S1E5: Defining a Data & Analytics Strategy - Lessons Learned and the Evolution of our Approach
S1E5: Defining a Data & Analytics Strategy - Lessons Learned and the Evolution of our Approach
This week we are joined by two of Moser's top Data and Analytics experts who are going to talk about defining your organization's Data & Analytics strategy. Shaun McAdams is Moser's Vice President of Data & Analytics and Warren Sifre is the Director of Strategy within Moser's Data & Analytics group.
Warren SifreDirector of Strategy
Shaun McAdamsVice President of Data & Analytics
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Angel Leon: Hello everyone and welcome to another episode of ASCII Anything presented by Moser Consulting. I'm your host, Angel Leon, Moser's HR advisor. Today we have a special episode for you. We will be listening in on a conversation between two of Moser's top data and analytics experts. They will talk about defining a data analytics strategy for your organization. With us today are Shawn McAdams, Moser's vice- president of data and analytics and Warren Sifre, director of strategy within our data and analytics group. Sean McAdams came to Moser in 2015 to help establish a division focused on data and analytics. Serving as a principal consultant, engagement manager, director, and now vice- president, he has helped advance the data analytics services to over 80 clients and 100 engagements across many diverse platforms. Warren Sifre has been involved in the IT community since 1998 and has worked in a variety of industries. Warren is a chapter leader for a couple of user groups and speaks at many conferences and user groups throughout the United States. Warren has a passion for data architectures and solutions. Serving as a director, he is helping advance the adoption of true data strategy in every engagement. Without further ado, here are Sean McAdams and Warren Sifre and their conversation on data and analytics.
Shaun McAdams: Well, Warren, finally. We've been talking about this for a while. What's it feel like to finally be here doing our first podcast?
Warren Sifre: I think it's pretty cool. I mean, the conversations we've had, the topics, the thoughts, all the things we've got prepared for the next 12 months is just exciting. So it's good to be here. It's good to get things moving in the right direction or at least in a direction.
Shaun McAdams: That's right. Yeah, and I'm pretty excited too. We've got a number of different avenues that we're going to attack this year, as it relates to data and analytics. One that we have that we're going to start today is talking specifically about data and analytics strategy. And we're going to do one each month, digging in a little bit deeper to it. First thing I think we got to do is define what it is, but then also establish some goals. For those that are going to listen in, what are some things that they will get if they tune in to this particular session around data and analytics strategy for the next 12 months?
Warren Sifre: I look at it from the perspective that if you're listening in, you're going to be hearing some things that you've heard everywhere else. You're goint to hear some pieces. Some of the stuff is going to be stuff you're familiar with, but we might shift the perspective a little bit or include some more avenues of thought and approach, and maybe be a little bit more overall encompassing than just theory target [ inaudible 00:00:03:01], which a lot of the messaging out there that we've seen, that we've looked at, researched, investigated and used as a platform for us to develop what we're going to share is something that we feel is going to be beneficial. When we're done with this, you're going to have a very broad understanding of what our definition of data and analytics strategy looks like. And at the same time, you're going to recognize the full breadth of conversations you need to have. Every point, every angle that's there, because I think it's going to provide that complete picture versus just focused on one thing.
Shaun McAdams: Yeah. I would hope that what the listeners will get from the end of the year is actual tactics for them organizationally, on how they deliver data and analytics. When you go out and you Google data and analytic strategy or define a strategy, I did that this week. Went in to Google and actually just put in data analytics strategy and hit I'm feeling lucky so I could bypass some of the ads and get to the very first thing that would pop up. The very first thing that came up was an ebook. It was an ebook from Gartner. Gartner produces a lot of collateral. It was called Design a Data and Analytics Strategy. I don't know, I feel like it's a pretty safe assumption to anyone who starts to listen to this podcast or would pick up this ebook that they think they're going to get the same thing out of it. I think it's important that we define what a data and analytics strategy is because if someone reads this book, which this week was the first time I've read that, they did produce this in 2019, it doesn't specifically tell an organization how to deliver data and analytics. There is a bright comment on there that says," Don't do nothing." But if you read this, it gets down to what I would say is the micro level. We're going to talk at a macro level, which I think you have to do so organizationally you know the things that you need to tackle to deliver data and analytics. It gets into a micro level, meaning let's take a use case or a set of use cases and what's the strategy around implementing that use case. The author goes to talk about three different trajectories of strategies, talks about four different types of things that the business may want to tackle. For example, maybe it's customer engagement and then how do I do that in these three different ways and what's that look like? Obviously, you get to 12 and then they throw the 13 in there, which is don't do nothing.
