Transcript
Speaker: Hey everyone, welcome back to Data Knowledge Pioneers presented by Workstream.io.
Speaker: And we're again exploring how organizations create shared consciousness around their data.
Speaker: I'm Nick Freund and we're speaking with leaders and practitioners around the acute problems they experience in creating and disseminating knowledge about your data.
Speaker: So specifically today, we're talking about
Speaker: The issue of fragmented and tribal knowledge and then how you can capture that knowledge, institutionalize it and ultimately enable your team.
Speaker: And really excited to introduce two awesome data leaders who always make me think differently about these types of topics.
Speaker: So first off, we have Michelle Ballen, who's the head of data analytics at Future, which provides one-on-one digital training with fitness coaches.
Speaker: And Scott Brighton-Author,
Speaker: And I think I butchered his name again.
Speaker: Close enough.
Speaker: Close enough.
Speaker: Close enough.
Speaker: He's the founder of Brooklyn Data Company, which is a very large and fast growing data consultancy, which among other things gives out these awesome t-shirts.
Speaker: So if you ever see Scott, you definitely should ask him for one of these.
Speaker: And you probably can get one if you email him at, I think it's a Scott at Brooklyn Data Company.
Speaker: So anyways, Michelle Scott, thanks so much for joining me today.
Speaker: Thanks for having us.
Speaker: Yes, that is my go-to gym shirt.
Speaker: Scott, I keep meaning to send you a gym selfie when I'm wearing the Brooklyn Data Co.
Speaker: shirt.
Speaker: I appreciate it.
Speaker: We do t-shirts and data very well.
Speaker: Those are the two things we do.
Speaker: Everything else is okay, but t-shirts and data we do well.
Speaker: I'm glad you both like it.
Speaker: They are very comfortable and I will
Speaker: plus one that I work out in this show quite a bit.
Speaker: Cool, well, so non sequiturs aside, I wanted to kind of start by talking about the problem of tribal and fragmented knowledge about your data.
Speaker: So like, what is this?
Speaker: And like, how would we define the problem?
Speaker: So Michelle, do you have thoughts on how you would define the problem?
Speaker: I have my opinion, but as a practitioner, I'd be really interested to hear how you define it.
Speaker: Yeah, I mean, there's cross-functional work streams in different pockets of every organization.
Speaker: They have different initiatives that they're testing out.
Speaker: They're learning all the time.
Speaker: And how do you make sure that the knowledge that they're gaining is
Speaker: via the initiatives they're testing, the hypotheses that they're validating actually gets disseminated and shared throughout the organization because something that the team might learn on a marketing initiative, maybe it was like a campaign creative, oh, this really resonated with clients from an acquisition perspective, might be relevant.
Speaker: to a member experience team who is kind of like doing ongoing sales and kind of keeping clients engaged and retained.
Speaker: And so how do we make sure that the learnings that are happening over there are being shared throughout the organization?
Speaker: Everyone has a kind of shared understanding of how the business is going, what we're learning every day about our users so that we can make more informed decisions and really have just more collaborative discussions around what we should be doing next to improve the business.
Speaker: I mean, I totally agree with what Michelle was saying.
Speaker: And I think this is always like a people process and technology challenge.
Speaker: It's, you know, you've got to create the culture of sharing this knowledge, but you also have to create the structures to it, the intranets, the documenting and code, and just build a culture of knowledge sharing, which is very, very, very challenging.
Speaker: But it's like one of those things with culture.
Speaker: If you see a good culture, you can kind of recognize it, but it's hard to think about all the millions of components that went into building that knowledge sharing culture.
Speaker: Michelle, you kind of had talked about that example of the marketing team has seen something and is building some knowledge.
Speaker: Maybe that's relevant to the customer experience team.
Speaker: Have you experienced this problem mostly like with one team sharing knowledge to another or do you experience a lot of like the data team sharing knowledge or capturing knowledge from like the business or do you see both?
Speaker: So I feel like I've definitely seen frequently teams giving their best effort to share with other teams, like the product team.
Speaker: Oh, we learned this thing.
Speaker: Let's put out like release notes or send around an update.
