, are not skewed and all biased and coming from a place where the data cannot actually be trusted. So I I think changing the culture, from my perspective, has to be the most challenging, definitely, and it's all about change management and giving the ability if people to kind of dig for data for themselves and creating that mindset shift as a their leader. How has a lack of data culture affected the adoption of some of the solutions that you've developed, even outside of creating a platform where people can do data for themselves, but as a solution for the data signed solutions that you would drop, and what are the ways that you've been able to battle through such an option issues? So the biggest barrier that comes from a lack of data culture is essentially the proliferation of data silos within the organization. You have data silos, you're reporting silos, you have organizations where you have fift, twenty, thirty reporting tools within the organization. You have, don't even ask how many data architectures sitting over their data is not in a singular place, reporting is all over the place and numbers are never matching, and then organization struggle to say, okay, which is the number that I should look at? I'm setting a goal to basically say my sales has to increase by ten percent next year. But with sales should I look at when you look at it? If there is a lack of that data culture or the lack of where you're not looking at a singular cover and data set, then all of a sudden these kind of problems become more rampant and they exist within organizations and it's not uncommon to have. So you have increased total cost of ownership because now you have so many of these self manage data and data sets and then no ability to control so if you have sensitive data that moves across the organization, you have no ability to manage or control that. And with things like CCPA that comes in, it becomes so important that very sensitive data essentially has to be managed so closely so that if a customer comes and says I need to remove access to this data, then you have to remove that data out of the system. Then it's not easy to do if it's all over the place and you have no way of identifying there it exists. So I think that lack of data culture does cause a lot of problems. Some of them are tangible, some of them are not, and some of them are visible and some of them are not, but it does. It causes a lot of issues. How do we make data essentially a priority in every conversation, with every initiative that takes place, we start talking about what are the success metrics for that initiative and how are we going to measure it, because you cannot manage what you can't measure and you have to be able to measure that. So how do we bring data into that conversation? Now it may translate to saying that we truly don't have data needs, but it is important for the data person,... data leader, to essentially have a seat at the table so that what that will allow is that, okay, now you have a whole list of stakeholder so sitting with you and telling okay, this is the initiative that is coming up, and then it allows for you to have the conversation and say, okay, this is what it is going to touch, this is what it's going to impact, then for them to have a top down conversation where they cascade that information down to their to their leaders, and then it goes down all the way the bottom of the of the enterprise itself. Now the other thing you have to look at is also bottom up. So the top down approach. Sometimes it's not very effective just for the reason that it's represented in a very different way. So to ensure that that message is reached, you have to ensure that there is also communication from bottom up. You show value essentially from what is coming out of your platform. Out is going to get impacted and see that how you can essentially provide that value for their business unit and or team also, and so that needs to be something that you have to be continuously communicating to them. So that's one of the ways in which you can ensure that that lack of data culture does not exist within the organization. It of course, happens in a piecemeal way. In some organizations that's easy because they already know what they want in terms of data centralization, or it exists, it's just that they want to truly democratize data. So, depending on where you are within the organization that are there's a communication that you have to do either from top down or bottom up, and that will help address the issues that basically come with that lack of data culture. That the communication is very key. That's really great and harping on the semantic layer that we discussed about and how also empowers the data culture. I've seen you speak about this quite a bay and we've mentioned this in our conversation so far. Is the importance of standardizing business metrics to be able to galvanize at their culture. Do you mind further expanding into that and how it helps accelerate their culture with an organized stations? Absolutely so. Let me give an example. In one of the organizations that are was working with, there was a key metric that was in use within the organization and we had thirteen different definitions of that metric. Thirteen, not one or two. We had thirteen different definitions of that organization. So you can imagine just what happened that when that metric actually got reported across and that was a metric that was being used to compute to basically as part of our strategy, to say that whether we were making progress in our initiative or not. So it was very, very important that that metric definition become consistent so that we know exactly what numbered we were starting from and what we were actually driving towards. I talk about this so much because this, again, is something that is rampant across organizations. Finance, for example, has their own definition, marketing has their own definition, sales has their own definition and then so do other business units, having their own definitions of each metric. So bringing all those stakeholders together and trying to physically understand as to okay, we have all of these definitions, but as an organization, we all need to kind of attached to one or one definition that we can so that when we are actually communicating, it is we are all speaking as one rather than speaking at six or seven different business units and not speaking six or seven different languages. So inconsistency in metrics essentially means where the metric is defined differently across different business units and is being used in different forms, but it is called the same thing and that leads to a lot of chaos and it's very, very important that organizations essentially come to us an alignment in terms of how they truly want to define it. That's really great and definitely a nightmare scenario when you have thirteen different definitions of the same metric. So if you're an organization or they a leader looking at such a situation where there is a lot of messy definitions of metrics, how do you approach the journey? If reaching consistency what is it entail and what does that journey...

