Being more effective for me starts with learning how I behave, and for that I need just the right amount of information. How does this scale when you are looking at a team?
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[00:00:00] Loris Marini: Time is the most limited resource that we have. And like many, I'm always on the hunt for better ways to make use of mine. This means blending quantitative and qualitative information and contextualizing it to gain insights about my own behavior. But this idea, of course, can be applied to teams at large. The hope is to reclaim more of our free time and spend it on what matters the most.
So how does this translate to organizations? Well, with the right information at the right time, we have the necessary ingredients to increase organizational efficiency, move faster, be more resilient and just respond quicker when things change.
If we know what's working and what isn't then we can course correct.
This whole idea revolves around us knowing what's going on. And as you can imagine, this isn’t as easy it sounds because data is often siloed and inaccessible. And even if this wasn't a problem, knowing how to analyze the data and then act on it is time consuming. Where do you start? What do you focus on?
To learn more about this, today I'm speaking with Chris Boys, founder of Umano. Chris is a strategic planner, a people leader, a connector and a change agent. He worked for a number of organizations including Accenture, Dun & Bradstreet, Social Venture Australia, and most recently, he co-founded Umano. Umano is a team analytics company that provides real-time data driven insights into a team's working habits, strengths and opportunities. Sort of painting a full picture of the performance of software development teams. So here I'm with Chris Boys today. Chris, thank you for being with me and welcome to The Data Project.
[00:03:33] Chris Boys: Hey Loris. Great to be here and thanks for inviting me onto the show.
[00:03:38] Loris Marini: Absolutely. My pleasure, man.
[00:03:40] Chris Boys: There's so many things you've raised in that little introduction. I think one thing that struck me was really understanding where to start in an organization. And if you're a large enterprise, typically you've got the resources and a team to help, which is a massive head start. You'll have a team of data scientists, business information and analytics tools, and central databases. Even with those resources, it still comes down to the basics. What questions are you trying to ask? What hypotheses do you have that you're trying to prove? And with various signals available to you, how can you go about finding those signals in all of the assets that you have to prove or disprove, and ultimately generate the insights to solve the original question?
You take that example in an enterprise that is ultimately well resourced and the level of difficulty that exists there. Then you start to come down in size in a company that doesn't have those resources, without dedicated data science teams that are just trying to do their best, it's immensely difficult when you start to see that complexity. Not only of what questions you're trying to ask, but then to go and find where those signals exist in all of your toolsets and all of the different data sets that are available, to stitch that together and to come to timely insights in order to help that decision-making it's near on impossible.
And this was a problem that I experienced firsthand within enterprise, and felt that there just had to be a better way to stitch those data sets together for our specific applications to help make better decisions in real time. This is to your point around moving faster with that decision-making to be more effective and ultimately winning back time to do the things that you love.
[00:05:47] Loris Marini: I find that this problem of data access is the common denominator to pretty much everything we want to do with data: whether it's applying it internally in the organization in order to improve fluency or efficiency or using it to decide what the next features on a product should be or just in general, how to manage our relationships with our customers.
I find it interesting that typically you have to choose between one platform that integrates it all and makes it easy to access all the data versus having to stitch it together yourself. And most of the time teams don't have the luxury of having those extra resources to do the stitching. And I can speak about this because in the past, I had tried to build a source of truth for a couple of companies I've worked for and the process is very time consuming. Every tool has a different understanding or it makes assumptions about how the data should be stored – the data model, but also what the different fields mean.
It's not just the engineering work of bringing this data together. It's also mostly the human work involved with agreeing on the meaning of data and the building of, what we call in data management a data glossary, making sure that when people look at a metric, they understand that metric in a way that is consistent across the enterprise.
Now, you see fields like master data management and reference data management become more and more important when you want to have that visibility across thousands of people. But I'm curious about your journey into Umano. Tell me how it all started. What made you realize that you had to do something and that something was building Umano?
[00:07:48] Chris Boys: You bet. My previous role in Dun & Bradstreet, which is effectively a data driven, multinational enterprise, was Head of Product for the region. In scaling the company and growing our revenue objectives, we were very clear on what aligned the north star in terms of everybody's efforts and direction. What I was less clear on in my role in product was how best and most effectively and efficiently to achieve that north star.