Speaker: I feel like personally, and this is definitely true for my role at like smaller organization startups.
Speaker: The data team is super well equipped to play this role of making sure that this knowledge is disseminated and shared across organization.
Speaker: But it's something that I kind of consider part of my team's mandate is like, we are business partners to every different operation.
Speaker: We're very close to them.
Speaker: We know what we're doing.
Speaker: We've been part of the strategy discussions around what data do we want to create to answer what questions do we have, right?
Speaker: And so I think it makes a lot of sense for our team, who's already part of all of these disparate conversations, to come together, pull all those learnings together, and make sure that the team across the board is being made aware of new learnings.
Speaker: And also, I think it's our responsibility to facilitate the conversations to get people talking about these learnings.
Speaker: And then it kind of spurs and inspires new ideas and new questions and just kind of gets this cycle going.
Speaker: I like to think of it as my team's, one of our responsibilities to create that shared knowledge and shared understanding.
Speaker: And do you scope that around data specifically, or is that, do you see that as almost like a broader mandate given how your team works across future or any other company you've worked at?
Speaker: I guess a broader mandate.
Speaker: Yeah, I mean, it's like we are working with the business stakeholders every day to learn new things and test out new hypotheses, validate those hypotheses.
Speaker: And then basically, like, how can we recap all those learnings and then make sure that everyone has access to them and has an opportunity to ask follow up questions and have an opportunity to have more in-depth conversations around what does this mean?
Speaker: Are we interpreting it correctly?
Speaker: And how can we use it moving forward?
Speaker: Scott, any responses to that?
Speaker: Yeah, I mean, I totally subscribe to the Michelle School of Thought on this.
Speaker: I mean, I would say that like the data team's mandate is not just produce a data warehouse or produce reports.
Speaker: I kind of think the data team is accountable for how data is used across the entire organization.
Speaker: and creating a culture where the entire organization is building, is kind of making data-driven decisions, which is kind of weird because that's a dotted line type of responsibility.
Speaker: It's like, how can a centralized data team be accountable for
Speaker: outputs and data-driven decisions that they're not in the room for.
Speaker: It's by kind of being that hub of knowledge and kind of disseminating the learnings, being good listeners.
Speaker: Ah, CX team, I hear you're thinking about this decision.
Speaker: This kind of other thing that the marketing team did the other week might be really relevant.
Speaker: it's like a multi-pronged kind of approach.
Speaker: You got to push out reports and analysis.
Speaker: You got to present as part of the data team.
Speaker: Just the data team needs to be talking amongst themselves and educating each other and kind of being, I don't know, like ambassadors for kind of the learnings and pushing them out in the org.
Speaker: They've got to be like ambassadors for this culture of data and data-driven decisions, which is, again, like it says, it's
Speaker: hard to do.
Speaker: And you've got to build trust, got to build relationships with your stakeholders.
Speaker: But if you do it right, the data team essentially becomes this like artery of kind of insight and knowledge that's kind of going out to all aspects of the organization.
Speaker: Absolutely.
Speaker: There's so much more to it than just documenting and pushing out the write-ups or the recaps.
Speaker: It's
Speaker: involving the stakeholders early on in the process, right?
Speaker: Getting them kind of part of the question generation of like, what questions do we have?
Speaker: Getting them excited about and bought into how we're going to capture the answer to this question or how we're going to validate this hypothesis and like really getting them involved and making them feel part of the process so that it feels that much more relevant to them.
Speaker: the way that we push it out, making it high quality design and something that people actually want to consume.
Speaker: And then also to Scott's point about being like empathetic, really listening and creating a safe environment for people to have conversations and discussions around the data.
Speaker: That's all part of it.
Speaker: It's not just simply writing up the learnings and sharing them.
Speaker: It's creating the whole culture, really.
Speaker: The way I think about the role of data and analytics team, it's like
Speaker: center of the organization, right?
Speaker: Or decision-making.
Speaker: But I think that's of the organization in general because of everything that both of you have been talking about.
Speaker: And with great trust comes great responsibility or great power comes great responsibility, whatever they say in Spider-Man.
