Data Made Me Do It

Wade: Yeah, Hi. I am Wade Johnston. I work at Civis Analytics as a Senior product manager here.

Suzanne: What is Civis Analytics?

Wade: Civis Analytics is a software and solutions company for [inaudible 00:00:21] data science. So we have two parts of the business. One is a data science software platform and the other is a solutions group. So, data science consultants that solve business problems with data science.

Suzanne: Data science is a topic that comes up a lot. I mean especially now in this ... everyone wants to be, you know, data driven, data driven. So, I want to dive in deeply. I feel like we are speaking in alliterations.

Wade: Ha ha.

Suzanne: Because even when you said Civis Analytics, I thought that sounds like we are about to go into a tongue twister or something. Let's start back in your career. We will jump to present day. You were at Nielsen.

Wade: Yup.

Suzanne: And, tell us, well first of all for our audience, maybe they don't know what is Nielsen?

Wade: Yeah, Nielsen is, I think the world's largest market research company. So they have offices and products across 103 countries, or at least they did when I was there 18 months ago. And they measure grocery sales. As well as doing the T.V. ratings. The T.V. ratings, the Nielsen family, that is what everybody knows them for.

The larger part of the business is actually tracking grocery sales that are sold through your Safeway's, and Jewel's, and Dominicks, and then selling, selling that data to the Proctor and Gamble's and Kroger or Procter and Gamble's, and Crofts, and those kind of companies.

We track cigarette sales, Coca-Cola sales, all across the world. And they package that up and give insights to those large CPG companies.

Suzanne: Right, like I am sure they have been around for like, what? Like 100 years, Nielsen?

Wade: Yeah, 1930 or something like that. So it has been a while!

Suzanne: So they're, they're kind of like the original data company.

Wade: I would think so. I think they would be happy to take that title!

Suzanne: Congratulations Nielsen!

Wade: Ha ha

Suzanne: On being the original data company.

Wade: Yeah. Arthur Nielsen, started off counting market shares. I'm not sure if he absolutely invented the concept, but he certainly commercialized it first. He sent out auditors to the different corner stores, and the different grocery chains, that were starting up. And literally counted the stores and then used statistical methods to estimate total sales within the United States, to start with, and then very quickly got to London, and then other International markets.

Suzanne: Did you think that was something that people had asked for? Or he just thought, I want it ... I started by doing this and then realized I could probably leverage; I am going to get all this data, then I'm going knock on the door and say do you want this data?

Wade: I guess I need to bone up on my history...

Suzanne: Ha ha

Wade: ...of Nielsen here Suzanne. I don't know. I think from what I understand from the history of it, they started relatively small. They started with just grocery sales. And then that took off pretty quickly! And they rode the wave, if you think of...we are getting into the history...

Suzanne: Ha ha

Wade: But if you think about the, you know, the first 40's and 50's, the great brands, the advertising, the T.V. All of those kind of factors came together and Nielsen was right at along with them. So, the great brands of say ten years ago were all Tide, and Kraft cheese, and all those kind of pieces and Nielsen rode that wave alongside them.

Suzanne: Yeah. So in many ways, because I think we're so obsessed with data right now. This is the word. It's everywhere. It's big data, and you got to be a data driven product team. And, and you do. And we'll talk about that certainly. But, I guess, because product management, one of the other things we talk about is, you know product management was Brand Management, originally. It was the early days of saying, well let's fight for our original line extension.

Wade: Yeah, but some of the earlier, earliest job titles that were product manager, was at Procter & Gamble as the product manager for the major brands, right.

Suzanne: Right.

Wade: The product manager for Tide, or the product manager for something else like that.

Suzanne: So, so, I guess what I am ... Less than the history of Nielsen though you did a very good job! I know I put you on the spot. It's interesting to think about how companies were leveraging data. The available data for a lot of years before, kind of Ad Tech showed up, before all of these analytics platforms, before Civis which isn't a very old company.

Talk to us about your relationship to data. I mean are you yourself a data person? Or are you just like hanging out with data people?

Wade: Yeah, I like hanging out with data people. We have got plenty of them here. I am not a data scientist. I don't program in Python RR or understand the algorithm that we use. But, my journey into was, I was, my first product job was actually managing products for an underfloor heating company. So, it was a, electric cables that go in the floor and heat bathroom tiles.

Suzanne: I love those companies for existing!

Wade: They're amazing! And the ... I was lucky enough that the Manager, the Hiring Manager at Nielsen, sold beyond just the content knowledge of a subject matter knowledge, and looked at some of the core skills that I got from managing this small business with selling electric floor heating and towel warmers; and introduced me into products as data, or data as product. And that, that gave me the entry point to understanding grocery data and fresh foods data. And then also understanding geospatial data and how retailers use the same kinds of data that people are thinking, oh my goodness, this is so new, we have all this geolocation data. And this was a year old product that they were doing. And being able to accurately predict who was going to go into different grocery stores.

So, that was my entry into that. I was lucky to go and make that bridge from a hard good product into a data product. And then, I think where I still am able to bring that value of the data piece, is, you have to understand what the question that they are trying to answer is. Like, what is the problem? It doesn't really matter that you have great data. Like, that's important. But if you don't have a good problem to answer or a good, or if someone is asking a question, and you are asking the right question, the data isn't going to help you. It will make a nice chart. It will make a nice spreadsheet, tabloid, dashboard, whatever. But, it doesn't answer the question, then what are you selling?