...look like? So what we do here is so first you start identifying what are the key metrics across your organization and we prioritize them. Think of it almost like a data journey for you. I wouldn't say it's a multi year journey, but it is a journey to essentially get all of this aligned. So you need to first understand what are the key metrics that are in use across the organization and specifically focus on metrics that are used across business units. So if a single business unit has a metric that they use, that's not of too much concern just for the reason that it's used just within that business unit. But if there are metrics that are used across business units, that's where the concern starts. And so you identify basically the list of those metrics, you sit in down with the stakeholders and begin a conversation. Now this is like the toughest part. I don't think the build out of the metric or anything is difficult, but it is this conversation where you have to get alignment in terms of metrics, and the challenge with these conversations typically have been is who's going to take ownership of this metric going forward? So you land on a definition. But then who becomes that owner? Who becomes responsible for that metric? So this conversation is very, very important and very, very key to basically have and you bring all of them together and you sit in and you tell them, okay, we have all of these, so let's come now up to an alignment. Now, in this case, that data leader essentially is the facilitator or the coordinator of these conversations. But you essentially are waiting for the business to make that decision in terms of what is the definition that should be used forward. So you document and you take it across to the stakeholders and say that this is what we have. Now, can you tell us that which is the one that should be used? Once they decide on that singular metric, we say okay, now who becomes the owner of that metric going forward? So, defining that data steward or the owner of that metric, that person will be responsible now for all communication for that metric going forward. So business changes, the way we look at the way we pivot our business or the way we look at our customers are product changes. For example, and the metric definition is likely to change. So this person then becomes responsible to say, you know what, just because of these changes that are happening going forward, this is what the metric definition is going to look like, and that communication needs to be done. They need to get alignment from all the other stakeholders and say, okay, this is what is, this is what it is going to be forward, going forward, this person is also going to be responsible for that definition in a business glossary. So you have a business glossary within the organization. So in some cases it's a fancy business glossary, like it comes as part of your data governments tool, but it can be something as simple as a shared excel document or a shared sharepoint site or a shared confluence document or something where you know that is all defined. Their definitions are put over there for the organization basically see. So in short, basically this person owns the metric end to end, and then we just go back repeat this exercise until we cover all the KPIS and metrics that are in use within the organization. So, as I said again, it's a journey. I wouldn't say necessarily it's multiar based on the size of the organization and based on the number of metrics that you have. But till you identify and get to that point, you just go through till you cover all the metrics. Once you have all the definitions in place, then the data leader essentially goes back to his or her team and then goes and has that metric and, in its definition, put as a metric within the semantic layer. So again this is why that cemental lear is very, very important. So if I have something called financial then financial sales becomes a metric within my semantic layered so whoever accesses are going forward will get the exact same value. And would you consider this like a massive low hanging fruit that can really accelerate their culture, given that it's not a multier journey, bet something that just requires a w man, people sitting in a room, and this is one of the easiest way their leader. It can make an impact in an organization. Oh my God, yes, yes, I think this is so key and so important and people fail to see that. You're absolutely...