Warren Sifre: I think the idea of us taking at the macro level is a big deal because it's easy to spot the micro level improvements that you need to do in a data and analytics strategy. It's easy to find those one things, but how those things roll up, how does it go top down? How does it make its way to ensure that the steps we're taking, the investments being made, the approaches being done and the processes put in place are culminating into the business strategy that's being set forth by leadership?
Shaun McAdams: Yeah. Yeah, I would agree with that. For those that are tuning in, when we talk about data and analytics strategy, we're being very specific. Here's our definition. What organizationally do you need to do to deliver those capabilities throughout your company? Before we get into that and how we establish that, I think it's also important to talk about our influences of how we got to this type of methodology. We've been delivering this since 2018. We built our maintenance service platform, Honeycomb, on these philosophies, but a lot of things went into influencing. Some technical and some non- technical. Technical influences would definitely be all the clients and the engagements and the things that we've done as an organization over the past five years, but even before that, before you and I both came to Moser, were influenced by those particular implementations. The good, the bad, the ugly of those implementations. One in particular for me was with a large healthcare provider in the Northern Midwest. I got to go in as a principal consultant to look at how they were delivering data as a service, along with two other consulting companies. We're coming in there to look at it and give our opinion on it. Most of it was technically focused. They were having issues with integration, but their main issue was with delivery. It was this isn't one service, you had tons of business units that were supporting this initiative to be a data as a service organization, but they didn't know how to work together. They didn't know how to work together to deliver this. This is an ITIL company. This is a company who should apply a particular service design strategy and just failed to do that. Or they scoped it rather way too big to be able to function. What was a technical history or a main influence you think that got us to the point of what we now advocate?
Warren Sifre: I've seen quite a few clients over the years and the clients usually come with a very technical piece. Hey, I need you to take a look at our data warehouse or need you to come up with a way to do X, Y, Z. A lot of times the requests are how to stop a particular behavior within the culture of the organization. Shadow IT, business units just running off and doing their own thing ultimately, and building things on their own without taking into consideration compliance and security and all the components that an enterprise must take into account. Most recently, I would say within the last year and a half to two years, I wound up at an organization that is global. Ultimately we're brought in, hey, you know what? We've got this data scenario. The client had acknowledged that they're outgrowing, out pacing and it's out of control. There's just a lot going on and they're not able to really manage it. We went in, it's like," All right, fine. Let's have some conversations. Let's talk about your immediate problem." From there, it continued down. As we started investigating and uncovering some of these pieces, the conversation had slowly shifted from what is it that you have that you feel is a problem, to how did you get here? What were the things that drove this manifestation of the solution that you have now that's become cumbersome? We started going down the path and you know what, let's figure out what these different business units really need. We went down the path of interviewing and having conversations with these business units. It's not necessarily rocket science, but it's like," Hey, you know what? What works for you now? What is it you like about what you're doing? What are things that you needed yesterday, that you want immediately? And if you had unlimited budget, what do you dream? Where do you think this will go?" Then as you start interviewing these different business units, you start getting these cross sections. You start getting the characteristics of how and why these business units made the decisions they did. I went with this tool, we purchased it. We funded it ourselves, got our own staff to manage it. Why? Because of certain characteristics or interaction traits within the corporate IT. As you start investigating these things, you start realizing that it's more than just technology that drove them to do this. It's process, it's people, it's culture. Then for us to go in and just provide a technical solution, we're not necessarily approaching the problem that's at the heart of it because until you fix that, you're not going to find yourself in a situation that is going to have the longevity of success. It's going to prove successful now. And then because the root has not necessarily been managed and orchestrated and curated, it's going to manifest again, slightly differently. Maybe three or four years from now we're going to be coming back and doing the same thing. And as much as we enjoy fixing these problems, we want to make sure that we advocate and push for something that really drives to the root of things and allow organizations to go forward and blossom from that point.