Suzanne: Right. Yeah, let's ... Let me just go back for a moment, because it's interesting that transition from straight up physical product to intangible. And, one of the things I say in my product management class, first lesson, is let's just talk a moment about what is product before we start looking at what is the role of the product manager. Because, when we think ... when we shift toward a product centric mentality, it is, I think about wrapping a point of view around something. When it is a physical product, it is almost easiest when it's non-tangible, like data, it's numbers and in a database, that's harder. Can you talk a little bit about how you, how you package data as a product? Or communicate that to customers?

Wade: I think that, it again comes to what are they trying to answer? For example, Nielsen on the retailers side has two big data products. They have a Retail Management product that tracks sales. And it just tells them what was the last week's sales for this region in this place. And that is telling them how the business is going. Showing them how they are comparing to their clients, or to their competitors, and how they think they are going to do next week.

The other part is a consumer product where they get consumers to scan the products that they buy, and that gives them demographic insights. And that allows them to inform and explain their marketing messages, their packaging messages, their promotional materials. So, if you don't actually have that end question in mind, it, it doesn't matter. So you have to start with that. And if the problem is I want to know how my competitors are doing or I want to know how I am doing against my competitors, then it comes to what data will answer that question. And I think that's, that's really the way that you do that.

So here at Civis, we have a very rich individual level data file. And we get to the same point of what is the client looking at trying to do? And then, can our data answer the question? And so, you have to ask, start with the end in mind, start with a problem and then you package the data around to answer that problem.

Suzanne: Well this is a good entry then, into the product manager role in general, right? The product manager, I talk about them as being the life line to the customer and really, their one job is to understand what is it that the customer or the user needs and wants. And then later, you know, how do we make this better, more optimized, et cetera.

I guess what is interesting in this world of experiment, experimentation, is arriving at the question isn't actually all that intuitive on its own. Maybe this is part of what separates a good product manager, good Product Team, from not, is the appropriate level of curiosity to say, "What do I need to think about, in order to" ...

Wade: Yeah

Suzanne: Do you know where I am going with this? It's like you are saying you can't sort of make any use of the data until you understand what the question is that you're trying to get insight on. How do you even get to the point where you are asking the right questions?

Wade: Yeah, and I think that's where ... part of this is like ... I attend a lot of other team meetings here at Civis. And I attend ... I tried to read broadly around what business problems people have in general. And then also, having a vague idea or rough idea of the kind of person you may be able to target, right? So we've talked previously about the concept of generative research. And, that may just be ... you think okay, we think we can sell to media buyers, or media planners. Let's talk to them and just ask them what they do all day. And spend half an hour, or an hour with for or five different individuals. And see what their job is. Ask them where their pain points are. Ask them what's annoying. Ask them what they wish they could wave a magic wand and get rid of.

And out of that starts coming the themes of the kinds of problems that they have. And then you can start narrowing down onto those specific questions. So, I think that if you're ... if you get ... you're right, if you get to your question too quick, you either ... you may get lucky, but you could end up asking a completely irrelevant ... or just solving a really small problem that you might get a little bit of traction with but isn't going to be a sustainable business. It's going to be is that a feature or a product kind of question.

Starting broad, trying to get a depth of understanding on the particular market or the group of users that you are trying to solve for, and then you can start to narrow in from there.

Suzanne: Yeah, I describe it as strategically surfacing insights. Or kind of imaging being an archeologist and you are just carefully brushing away at the edges or sometimes not so carefully just sort of pushing, putting your finger into a soft spot on...

Wade: Ha ha ha ha

Suzanne: Does that hurt?

Wade: Is that where your pain is?

Suzanne: Ha ha ha ah

Wade: Is that where your pain is? Stop doing that, yes there's my pain. It's okay, it's okay.

Suzanne: So, if I understand correctly then, in the context of Civis, you're selling, you're selling the solution of data to other companies themselves so I am curious about what in your mind is the distinction between how much of this work we should own in house versus when does it make sense to engage a Civis, or a Nielsen, or you know, some other organization that provides that level of service? When is the job of collecting data too big for an organization?

Wade: So, if you think about a company's sort of data life cycle, of ... the initial part is that they have to be able to collect the data. So, we have a client we are about to start working with, they have thousands and thousands of employee survey's, they have thousands of HR interactions, and it's all sitting in either Excel spreadsheets, or random databases that are ten years old. So it's all over the place.

They really can't hire for a data science position because they don't even have the data ready to go in there yet. So they're engaging with us to help on some of that discord Data Management piece, and we're going to help them put that together. And, then start to do some data science with them.

Now, some of other clients have been in similar situations and we've helped them get to a point where they have a good Data Management operation. They have a good data hygiene and processes in place. And, they know what to hire for now. They can, they can see a skill set that says, okay, here is a job that a data scientist can do and we need this kind of skill set. We need someone with this kind of knowledge around these kind of business problems or this kind of algorithm that can help us with that.