...that. I like that. I like the low hanging fruit. Yes, this is something that can be so, so easily achieved and can be done with just some very quick communication between teams and someone just taking ownership or something so simple, something which they're already working with and they just take ownership of it. So absolutely I think this is something that is very key to bringing at least landing and bringing that data culture much, much closer to what you're looking for. That's really great and I think this marks are really great segue to discuss kind of the role of the data leader in the marketizing data. I think the past few years we've definitely seen the role of the data leader, or whether that's a VP of analytics, are their science, a CDEO, a chief data I don't thics officer. It evolved into much more of a culture steward and a change management steward rather than someone who just sets the data agenda of the data team. So how do you how have you seen this evolution over the past few years and what do you think of the data leader's role in the mocketizing data today? So I think that role has evolved in leaps and bounds. So if you looked at traditional data leadership, essentially the data leader was an order taker. So his or responsibility was just quite the data into the system and make it available to the end user. There was nothing beyond that. You attacked reporting tool on top of it and that's about it. They were an afterthought, typically when initiatives launched or projects launched. But over the past few years, when people started saying, Oh my God, data is the new oil, day ties, your latest asset, and everything is all data, data, data, and people are like going all completely crazy, this role has not completely like three signature and now what has happened is that the data leader has a seat at the table and their responsibility now is just not building those data pipes, but now they are actively responsible for, you know, changing the data culture within the organization, making sure that organization is data literate, that data is being used very effectively across the organization. In words, it's basically they are like they become an evangelizer of data and they have to become that change agent which they originally, like traditionally, were not so now in with basically the just having like a roll of where all you had to do was like pure data engineering, now you have become like a data stretages, your data transformist. You have to think about how you're going to monetize data. You're to think about. You have to pretty much think about everything from a data perspective. You have to see that and ensured that the value that the business thinks that they're going to get from the data, you have to prove and provide that value to the organization. So the role has completely shifted where the owners of all of this is now falling directly on the data leader. So whoever is the data leader and who's sitting in the space has a great responsibility, and I'm not saying that they were not responsible before. It's just that a heavy responsibility has now been placed on them where they have to be so vision revery, so forward looking, in addition to just being someone who can execute. And what do you find or key guiding principles to succeed as a Daia leader and such a stressful as well as high pressure environment where you have to really decide on and drive the data strategy of the entirety of the organization? If you think about guiding principles, I think one is communication. Is a big, big, big key right. So when the leader gets into this role, he or she may or may not have access to what they need to do their task effectively. They have to go out reach out and ensure that they fully understand what is it. That is the goal, if it's not something that they understand at this point. And what you have to do also is that you have to have, as I said, a strategy in place. That strategy would kind of help you dictate as to what your next steps are going to be, what you are actually going to do. So that strategy is very important, not only like a data strategy, but you also have a forward looking technology strategy for your team and for your organization itself. So communication, getting...

...that executive support, having a strategy in place, essentially, a lot of what we actually talked about in terms of the components of data democratization become also your principles for what you need to do to basically make your life much, much easier. And communication holds a big place, because you not only have to communicate to your stakeholders continuously providing that value that they're looking for it, but also to the folks who are your end users and continually telling them in training them and telling them about the value of the data as well, so that they adopt your platform. So I would think in terms of guiding principles, I would probably go back again to my components of data democratization and say that, okay, these are this is almost like a checklist for me, and let me and share that I'm going and performing each of these steps. This should help reduce that stress that data leaders now face. And then, of course, you keep yourself very engaged with the industry. Attend there are a lot of data and analytics conferences where hot leaders come, they share their best practices, they share their thoughts and challenges in terms of what's going on, where they are challenged, and hearing and listening to other people going through the same journey essentially helps you. You know, you have buddies who are essentially are going through the same journey as you and you learn a lot, essentially, coming out of those. I can imagine, and you mentioned the here, communication being super key, of course, that they leader cannot do it all by themselves. How do you choose your collaborate collaborators when imbarking on these large transformational projects, and what does successful collaboration look like in a constant context of these data transformation initiatives? So I see collaboration, how happening in multiple forms. So again there is this whole piece of where you have a data strategy and you have priorities for your data itself. So in that case it becomes a little easier because your collaborators are kind of already defined and you already know that they have a strategic initiatives which tie to all oranizational goals and they become your partners very easily. But if you are also building out and you are also looking to see that, how can you how can you bring more business into your platform, you basically reach out to business units. Again, that's where communication becomes very important. You almost have to be, I wouldn't say like a know at all, but you have to be someone who's aware of things that are going on within the organization itself. You reach out of business units and say, you know, what I've heard actually that you'll have this big initiative that you're imparking on and data. Maybe your concern for you is there any way, essentially that we can step in and help you out? In some cases you are able to get this project funded, but in some cases it's almost like you have to prove it as a proof of concept, and once you prove it, then you have a collaborator for life, and that person also becomes your data champion and allows you to promote it. So that's one kind of collaboration that you can do. The second kind of collaboration that you do is going and talking to the top folks. You can go down to the bottom folks and you talk to them and sit down with them, understand what their challenges are and you do a similar exercise in terms of how do we how can I help you? There is opportunity here, I see. And how can I make this easy for you? Go to them, reach out again. It may be funded, may not be funded. Again you do proof of value and again you win a data champion for life or you win a collaborator for life. I have done this successfully across my organization and I have seen that one is not only that these Jay that champions become my it's like the train the trainer kind of mode. They become my champions for across the organization. So there have been meetings where they sit in and I have not sat in and they speak to the capabilities of my platform much better than I ever good, and so that becomes an automatic showcase for my platform without me having to say anything. So, as I said, you find collaborators in many forms. You just have to be aware of what's going on from a data standpoint around your organization and you go reach out and you just have to go with the attitude as possible that you're just...