Speaker: But Scott, I'll set you up with this one.
Speaker: What do you think is like the most important context that consumers of your data need to do their job or to, you
Speaker: kind of make better decisions, right?
Speaker: Is there anything that you think is particularly important?
Speaker: I mean, I think just knowing about the universe of data that's available.
Speaker: And so I've been like super into enablement lately.
Speaker: And I mean, I probably should have always been into it.
Speaker: But so Brooklyn Data, we implement data strategies and data infrastructure for clients.
Speaker: We train the users, both the data team users to continue to build and maintain the infrastructure and stakeholders.
Speaker: But now we're doubling down on the stakeholder enablement office hours.
Speaker: We'll probably do like five, six, seven, eight, two hour long training sessions with the stakeholders to make sure they really feel comfortable using the tools and understanding what data is available and how to self-serve.
Speaker: I think that's so important.
Speaker: Also kind of building that, you know, here's the category of decisions that you stakeholders should be able to take on your own.
Speaker: Here's the category that you might want to ask kind of, you know, the advice of the data team as it might be kind of uncharted territory.
Speaker: And then here's the category of decisions that probably should be more data team driven analysis.
Speaker: And just building those kind of the knowledge and the relationships so the stakeholders know when to raise their hand and ask for help.
Speaker: And kind of to kind of phone a friend.
Speaker: One other plug I would say is that I really recommend Michelle's blog post on adding annotations to analysis.
Speaker: I regularly send that a link to that blog post out to people.
Speaker: It's really cool.
Speaker: More organizations should do this, which is essentially annotating key dates and kind of milestones so that your data has context.
Speaker: Because without context,
Speaker: data is very hard to interpret and easy to misinterpret.
Speaker: And then the kind of challenge is like, unless you document it, context walks out the door every single day.
Speaker: And that's when people leave.
Speaker: And when people leave the organization completely, you can lose context and never get it back.
Speaker: And so I think like highly recommend Michelle's blog post.
Speaker: I mean, there's not much to the blog post.
Speaker: It's just keep a list of dates and what happened on that day and make sure that that's a living, breathing, cross-functional document.
Speaker: But I mean, sometimes it's the simple things that matter.
Speaker: Totally.
Speaker: I think that's also part of creating knowledge, right?
Speaker: So something that I'm big on and I think is very common now among data leaders is like, how do I become a more proactive versus a reactive team?
Speaker: And so something that I did like early on in my career, I would get the request, why is conversion rate down?
Speaker: Why are repeat buyers, whatever?
Speaker: It was probably from me.
Speaker: Yeah, me too.
Speaker: For everyone who's watching, Scott and Michelle used to work together way back.
Speaker: It was at Casper.
Speaker: Was it Casper you two worked together?
Speaker: Uh-huh.
Speaker: And we're forever friends now.
Speaker: Forever friends.
Speaker: And, yeah, I mean, getting that question and having to dig into the data, and you never found the answer.
Speaker: It was always like, oh, we think it's a seasonal blip, or...
Speaker: You know, something that's known issue, there's a known bug on the engineering team.
Speaker: And so keeping a record of those logs now, it just helps if we do ever see a shift in a metric.
Speaker: And this is something that I kind of train the whole organization on, especially my team is when a metric shifts, we should know already why that was.
Speaker: Yep.
Speaker: Even if, you know, ideally we would run a regular experiment so we could say we launched this feature to 50% of people and we know that it was hurting conversion rates.
Speaker: So that's why we know conversion rate went down.
Speaker: But if we're not able to do that, at least having that record of, oh, we sent a huge email blast that day.
Speaker: So there was significantly more traffic that was low quality, not high intent purchasers.
Speaker: And so it's all expected.
Speaker: And then that way we can avoid that reactive work.
Speaker: But it's also creating that knowledge of,
Speaker: When I see this metric, I can quickly reference that changelog and say, oh, this makes sense.
Speaker: And now we don't have to spend time.
Speaker: Reverse engineering and segmentation and stuff.
Speaker: And it's so easy to do it incrementally every day.
Speaker: Just do it as it happens.
Speaker: Yeah, exactly.
