Click here to watch the episode on our YouTube channel.
In this Expert Insights interview, Greg Brown, BI Consultant for Blue Margin, hosts Anthony Algmin of Algmin Data Leadership, host of podcast Data Leadership Lessons, instructor at DATAVERSITY (dataversity.net), and author of Data Leadership: Stop Talking About Data and Start Making an Impact!
Blue Margin helps private equity owned and mid-market companies organize their data into dashboards to execute on strategy and create a culture of accountability. We call it The Dashboard Effect, the title of our book and podcast.
Visit Blue Margin's library of additional BI resources here.
For a free, downloadable copy of our book, The Dashboard Effect, click here, or buy a hardcopy or Kindle version on Amazon.
#PowerBI
Click here to watch the episode on our YouTube channel.
In this Expert Insights interview, Greg Brown, BI Consultant for Blue Margin, hosts Anthony Algmin of Algmin Data Leadership, host of podcast Data Leadership Lessons, instructor at DATAVERSITY (dataversity.net), and author of Data Leadership: Stop Talking About Data and Start Making an Impact!
Blue Margin helps private equity owned and mid-market companies organize their data into dashboards to execute on strategy and create a culture of accountability. We call it The Dashboard Effect, the title of our book and podcast.
Visit Blue Margin's library of additional BI resources here.
For a free, downloadable copy of our book, The Dashboard Effect, click here, or buy a hardcopy or Kindle version on Amazon.
#PowerBI
Welcome to Blue Margin's Expert Insights Series. We're glad that you joined us today. This series is for private equity and mid market executives who want to use data and dashboards as a short path to increasing growth and profitability. I'm Greg Brown from Blue Margin's consulting team. Today I have the pleasure of interviewing data luminary, Anthony Algmin. Anthony is a data leadership professional who has led data transformations in many industries with roles as a consultant, data architect, program lead, chief data officer, and entrepreneur. He's the author of the first published book on data leadership, entitled Data Leadership, Stop Talking About Data and Start Making an Impact. Anthony currently serves as the convergence platform program lead at AbbVie, building a massive data and knowledge analytics platform transforming how R&D discovers and develops new medicines. Anthony has led over 100 public speaking appearances and was one of Dataversity's most popular online training instructors. I first learned about Anthony listening to his podcast, Data Leadership Lessons, which has a full five star rating by the way, and it explores how leaders can make the most of their data. So today is a meta view into data leadership from an established leader and someone who has interviewed many other luminaries and data. Welcome to the show, Anthony. Thank you, Greg. It's a pleasure to be here. Anthony, excited to have you here today to talk about making data useful and your depth of insights on the topic that you've collected over your two decades long career and data. For those listening, what are like the top one or two lessons that you've learned along the way about being successful using data? Sure, I think the first and foremost, is that really, it's all about the business. It's all about, "What does our business actually do? How do we leverage data to do that better?" I mean, really, at the end of the day, that's what it all comes back to. And so if we want to be effective with data, we have to impact our business. So that's number one. Second thing is that there is no value in data unless you're creating change. And so building upon the first lesson, which is it's all about the business. The second thing is it's all about the change to that business. Data informs what we're able to understand about our business, about the situation, about our industry, about our customers. I often say it's the closest thing to truth that we have in our organizations. But that truth, just knowing it doesn't really accomplish anything. And it's about putting it into action, starting to drive new decisions and activities. That's where it starts to create new business processes, and those business processes lead to the business results that we care about most. So data is a change agent, but it's really all about the business in the end. And are those the sort of lessons that inspired you to found Data Leadership Lessons? Absolutely. I mean, for me, I have this background, where I started in technology, I was doing all this technology development. But even prior to that I studied business, I was always business first. But then I realized that a technology and then later data toolbelt could really help me drive better business. And so I started to put these things together, and then eventually found my way into consulting, where I'm talking to business executives, I'm talking to technology teams, I'm talking to a whole bunch of different people across a bunch of different industries. And I realized what is evident, has long been evident to me, isn't always apparent to everyone else, because they're focused in their function. And for me, it was always taking that big picture view and whittling it into a function that I could help propel forward. And I realized that this was a message people needed to hear. And so that's why I had written the book. That's why I do consulting the way I did in the past, why I'm doing the work that I'm doing today in industry, and why I had the podcast, because the podcast gave me an opportunity to look at these challenges from a bunch of