S2E16: Recapping This Season's Data & Analytics Episodes

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This is a podcast episode titled, S2E16: Recapping This Season's Data & Analytics Episodes. The summary for this episode is: <p>Shaun McAdams, VP of Data &amp; Analytics at Moser Consulting, joins us for a recap of the conversations we've hosted covering Data &amp; Analytics this season, in addition to giving a sneak peek of what we'll be talking about next season with our Data &amp; Analytics team.</p>

Angel Leon: (singing) Hello, everyone, and welcome to another edition of ASCII Anything, presented by Moser Consulting. I'm your host, Angel Leon, Moser's HR advisor. Today's episode continues a series of podcasts centered around data and analytics. In this episode, you'll listen to Shaun McAdams, who is Moser's vice president of data and analytics. This is a wrap up for this year's series of podcasts from our data and analytics group. Among other things, Shaun speaks about what you can look forward to in next year's series of data and analytics episodes. Without further ado, here is Shaun McAdams.

Shaun McAdams: Thank you, Angel. This is Shaun McAdams, vice president of data and analytics at Moser Consulting. I want to do a few things today as we close out the podcast for data and analytics for 2021. I want to take a look at some of the things that we've covered. If you haven't had a chance to listen to them, you'll have a little bit of an idea about what we discussed this year, things that we're going to take a look at next year that I'm excited about a couple key things, and then answering a specific question. I think this is really the main question that those of us that are in the space of managing data and analytics have to answer, and it's how effective is my organization at leveraging data and analytics to power business and operational models? It's a tough question, and I think there's three specific areas that we need to think about in order to answer that question. First, it starts off, how effective? We know that we're going to need something by which to measure this particular question, a maturity model. I want to take a look at that. How do I measure organizational maturity using data and analytics? There's a couple ideas that we have to share today. What is data and analytics? If you're going to measure effectiveness of how your organization is leveraging in, what is it? We've spent a large portion of this year's podcast talking about data and analytic strategy. I'm going to recap that a little bit. How do I deliver data and analytic products? Then last part of it, to power business and operation models. I think that's one of the most important and key concepts. How do you know where to focus? What should be our focus? What should be our mindset? That's really around the economics and using some economic concepts in order to know what initiatives to look at. When you think about economics, that's pretty much the social science of studying choices, choices that individuals make within the business. Obviously, it's applied to government entities and societies as they cope with specific economic factors, and we're going to cover a few of those. But when you look at the perspective of economics and as it relates to data and analytics, I think it's important that you understand the difference between an accounting perspective and an economic perspective. An accounting perspective would look at something, the value of something as it relates to its exchange. What is somebody willing to pay for this particular service or this product, this commodity? Or what did you pay in order to receive that particular service or commodity? That's the accounting perspective. A lot of times when people are looking at data and analytics, and what we should focus in on, we look straight to that accounting perception and we're trying to understand, from a monetary perspective, how much do I invest, or what is this going to cost? What is somebody willing to pay for it? One of the things that I challenge us to do as leaders within data and analytics space is change our perspective a little bit, and that we should be looking at the value and use, and that's the economics perspective. That says how much value can I create from using this commodity, from using this asset? I really think that that's the key perspective that we need to look at when we're trying to figure out what do we focus on. A lot of people want to become data- driven. When I say a lot of leaders, we want our organization to be data- driven, and that really doesn't describe so much the organization. It describes the decisions that an organization is making. Being data- driven means having data, collecting data, and it focuses our minds on data. But there's a misunderstanding that having data is valuable in and of itself. What we really want is for our organizations to be value- driven. Instead of having data, collecting data and our minds focused on data, it's having ideas, collecting ideas and having our minds focused in on ideas because the value of data is actually determined by how you use it. Although we want our decisions to be data- driven, we want the organization to be value- driven, and that is in line with that economic mindset, how much value can you get from using an asset, in this case data assets? Now, when you think about creating monetary value out of your data assets, I'm not necessarily talking about selling it, and that's the value it's going to provide. If we look at some economic factors like the law of supply and demand, right? This relationship between the quantity of a commodity that a producer wishes to sell and the quantity that consumers wish to buy. When you apply that to data and analytics, what you find is not all data is of equal value. Not every piece of data is as important as another. What is that supply and condition? What is the quality and the accessibility, the completeness of data? Do we have processes in place for validating and valuing and prioritizing those particular demand? Again, we're looking at the economic perspective of data and analytics. Supply and demand is one of those we need to think about. Scarcity is an economic factor. When we apply it to data and analytics, scarcity usually being this balance between seemingly unlimited need and the fact that we have limited resources, we have limited financial resources, human resources, time. The ramifications of that for data and analytics is not having all the data we need in order to meet particular needs within your organization. It also comes in not sharing data. Silos within the organization holding captive specific knowledge so that we can make better choices. We think about knowledge and wisdom. One of the things I say a lot is knowledge is information, you're learning it. But wisdom is the correct application of that knowledge. One of the ramifications that we see within organizations for data and analytics is what I'll call this shadow power, shadow IT. We have data silos, we're not wanting to share specific amount of information. That's a way that this economic scarcity affects data and analytics. It also means that we have to focus on the most important thing. We see that play out within the decisions we make for an organization. If you put that scarcity model as