So, they have to have a level of sophistication and just where their data is, and how they are storing it; before you can go out and hire a data scientist. I think that anecdotally, this seems to be a pretty large problem where companies will be, "we need a data scientist!", you know, "go get me a data scientist", right. Then they hire a team of people, they give them some, some open source you know, access and maybe SaaS database or something. And, they can't do anything. Then 18 months later, they are like, "What am I spending all this money on data scientist for? It takes me a month to get anything from them. They can't do anything." And you need to have those ... the factors in place before you really want to engage either with a software solution or actually hiring the full time employees to take advantage of that.

Size of an organization doesn't seem to actually matter that much. We have organizations that we have been working with for years, that, they were really small when they started! They were less than ...

Suzanne: How do you define small?

Wade: So there were less than 10 million dollars in revenue, right. They were ... now that's not really small I guess, but you know, they were at a five to fifty person organization.

Suzanne: Right.

Wade: And they started using our software. Worked very closely with our internal data scientists. They hire a data scientist, or we helped up skill some existing business analysts. But then we also have these massive organizations that frankly just have no idea what's going on. And you have, they need someone outside to help them tell the honest and hard truth, that then they can start doing it. So,

Suzanne: Right.

Wade: You can run; I had this discussion with one of the other product manager's here, and he was floored that this massive profitable you know, billion dollar revenue company didn't have this sophisticated data operation. And frankly they didn't need one. You know, they knew how to run their business. They were making all the right decisions to get to where they were then. But now they were looking for more. And so they were like, "We have this asset. We don't know how to do it, help us get better at it and go forward".

Suzanne: Right.

Wade: And there's thousands of companies that are in that same exact situation. So that kind of ... we got the business to a certain point, we have all this data, we should be doing something with this. We've probably have got an opportunity that somehow

Suzanne: Ha ha, right.

Wade: What do we do? And that ... When the leadership team gets to that kind of question, then they have a decision of: Do we fully hire a team, which is quite risky if you have got to build the team and get the Manager to invest that kind of money, or do we try something for about six months without an engagement. Whether it's with Civis or one of the other consultant companies or however that works.

Suzanne: Right, right. I love that, that the entry point is so, it's so fascinating because it's such a real problem, right? Even in product management at the smallest scale, and I am talking like four people small...

Wade: Yeah.

Suzanne: One of the things that comes up a lot is, if you are going to go out and do customer interviews, have a plan in place for how you are going to organize that information before you go and do it. So, certainly you could see how multiply that across a few different employees, or a few different departments, or several years, there could be a quickly this proliferation of different data. Is there a right way to start to organize your data. Let's say, like how I just bring all of these questions, will you please answer all...

Wade: Ha ha ha ha

Suzanne: Of the universal ... okay, but no. This is perspective based, but you know where I am going. I'm starting a company. I see that's a problem that I could be facing down the road. Can I do anything early on to prevent myself from becoming; I got these 1000 spreadsheets, theses 1000 survey's.

Wade: I think there is. The way to start, and the way to make sure you don't get into that kind of Excel hell, is be aware of the size of data that you are going to have. Or that you have currently. And then make sure that you make the step up at the right time.

So if you have dozens of client interviews, Google Sheets is a perfectly fine way to keep your data in handle right? You can share it amongst the team of four or five very easily. Everyone has access to it. You can put it in nice columns and keep it structured and keep it organized. But then when you start having you know, hundreds or thousands of website visits, and you are trying to bring in Google Analytics, or you are doing outbound marketing campaigns, and you have hundreds if not tens of thousands of impressions, then you are getting to a level of data scale that requires a database. That requires something larger than that. And you don't want to be stuck trying to squeeze all this larger data sets into an older or inappropriate technology.

And I think being conscious about the stages of how much data you are actually going to be having, will help you get ahead of the game. And you don't blink and then two years later you've got thousands of Google Sheets or hundreds of Excel spreadsheets and fragile macros that if Joe leaves the next day nobody knows how to run the macro again. That kind of stuff. I think ... and it's, it's so ... there's no real excuse not to do that now. It's easy to get onto Google cloud platform or AWS. These things are available and you can go and start experimenting with these things relatively early. That is not to say that you should be spitting up databases when you are in the dozens of people, but once you get there as you get closer to it, you need to be conscious of those moves across.

Suzanne: And is that a fundamentally data scientist that ... I guess this shines the light on the question of you know, are all data scientists created equal? I would imagine there's a specific version of a data scientist who is very good at organizing the inputs and setting up these databases versus somebody whose skill really shines from creating models or gleaming insights. Who is the right person to bring in, if you do it internally to say, "go organize all of this stuff."

Wade: One of the things we have been talking about here and one of the needs that we have is a role called a Data engineer. I joke that if more people knew about that term, that job would be even in higher demand than data scientists. Because, data scientists, great data scientists, some of the people we have here are these amazing unicorns of a variety of skill sets.

product managers like to figure themselves out as multi-tasker's and you know, great utility players, but, good data scientists are able to have some level of devops. They can set up databases and do this infrastructure, they can code, they can do interfaces, they understand the math and the algorithms and these kind of levels. And they're a rare breed. Like, that kind of level of skill set is really, really rare. And then be able to talk to people. You actually have to get some information out of them ...