different lenses with all the different guests that I had across many different industries, many different perspectives. Some were in the data space, some weren't. Some were just entrepreneurs or senior leaders. And to me, there was always a thread there that we could connect back into that say, "Okay, well, where's data playing a role here?" And at the end of the day, it was always about the business. It was always about saying, "Hey, how can we do something that helps people, or makes more money, or does something different in a way that we hadn't done before?" And kind of coalescing that lesson over and over again, from a bunch of different viewpoints, to me was one of the better ways to try to convey that to the people that needed to hear it. Yeah, makes complete sense. I like what you said a lot about data, and data of course, as a change agent. I'm kind of curious if there were situations where you consulted or worked with companies, where that wasn't fully being appreciated. It was more of, "Well, we want to leverage data, but we haven't thought a lot about how it's going to change our business or how we want it to change our business." Were there times where you encountered that, where you had to help companies understand what that change could be and how it would affect their business on an everyday level? My flippant answer is like every day of my life. But in reality, an example I'll give is when I was working with Chicago Transit Authority, where I was first as a consultant with them and later became their chief data officer. It was one of those things where the data component of buses and trains, this is Chicago's main bus and train service, right, data is everywhere in that and it permeates everything. But the ability to use it was really poor at the organization when I first started consulting there. It was poor to the point where the technology group was focused on keeping the system is running and serving the operational needs. Which makes sense, you need to know when the buses are running or what have you, but there's so much more you can do from an analytics perspective, where you start to dive into the data that those buses and trains and the fare system and all of that give back to you. And you can start to think about trends, and you think about efficiencies. And you can think about, "Wait, how could we potentially improve our maintenance schedules by looking at the data from these different devices, or different sensors on our trains or on our buses, or where we might have loading issues in different stations, or where there's a tendency for buses to bunch up together, and this is the condition. I can think of so many examples in our businesses today where data helps inform something that illuminates. It helps you understand the core nature of your business better. And the buses and trains are just staring you in the face, but it's not such that everybody could connect the dots and get back to say, "Well, I know this data would be helpful, but it's really hard for us to get to it. How can we get to it?" "Oh, the systems don't relay that data, it's hard to query them, or we're not allowed to, the data's closed off." There's all of these things that stand in the way, and so what's conceptually easy to understand, can be very difficult and implementation. But that's why we do this data work, is that we start to try to chip away at that, make things a little bit better, a little bit better. And all of a sudden things start to feed on themselves, create momentum, and then we start to move faster and faster. That's really what data can enable you to do. It's a momentum generating engine when it's when it's harnessed the right way. getting it in a way where you can serve it and have that visibility into things like you just described. Like, "We have these buses bunching together, we can see that now." And that starts that conversation around,"Okay, what process do we change? How do we fix that?" Because now we know that we have the best version of truth, I think in terms of the data, and then we have the ability to now look at that and say, "How are we going to make those changes? Or what's the process change that is going to happen there?" So yeah, it makes complete sense. You know, there's a default kind of thinking when it comes to change or when it comes to incentivizing people to start to move towards the actions you're intending. And to your point, we tend to start with thinking,"Okay, do I want to approach this with the carrot or the stick?" Right? Like everybody thinks carrot or stick. Don't start with either. Start with flashlight. Start with just,"Point a light at that thing." And say, "Did you guys know our on time performance was 25% this week?" I don't have an answer yet. But I at least know the problem and I can quantify it. By putting that in front of you, now all of a sudden, weird, strange things start to happen in the background, where people are like, "Oh, well, maybe I can make that better if I do this or that or whatever." You don't always have to be prescriptive. You don't have to say, "Here's the carrot, here's the stick, here's how I'm gonna force you into this change." No, sometimes just bringing it to people's attention is a good place to start. They're amazing at finding ways to solve problems, if you let them know there's a problem to solve. Yeah, I think that's such a valuable lesson because, from my own experience in my past career working in that role for seven, eight years, you start to feel like, "I know everything, I see everything, I'm very well versed in how this business operates, this industry operates." But because