Speaker: And it's across, yeah, the whole organization contributes to it.
Speaker: And it's something that I've done at my last five jobs.
Speaker: So.
Speaker: Yeah, I think regardless of the exact methodology, and I will plus one that Michelle's blog post is great.
Speaker: Michelle, I think that your point about incrementally doing this, right?
Speaker: if it becomes a habit and you're building that knowledge on an ongoing basis, right?
Speaker: Like you then have it as opposed to how do we create this knowledge from scratch?
Speaker: And that's when I talk to data leaders about how do you build out knowledge?
Speaker: A lot of times that's what tricks people up is, well, how do we go from a state of zero to one here?
Speaker: And it can be a lot of overhead if you haven't been investing on it the whole way.
Speaker: And I would just add is, yes, it would have been better if you start a year ago, but, you know, the next best option is to start today.
Speaker: If you are a growing company, you're creating more data in the next six months than you did in the last two years.
Speaker: And so just like...
Speaker: Don't cry or obsess about cleaning up the old data or the missed opportunity to annotate the old data.
Speaker: Just move forward and focus on the new data because if you're growing, you're creating so much more data to the point that the old data is almost irrelevant.
Speaker: I remember when I started at Casper and we moved to a new setup and I was like, what about the data from the first 10,000 customers?
Speaker: Who cares?
Speaker: It's important, but just focus on the next 500,000.
Speaker: You know what I mean?
Speaker: Scott, I wanted to go back to
Speaker: One of the points you made about when you're at Brooklyn Data, when you're training stakeholders and a lot of what you're trying to do is help them figure out like, what can I answer myself?
Speaker: What do I need to phone a friend for?
Speaker: Is that like more idiosyncratic business to business?
Speaker: Or is there like a broader framework that you have there that you kind of like rip and replace and use, like regardless of who you're training?
Speaker: Yeah, I mean, I think it's two indices that decide which category it is.
Speaker: Your familiarity with the data set.
Speaker: So if a business stakeholder is working with the data set, they're extremely familiar with
Speaker: they should be able to kind of analyze that without phoning a friend.
Speaker: As soon as they start to getting into new data sets, as much as we try the data world to make sure every single data set is documented and joins together perfectly, it's just unrealistic.
Speaker: And so as a business stakeholder starts to join unfamiliar data sets or new data sets or just join multiple data sets, that's when it's probably worth raising a hand.
Speaker: Kind of the other index is the importance of decision.
Speaker: If we're deciding on kind of something small versus a board presentation.
Speaker: So I think it's just like, you know, it's like a matrix of familiarity or newness of the data set and kind of importance of the decision.
Speaker: That's when you kind of decide when to raise your hand.
Speaker: If nothing else, we know you run a consulting company because you just introduced a two by two matrix into the discussion.
Speaker: You know, if you can't solve a problem with a two by two matrix, it isn't worth solving.
Speaker: So sorry, so on one axis of the two by two here, you're saying technical complexity and then one is business importer impact?
Speaker: It's familiarity.
Speaker: So, you know, if I'm a business stakeholder in marketing and I'm analyzing the marketing data source I always look at, that's kind of, I'm very familiar.
Speaker: If I'm analyzing shipping data, I'm unfamiliar.
Speaker: And so as you kind of get to like data that you're less and less familiar with, that's when you should start raising your hand.
Speaker: And then it's like business impact or importance, which is not always the same thing.
Speaker: Like, again, a chart in a board presentation
Speaker: might have low business impact, but it might have high business importance.
Speaker: And so it's kind of like important as things become either more important or the data set is less familiar to you or you're less familiar with the data set, that's when you should kind of raise your hand.
Speaker: I don't know, Michelle, what do you think?
Speaker: You know, answer only in two by two matrix answers are the only accepted form.
Speaker: Yeah.
Speaker: I mean, granted, I've only been in my current organization for seven or eight months now, so things might change.
Speaker: But the way that I've been approaching it, my team has been approaching it is we're building out these self-service tools in this foundation, of course, so that people can be self-serve to an extent.
Speaker: I would say like opportunity sizing.