Shaun McAdams: I think that's one of the goals that we have internally with then doing these podcasts is that so often we're pulled into conversations that I would classify as those renovations. When you talk about and describe the challenges that a business has in the delivery of data and analytics, we're basically pulling back and saying," Well, you didn't really need this room and that room. Let's go back and let's try to make use of what we have, but we may have to reconfigure this a little bit because you bought a house that doesn't quite fit your needs." I think that is one of two non- technical influences that we have. I would classify that one with this concept that I talk about of this mathematical concept that we got taught early in grade school, lowest common denominator. How do we boil down? Because we have done implementations on- prem Hadoop, in the cloud Hadoop, on- prem Microsoft, Oracle, SAP, in Azure, in Google cloud platform, in AWS. And we've had success in all of those where we're talking about over 75 clients, over a hundred engagements. I think that its success is attributed part to the philosophy that we have to boil things down to the lowest common denominator. What do you have to do well to deliver data and analytics? I think that's one philosophy that should come through as we have these podcasts and these conversations. One of the challenges there is that as we go through this one today, and we talk about it, I think a lot of people will go," Okay. Yeah, that makes sense," and they'll miss the power in being able to step back and then approach and apply it to the organization. I think too often we've done our main delivery of how we look at data and analytics over and over and over with no objections. We talked about that pretty early on. I think it was a year in, where we're talking with organizations and we're looking back at all the conversations we have, and it's like," Hey, Warren, we're not really doing too heavy objection handling here." The message is resonating. It means something. But I think because it has been simplified, it's easy also for listeners to not see the power that exists in these philosophies. I think the last influence would be the starts with why ideology that Simon Sinek had portrayed. Listeners are going to see that philosophy when we really dig deep into a number of aspects of what we're going to talk about. That philosophy essentially says," Hey, let's answer the why, the how, the what questions," and in that order. Let's start with answering why. For us, when we look at why we exist and we talk about What makes up a data and analytics strategy, all those pieces come together. We say we exist to help organizations define and implement a data and analytics strategy. We're pretty explicit to that too, because organizationally our structure and our practices are data, analytics and strategy. So we're living it out pretty explicitly. But we say that that strategy is made up of data functioning through tech, which is predominantly the reasons why organizations call us. They're having issues with technology, or they want advice around technology or data and some of the things that go into it, where we talk about data management, data governance. But it's also people operating under a process. Those four pillars; people, process, data and tech.
Warren Sifre: Many times it's that process that floors them in that conversation because they're like," What does this have to do with the technology and the data we're talking about?" That goes back, that uncovering of, hey, there's a route here and we can implement things, but if you don't have a good foundation to manage and orchestrate this and work through this and develop that culture that can support the technology and the data that's being put in place, it's going to run amuck again. That's that data process that the people and process part that I think is the forgotten combination. When we bring that up in these conversations, they're like," Wow, okay. That makes so much sense. I understand how we got where we're at now." Because we simplify, because we take it down to those pillars, we find ourselves having some very constructive conversations. The conversations are very much tailored to the situation the organization's at because every organization is at a different phase of this life cycle, data maturity. And implementing data governance and security policies and all these other pieces that we get called in to try and to advise and consult on, you got to have processes to match that too and what does that look like? That's usually the end of the formula, as I mentioned, a few moments ago, that it's the forgotten part or just overlooked.