Suzanne: Which usually, immediately goes against all of those other skills that you’ve highlighted.

Wade: Ha ha ha . We're very likely to have a lot of, a lot of great people here that have all those skills sets, but they are rare. They are really hard to find. And, but getting that Data engineer, or getting that data in a good place, gives you more flexibility. For when you have a data scientist who maybe they are good on the math, maybe they are good on the algorithms, but they are not as good on the computer science. Or maybe they are good on the computer science, and have some idea on how to use some of the packages, but they don't have the infrastructure knowledge that they might need in a different kind of organization.

So, it's, it's a reason why people are scrambling for quote on quote “data scientists” because to get that full breadth of skill set, it's just rare, it's just really hard. And even large organizations they might have two or three people that can do those kind of works. So it's a real challenge for them for sure!

Suzanne: So, long story short, it's just easier to call you ...

Wade: Ha ha ha

Suzanne: And say can we please engage Civis to make some sense of all of this?

Wade: So this is the thank you for opportunity for the plug, right?

Suzanne: Ha ha

Wade: The value of our platform, I'll keep it short, is we take care of all that infrastructure pieces. So that your data scientist's have, they don't have to worry about that set-up. They don't have to worry about the security piece. They don't have to worry about who has access to the data. They can get there, switch it on, load their data and be doing actual value added data science work in a matter of hours. And that, the market is at a level of maturity now that you are seeing this phrase and concept of data science platform, that even 18 months ago wasn't even really in the vernacular.

When I joined Civis, people were like: What do you they do? I was like well, we build the data science platform. They were like: What is that? What do you need a platform to do data science? And, and now you are seeing it in the Forester reports, and Gardener reports, and different kind of analytics, analysts reports; that are saying here is a market for this kind of software category.

Suzanne: Right.

Wade: And it's driven by the fact that the unicorn data scientists, just can't do it all. And they are just not going to exist, and companies need software to help them with that.

Suzanne: Right. So, Civis, if I understand correctly, really has two things going on. One is you provide the consultative data team. So you would go to an organization, like you described, who is looking to answer some questions that they have. And you would guide them through that. And then you have the platform.

Was the platform initially built simply to support the consulting arm? So it's like that kind of ... It's like Basecamp. First we built it for ourselves, then we realized, oh other data consultants like us could benefit, potentially.

Wade: For the listening audience, I am nodding my head. As my mother used to say, she can't hear my head rattle. So, I would say yes. Yes, that is correct Suzanne! We, I wasn't here then, the tools that were being built, it used to be called Consul, and they were building these data science tools for the consulting organization about 18 months or so into the organization's existence, there was an effective pivot of saying, we are going to create a product. We are going to package this as a product, they hired product managers, we increased the number of engineers and focused around: How do we make this an externally facing, clean user experience for people who aren't as experienced as some of our consultants and data scientist's here. And, while maintaining the consulting business, how do we then grown the software business as well.

Suzanne: Right. So is that primarily your responsibility then, in the product management, is to steer the platform and its identity to the market, sort of beyond an internal tool?

Wade: That's the product team's responsibility in general. My specific role within Civis is more focused around actually some of the data products, and the access to the data we have. But we have a platform team of product managers, and then we have a data and apps team of product managers, and I am on the data side.

Suzanne: So, I'm an organization, my data is a mess. I can engage a consulting team to come in. They can clean it up for me and then ideally we say: Look now you can do it by yourself, it's kind of like the parents you know, you're riding all by yourself; so that's the goal longer term for the platform is to empower people to manage their data more effectively through this tool.

Wade: Yeah, that's one path. We have found that, as I've said, there's a lot of profitable, well run organizations that just need a lot of help with their data, and we can get them to a level where it makes sense for them to hire and go on for themselves.

There's also other organizations that have the data scientists already. They have workflows. But, their needing to be able to scale and expand the impact they have on the organization. So, they'd know how to run all these models. They might have some home grown solutions. But, their Managers, or the other stakeholders alike, we need to get this out of your laptop. We need to get this out into the field. We need to get this to the people that are actually making the sales and marketing choices and using their data. And for those kind of customers it's really very much a turn the switch on and away they go. They can really start using it right away!

Suzanne: One of the things that I talk about a lot is how they activities of a product manager changes as the company moves through its lifecycle. And this is interesting, because I would imagine Civis is much earlier in its lifecycle than many of the clients that you serve, in their own way. Maybe we will come back to that.

Where I am interested in, is where the clients that you are serving are at scale. You are talking about at minimum revenues of 10 million and probably exponentially up from there. In that world it's a game of inches. Right? One insight that could help you increase lifetime value by a month, could translate to millions, if not hundreds of millions more in revenue. Are there some universal questions that companies are asking themselves irrespective of scale? Or does it simply become every situation is going to be unique?

Wade: My impression, that is, is very much on the universal side. I think that effectively companies are trying to answer two to three basic questions. Especially those that are B2C -. like dealing, and even B2B to a certain extent, but the B2C dealing with consumers. Is that ... You are either looking for new customers. You are either looking to get your existing customers to spend more. Or you are looking to keep your existing customer's. And across those three kind of business problems, you have then within there the different tactics you can take so. For example, "Where should I put my new stores? How can I get new customers with new store locations? What deals should I send to people to get them to come back to my site? How can I increase the share of, of wallet with regards to either food purchases, or laundry detergent purchases or whatever it happens to be?"