Speaker: Oh, we want to send this email to all people who have been deactivated for X months.
Speaker: Like I can quickly pull that list without being someone on the data team.
Speaker: In terms of like doing deep analysis and trying to uncover new trends, I think the self-service tools make it possible for my team to move a lot faster.
Speaker: And I almost encourage more of that business partnership where in the meeting together, the marketing analyst and the marketer will have a discussion, figure out what questions do we have kind of on the fly.
Speaker: use the self-service tools together with the data person kind of driving and sharing their screen, almost like Figma, but like data and answering those questions on the fly together.
Speaker: And that's why the self-service tools are so, so great that we built out that foundation and really encouraging them to lean more on the data.
Speaker: Cause it's just unrealistic for the marketing team to become experts in this data when they have all their tools that they need to become experts in and need to own.
Speaker: So I really encourage more of the business partnership and
Speaker: The exploration and the kind of question generation and the hypothesis generation happens together with the data person driving the data side.
Speaker: And we don't do as much training on the self-service tools.
Speaker: Again, quick opportunity sizing, how many people, oh, I need to pull a list of all these users who I need to contact or roughly how many people have used this feature just to get like a quick gut check.
Speaker: But in terms of doing analysis to uncover opportunities and then especially for analyzing the incrementality of different efforts, that would be owned by the data person.
Speaker: Still using those same tools, self-service tools.
Speaker: And by building that, it's almost like we're enabling ourselves to move a lot faster and be more effective.
Speaker: But I don't know, maybe over time, I'll realize that this doesn't work and we'll need to do more training and lean more on the operators.
Speaker: When it gets bigger, I think as I've started to work with large and large organizations, it's become...
Speaker: more apparent that the data team can't be everywhere all the time.
Speaker: Right.
Speaker: And I totally agree.
Speaker: I think when you're kind of early and mid-stage size, having the data team as deeply plugged in makes sense.
Speaker: But then, like, as you start to get a big company, you actually have this, like,
Speaker: you have the data team, you have the business stakeholders, and then you start to have like a business analyst, which is like this whole new like role that is, you know, not in this world or that world.
Speaker: You know what I mean?
Speaker: Yeah.
Speaker: And we do have, there are like certain evangelists on each team who are taking it upon themselves to learn the tools much deeper so that they can kind of be that, play that role as well.
Speaker: There was something else that you said that now I'm losing my train of thought.
Speaker: Don't come back to me.
Speaker: We'll come back to it if you think of it.
Speaker: We'll add it in post.
Speaker: Yeah, exactly.
Speaker: We can always just say that.
Speaker: We'll add it in post and it just makes it sound cool.
Speaker: Scott, you were kind of talking about your perspective that the dynamics maybe change a little bit as companies get bigger, right?
Speaker: And I totally agree with that.
Speaker: Given your work, is there a threshold there where you think what Michelle has been talking about truly, truly breaks down?
Speaker: Is it thousands of people?
Speaker: Is it less than that?
Speaker: Any perspective on
Speaker: enablement strategies and self-service strategies given company size?
Speaker: Yeah, I think it isn't a threshold company-wide.
Speaker: You might find that it'll happen at team-by-team basis as each team gets larger and larger and has the resources to dedicate to have their own dedicated analytical resources.
Speaker: And also, the data team starts to specialize.
Speaker: They start to become like the data platform team.
Speaker: Instead of this kind of data and analytics team, you've got this data platform team that their whole job is kind of landing data in the data warehouse, cleaning it up in the data model.
Speaker: You might even find that a certain size, you don't even have a data platform team, you have the data integrations team.
Speaker: And then the data modeling team and just at certain sizes, companies get larger and larger, people inevitably specialize.
Speaker: And there's just more and more hops between the person that's modeling the data and setting things up and might have one aspect of context and the business stakeholder that's kind of further and further removed.
Speaker: And that's why training, enablement, data catalogs, discovery tools, sharing reports and insights in monthly or weekly newsletters to the company.
Speaker: It's funny when, you know, my background in smaller companies, I would sit there and I was like,
Speaker: This seems like the silliest thing in the world to send out a newsletter of insights.