Shaun McAdams: Yeah. It's funny, I go back to this article from Gartner, which I'd made a lot of notes about and was fairly critical just because I get tied into if you talk about designing a data architecture or a data and analytics strategy, then it should be tactical things of how you deliver data and analytics. And it doesn't really deliver on that, but there is some good information in here. But in one of the slides, the author says this," In a digital society, data is as important as the classic business drivers of people, process and technology." I mean, that should be the sentence that he started with, because that is what is needed in order to deliver data and analytics at an organizational level. You're just adding data to those classic business drivers and that's what we've done. We want to attack the particular challenges that are specific to an organization. The application of this may be different to each individual client just because of their uniqueness, their differentiators, their business strategy that you're trying to introduce into it, but it's people, process, it's tech and in this case, it's data because it's data and analytics. So as we move on, we've got to dive deep into each of these areas. And we're really pretty explicit to this particular slide. If you're looking at the video, you'll see it. If not, you can picture in your mind a line that says data and analytics strategy and above that set a couple of pillars around people working through a process and below it data coming up through technology and those things are coming into data and analytics strategy. To the left, you'll see business strategy being introduced into that so that we can create these insights that the organization can take action that drives a business value. Now, we're super explicit to all these areas. I already talked about our organizational structure. You being the director of strategy, we have a director of data, a director of analytics. We have a vision statement that says," We want to become a trusted advisor that innovatively applies data and tools and techniques that deliver business value." That's the book end of where we want to get from our data and analytic strategy. That's also our vision statement. Those things align. Then we have a mission statement, which we ask our consultants to live out every day. To design the right solutions, to implement best practices, to get better at tech, but also get better understanding our client's business strategy. The things that we're ultimately going to introduce into what we're talking about. We have a slightly different take on analytic maturity as it relates to insights, these things that are created. If I go back to a very, very common model with Gartner, they would look at analytic maturity going from say, business intelligence up to artificial intelligence. And they would graft that over value and complexity. As you grow, you're going to get more value out of more advanced techniques, but it's also super hard. We've flipped that a little bit. I think we flipped that one: because I don't think in every instance it is super hard and I think it's getting easier the more software we see developed.
Warren Sifre: I think the challenges that most organizations encounter in trying to achieve those advanced analytics platforms and levels is been traditionally the maturity model they have within the technology and the processes of how they curate data. And how data is staged and made available so that when a platform is introduced or a concept or a piece of technology is introduced, it has to abide by certain guidelines and principles to ensure that it is leading towards that ultimate goal of being able to achieve those additional levels when it's appropriate. Like funding, technology, the data, whatever stage your organization's at. But having that platform to be able to launch and get there. We find ourselves in situations that, hey, we've got all this ML stuff, this is great. How are you doing it? Here's all this Excel stuff, here's these documents, these things here. They're like," Wow, okay, I'm glad you got functional, but you may want to address the scalability, the technology, or the approaches taken on how to be able to expand and leverage things in a way that allow you to be more productive and efficient in how you're doing that model. Or retrain that model, inaudible available in that model." That goes back to that maturity in platform and that goes back to analytics strategy and where the business is going. We understand what top level one side of the business, then we know what type of people we need to execute the process and what kind of technology we need to acquire the data that combine and give us those insights and thus start driving those business strategy goals forward.
Shaun McAdams: Yeah. And the type of tech needed to empower those people. As we have future conversations, we will break down how we see insight. It isn't follow that particular Gartner model. But then with the automation of those. We talk about actions. Really, if you're looking at going from BI to AI, it is the automation of insights that have been derived and what are you doing with those? We'll break down into that. Of these four pillars, now we've got to look at tactically over the course of this year how do we want to dive into each of these? I think rather than going say top down or bottom up, or starting with tech or starting with these things, we should start with the thing that we see as the biggest gap within the clients that we're currently servicing. So when you think about the clients that we've had historically and you look at gaps in people, gaps in process, gaps in tech, gaps in data, what is the biggest challenge you see organizationally?