it relates to data and analytics, it means you have to focus on the things that are most important, the things that provide the most value. When you look at law of supply and demand, and scarcity, then you start to understand some other economic concepts like postponement theory. That would say let's implement a strategy that looks to maximize the possible benefits and minimize risk by delaying a specific decision until we can get more information. We're going to postpone making a decision because we think that waiting outweighs a potential risk for making a bad decision. That plays out directly with the use of data and analytics of not having enough information, or maybe you have the data, but you haven't transposed it into information. When we talked this year about delivering data and analytic products, data as a service and analytics as a service, you have this transformation of data into information for the organization. Maybe you have the data, but just hasn't been transformed into information that you need in order to make those decisions. One of the last areas that I want to look at for economics is efficiency, right? The relationship between ends and means. If we describe something as inefficient, we're usually claiming that we can achieve that desired end with less means, or that if we employ the same means, we should be able to produce more of the desired ends. The focus from a data and analytics perspective, there is on driving optimization, on identifying inefficiencies, on prioritizing, again, those most important use cases. There's capital that exist in every organization, these nonfinancial assets of which data is one. The ramifications of that capital is being used to drive optimization. It's being used to mitigate risk, uncover new opportunities, and even delivering a better experience, a better experience for our customers and for our employees. If we want to look at that question about how effective is my organization at leveraging data and analytics to power business models, and you look at that last prepositional phrase there, to power business and operation models, we need to ensure that those that were empowering with data are using it for the most important things, the things that are through that economic perspective. Now, if we go back at how effective is my organization at leveraging data and analytics, we really have to define what that means, what data and analytics means. This year, we focused a lot on strategy. When we engage with organizations and people think about a data and analytic strategy, they really think a lot of times about a particular use case. They're really focused in on that micro level and just that one particular use case. We want boil that back and look at the macro level. We want to look at how does the organization deliver data and analytics so that we can set a strong foundation, and then everyone knows, hey, this is how we're going to deliver this type of service, and here's the part you play in it. If you go back and you listen to the podcast where Warren and I are talking about data and analytics, we talk about those at the very foundation. We say, " Hey, here's why we exist." We give that basic reason of why, and then we say how? We say that organization should deliver data and analytics, not through functional areas and organizational charts, but rather through supporting platforms, data engineering, and insights, and that that delivery channels correspond directly to the service models that you want to implement as an organization. If you want to be a data as a service, you're going to predominantly have focus within platform and engineering. You're going to have a lot of people in the space of insights taking data and a self- service IT type of setup and using it to create insights. If you're just getting started, we advocate your analytics as a service model. You have of a core team that is doing all of those particular activities, the platform, the engineering, and the analytics, so that you can start to build out the policies that support data governance and start to educate the organization. Our manager service that we run it Moser, we call it Honeycomb. It's a analytics as a so service model. It's built to help organizations get value out of data. They're not in a position or have a desire to want to invest in software and people, they just need a partner. They need a partner that's going to support platforms, engineering and insights so they can get those value- based analytics into their decision maker's hands and make data- driven decisions. That's the model that we use in our consultancy services to help organizations. Every client we've talked with has a gap somewhere within that because that's not the mindset that they approach delivering data and analytic products. We talk about some core concepts for a platform. Things that you want within that platform to support analytic workloads, engineering, data engineering layers. We described those as transient, raw trusted and refined and the activities that you should, and each of those areas that sets a strong foundation for governance, where you're going to assign policy and then implement enforcement through process, and then lastly, how we look at analytic maturity. That's the last part that we need answer in order to answer this question about how effective is our organization at leveraging data and analytics to power business and operational models. One of the things you can do when you are analyzing maturity is look at the micro level of that use case and classify where it is from BI to AI. That's one of the things that we really advocate. There's a very common model that exists for analytic maturity that Gartner had put out some time ago, and it charts of the growth of BI, business intelligence, to artificial intelligence over value and complexity. If you read that chart, what it says is that you'll get a low level of value, but it's also not very complex to do business intelligence and you get a lot of value, but it's also super hard to do artificial intelligence, neither of which is true. It's not something at Moser that we advocate. Rather than doing that, we flip that curve around and we say, " What we're trying to focus in on is how do we reduce the amount of human input necessary to make a decision? And using techniques to learn about the decisions that the organization is making using these particular analytic products, and then how can we use machine learning in order to optimize those?" One tool that you can use in order to analyze analytic maturity is look at the actual analytic products and use at the organization, and where do they fall on this scale of business intelligence? Going from descriptive analytics to diagnostic, predictive, to prescriptive, where does it fall? What is our ability to further increase the maturity of that particular insight as the organization is using it? The other thing that you can do is look at how you're getting your business from business monitoring into digital transformation. There's five areas that are very commonly talked about for this type of maturity model. Go from business monitoring into business insights. The way you get from there is applying those key business use cases, right? Go back to our economics discussion, what we're focusing in on. We're going to