I was responsible for a new function that was created that included product plus data scientists with respect to research and innovation, as well as engineering in terms of prototyping and iterating and building new solutions to take to market with those data sets. And with the resources that were made available to our team to create new products and to ship to market was the inevitable reporting scenario that we had to show we were on track, that we were managing resources appropriately and effectively. And if not, we needed to manage those stakeholder expectations accordingly.
What I found in that experience was that the reporting lacked efficiency. It was manual. We were building PowerPoint and Excel spreadsheets. We had to get different data points or signals that tracked our progress from end to end, based on the different tools that served different ways of working across each of those functions to then, as you say, unite it into a coherent narrative of where we're at with respect to our north star and the resource allocation. And it was a nightmare.
We were spending, on a monthly basis, two to three days a month on it. I had two or three people allocated to that resource on a monthly basis. And it was so ineffective. By the time we stitched all of that data together for our monthly reporting effectively, we lacked currency in that we'd already moved on and the month had passed. We now needed to get new data sets. I felt that there had to be a better way of providing this loop of learning. Because ultimately that's what we were doing in reporting. It's a learning tool to instil confidence, to set expectations, and to ensure resources were being allocated appropriately.
What inspired me to set up Umano was the concept of learning. Real time learning focused on the conversations that data can fuel as opposed to spending all that time preparing and aggregating and collating and curating and analyzing, etc. So that was the inspiration. I wanted to really get data not only into the hands of my stakeholders, but also into the hands of my practitioners, my teams who were on the front line, doing what they needed to do to execute according to plan.
And if they were armed with the same information, with the same insights as leadership and as management, they could be far more accountable in the way they made decisions in real time. They could self-direct their own progress and learning, and ultimately be in their own loop of learning to improve their own effectiveness and efficiency.
And what more beautiful outcome to see than teams that are empowered by data, in an innovative learning culture, taking responsibility for their own way of working and firing on all cylinders. So that was primarily the vision and inspiration for Umano.
[00:12:06] Loris Marini: That's amazing. There are so many points you raised and I'd like to dive a little bit deeper into the mindset, because we are very similar in this regard. Me and you, I feel like we are both passionate learners and we want to use information to improve our ways, our behavior, how we work and save time. But not everyone shares this.
I do know a lot of people that are taking a different direction and they're going, “Ah! Too much data. I'm already stressed.” It really feels like an extra thing to worry about. And there's so much of it: the variety, the veracity, the velocity – it all feels really complicated.
This is sort of the mentality of people that feel that it'd be nice to be in a workplace where things moved faster, where the data and information and knowledge layer is well-connected and everything is available all the time without asking a thousand people for a spreadsheet to get the information you need.
Sometimes it is impossible and they feel overwhelmed when trying to fix the problem. Even just the idea of fixing the problem is overwhelming. And so, there are two elements. One is the mindset. We really want to do this. This matters to us. This is the mindset, the psychological stance. And the second is how do we do it in a way that's efficient and it’s not going to cost us a one week of development time every month?
[00:13:49] Chris Boys: I love talking about the mindset because I think fundamentally this is the bigger question of humanity. That is the name of Umano: humanity in Italian. In understanding the way, we as humans, work is fascinating. In the context of learning, I come from no theoretical framework, other than pure practical observation of the way people apply themselves in different contexts.
There are those that come with a fixed mindset, and those that come with a growth mindset. And you would be familiar with the work of Carol Dweck from Stanford who focused their education context around how you build and re-instil a growth mindset that is continuously learning, adaptive, resilient, and evolving, relative to a fixed mindset, which fundamentally comes from a place of fear – not wanting change and the idea of control and maintaining status quo.
When you walk into an organization, you're going to encounter people on every level of that spectrum and the extent to which you can inspire people to move up the spectrum and into a place of growth is key and data doesn't always do that.
Data can, in the face of it, drive fear because people build information silos and power through withholding that information and controlling the flow of that information. And so if you, through transparency and visibility and observability, disrupt those patterns, challenge change, avert control into almost like a democratization of control and accountability, you're absolutely going to come up against barriers.