Warren Sifre: I think organizationally I've found that process is the biggest gap. Traditionally what's happened is we're implementing, bringing in technology. We're setting up a framework of how this technology is going to be adopted and used in an organization, but we haven't taken process into account. How exactly that's going to work and how does that process align with the business strategy? How does that process align with the long- term goals of that IT organization? Do you want to own all of this technology and all the development and all the support and all the pieces? Or is there some self- service avenues that you want to address and approach, which means your process needs to change, unless the way the technology is implemented needs to cater to that process. A lot of times it goes the other way round. We've got this technology, it's been put in place, it's working, it's functional, we've got all this cool stuff and it's like," How do we manage this? How do we scale this out?" It's like," Well, you need this process." Oh man, that means we've got to refactor.
Shaun McAdams: That goes back to the original use case that I said was a strong influencer for me because yeah, that organization technically may have bit off a little bit more than they could chew. But the real issue is they had a bunch of different business units that played some part in the delivery of data and analytics for the organization and they didn't know how to work together. So when we talk about process, it's more than just workflow. It's more than where is data or where are requests coming in and then how are we satisfying those? It really is the mindset of how we approach those. A lot of people want to have a data- driven culture. I really feel strongly that the only way that I've seen for an organization successfully truly deliver that, that it's not just words that we say that we do, but behind the doors we're a complete and utter mess, is to have a process that sets the mindset for how organizationally you want to attack these challenges. So I agree with you. I mean, I think that process isn't the common conversation that organizations want to have when they come talk with Moser, but it's the one that we almost every conversation end up influencing in and asking questions around. That's where we'll start. Next month, when we go through and we start to build on defining a data and analytic strategy, let's tackle process. We'll use what we do internally within our managed service for Honeycomb, so our delivery model. We'll look at our agile methodology, the approach that we apply to agile methodology and then other applications. Because that's one thing we're not super explicit about in that every client has to use their terminology, or they have to adopt our particular SDLC. Again, we've approached it through this least common denominator, and this starts with why non- technical influences. So definitely if you listen, you're going to want to hit subscribe. You're going to want to look to catch that next one that comes out as we dive deeper into process. Now, if you're a listener and you look at what we've talked about and you just can't wait a year to have this information coming to you, obviously you can reach out to Moser, to myself or to Warren. We can have those particular conversations. More than that, we are going to be releasing an ebook that has a lot of the content that we're going to be talking about over this year as well. You won't get our natural banter within that, or some of the applications of our history and stuff that you would get by tuning into this podcast. But I'm super happy that we're doing this. We've been talking about it for a while, like we said at the beginning, to be able to get started. I'm also excited about the things and conversations that we're going to have outside of this particular topic that we are going to do once a month for this year.
Warren Sifre: Oh, yeah. I mean, I think aside from going through this process, as events change, things happen in the market space, we might throw out some extra bonus podcasts out there giving us our thoughts and how we are taking into consideration those changes. Because I mean, some of those changes are pretty monumental. They're foundational and they're big things, especially when new compliance regulations goes around. I mean, a couple of years ago, GDPR rocked the world. People are still trying to figure out how to make that work. The concept of forget me and all these different implementations that now have to be retrofitted into existing pieces. So as things like that crop up throughout the year, we may have some bonus content for you that are some thoughts and get some great consulting, I guess.
Shaun McAdams: That's it. We've held these philosophies pretty close to chest over the past couple of years. I mean, we've lived these well, three years, really. 2018, 2019 and 2020. What we're going to see this year is us release that, open source maybe those methodologies and us really focus in on the application of those, which we do anyway. Appreciate everyone that hung out with us, that listened and I'm definitely looking forward to continuing that conversation. We'll see you next time.
Warren Sifre: It's been great. Thank you, Sean.
Announcer: That's our episode for this week's edition of ASCII Anything. We hope you enjoyed Sean and Warren's conversation on data and analytics. We will be back next week with another episode of ASCII Anything, presented by Moser Consulting, your technology partner. Until then, so long everybody.