...going to probably get pushed out or you may not get funding. But if there is value, you know that has to be unlocked, because sometimes people don't see the value that has to be unlocked. But if you see that value to be unlocked, then you almost you force take that thing and you say, okay, you know what, let me prove it to you. There's no cost to you, I do it for you and you tell me whether this is not going to change your life. And almost like ninety to ninety five percent of the time it has changed. I think that sees a lot of success and for me, if you ask me, that's the best way in which data championing or collaborations with others have been up democratize my data better within the organization. That's really great and I love this virtue is psycho model that you're proposing where you start off with a low hanging fruit. It creates more evangelism of the data, generates a lot more opportunities to do a lot more low hanging fruits and it's like a cross pollination if data within the organization. Do you mind sharing, to a certain extent, successful low hanging fruits projects that you've worked on? They were able to create their champions, and how do you prioritize which projects to go after first in your data journey to be able to create this momentum and keep it going? Yeah, I have a couple of use cases that I could actually share. So in this one organization of ours, we had this whole team of marketing analysts, essentially, who were there and one of those analysts essentially was spending seventy percent of his week on doing this repetitive task. Okay, so he was given an exercise and spend seventy percent of his week doing all of this manually. Then again he repeats that same thing seventy percent of the week because the parameters have changed and or shifted and it's a challenge. So you have this really intelligent individual who is here and that person is doing a manual task, spending seventy percent on his time on probably something that he was not actually hired for originally. But there is so much more value he can actually provide to the organization. So we reached out first to his manager and then eventually to him and we said, okay, we can help support you and we can actually automate this whole function out for you, and he would look a little worry and he's like, there's no way, this is so complex and everything. We said, why don't tea, just give us a chance and we can show basically what you can actually do. So it took us a good two and a half to three weeks to basically get everything that he was doing manually automated. And then not only did we like automate what he had to do, but we also gave him the capability that tomorrow, if he has to injeorship the parameters of variables within that that set, he can do that with just the click of a button. He saved not only that seventy percent of the time, but just because he had all this additional time, he got to work on more fun stuff and he was so happy at the end of all of this that he became like our champion for life. He became our key mouthpiece in all meetings. He would just keep saying, Oh, if there's anything that you'll want to get done. This is the team who's going to do it for you. It might team's work was done because we are like, okay, we don't have to go and market ourselves anymore, we don't have to prove value anymore, we don't have to showcase anymore. We have someone who's going to do this automatically for us. It's so simple, right, as you very nicely said, low hanging fruit. These are like low hanging fruit that you just have to go figure out. Again, you know when they exist and go take your opportunity and go after it. In another organization I was working there was a very critical problem of where they were doing this computation of onhand inventory manually and it was at a very, very, very granular level. So there was so much, so much data to crunch that and again, since they were doing it manually, they were able to manage it only twice a year. It's a very critical thing that needs to be done actually at a weekly level, but one, given just the size and nature of the data and the fact that they had to do it manually, just allowed them to do it twice a YR. They could not do beyond that. Even that twice a year was like a nightmare for them.