And those are all pretty standard business problems. If you take the ... The way data science comes in, where we can help, and where the kind of work that we package up, if you take a, what they call a churn analysis, or the flip side of that is a retention analysis, of who amongst my customers are likely to stop buying from me? Or who amongst my employees are likely to quit? And what data can I use to help predict that? And, how can I then focus my limited resources on the people that; I don't actually want to advertise to people that we are going to stay anywhere, because then that's kind of a waste of money. I wanted to advertise to the people that are on fence. That are thinking, okay, I may go somewhere else or I may stay, but if I can get them the right offer at the right time, then I can encourage them to continue the relationship with my organization. However it happens to be.

Suzanne: Let's go back, you said something earlier in the conversation about generative research. And last time I was here in Chicago, I actually heard you speak on this topic. So it's interesting you represent this large scale data science organization and product, but you are a real advocate for grassroots qualitative research gathering as well.

Talk a little bit, if you will about your perspective on that process. You know, how do you become effective at conducting; Well, what is generative research to use the term and how do you become effective at conducting it?

Wade: My definition of generative research is where you are asking wide open questions. You want to get, and I guess you want to generate those ideas. You want to look at what is the baseline problems, interests, challenges that a potential user or a market is trying to address.

That research can be done virtually, so through online research, or just looking through market, understanding market information. But you also get great knowledge having those direct one to one conversations. In the talk I gave, I mentioned a company called Respondent.IO. And we've used them to, I think great success. And what they do, they are effectively a matchmaker between product teams that need to find someone to talk to. And people that want to make 50 bucks on their lunch hour. Telling me about what they do all day.

So, it works out great for the respondent, the person who is actually giving the information. They get some money for their lunch hour and just talk about what they do. And it's great for product people, especially when you don't have the hundreds or thousands of clients that you can then pick up and call them and find them. It's worth the money to spend to get these people.

Now how do you get good at it? Do more of them, right, to just keep going and like make sure that your stakeholders and your managers understand that you are going to get better over time. So to bullet it out, you really need to prepare ahead of time. Make sure you know what you want to get out of that conversation, and what questions you are going to be asking.

Even though it's a generative process, that doesn't mean you are just there and just going to randomly have a conversation with the individual. You want to make sure that you are focused on identifying what their problems are. And getting them to talk as much as possible.

So this is a good tip for any kind of user research. But it's to talk as little as possible on there. And get really comfortable with awkward silences. It's human nature to kind of just dive in and especially, you wouldn't want to do it on a podcast, where you just have the radio silence. The dead air between you and the person on the phone.

Suzanne: But maybe we should try that as an experimental episode...

Wade: Ha ha

Suzanne: Just sort of like a standoff of who is going to cave first.

Wade: I will cave first most times, as I just said. So, we have great UX researchers here. And their recommendation and teaching to us product managers has been, you count to six. You take the time and you just count your breaths. And eventually, the other person starts to talk, and that when they get into what is really bothersome to them. People don't tell you the good stuff right away. Right? You got to kind of let them do the surface. They're kind of protective and then you have to let them dig a little deeper, and you are like : What is really annoying about your job? Or What is really the big problem that you face. They're like "oh no, it's great, it's really good, it's really exciting!"; It's like, really? And then you just kind of don't say anything.

Suzanne: Ha ha

Wade: "Well, actually" and then you get some good responses. So yeah, be quiet. Ask short open ended questions. And then shut up and get out of the way.

Suzanne: Right. Well, what's interesting and what I would like your perspective on, so, we certainly talk a lot in the show about the importance of generative research when you are at the idea phase, right? Or when you are pre-product market fit is going out and talking to people. I have a sound bite I use, which is: Pre-work, is free work, right? It's like go and gain that insight, if you can save yourselves a 100,000 dollars by realizing you shouldn't build this thing, that's a win.

Where I think it's relevant though, is for larger organization's. Because, this qualitative research doesn't stop at that phase. So I think what happens is, okay, we've more or less agreed that that is an important process for starting up. And then we get bigger, we engage Civis, we have all these cool data platforms so we get comfortable looking at models, looking at graphs. And we forget about talking to people.

What advice could you give to somebody who is a product manager at a large organization like the one's that are your own clients, to remember the importance of qualitative research and just for getting good at it or incorporating it into your daily or weekly rhythm?

Wade: Yeah, so I had that experience when I was at Nielsen. So we were working on products and we had hundreds of clients. And these clients were large multi-national CBG clients. And getting out in front of them is really challenging. So you have to just get on as many almost random phone calls as possible. It's almost like product management by osmosis, right? You just get in amongst the discussions that are happening. And that allows you to start recognizing themes over and over again. I don't know if it's Jason Fried or DHH at Basecamp, they both have this kind of approach where they don't necessarily have like their prioritized lists and they don't go and sort of score and make sure things are getting the one, three, and nine kinda low, medium, and high values. They just listen as much as possible. To as many people as possible. And over time, the right things bubble up.