I think this is why any solution that involves data and insights needs to inspire growth. It needs to be framed in the context of evolution. Evolution is one of the driving forces of life in the way that it inspires nature and humans to constantly grow, adapt, evolve, and change.
We know the quote, we've seen the Eddie McGuire movie, Adapt or Die. And it is like that at work. If you stick your head in the sand and you're not listening and learning, everyone else is going to take off around you because they are evolving and they are learning and they are adapting and changing.
Part of this mindset plays into why the agile way of working is so big in our lexicon and work right now. That's been a 10-to-20-year process of instilling a mindset of change and growth and learning. I think part of that mindset needs to include this notion of sort of servant leadership, if you like. We can't leave people behind.
Those that are afraid and those that are nervous or have had a bad experience with data being imposed on them in some way, shape or form. The extent to which a servant mindset ultimately is there to set others up for success and not be self-oriented or self-gaming. That's also critical in the mindset.
And finally about the mindset, the notion of using data to really, again in the team context, drive shared accountability. We're not here to pick on or nail down one individual's performance, but we look at the holistic and group performance as critical, reinforced by alignment. Data is critical to drive team alignment so that we're all moving in the same direction.
So those notions of a growth mindset embedded in evolution in servant leadership, shared accountability and alignment are critical components to a mindset of working with data and building a data-informed culture. In terms of how we do that, I think, it's again the conversation with the team, because data is the means to the end. It is the means to greater collaboration, more accountable conversations, better alignment of resources.
Any healthy, adapting, evolving team organization, or construct needs a feedback loop. We all have feedback loops. We live with those in whatever context. And so that feedback loop needs to embrace both quantitative and qualitative data for insights to be trusted and most importantly to be applied.
[00:19:17] Loris Marini: One second. Before we go to that, I know that you have a lot to say regarding qualitative and quantitative, and I can't wait to hear it, but I wanted to take a second to dive into the resistance, the forces that oppose a person that, at the cognitive level thinks, “Well, yeah, I’d love to have more data around my behavior and my performance and use it to improve.” Who doesn't want that?
But when we're faced with the numbers and the stats around our own behavior, it can be confrontational and it might feel weird. I do feel weird every week when I do my alignments. I do my weekly reviews and I look at what I plan to achieve and what I actually achieved. And it doesn't feel good every week. And so, it's understandable that we sometimes we try to push away that feeling.
I'm thinking about the episode of Work-Life by Adam Grant, where he explored the culture of an organization where they had a so-called candid feedback policy. Everybody was encouraged and rewarded if they shared exactly what they thought or what they felt on the spot without feeling any type of fear. And it's interesting because the founder of this organization, which I don't recall the name (Bridgewater), but I’ll put that link up to the episode in the show notes (https://www.bridgewater.com/).
When he was interviewed by Adam, he said something like, well, I've been doing this for 20 years, I pioneered this way of working, but I still feel that every time someone gives me a feedback that contradicts my prior belief, I still feel the amygdala kicking in, and the fight-or-fly response that prevents us from having that clarity to think about what's really being shown to us and how to use that information in the best way possible.
So that makes me think because if someone that has been doing this so successfully and created an entire organization with that culture still feels that way. On the one side, it makes me feel good because that means I'm not missing out on anything. It's not a gift. It's something that we can practice. But also, some resistance is always going to be there no matter how much we practice. How do you cope with this resistance?
[00:21:56] Chris Boys: I think on the case studies of where this was done successfully, to add what you've already suggested, Patty McCord from Netflix in her book, talks about radical candor and building cultures of radical candor. Ray Dalio who wrote Principles and founded Bridgewater talked very clearly about the power of playing to candor and how freeing that actually is irrespective of whether someone likes to hear it or not.
It's actually the truth we need to hear. It's not platitudes of where we want to feel more comfortable because it's those platitudes that play into comfort that re-instill the lack of evolution and the lack of adaptation. Because fundamentally what drives adaptation and evolution is tension. Tension is the magic that none of us actually realize with its power has the ability to transform. Tension is where we actually get the kick up the butt to get into gear and create change. Tension is what drives problem solving. Tension is where birth is created literally.