Speaker: I look around to the left and right, everybody that needs to know knows.
Speaker: It's the real deal.
Speaker: Now that I work with much larger companies, sometimes you sit there and you're just like, if they only knew what they knew, you start to go into larger companies.
Speaker: Not only is it an issue of getting data from this kind of centralized core out into the far regions of the universe, they might actually have five data warehouses.
Speaker: And there might not even be just like,
Speaker: One kind of centralized core that's having issues kind of getting to the periphery of the business, there might be five decentralized central nodes.
Speaker: It just gets more and more complex where, you know, training enablement, pushing out knowledge becomes really required.
Speaker: I think a lot of this ultimately bakes down to just like the problem of information asymmetries.
Speaker: And there's just a lot of them, I think, around data and all the ways that you're talking about, right?
Speaker: But
Speaker: getting to the touchy-feely vision of, hey, we have shared consciousness around your data, which is a standard we throw up sometimes when we talk at Workstream.
Speaker: It's about breaking down those asymmetries and those barriers of knowledge and how do you get them to go across those organizational boundaries.
Speaker: Michelle, like on that, this starts to get meta.
Speaker: So bear with me.
Speaker: When you think of all of the self-service tooling you've put out there, other pieces of data or data assets that you've been like, you've put out there and enabled the organization on, are there times and places where you feel like you actually lack knowledge about how that's bringing value or like data about the data?
Speaker: Like,
Speaker: Or is that you feel you're not feeling that yet because you're new or that's totally off or?
Speaker: Yeah, I think it's just a small organization that I have an opportunity to talk to people one on one.
Speaker: And I also can kind of just by monitoring Slack and get a feel for how this is being used and whether it's actually being absorbed and valued.
Speaker: So.
Speaker: We don't currently do any sort of qualitative surveys around how people are feeling about data or the data products.
Speaker: We might at some point, but I feel like I have a good sense just from the fact that it's a small organization and I can kind of see everything that's going on.
Speaker: What are you ultimately looking for there?
Speaker: What's used?
Speaker: What's valued?
Speaker: How do you break that down and try to understand the impact when you're being successful?
Speaker: Certainly, you know, when questions come up in Slack, are they being answered pretty quickly?
Speaker: Also, all of the effort that my team is doing, is it actually generating the output that the teams expect or that their team is actually able to use, right?
Speaker: So basically limiting the amount of wasted work that our team is doing.
Speaker: And that starts with when a question comes up, are we part of the initial discussion, initial strategy discussion, and thus we understand the context and we can make sure that we've worked with the team to think about how we're going to think.
Speaker: and figure out what are the questions we want to answer and make sure that the team, all the work that we're doing is ultimately useful for what the problem at hand is.
Speaker: Right.
Speaker: So I feel like back before I learned how to be better about process and working with stakeholders and understanding what their challenges were, there's a lot of wasted work.
Speaker: And I feel like today we have basically no wasted work and everything that we're doing to like build upon.
Speaker: So when we get to the next question, oh, we can leverage this foundation that we built.
Speaker: and tweak it a little bit, make it a little bit more flexible.
Speaker: And now we can open up a whole new area of opportunity for us to answer new questions.
Speaker: So I think it's limiting time between asking a question and getting an answer.
Speaker: The amount of work that our team is doing that is actually useful versus wasted effort.
Speaker: And obviously usage, but what good is usage of our tools if people are making the wrong decisions?
Speaker: So I don't read too much into that, but currently it's, you know, engagement is high.
Speaker: I have a question for you, Michelle.
Speaker: I find that it's a tough decision how generalizable and flexible to make something.
Speaker: I kind of feel like sometimes I've been so jaded by not making something flexible enough that sometimes I err too much on the side of making things flexible now instead of going for quick and dirty.
Speaker: I'm not the right person to ask because I am all about spend the time upfront, invest the time to think through how this should be structured upfront and you're going to save time.
Speaker: I understand like quick and dirty people want to answer questions quickly, but I just think if you spend the two times more amount of time upfront, you're going to accelerate everything in the future.
Speaker: And that's where we are today at my company.