To answer your question specifically, if I'm in a large organization, you have to broaden your network as much as possible. It's really important to go to, whether it's their employee research, er, employee resource groups that are quite common in large organizations. Or go to the mixers. Go to the luncheons, and the breakfasts, not just for the free food. Don't just talk to the other product managers and engineers, you probably spend a ton of time with them already. Go find the random sales guy that you haven't talked to before or the person who is in marketing and doing different kinds of pieces. And you just have to go up and say hello. You have to be, Hey, I'm Wade from products, what's going on in your world, and just start talking to people.

And that's something I did at Nielsen. I had great opportunities at Nielsen to work very cross functionally. Which is a ton of fun! But you have to maintain that network. And it's really on the soft side. It's not that your boss is going to just say, "oh, here Wade. I've set up 14 meetings for you across the different organizations. You just have to go out and hustle.

Suzanne: Yeah, well I love that you brought that point up because that's what my question was going to be is; Well, first of all let me frame up what I think that you just said there. Which is, in a start up organization, you're going to go out into the field. You're going to talk to people. Flag them down on the street. You are going to use a service like Respondent.IO. Later, as your organization grows, you'll have actual customers that you can reach out to. But as you grow, and grow, and grow, your direct access to those people as a product manager gets filtered through a Customer Service team, a Customer Research team. So the new kind of get out of the building is more like, go somewhere else in the building and talk to somebody who is still in the front lines with those customers. And do it of your own accord.

Wade: Absolutely, absolutely! And your ... that's not enough. But it's necessary to then build the relationships and trust with those sales teams or with the customer facing client service teams, so they know who you are. Nielsen had over 1,000 people just working in their[inaudible 00:39:12] office. And, making sure that people know who you are, so if they have a client call coming up, or if you put one of your random emails that goes to this large distribution group. They will be like: "oh, yeah, that came from Wade. Yeah, I had this question, I can go back to it." And having that personal interaction is really, really key. And that's going to help you both get that kind of filtered response from the client, but also give you the opportunity and build the relationship to get out actually in front of the client and work that way too.

Suzanne: Would you consider Civis to be a startup?

Wade: Hah. Ha ha ha.

Suzanne: Are you running through your mind like with the lawyers are like, don't say that, you are not allowed to say that!

Wade: The reason for my hesitation, is, it gets to the meta question of what is a start up? Right?

Suzanne: Right.

Wade: Or what is a small business? I think we are a start-up. Because it fits this definition. So, to me the thing that distinguishes a start-up from a small business, which is where my first job was at, is a start-up is oriented, and funded and built towards becoming very, very large very quickly. And that is, that's inherent in the goal. It's not just to be a good mid-size consulting and software company for the next 20 years. It's oriented around; we are going to be the data science platform of choice. And we are going to own this new market that is being created. And so, I think it's more attitudinal than size. We joke about Uber as being the world's largest start-up. Right? They are not the best example at the moment, but having that; and Amazon actually is even a better example. The Jeff Bezos's theme of that day one, we are still at day one for Amazon. Which is nuts, because they are billions, and billions, and billions of dollars. But they are still growing at 30 percent.

And so, is Civis a start-up? Yes, because our goal is to really own and disrupt this data science platform market. So that’s a good one Suzanne. Ha ha ha ha.

Suzanne: Well, okay, so I ask the question mostly because you were at Nielsen. I mean how many people at Nielsen?

Wade: There was 35,000 globally.

Suzanne: Right. 35,000. They've been around since the 1930's or something like that. They're established. They are a mature company.

Wade: Yup.

Suzanne: And, so I'm curious to hear your perspective as a product manager, going from an organization that is very mature, very established, probably very processed oriented, to being at a start-up and how that has changed your understanding of the role. What your day looks like as compared to what it used to look like.

Wade: I think the...I used to joke...so my first job at this floor heating company, I was the 13th employee and it grew to about 50 people. And then I went to Nielsen, which was 35,000 people. And now I'm at Civis, which is around 100, 150 or so. And, the core theme throughout that is sometimes you just need to get people in a room to make a decision. And, it's easier when there is only 50 of you. But, whether you get everybody on the phone and you say hey, this is the problem, let's hash it out. That's the similarity through all of those, those different kind of roles and jobs that I've had.

The unique part about the product piece, is the opportunity and the ability to have a wide open mind at Civis, is so much greater. And I assume at all the start-ups relative to somewhere like Nielsen. At Nielsen, I had a product, we had a limited development budget. The revenue we had to protect, and it was go do that. Engage with your Client Service teams. Engage with your sales people. Make sure they understand how best to do that. Work on the contracts as they go through...

Suzanne: Do everything you can within this tightly defined area that we have created for you.

Wade: Exactly. Actually, the best time that I had at Nielsen, was for three month, is when I didn't have a boss. It was in like some weird reorganization, and I got to own things for three months. It was the best time of my life there. But anyway, you have this very defined set of goals. And they are defined by four layers up the management chain. You really have no idea how they got there, but it's like, okay, I'm going to execute this and maintain the business.

Here, the goals are open, right. We have the vision and mission of what we are trying to do. But the exact way that we are planning on doing that, there is a ton of opportunity for influence, and input, and discussion. Which is obviously a lot more fun, right. At least, with me. There's an excitement, and risk, and challenge to that, that you don't get. Or at least I didn't find in a large organization.