[00:23:18] Loris Marini: Not to be confused with strain, right?
[00:23:21] Chris Boys: Very true. But how do we reframe the mindset that we all have around tension? For me, I totally agree. There are times when I respond to a feedback loop through fear. And it's the old thoughts and belief patterns that play up from experience that's passed that are being projected onto this current situation, as opposed to suspending judgment, suspending the past, seeing this experience for what it is: a learning opportunity from which I can either react or respond.
And to react is to play into those old experiences of the past, it's to react into the mindset of fear. And it's playing to our so-called smaller selves, which is the space of ego. Or if we suspend that judgement and look at it from the place of, in the present without the past, and see it as that opportunity to learn, then we start to play into our higher selves.
And we start to play into a space where we see it as a learning opportunity to evolve, to grow and ultimately, to play bigger, to be better. And to realize that potential, that innate potential that we all have fueled by this learning opportunity, which is fundamentally a feedback loop triggered by data, whatever that is.
[00:25:02] Loris Marini: I find this sweet spot .. crucial. If we judge ourselves every time we feel bad about something, it’s literally the beginning of a self-destructive feedback loop where you can't think, can’t act, can’t change – you can’t make the most of that information. So it's totally okay to feel strange and cornered sometimes. What changes is how we react to that feeling and how quickly we can equalize the response of the body and the brain together as a whole system to accept that information.
What’s funny when you look at the definition of information, the mathematical definition, it’s nothing but the quantity to measure how unpredictable something is. If you read a book and the same word is printed over and over and over on each page of the book, that book conveys very little information because you read one book with one word. Whereas a book that uses many words and sentences, both long and short, and you can never sort of tell what's you are reading next, that's an information-rich book.
So in this context it’s kind of the same. When you have a piece of evidence that reinforces or confirms what you really know about yourself or what you believe about the team then it carries very little information - it's highly predictable.
When you encounter a signal that is completely different, it could be an outlier. It could be because of an issue in the data itself, or it could be real information. I think one of the other really interesting topics here is trusting the data. If you decide, as a team, to use a digital information system to measure and improve the performance of a team, you better know that what you’re reading actually reflects what you're measuring or what you believe you're measuring.
What are the problems that might result from an incorrect or corrupted data layer and how does Umano come in, in terms of the idea of the platform, that we are taking care of that data quality. We are making sure that you can trust the numbers.
[00:27:40] Chris Boys: It's been a long journey getting to that place. When we came out with the platform to begin with, we learned very quickly the power of needing to win users trust before we could support them to make effective decisions to improve the way that they worked. And ironically, in the first release of the beta, we had the machine learning models, magnificently curating all of this data that we had available to us to present back to the team a rating system and a behavioral profile.
And the team response was: that's magnificent. We know the data and we can see what's going into your models, but what the hell is this that you've produced? And why would I trust something that I don't know how you’ve curated, calculated, and manipulated to tell me something that is meant to spur me into action?”
Trust is critical with respect to getting or enabling an end-user to see themselves in it. The second they can't see themselves; it creates suspicion, it creates doubt. We see so many of the principles relating to machine learning models and AI now about the ability to unpack and see how models are calculating and creating output or insight.
So firstly, for us product decisions that relate to simplifying models with which we curate that data to provide insights, to make that transparent. And secondly, to surface additional context and data that reinforces the insights that almost draws it back to the raw data input itself. And in that interface, they can see direct link from their own artifact. The output of their data into the insight that's been generated. And the path by which that connection can be made needs to be very easily accessed if not imminently recognizable and not buried somewhere in the detail that you hope your end users will never find.
So again, the principle of transparency goes hand in hand with trust and that permeates every product decision that we've made in Umano. I think the other key product decision that we've made in relation to trust, particularly given our application for generating team-based insights is that we reinforce the principle of a team construct.
We don't look at individual activity within that because the team is there to collectively share and realize the objectives that are set for them, the goals that the company or that they set for themselves.
[00:31:17] Loris Marini: This is super important, right?
[00:31:19] Chris Boys: And similarly in that context, another product decision very, very early on, was to remove a lot of the individual profiling that was going on. Another product decision that was made early on to reinforce trust was where we were thinking we were helping teams by providing benchmarking data, but the benchmarking data was relating one team to another team. And that just didn't stack up.
