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Alter Everything Podcast host, Maddie Johannsen sat down with Manuel Coello, Senior Director of Data Analytics at CVS Health, for a conversation about analytics, transformation and positive team dynamics. Manuel includes tips on what skills to focus on when you're trying to demonstrate value to your organization, unique ways to engage your team, and how to get sponsorship for your analytics initiatives.


Special thanks to @andyuttley, one of our winners of the 2020 theme music competition, for the awesome theme music track for this episode.

 

 

Panelists

 

Maddie Johannsen - @MaddieJLinkedIn, Twitter

Manuel Coello - LinkedIn 

 


Topics

 

 


Transcript

Spoiler

MADDIE: 00:07

[music] Hi. I'm Maddie Johannsen and this is Alter Everything: a Podcast about data science and analytics culture. It's been a while since I've been your host and I'm excited to share this one with you. In today's episode, you'll hear a conversation I had with Manuel Coello, senior director of data analytics as CVS Health. In the conversations I had with him leading up to our actual interview I knew he was a treasure trove of information about analytics transformation and positive team dynamics. So once we actually recorded the episode turned out to be the perfect toolkit for data enthusiasts to share with their teams. Manuel includes tips on what skills to focus on when you're trying to demonstrate value to your organization, unique ways to engage your team and how to get sponsorship for your analytics initiatives. I hope you're able to gain as many takeaways from this episode as I did. It was a lot of fun chatting with him. Let's get started.

MANUEL: 01:04

First of all, thank you for having me. As you know my name is Manuel Coello. Born and raised in Mexico as you can tell by my accent [laughter]. And analytics was pretty much the key for me to come to the United States about 20-plus years ago. One of the Fortune 500s was looking for somebody that can initiate the analytics department. Imagine that. 20 years ago. And they found me down there. So I came here and it has been a little over 20 years in the analytics space.

MADDIE: 01:33

That's amazing. Yeah. 20 years, that's really cool.

MANUEL: 01:36

I don't know if it's an advantage or disadvantage. I think it is an advantage but at times I feel it is a disadvantage because you have to unlearn some of the old techniques and old applications and try to learn the new things. And things are changing and evolving so rapidly.

MADDIE: 01:54

[music] I really enjoy speaking with people who have been in the industry for a while and Alan Jacobson, friend of the podcast and Alteryx's chief data and analytics officer, had a similar experience to Manuel's when he started his career and he shared some memories on a recent interview with Federal News Network.

S3: 02:10

I really do believe these are the [inaudible]. If you really look over really what's been a short time period-- if I rewind to when I started in my career I think back to the computer that was on my desk. I think it was a Pentium II. I don't know how many of your listeners remember those. It had a clock speed measured in megahertz, it had a single chip, it was not very fast. And I compare that to my machine today which is probably the equivalent to a supercomputer from back when I started my career and it's now measured in gigahertz, it has eight cores, it has a GPU on top of CPUs.

MADDIE: 02:46

You can hear more of Alan's interview linked in our show notes.

MANUEL: 02:51

And things are changing and evolving so rapidly that trying to be relevant every year is a challenge and is something that we'll need to continue to evolve into whatever is next.

MADDIE: 03:01

Interesting. Yeah. I'm glad you brought that up because I would like to dive into what it means to build that digital workforce and what that might mean for analysts. So in your time in the analytics space have you seen analytics always as this cornerstone or this foundation for building the digital workforce or is that something that has happened more kind of recently?

MANUEL: 03:29

I think it has happened maybe over the last five years and, of course, the last two years even with more intensity. The previous formula was data analytics. Was data plus analytics. And using those two segments you ended up with whatever project or analysis that you were doing. And there were two different components that were added pretty much in the last five years. People can argue that it has been there for longer. And those two other components are the RPA - the robotics process automation, as well as artificial intelligence.

MADDIE: 04:07

Whoop! Hold on a second. What was RPA and what's artificial intelligence? And what even is analytics? Manuel, please help.