of your job and the functions of your job, and in the way data is either served to you or not served to you, you start to develop that sort of false confidence and have those blinders on. And so I think that's just an incredible reminder for everyone to keep in mind. Especially when you feel like "Hey, there's more that data could probably do for my business." Well, that definitely means there's things that you would react to and feel differently about if you had that scoreboard, if you had that visibility into the data. Absolutely. It reminds me of a term I think I coined during a podcast episode. I was talking to George Firican, and we were talking about how being data driven isn't what people always think of as being data driven. A lot of people think, "Well, if I am doing the thing, and I see this data, and that data supports what I'm doing, then I'm data driven. But in reality, what you are then is data justified. You've decided to do a thing, and now you're looking for the data to validate that your decision was correct. And really, that's not what we're going for here. And I think the thing that changes the dynamic instead of being sure of yourself, being confident, it's about being curious. Data driven is about understanding more, learning more, being curious about the world around you, and seeing what the data is telling you. If you already have a hard and fast lens that you're going to interpret everything through, the data is not going to tell you anything new. You're already closed off to it. It doesn't matter. So if your actually curious, and this is one of the most important traits of consultants and of anybody, is, be curious and recognize there's a whole lot more you don't know, than whatever amount you do know. And so don't ever come into a situation, especially a data driven situation, like you have all the answers. The data has more answers and probably still doesn't have all the answers. You really need to recognize, "Hey, we are all dealing with a sliver of the available information, we better act like it and recognize, hey, our job here is to be curious to try to find, to hypothesize, to look and see what might work but then to always be open to new information that might be able to guide our future behaviors even better." Yeah, absolutely great insights there. Well, and pick your data, shiny object of whatever in last 20 years. Whether it's AI and machine learning, or we can even go back to big data, or dashboards, or whatever it is. They were all presented as this quick fix, or the shiny object that was going to solve all these problems. And yes, they have their place, but they don't solve all these problems simply. And I think we put all this marketing jargon around these things like their simple. And the fact is, is that to do data well, and to do data well at scale especially, it takes some real effort. It takes some real work, it takes some real coordination to get it right. I think of technologies always as amplifiers, right. And I think of it as, "We're doing a recording here." And if you record and the recording is poor, or the speakers are poor, or the cables that connect them are poor, and you amplify it, you turn up all of that amplification really loud, you're going to have a very unpleasant sound coming out of that. But if you record beautiful music very well with high quality equipment, and you then turn up the volume, now everyone for a much wider area can use that and enjoy that for what it is. Data works the same way. If we just jump straight to that amplifier, "Let's turn it up to 11 and see what happens," we're going to have some challenges. And that's what scares me about some of these jargon terms, or technology amplifiers, or what have you. They have their place, certainly. But it's part of a broader balance of everything that needs to be thought through. Yeah, very well put. Completely agree with that. And you touched on it there, Anthony, in your last answer. But, I think a lot of mid market companies and larger companies, they of course find machine learning AI in general to be very alluring. And I saw a stat recently from Grandview Research saying that AI adoption by organizations is projected to grow at an annual rate of 38%, between 2022 and 2030. And we all know it's gonna become more prevalent in our everyday lives and business lives. And I heard you kind of pose this to someone on your show once. But how do companies move into those fields if they can't master some of the simple things like Master Data Management and real data governance? Can we reconcile that? Can companies create value with those sorts of projects, even if they haven't mastered some of the simple stuff? There are more and more ways to tap into some of that power, that you don't have to grow it all yourself. So are there ways to do this? Absolutely. And I think there's more and more libraries out there, and more and more libraries from a from a programmatic perspective, not actual libraries. But more and more things you can tap into if you're writing code that will make some of those real complex things more possible. What I really want to encourage anyone out there, though, to realize is that your best bet in terms of amount of effort or amount of investment, or what have you, is probably still in the basics. Like, get the core things. If you don't have really clean transactional sales information, customer master information, vendor, and inventory, all of the basics of your core business operations, if that's not all taken care of, I don't know that I'm going to entrust that to AI and machine learning models. Those are the things that you