We were applying a principle almost like a professional sports game, where of course teams should be compared to teams. And as per the winning outcome on the scoreboard, that's ultimately what both of those teams are there to do. Well in a corporate environment, in a company or an organizational environment, that's absolutely not the case. Every team has a different objective.
The composition of that team is always going to be different based on its objective. You're comparing apples with oranges. For us, what we did and what we learnt again in order to reinforce the principle of trust was to remove the ability to compare teams, and therefore an eventuality that may lead to stack ranking teams, and grading them highest to lowest into a situation where actually the benchmarking became self-comparative.
We should, in the same principle of personal growth and development, only be benchmarking ourselves against ourselves at the extent to which we're growing and evolving our own practices over time based on what we did historically.
I guess the final point that I'd make around the principle of trust is in our context of supporting teams to grow, helping them compare against themselves in the way that they have peak or highlight experiences where they may not be performing at their best, but then showing them those highlight or peak experiences, what actually underpinned those, there's a clear set of opportunities or a pathway to help them grow.
That experience is personalized. It's tailored purely on their own experience. It's reinforcing to them in the way that they work as a discrete entity to trust themselves in that context to grow and to take action in order to grow.
[00:33:53] Loris Marini: That’s beautiful for a number of reasons. The first one is: it's all about, again, the intention, right? Setting up the reason. Why do we want to do this? This is not about deciding which team to cut funding for.
[00:34:09] Chris Boys: Spot on. I think that to get very clear and we encourage our teams. And in fact, we're going to be building this into the product where we have a team charter, which is the first experience where a team defines their intention.
How is it that you intend to use Umano to drive and support your performance and how will you agree not to use your Umano’s data and insights to influence or impact your performance? Knowing both sides of those that agreeing collectively to both sides of those is so reinforcing of your intention. And once you're aligned on intention, again, you come back to the magic of teams achieving great outcomes.
[00:34:52] Loris Marini: Yeah. We talk a lot about monetizing data and the monetary value of that information. And I keep seeing data as the raw ingredient to realize, to extrapolate a potential, like something you can use to do something else, whatever it is that you want to do. But like all tools or instruments or enablers, it’s not the full story. It's just the means to an end.
Setting the intention, like the driver behind the seat. I know that you raised this point a couple of times in our previous conversations around the difference between data-driven and data-informed.
And there's a key difference there in data informed we own the fact that as human beings, we are in control. The reason why we are so obsessed with data and information is because, well, we need to know what's going on. If we want to be in control, we do, that's, it's just a need, but then obviously how do we use that tool and where we go is entirely up to us. In that sense, I find this approach sort of empowering.
[00:36:06] Chris Boys: Yeah. We have to remember that data is there to serve us. We're not there to serve data. And in serving us, what does it do to help take behavior change? Because fundamentally that's what this whole game is about, to your point. And in serving us, I think we really need to, almost back to the kind of the point around intention, we need to get clear on the why, and we need to get clear on and align around the power that it can have in ultimately helping us to make decisions or not. But we get to make the decisions.
The data is not making the decisions. And I think the other point that I'd make is, we talk about data and in that notion of typically of a quantitative form, the extent to which we also need to recognize and appreciate data in the qualitative form is equally important to serving us and to serving whatever intention we set up to achieve.
I love the contrast of those two artifacts, because one is highly rational – it fundamentally dissects things into comparable elements and the other is purely narrative. It's subjective. It's loaded with feeling and with intuition and inspiration.
Coming back to being data driven versus data informed, data driven, you see these cultures of being slave to the quantitative, that there's no room for interpretation or context. Whereas data informed has the magic of both and the power of both and data informed really lets you look at that data and to your point earlier, so, well that's actually an outlier. It has no reference or context in this conversation. I'm not going to deal with it. It's not serving me as opposed to “ah, that makes sense. And now when I overlay context or narrative, yes, I can make decisions with this to change behavior or to change an outcome.” You need both.
[00:38:33] Loris Marini: Yeah, absolutely. And one of the more practical reasons as to why data is incomplete is because you can gather data about everything but when do you stop? There's always a monetary aspect in the pipeline, you know, the seven activities which Ben Jones mentioned, of any data team.