MANUEL: 04:17

So let me give you my own definition. The textbook may be a little bit different. And this definition has evolved as we continue our journey. But for me, analytics is having the ability and the creativity to get relevant data so you can perform intelligent and meaningful summarizations that can generate value. And that for me is the most important part of the equation. Can you do something with purpose, on purpose, to generate value? In the question for the RPA and that has been revolutionary since many new tools have entered the market that are more affordable and they're easy to use. And now we're not just limited in the digital world which is only the data but also we can do things that manually through robotics. One example is historically you have to log into a system to try to trace some information and get an invoice for instance. Now we can just have a robotics process automation that can do that for us. Has their own employee ID, has their own system access and we can just easily instruct that person - in this case, the digital workhorse or the robot - to log in and get us the information that we need that we can incorporate later on into the analysis that we're trying to do.

MADDIE: 05:46

Artificial intelligence that's perhaps the most difficult piece to incorporate into the equation. How can we partner with machines for them to do the work that we always do? We can do things in a natural intelligence but how can we do in an artificial intelligence environment? Or how can we give the machine all the inputs that they need so they can start creating conclusions or decisions that only a person can do? So that's always a challenge.

MANUEL: 06:20

[music] Got it. So Manuel was just saying that back in the day the formula pretty much consisted of data plus analytics and that's it. So now there's been an transformation by incorporating more advanced processes and technologies.

MADDIE: 06:31

And there has been an explosion in products and tools and now they're more economical that people can really acquire them and implement them. Or the company has been already purchased and you can leverage the investment from other departments. And the digital workforce is something that has been very close to my heart for many years. And the whole concept is can we create a digital workforce that can perform some of the work that we do? And it is definitely a very ambitious goal. Especially when you are trying to teach a machine to do the work that we do in order to connect the dots, have the critical thinking, having all this exploratory capabilities in order to achieve something. That's always been a fascination for me.

MADDIE: 07:21

What do you think the state is of these businesses really leveraging AI, RPA, and analytics to really drive that value?

MANUEL: 07:29

So as you know I specialize in audit analytics and internal audit. And right now I work in CVS Health and previously I work in other Fortune 500s. And, of course, I interact with many other organizations at least in my area. And one conclusion that I have is there is a lot of ignorance about technology. And by that, I mean people are ignoring the benefits of technology. It's not like they don't know. They're really smart. They're incredibly smart. But they are neglecting. They are just ignoring the benefits and the transformation that they can acquire in their jobs just like implementing some new techniques. And it is sad. Now there is people that are kind of leveraging. They have the resources, they have the sponsorship and they have the skills and they're going really, really fast. And one of the things is because they have to. In order to be competitive, in order, really, to do things in a more meaningful way they have to evolve into a more technological way. Something very interesting I have a 16-year-old soccer player in the family and his team is using analytics to win. So imagine that. A non-profit organization, little club here in Connecticut, and they actually analyze the video and from the video, they extract all the plays and they know exactly what are the strengths, what are the weaknesses, they have a heat map where every player is throughout the game and based on that they create the training session. It's just fascinating how everyone is using analytics for their own purpose to be more effective and create more value.

MADDIE: 09:20

Wow! That's amazing [music]. That's really interesting. Personal anecdote. My dad is a high school basketball coach and has been for the past 20 years. And he uses a similar technology to analyze his game film. I remember growing up when he and his coaches would have to spend all this extra time looking over film and manually marking on paper where players were shooting from and what their shooting average was. I mean they'd had to have different symbols for if somebody took a shot and they made it or if they took a shot and missed. Super complicated. But now it's all done for him which is pretty sweet.

MADDIE: 09:53

What are the skills that you look for from the members of your team?

MANUEL: 09:57

Oh, good question. Well, the first one is intellectual curiosity. You have to be curious. If you're not curious I don't think you should be in analytics [laughter]. If you don't have this thrill of digging into the data, the self-exploration, the discovery approach and be curious of why things happen you may not belong in analytics. Oh, by the way, if I can share my story of how I ended up in analytics. Happens pretty much in the beginning of my career. I was working in Ernst and Young and I noticed one new person in the office. So being the curious guy that I am I kind of approached this person and introduced myself, "My name is Manuel Coello." And he introduced himself. And I said, "Oh, you are new, right?" He said, "Yeah. Coming from this office and one of the things that I specialize," - again this is 20-plus years ago, "is in analytics." And I said, "Oh, what is that?" I was out of college and analytics wasn't that big back then. And he said, "Well, look at this." In this case, it was one of the audit tools for analytics and he click a button and suddenly a beautiful visual came up. And he said, "See these outliers? You can see." And he was showing me what is called the Benford's Law. And the Benford's Law analyzes the frequency of the first digits of the numbers and based on the patterns identifies some of the outliers. So that, for me, was perhaps the revolving door that kind of brought me through analytics. I then realized that through the push of the button you can create some models that can give you the answer right up front without you doing extensive work. That for me was incredible.