Speaker: Like we invested that upfront work and now we very rarely have to go in and make changes to our data models and
Speaker: Almost anything is possible.
Speaker: At least anything that we want is possible today.
Speaker: So I'm a big fan of move slowly to move faster in the end.
Speaker: Me too.
Speaker: But sometimes I just like, I want to challenge myself.
Speaker: It's like, am I being, am I over engineering?
Speaker: Am I putting too, you know what I mean?
Speaker: I know.
Speaker: I mean, yeah, definitely there are people who would argue against it, but I just, I see what happens.
Speaker: It just creates swirl and it creates spaghetti code and it's just like,
Speaker: all this buildup of tech debt that now you're having to break down and change management of, hey, why is this different than that?
Speaker: And yeah, I'm just a huge fan of being very thoughtful, deliberate upfront and building systems at scale.
Speaker: Totally.
Speaker: I totally agree.
Speaker: I just like, I always try to like keep myself in check though.
Speaker: It's like, am I making this too complex?
Speaker: But it's hard.
Speaker: You just never know the answer.
Speaker: But I think you and I have experienced,
Speaker: the kind of the downside of not being thoughtful and putting flexibility.
Speaker: Yeah, I'm process obsessed at this point.
Speaker: But I think it's working.
Speaker: I think it's working.
Speaker: Love it.
Speaker: So that kind of kicks off one of the last places I wanted to dive into the two of you, dive into with the two of you, which is kind of when you think of these problems of tribal knowledge or facilitating institutional knowledge about your data,
Speaker: Is this fundamentally a people problem?
Speaker: Do you think it's a process problem?
Speaker: Is it a technology problem?
Speaker: Is it some combination of the above?
Speaker: I'd just be curious what the two of you feel there.
Speaker: Scott, take it away.
Speaker: Well, perfect.
Speaker: I want to go first because you're going to knock it out of the park with the answer.
Speaker: I don't want to follow you.
Speaker: I think it's people, process, and technology.
Speaker: And the solution that's right for you at a 50-person business is not the same as for the same business a year later when there are 100 people or 200 people.
Speaker: It's constantly evolving.
Speaker: If you look at
Speaker: You know, Airbnb is a really great example.
Speaker: They're spinning out phenomenal tools constantly on how to navigate data, how to understand SLAs, how to kind of enable people across the organization.
Speaker: They're also ripping out the old shit every kind of couple years and putting something new in because they're a completely different company every couple years.
Speaker: The technologies are enabling you to do even more than they could a couple years ago.
Speaker: And so it's a journey.
Speaker: But I guarantee it does not happen by accident.
Speaker: You have to have a strategy.
Speaker: You have to be thoughtful, help put the data infrastructure, get everybody working on a single repository of data and code, document the big changes and insights in one place, create tools to help people explore data, train them, create the Slack channels that anybody can ask a data question and get a quick response.
Speaker: If you look at any high-functioning data-driven organization, there's not one of those specific things that's driving it.
Speaker: It's the combination of all those things.
Speaker: And that's built intentionally and over time.
Speaker: Well said.
Speaker: Michelle was nodding for anyone who was listening.
Speaker: Is that okay?
Speaker: Yes.
Speaker: I completely agree.
Speaker: The only thing I would add, just a thought that I've been really trying to reinforce with my team over the past few months, and it's something that actually Emily Shario mentioned in one of her newsletters, or one of her blog posts, rather, is that absolutely it is people in process just as much as technology, if not more.
Speaker: And I really encourage my team, like even if they are not managers directly managing someone, they are still leaders of the organization.
Speaker: It is their responsibility to lead this charge and create this data culture and really evangelize how and create.
Speaker: How are we going to share this knowledge?
Speaker: How are we going to use it?
Speaker: And how are we going to be better and maximize the value of our data over time?
Speaker: So cool.
Speaker: Before we wrap up, is there anything else around
Speaker: Fragmented knowledge, tribal data knowledge, anything in this area that you want to talk about or it's tickling your brain and you have to share it with everyone?
Speaker: I guess the only thing I've been thinking about, I'm sorry, Scott, if I cut you off.