So again we intervened, we got into play and they were a very, very, very skeptical team, very skeptical, and again we said free of charge, the will just go and prove it to you and then, if it works, all you do is basically have this thing run on our system going forward. And we went we built it in into our new platform. So not only then did we save on resource efficiencies, now basically the process even ran weekly, as it was originally. This basically desired. So that was what the actual intent of that work was and again freed up a lot of time and then they could actually focus on other things that were sitting on their plate which they were not able to focus on because of this whole nightmare thing they were doing. So that Kata champion again became so happy that he worked with us to eliminate that data silo and the reporting tool his team was using. So he said, you know what, we don't need this. Your platform is the one that we want to start building everything on. So when it came to migrating everything from his tools and his platform on, he became that champion for his team. He went and spoke to his leadership and said that this is what we need to do and we need to move out of this. So again, easy, lowhanging fruit, something we can all get to and something easy to do as a as a leader, as a data leader, as I said, you have to be like a communicator and an evangelist, and as long as you are able to get to the root of the problem and then figure at to help fix it for them, you are going to be able to successfully ensure that your organization is using data in the right way and they adopt your platform, which essentially was just built for that purpose. That's really great and I love these stories, especially when you showcase the enthusiasm that it created. Now, because a day leader, you want to balance out between the low hanging fruit and the strategic, long term projects. In even the more technically the man thing. They assience projects that required machine learning, highly complex algorithms and tools, the only sub matter experts like dat scientists, their engineer's machine learning engineers know how to master. How do you balance out that road map between short term and long term wins, and what are some of the exciting kind of long term projects that you've worked on as well? As. So this is how I manage and this is specifically for me how I manage. So the way I resource and the way I think about it is seventy percent I work on strategic initiatives and percent is essentially ad hoc, where my low hanging fruit falls, and any other ad hoc needs and no request that just pop in. And then twenty percent is technical debt. Okay, so we do accumulate technical debt and stuff starts getting older and then how do we keep it always new and fresh? So that's how I do the breakdown. Now in some cases what happens is that just because of timelines, one becomes ninety percent and then the others just become a little lower. But overall, as an other and average, I maintain it like this. Seventy, ten, twenty percentages. So just a little. It's just allows me to say, okay, I have managed and we should be able to get all of these things done and it for me, it has worked basically pretty effective now in terms of use cases. That so there are a lot of use cases that I exciting, ones that I've actually worked on, but one that I was very, extremely proud of, not to say I was very excited about it. But I was extremely, very proud of was when, in one of the organizations I work with, where we migrated to the AWS cloud, red shift at basically just come out. I think it was about eight months old, so we were almost going to be close to they probably were all having Beta customers and not production customers, and we were going to be one of the first ones going into like going into the AWS redshift. And the interesting thing was that my team had basically traditional data engineers, including myself, so we hadn't had access to cloud technologies and we hadn't done that and I created a training plan. With one and half months, all of my team became like cloud, I wouldn't experts, but...

...we became we knew everything there was to know about the aws cloud and we probably could talk about that in auseparate session in terms of how we did that, but I was so surprised at how my team just they just all picked it up and in six weeks we were already we built out the whole architecture, our whole architecture. Okay, and this was not only the batch architecture. We built a full streaming architecture in the cloud and we launched on time. Of course there was a lot of money savings because we were migrating out of a very expensive platform as well, and then, of course, aligning with the broader technology strategy for the organizations. And then, in addition, what we were able to do is that we were able to build a fraud detection model on this platform and which would actually run in near real time and then spit out, essentially back to the application. It would spit out a score to say that whether this transaction was going to be fraudulent or not, and that saved so much of the resources on the risk management team because otherwise risk was going through these things actually manually. So our ability to basically be able to score the transactions and we had about a ninety two percent accuracy, I made it all of worthwhile. So a lot of exciting use cases to be work on, but this one I'm very particularly proud about. That is so nice and as we end on as such an inspiration. Now talking about kind of the the value of faring a training plan. Do you have any final call to action you now, before wrap up, I probably repeat what I said when you ask me to define data democratization. So again, data demecratization is more than just bringing data all together into a central location. So it's not only about size, solving data silos or centralizing data right. It has to be. It has to be truly an interface that the user can become self so get. It has to be an interface where the organization truly can say, yes, we are moving in a data driven fashion. It has to be an interface in which you Provide Comfort, you provide the tools, you provide the access and then you provide all the processes, you back in processes of governance and privacy and all of that and help, basically, the organization succeed and becoming a data driven environment. So I think for me, in terms of call to action I tell all data leaders is that focus on what that end view for the customers should look like rather than just focusing on fighting data in and just bringing all that data in P A central location. Thank you so much, me now for coming on the PODCAST. I really appreciate it. Thank you so much, a Dell. Thank you for letting me speak. You've been listening to data framed, a podcast by data camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time,.

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