MANUEL: 11:53

So after that I stuck around this guy forever. He pretty much was my mentor and since then I have been doing analytics. So being curious - going back to the original question - I think that's perhaps the first I'm looking for in employee. Having the curiosity to do it. The second one, the can-do attitude. Everything that we do is difficult. We're going to have challenges pretty much in everything. In everything that we do. We don't do easy. We do difficult. All the projects that we do are difficult. If they're easy we typically decline them and ask the users to do it themselves. So everything we do is very complex. And having the can-do attitude, whatever it takes to do it, that's very important. The third one, having the cultural fit. Somebody that enjoys analytics and collaborates and can open to the diversity of thoughts. Using my team, we're about 14 people. 10 here in Hartford, Connecticut and 4 in India and we get along very well. And we're very different. I always call a nice dysfunctional family [laughter]. Different backgrounds. Some are math backgrounds; economic background; we have a bunch of CPAs; we have different ages, some people are out of school, some people already have 10-plus years in the industry. So it is amazing how nicely we complement each other. So somebody that plays nice, that is open to points of view. That for me is very important.

MANUEL: 13:30

And the fourth one is having the speed. Everything that we do is in a timeline. So having that kind of aggressiveness, being hostile to some of the tasks, that is very important for us. Somebody that is very focused, that can escalate things quickly, that can collaborate early on. And finally, of course, create value. Understand why we do things.

MADDIE: 13:54

That's great. So you mentioned kind of your dysfunctional family [laughter]. What are some tips that you have for creating that kind of environment? Especially because you mentioned that you have people all the way in India and Connecticut. How do you kind of bring those together and create this really warm and exciting and collaborative environment for your team?

MANUEL: 14:19

Good question. As you know, the Agile methodology came up maybe in the last 10 years. And when people work in a more Agile way it's not the kind of waterfall that you have a project plan and people start working independently in their own task and then somebody pulls that together. Now with the Agile, we meet every day. Every day. So we have the Scrum meeting. Everyone knows what they finish the previous day, everyone knows what they need to do that day. We have the face time, that kind of felt good time and then people go there. And throughout the day we really don't talk too much by phone or through emails. We just kind of tap each other on the shoulder and start running ideas by each other. Having that kind of fun environment. That kind of supportive approach is just really nice. And, of course, the culture is if we win we win as a team. If we lose, we lose as a team. The blame is shared. It's not one person. And having kind of that team environment is just a fascinating way that people can really relate. Being part of the team, trying to do something kind of bigger than themselves. That is perhaps one of the things. The other things every year in December we get together and we decide our destiny for next year. So every year we can all sit together, "It's okay guys. This is what we accomplished this year. Good job. What are we going to accomplish together next year?" So everyone is part of the vision of the year.

MANUEL: 16:00

And we brainstorm for hours. And people start kind of saying things that we should do next, things that they hate that we should stop doing, and people kind of bring what they want to do for next year. And I take all the ideas and I put one PowerPoint presentation, one page that shows all the imperatives. 'This year, this is what we're going to focus on'. And then we put all the plan together. This is how we're going to accomplish that. From there we create the objectives so now everyone knows the objectives for next year. And we just keep reminding ourselves, "Okay. This is what we're going to accomplish this year. This is how we are to accomplish. This is our playbook. And everyone knows how are we going to do it." So the digital workforce was one of those six imperatives and everyone knew their role on that goal.