Speaker: No, go for it.
Speaker: This concept of monthly business reviews.
Speaker: So I think every organization that I've been part of, it's very focused on the metrics and it's very focused on why did this happen?
Speaker: Why did metrics shift month to month?
Speaker: And I don't know that it's very actionable, those learnings, and really translate into new decisions or new questions for the business.
Speaker: So
Speaker: What we've been doing at Future is that's like a small portion of the monthly business review, but the rest of it is all about here's everything we learned this month, all the things we tried across all the different channels.
Speaker: It's focusing more on like what we learned last month versus what performance was like last month.
Speaker: And I don't know, I'm just curious.
Speaker: I don't know if this is an opportunity to put out a survey or maybe just a Slack post asking people, what is the balance of their monthly business reviews related to just kind of reporting on KPIs versus recapping everything that we learned that month?
Speaker: Definitely interesting.
Speaker: I mean, I think something to that is if you're focusing on learnings, like what have we learned?
Speaker: arguably that will push you forward much faster than just talking about the metrics because the learnings tell you what to do next.
Speaker: The metrics just tell you what happened and maybe you can
Speaker: You kind of have to get a little past that.
Speaker: It's fun.
Speaker: The only thing I would add is a completely different direction.
Speaker: Sometimes I forget that stakeholders need enablement and training on how to make charts, how to interpret charts, how to interpret data.
Speaker: And so often we're just going straight to how to drag and drop in Looker.
Speaker: When we're kind of skimming this, it's like probably the most important thing is how to make a compelling chart, how to do an analysis.
Speaker: It's very funny.
Speaker: You know, Michelle and I were at the Marketing Analytics Data Science Conference last month, and the very last talk was by a guy named Bill Shander, and he did like an hour and a half session on data visualization.
Speaker: You know, it's like I've been making charts forever.
Speaker: I thought it was spectacular.
Speaker: Like, I mean, I literally thought that like doing, and it's as little as a really good structured hour and a half training or something like that on just how to interpret a chart.
Speaker: I actually think that would be beneficial for the vast majority of organizations.
Speaker: Like too often we focus straight on the data.
Speaker: Let's focus on actually how to interpret data and how to build charts.
Speaker: I think, again, it seems silly, but I think too often, and I know I'm extremely guilty of it, I go straight to the numbers and skip that chapter of the enablement book entirely.
Speaker: I really like that.
Speaker: Yeah, I was going to plus one that one for sure.
Speaker: I mean, I find this, you know, as a early stage founder, I spend like relatively little time like building charts.
Speaker: And hopefully no one can hear my child screaming in the background.
Speaker: But I don't spend that much time visualizing data anymore.
Speaker: I used to a ton like 15 years ago.
Speaker: And so I get in there and do it.
Speaker: And I have lost my skill set where it takes me 10 times longer to do anything.
Speaker: So I think it's a skill that is actually harder to do well than you would think, especially if you're in it all the time.
Speaker: And it goes back to Michelle's point about how do you make the experience?
Speaker: If you're being thoughtful, it's as much about the experience as it is about the underlying data, right?
Speaker: What are you doing to that child?
Speaker: Oh, that's multiple children now.
Speaker: Can you still hear that?
Speaker: I don't know what's going on.
Speaker: There are three of them from the audience.
Speaker: There's three of them here in the house.
Speaker: And one of them just woke up from her nap.
Speaker: The newborn, I can't hear her, and the five-year-old, I think, is the one who woke up, the two-year-old.
Speaker: So anyways, before my house erupts, we can wrap.
Speaker: Thanks again, everyone, for joining the Knowledge Pioneers.
Speaker: And again, Scott and Michelle, thanks so much for coming on.
Speaker: And to anyone who's listening, if you want to hear more, join us next time.
Speaker: We're talking with Ben Stansel from Mode Analytics and Daniel Menheim, the Director of Data Analytics at Dr. Squatch.
Speaker: And we're going to be talking about the workflows between data and business teams and why and how they're often very broken.
Speaker: So thanks again and have a great day.
Speaker: Hi, everyone.
Speaker: Bye.
Speaker: See ya.