go from business monitoring into creating these business insights that are predominantly first going to look at prescriptive recommendations for business optimization. We want to go from business monitoring to creating business insights to business optimization so that we're getting better at what we're doing, and we're applying those economic models of efficiency, for example, in order to get better. Then we're going to look at how we monetize those particular insights. Go from business monitoring into creating business insights. We want to drive business optimization. We want it to monetize our particular analytic products through the organization. Again, applying those economic perspectives to get into digital transformation, to create competitive advantages. There's a number of resources that are available for us to look at, and examples of organizations that have delivered services in line with this concept. You think about the models that organizations had for selling books, right? You can look at organizations like Borders, and then now Amazon, right? You can see the application of digital transformation in the delivery of those particular products. I believe that data and analytics stands at the core of our ability to find those particular efficiencies that create the path, the roadmap toward data and analytics, and most organizations we talk to, there's this gap. There's this gap that exists between descriptive analytics and diagnostic analytics. You're looking at the maturity of your organization. You're going to create some data awareness. There's a lot of organizations that are doing that in very manual processes, looking at operational systems, extracting and working with data in Excel. They're creating some reports that answer what's going on, what has happened. These descriptive analytics, these facts that created. We see this in even some traditional data warehouse designs. But there's this chasm that exists to get from that type of architecture that support analytics into a modern data framework, a lot of which Warren and I have talked about this year so that we can apply machine learning to get to predictive insights and we can correctly apply knowledge that's wisdom through prescriptive analytics, and we can start to create digital transformation opportunities. At Moser, we have all these collateral within our consultancy services, if you're interested in applying those to your organization or taking a look at that. We have a playbook that we put out this year that talks specifically about strategy. I definitely want you to reach out if anything that we're talking about in answering this question resonates in these areas. How do we get our organization to really prioritize their needs based upon economic factors that actually drive business value? Because that's where we want to get. You go back to the very first podcast, Warren and I, we talk about why do we exist? We said, " We exist to help define and deliver a data and analytic strategy," and that strategy was data functioning through technology, which is predominantly the reason why organizations call us because they look at us as an IT service shop or professional services shop. But it's also people operating through process. It's these same very common key business drivers, people, process, technology. Sometimes people refer it as people, process and product. Now, we add data as a layer because that's an area of focus for us. People, process, tech and data. But if we can answer questions on how we deliver those particular areas and we want to mature those particular areas, the types of philosophies and guidelines that we want to implement, then we can start to introduce business strategy that are guided by value- based mindsets in order to produce insights that the organization takes action that drives business strategy. That's a very key concept and the reason why we exist. It's at the front of every presentation that we give because we want to set that foundational understanding of this is how we look at data and analytics. Well, as we go through next year, there's a couple key topics that we're going to focus on that I'm super excited about. Goes a little bit deeper than areas that we covered this year, which again focused predominantly on data and analytics strategy, that's around data governance. How does Moser look at data governance? What is our framework? How we do assessments on that and consultancy services and audit services and so on and so forth. We'll be looking at that here within 2022. Another area I'm excited to get into for data and analytic products and talking about is the human- centered design approach. I think the design thinking is becoming involved in more conversations for people that are in data and analytics. We've had a very strong focus this year within human- centered design for our analytic products and trying to increase quality of our analytic products, trying to increase data literacy, and ultimately, customer engagement and satisfaction. We want the products that we create to really drive business value, to reduce the time to make decisions, and we want those to be created based upon the consumers. Applying human- centered design principles is something that we'll get into in 2022, and I'm really excited about that. I hope that this year you've enjoyed the podcast around the data and analytics. If you guys have any questions for Moser for the data and analytics division, make sure you want to fire those in. This podcast is named ASCII Anything for that particular purpose. We want to use it to create credibility within the community and get information out, and you guys learn a little bit about Moser, about our resources, about our offerings and how we can help partner to solve particular challenges that are being faced within your organization. But we also want to solicit particular questions that you have so that we can meet our goal of being a trusted advisor. I appreciate you guys' time this year and listening to us through these podcasts, and look forward to continued discussions around data and analytics in 2022.

Angel Leon: Thank you for listening into this week's edition of ASCII Anything, presented by Moser Consulting. We hope you enjoyed Shaun's recap of our series of podcasts centered around data and analytics. Our data and analytics inaudible will join us again next year for another series of podcasts centered around that topic. Join us next week when we continue to dive deeper with our resident experts and what they're currently working on. Remember, if you have an idea or a topic you'd like us to explore, please reach out to us through our social media channels. In the meantime, please remember to give us a rating and subscribe to our feed wherever you get your podcast. Until then, so long, everybody. ( singing)


Shaun McAdams, VP of Data & Analytics at Moser Consulting, joins us for a recap of the conversations we've hosted covering Data & Analytics this season, in addition to giving a sneak peek of what we'll be talking about next season with our Data & Analytics team.

Today's Host

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Angel Leon

|Director of Personnel

Today's Guests

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Shaun McAdams

|Vice President of Data & Analytics at Moser Consulting