As far as the actual day to day. The day to day at Nielsen or I’d say for all large organizations, was very much phone calls, living in the matrix of the different organizations, and talking much more to people who talk to customers. As opposed to getting to talk to customers directly.

Here, it's much easier for me to walk around the corner and ask the Client Success person, hey can I join in on your next touch base with XYZ client. And they say yes, sure okay, of course. And then you get to sit and talk and say. Hey I've got this one question and I know it's your time but do you have five minutes that we can hash this thing out. That's been the big difference.

At the same time, I make sure I say this all the time when I talk about working at Nielsen, it is a great organization! I learned a ton there. They are incredibly family friendly and work life balanced friendly organization. And depending on where you are at the stage in your life, and career, and how you want to do it, you can get a lot of value an interest out of working for those larger companies for sure.

Suzanne: Yeah, right, well I appreciate that you set up that distinction and I think it speaks to an important topic that we bring up a lot on this show. Which is the right product management role for you, will depend a lot on where you thrive. What I'm hearing from you is, that you thrive best when nobody tells you what to do, when no one is checking your schedule, just like wind me up and let me go. And great! And there are other people who feel, I want the stability, I want the structure, give me that box, and then I will do the absolute best job that I can do inside of it. So, knowing what motivates you is probably an important part of making a decision of where to end up.

Wade: Yeah, I also think there's, we think of kind of career in terms of the jobs that you have throughout your career. I don't remember whether, where the reference came from, but I read somewhere if you look at your career more as chapters in a book, as opposed to a path, it gives you a lot more forgiveness if you take a sideways move or you do something different. And it's not this continual progression from Junior, to Senior, to Director, to V.P., to CEO of the company right. There's having a recognition that, hey, I want to learn about this kind of job, or this kind of industry and I'm going to take a sideways step and go and do that. Or backwards step, or no step at all, is a much more kind way to treat yourself when you are thinking about different career paths.

But also to your original point Suzanne, I think the knowing and finding where you are good at and where you are going to be comfortable and fulfill that for whatever time period that happens to be is an important thing to think about and is a level of maturity that sometimes takes a long time to get to! But once you have it and you think yeah, I'm in a good spot right now. That's, that's really fun when that happens. And, it might only last for 18 months and then you are like, eh, I'm ready for something different, I've got to where I've needed to be. But, getting to that level of self awareness in your product life cycle, I think is really important. And it's good to do! It give you an immense satisfaction when your mental states, your skill set, and your job sort of all line up together, is a really wonderful thing!

Suzanne: Yeah, holy trinity

Wade: There you go! Ha ha ha

Suzanne: Well since we kind of organically arrived at advice for being in the world, what advice would you offer to anyone listening in who wants to make a switch into product, so this could be, hey I'm doing something completely different, but product sounds like it's for me. Or I'm a data scientist and I would like to be a little bit more strategic, or a little bit more holistic, or any number, of what I call these tech adjacent rolls. How do you get started in product management?

Wade: I think the recommendation I give when I have the coffees and things like that when people ask, There is a medium article called "Minimum Viable Product Manager." So that's a good place to start! That will get you, is this something that I want to do and how many of these ... how much of the [inaudible 00:48:43] diagram do I actually fill out? Where do I need to increase? That's a good place to start.

If you are coming from the marketing side, or the business side which I did, getting an understanding of how to talk to engineers, understand what is needed to communicate those business goals to the engineers, and then trying to get either a side project at your current company or volunteer for a project to be able to say, hey I ran this website build for a non-profit that I'm involved in. Something that shows that you can stretch yourself a little bit outside of where you are.

And then you also have product meet-ups that are great product meet-up scene in Chicago somehow, we have three or four meet-ups it seems every week if you really want to go to all of them. But start to go to those. Mind the Product has meet-ups here in Chicago and General Assembly hosts some as well. Get to those. Get to meet some other people. Most product managers think that we have the best job in the company, I certainly think that I have one of the best jobs in the company, so we're always happy to brag about it and tell you how great it is. So find some, say hey, I'm new to this. Do you mind having a coffee. Meet up with somebody, and that should get you started.

Suzanne: Cool! What about hard lessons learned. Either ones from your own career or you know, that you have seen in maybe junior product people, where it's like a classic mistake.

Wade: Yeah, and I think you kind of got me with your comment earlier. I think one of the challenges that I have and the lesson that I need to remind myself of is the wind it up and let it go, kind of approach of “Alright, we've got our project, let's get going.” And not engaging stakeholders. Just over communication. Not engaging them enough. Because then you are like excellent, we've got it, they're on board, here we go. And we are like three months later like, hey, look at this great thing, and you are like, eh, not really Wade. What's going on with it. And you are like damn. And making sure that you keep that communication going and building that support all the way through the process is something that you absolutely need.

You are not CEO of the product. Let's sort of get rid of that turn of phrase, which I just think is a terrible phrase. You are a herder of cats, to move towards an objective and you need to get as many people as needed in your organization on board to be successful.