really need the attention and direct eyes on. But if you're starting to think about, "Hey, how might we go to market in a more effective way, or target different groups and different demographics, or would this new product idea that we're thinking about resonate well with this group," or whatever. There's opportunities there where you can start to do things because the consequences are low. Yeah, it might point you in a direction, but you're not banking this quarters numbers on whether or not your AI was perfect. And so that's where I think some of these prospecting mechanisms are useful, and may provide you that kind of amplification where a little noise is okay. And so I would certainly explore it, especially if you're reporting to people who have read something or have gotten interested and say, "I want to do more data stuff. This AI stuff sounds amazing." "Hey, yeah, let's do that. While we're doing that, let's also spend a little bit of time getting our point of sale system fixed. Like, let's do those things, too." So shifting gears just a second, Anthony. You know, here at Blue Margin we really focus on helping companies organize their data, establish modern data architecture solutions in the cloud, and build reporting to grant visibility into the growth plan and how its components are performing. We've always found that if you build that scoreboard, you create that visibility that we've talked about, and you show stakeholders how the business is performing in real time and where the gap is, that it lights a fire and builds some urgency and team accountability to the established goals. I'm curious if you've seen the same things in terms of just generally granting visibility through data and starting to tell the story through data, does it feel like that alone can provide that spark to businesses in terms of moving the needle on their growth plan or helping them fulfill their strategic objectives? So I've definitely seen that. I like the idea of data storytelling, I like the idea of illuminating truths through dashboards and data and building data literacy within organizations. But I find oftentimes, that people think just because they use data, or they look at dashboards, they view the reports, that magic happens in their business gets better. I see a lot of energy and investment wasted on simply providing new interfaces into the same data that they've been working with forever. You get a new report that maybe has more detail, but then you have to ask yourself, when you've gone through this investment, you've done something to change the status quo, change what people have, you have to then think, well, what are they going to do differently as a result of this? And I don't hear that question asked nearly enough. It's that when you provide a new capability, what will this impact to your business? What will this change in terms of your business's ability to deliver on its core objectives, reduce costs, improve revenue, manage risks? Something that's tangible, should be the output of anything, whether it's an information system that provides dashboard reporting, or it's something that drives automation in your factories. All of these things should point towards an improvement to the business. And so that's the thing that I think we still fall short of a lot of the time, because we see and focus on the data capabilities themselves, but not realizing that there's endpoints to this, and they have to anchor into our business in ways that we're getting insights, not into just any question about our business, but ones that can manifest in improvements to our business processes. And so that's where, especially when we're talking about dashboards, or other kinds of traditional business intelligence types of information systems, that's where you really have to push forward. And I think that teaching people to be change agents through data is a different story than saying,"Here's what the data means in this place." And I think that we need to do a better job of creating those change agents versus simply teaching people,"Here's what the data can tell you." What we need to do is say,"Here's how the data can move you." And that's, I think, the next extension point. I don't see that happening very well, broadly speaking. And it sounds like it's also a matter of saying at the outset of a data initiative, like for us, for example, in helping to develop customized reporting or bespoke reporting that really aligns with the value creation plan of a company, it's really important up front to consider,"Okay, before I develop this reporting or these dashboards, A, what change are we trying to impart on the business and then B, really verifying what the ROI of that project is going to be. Because the last thing you want to end up with as "Well, these are beautiful reports and dashboards, but it has really no impact on our business. And it's not helping us change and evolve, it's just simply displaying things to us." And if you verify those things up front, you can assure that maybe there's a bit more value in that initiative than if you just say,"Well, let's build it, and then we'll see what change and returns come later down the road." Which sounds kind of crazy, but I feel like businesses sometimes say that, and then realize, "Oh, we should have actually pressure tested this and made sure it was going to create the change and the value that we were hoping for" You can base it on where you see market opportunities. But ultimately, it really has to be part of your business strategy. Your business strategy has to say, "Here's what we do. This is what we do and what we don't do and where