You start with collection and organization and management, and those have a cost. Of course, in an ideal world where we all have an infinite bank account and energy doesn't have to be conserved then we can build in imaginary source of energy and spend all the time in the word acquiring every single possible bit. But that's not the universe we live in. Resources are finite and at some point you have to draw the line and say, “this one we care about, this other data, not right now.”
I wonder about the level of the product, when you had to design one. How did you make those decisions?
[00:39:36] Chris Boys: We're still early stage, right? So we've only just released a cloud beta version and the roadmap that we want to achieve. All of the things that we've talked about today is huge and immense. However, we started with purely looking at the quantitative components, because for me so much of the pain came back to where we began in this conversation around automating the end-to-end process of collection, curation, and presentation. In a way that I could then intuitively assert or insert my narrative on top to tell a story about where we're at and what we need to do to improve.
And so now that piece of the puzzle is done, we're effectively extracting and automating and combining insights from tools such as issue trackers, chat channels, wikis, git repositories. The next great stage of the roadmap is to be able to allow teams to insert the qualitative insights the qualitative context where they can provide clarity around context, where they can start to really make notes on what we're impacting the outliers and where those patterns exist and continue to exist then it's much clearer to be able to do something about it and to take action and manage stakeholders with respect to either getting additional resources or pushing back, whatever that might be.
So that notion of being able to blend that quantitative and qualitative is critical. And then we've got a whole lot of underlying models that start to look at the correlations and the relationships, which is really exciting.
Because again, to your question around where did you start? You start to look at inputs that drive great outcomes. My original goal and outcome were to achieve a particular monetary goal as a company. Well, what are the inputs that fundamentally drive that? What are the behaviors of a team to get us there in the most effective way?
The input data coming from those tools that we collect from was stage one. We then want to move into output, the direct artifact as a result of those behaviors, in addition to the qualitative context that I've already mentioned.
Downstream, the magic starts to happen when we stitch together the outcome data and you close this feedback loop. And we start to see the inputs delivering the outcomes that our team aspires to achieve in delighting their customers.
And so that outcome data with respect to not just purely financials, we actually want to understand things like NPS, service desk tickets, the things that give signals to the quality of the customer experience in consuming and using the software that's shipped by a team.
[00:42:58] Loris Marini: And that's where the stats and the machine learning comes in.
[00:43:00] Chris Boys: Yeah, spot on. And so you can start to get a picture of the power of what this data set then enables in delighting customers, in building great team experiences, in liberating work cultures from inefficiencies, through all of this transparency that we can create.
So, yeah, that's the grand plan and we're at the beginning.
[00:43:28] Loris Marini: That's the vision and I share that too. Now, if you were to ask me what I wish I would have accomplished in the next 10 years, I would say definitely help a lot more organizations reclaim control of their information and knowledge assets. They're just all over the place. And we know data problems are everywhere, you know, the bigger the size of the organization, the bigger of the problem. And so it's more evident in larger organizations that fragmentation.
[00:44:05] Chris Boys: Yeah, definitely. I think though, if you were to ask me the benefit in that, like the outcome or what I want to see in 10 years, I think. But to me, that's a means to the end. The ultimate end is just charged teams, from a position of being totally fired up to be more confident in the way that they can function, collaborate, execute, and delight their customers is what I just dream to see. And in that context, just seeing the shackles of inefficiencies kind of fall off companies and fall off teams in the way that they go about creating better products and services and ideally a better world.
[00:44:51] Loris Marini: Absolutely. We'll have to close the information loop. And I'm so glad to hear that people like you are working hard to make it easier for teams to take ownership of their own data, their own behavior, and strive to improve it.
[00:45:03] Chris Boys: Thanks Loris.
[00:45:04] Loris Marini: Chris, this has been a fantastic pleasure. So thank you again for taking the time in sharing your wisdom, your expertise here, the over at the data project.
And, uh, I'm sure we'll have plenty of opportunities to follow up and have more interesting discussions around data and human behavior.
[00:45:19] Chris Boys: I hope so it's been a joy. Thanks very much.