Suzanne: Yeah, it's we had a guest of the show speak to that some time ago. Same thing. We went very far down a path of building a thing that when we went back to our supervising team, it was like no, you have missed the boat. Those are the moments when we can inadvertently move into more sort of waterfall type of processes. And I say this a lot too that the better we get at product management, the more susceptible we are to the blind spots. Because we forget, in a way when you are new, you have kind of all the steps that you've learned, more or less, you are like okay, no here is where I have to go ask the questions. Oh no, here is where I have to go get buy-in, then later you kind of forget and then you forget to get buy-in from your own team.

Can we proceed? Bring it back. Be agile. Come back after two weeks or three weeks and say here is what we are finding. Can we still proceed?

Wade: Yeah, absolutely. I think that the ... It's a good example of the value of checklists. Like, when you first ... and the job ... and you are like step one, step two and even a more experienced product manager, and we use it here for our launch activities is we still have our large checklist. Because you don't want to forget stuff. And you want to make sure you get it through and you do everything correctly. And having those kind of structured reminders is a great way to make sure you do the complete task as well as possible.

Suzanne: What, you know you said you think you have the best job in the organization. What is it that you love so much about product?

Wade: You are in the action, right Suzanne. You get to talk to everybody. You may not have guessed it but I'm a bit of an extrovert, so, you get to be part of building something which is really cool! And you are kind of that coordination point of the organization. You are adding value, and you're getting ideas from people and you are making it real, whether it's you know, physical floor heating mat, or if it's a software product, or a data product that gets out there. And then, when the clients are actually enjoying it and we get the feedback through client success or whatever, it's like, yes! We made the right choice then that was good, so.

Suzanne: Since I've been putting you on the spot this whole time, normally I ask our guests to provide some recommended resources in the form of books, blogs, podcasts. Maybe you thought I was going to ask you that question. But, given how much you are immersed in this data science space, I'm wondering if you have any recommended tools for product teams who want to start getting better at data collection, data management. It can be whatever scale. Obviously Civis is one of those tools. But are there others that you just personally find that are better or ... You didn't think this was going to be easy?

Wade: Ha ha ha. Get good, especially if you are a SaaS company. A software as a service product manager. Get familiar and comfortable with using Google Analytics. It's confusing at the start at making sure that the pages are tagged correctly.

So on that one, I would recommend, which is kind of adjacent to some product management skills. Some I am even practicing and trying to get better at is learn SQL. Learn Query Languages for databases. So that way, if someone is setup for data, you aren't reliant on other people to do that. It's pretty straight forward. If I could do it, I'm sure most of my listeners, or your listeners can Suzanne.

Suzanne: They're our listeners.

Wade: Yes, yes. There you go. I'll be back next week. Try the buffet right?

Suzanne: Ha ha ha

Wade: But, no. So get good at those kind of tools. How technical does a product manager have to be? SQL is not very technical. So I'm firmly in the non technical product manager camp, and if I can see the value of that, I think that would be the other piece. I don't know if that answered the question?

Suzanne: Yeah, no I think that, that is fair. And I also, you bring up that nod ... I wanted to mention this earlier. What's so interesting to me about your role, well, your strange history and obsession with data scientists. Because you are not a technical person. And you are not a data scientist as you describe. And I think that a lot of times, product people feel, product managers in particular, because we are generalists, we sometimes feel inadequate. That we are not the specialists. So here you are, a guy, you are not a data expert. You are an expert on data products maybe, right? Or an aspiring expert on data products. So I think, understanding what is the right amount of knowledge that you need to have to be good at your role, and not worrying about having all the domain expertise all the time.

Wade: Yeah, I think this kind of brings it back to that first principle. I think that product managers are generalists. And part of the downside of that is people kind of don't necessarily see the true skill set that is there. And I think the work that you are doing, Suzanne, with 100 Product Managers and other kind of groups, are seeing this is a discipline, right. There is an art and a craft to asking the right questions, interpreting the data. Excuse me. And then making a decision. And being able to make that decision, and saying we are not going to do this, we are going to do that. And I will own that. And if it's wrong you can talk to me. And if it's right you can compliment the engineers. Right, like that's how it works.

And, I think that getting those core skills of communication, stakeholder management, engagement with people, asking good questions, is something that product managers shouldn't sell themselves short on. We're not just personable people that run good meetings, right. There is a craft to what I think good product managers do. And sometimes that gets lost in the shuffle in the generalist kind of approach.

Suzanne: Great. Thank you! Last question. Do you have a life philosophy, work philosophy, cheer that you sometimes run through this office and yell out to guide you right.

Wade: I don't yell it out too much, but I think, or at least I hope my behavior reflects it. I try to ask critical questions. That's ... thinking about this kind of question from your other, your other podcasts, I think that's what I really try to get across both either when you are researching. But even more importantly, when you are truly trying to solve the problem, is getting to the heart of the issue in a very constructive, polite, not passive, but calm kind of way of saying. Are you sure? Is this what our question is? Are we doing the right thing here? And practicing that kind of inquisitiveness and asking those good questions; not tough questions because that could have means adversarial, but just good critical questions, I think is key to good product management.

Suzanne: Can't think of a better way to close. Wade Johnston. Thank you so much for being a part of our project.

Wade: Thanks Suzanne.