we're going." And that shouldn't just be executives going to an island somewhere and coming up with a wish list of things they would like to do as a business. This has to be rooted in reality. And so there should be someone representative, often a chief data officer or something like that, representative at that strategic level to say, "Hey, yeah, we're capable of driving this kind of behavior, this kind of action or enter this new market," or "We just don't have this capability set at all. We don't have the talent today, and what's probably going to take us two years to ramp up to do this kind of thing based on the norms of this industry or norms of businesses at our relative scale," or whatever it is. And that's where I think we need to root both our data expectations, and our business strategy expectations, and our individual expectation. It has to be rooted in some sort of pragmatic anchoring in reality, and then also an understanding of what's possible or what's ideal from a future growth perspective for the business. Yeah, absolutely. Absolutely agree. Well, Anthony, at Blue Margin we believe that leaders are readers, and for our audience of midmarket and PE professionals who are looking to extract the biggest bang from data, are there any books or podcasts that you would recommend? The Phoenix Project by Gene Kim and a couple other collaborators, that's a classic. That's like required reading, I read that once every year. It really is important to understand how operations play in a data and technology space. So the Phoenix Project, Gene Kim, that's a really important one in my opinion. I also do a lot of work in collaboration with Dataversity. I just came back from one of their events last week. And talking to other data practitioners, and it could be a Dataversity event, which I'm fond of, or online training and stuff like that has its place as well, especially for functional learnings I've done a lot of training courses at Dataversity as well. But really any event where you as a data person, and as a person, either your a business leader who's trying to use data better, or if you're a data professional, or an aspiring data professional, getting to a place where you can go and talk to other people using data in that. Because a lot of the time, you might be the person. Especially if we're talking mid market, you may be the person in your organization thinking this way, trying to build these systems, trying to lead these groups. A lot of times, I mean, I worked for a company that had maybe 100 people total, I had a four person team, and we built all the business intelligence for the entire organization. And that was a trading firm. But like you have a lot of different hats that you wear. And you need to be able to connect these dots and then anchor them into what's really meaningful for the business. And there's different skill sets involved with that. And it's challenging in any organization. In very large organizations like I work at now, they are challenging in different ways than a midmarket organization is, or even a PE organization is really interesting, because sometimes you're going to have different collections of businesses that are in different areas. And depending on whether you're working for the central group, or one of those other businesses, some of those challenges are going to be unique as well. And so just trying to put things into a broader context, by collaborating with others, working and trying to understand beyond your own bubble, that's gonna be really key. But I think there's a lot, so many, resources out there. I would also say, pick up a little Python. If you don't do a little bit of programming, or a little bit of understanding how actual data flows from a technical perspective, you're really going to struggle to have good conversations with those technical leaders, or those technical developers, data architects and the like. Do some of that. And again, online resources are plentiful. You can even watch some YouTube videos. Whatever you prefer. It's amazing, I just think back at the beginning of my career, I had to just decide which book did I want to buy, and go buy the book, and I read it on the train, I'd read it at home. Now, you can do all this research on your phone, on YouTube alone, right. And so it's really a matter of how you learn and what's best. But I definitely think talking to some others, now that we can talk to people again and see people, it's invaluable. It will give you a perspective that that no book can in reality. Totally, and I love your comments around, you know, you can be a one person team, a two person team inside a company that needs to be sold a little bit internally on why we're allocating this money towards data and data strategy and why we want to embark on this initiative. And really talking to other leaders and getting their perspective, and what's worked for them, and how they've navigated those things is really critical. And I also do remember that time before YouTube and the availability of everything online. It was so much more difficult. It's almost so many choices and resources it's good to have someone like you pointing us a little bit in the right direction. Anthony, I just cannot say what a pleasure has been to interview today. We really appreciate the time. And I look forward to connecting again in the future. Thanks, Greg. Yeah, this is great. I appreciate you doing this. And it's been a pleasure to be on your show. And hopefully, all of you out there have an opportunity to dive further into this data space and help make a difference going forward. Awesome. Thanks, Anthony. Appreciate it.