MADDIE: 17:03

It sounds like such a culture to be a part of. It sounds really fun, engaging, and fulfilling too. Being able to have a say in what you want your destiny to be for the next year at work sounds incredible. So when you talk about different departments in organizations being more skilled than others in analytics and being advanced with these sorts of technologies. What are some tips or some skills that you try and instill in your team to really kind of help drive that transformation that really has a benefit across the organization and not just within your department?

MANUEL: 17:41

When you want to kind of expand to other people it has to be in the mindset. Before you transform the tools and the techniques and the approach, first the mindset. If people don't get it they won't go along. And as you know change is difficult especially for people that have done it for a long time in a specific way trying to introduce something new it is difficult. But trying to make this fun, bring them in the design approach, having small wins - that has been a big change. In our case, we use many different approaches for this kind of transformation journey. The first one is we have a conference every year. So it has been [inaudible] for years. So we have had so far three conferences where we just talk about analytics. And we bring executives and big thinkers and people share, "Look what we have done with the use of analytics." So everyone being exposed to techniques, technologies, business cases. That has been perhaps one of the biggest ingredients.

MANUEL: 18:59

The second thing is from the [inaudible] department we actually host or rotation people come from a rotational assignment for a month with us. And in these four weeks we get them tools, they participate in all the meetings, they do some specific projects. That has been a transformation for that person. And that person goes back to the teams and just keep expanding and expanding and expanding. So that was another thing. The other thing that we did is we do a gamification. As you know there are always 40% of the people that love analytics. There's another 40% of the people that they're interested in analytics and potentially they'll start using analytics in the future once the first 40% start using it. And then there are 20% that they won't do anything no matter what. So as you can see the biggest opportunity is with the second 40%. So we create a gamification. So we do these 20-25 things in analytics, we get data, we create sponsorship with the descriptive analytics, with robotics, and we apply our data scientists with some modeling. So we listed everything that we do in analytics and we create a game. We say, "Okay, guys. Anytime that you do anything from this in your projects you start accumulating points." That was a game-changer because now everybody starts focusing on the thing that we thought were important. And something magical happened the first year of that competition. And by the way, we told them we won't be cheap [laughter]. It's going to be every quarter. And if you win everyone gets a trophy. People love trophies even if you are in the [inaudible] you still love trophies. We say, "You take the afternoon off, one of the Fridays, and you have a nice dinner up to $100." So we were not cheap.

MANUEL: 20:57

And something magical happened. The first four quarters, guess what, four different people won the gamification. So the expansion in the use of analytics was just amazing. And you mentioned there is no transformation unless that everyone benefits. That was a key for us. Make it fun, work in the mindset and then bringing people into our world so that they can introduce themselves in the use of analytics.

MADDIE: 21:24

This reminds me a lot of what we see at our Alteryx internal user groups and user groups in general. Analytics teams coming together and saying, "Hey, let's all get together for a lunch and learn or let's race to see who can solve the most weekly challenges on the Alteryx Academy this month." People really do tend to change. But who's going to complain if you make it fun?

MANUEL: 21:43

Lesson learned is that the transformation is being done by the people. It's not by one person or one department. It's actually by the user. The people that are actually benefiting from the transformation are the ones leading the revolution so to speak. So having the actual end-consumer leading the analytics that perhaps was the key to advance quickly.

MADDIE: 22:11

Yeah. Definitely. And I wonder too having this kind of, like you said, grassroots end-user powered transformation, what are some of the challenges that you've seen in really trying to either help kickstart this or have you seen any sort of resistance from end-users? Or how do you kind of combat those challenges?

MANUEL: 22:35

Well, very good question. When you talk about challenges typically there are three. They call the three S's. The skills, the staff, and the sponsorship. So in most cases, you have the skills because there's a lot of talent out there that people can do the job. You've got the staff. Companies now they have resources and they value the investment so that they have people. But the sponsorship that's the most difficult thing to get. And without the sponsorship, you really cannot go the distance. So a few things that we're doing here is always recognizing those influencers, the people that actually is going to help you in your journey and get that right. So when I joined here my boss told me, in my first days, he said, "What can I do to help you?" He was just excellent leader. And I told him, "If you can put me in front of all the top executives of this company that will go a long way. So he just sent an email to the 20 top executives and he said, "Hey, guys. I just hired an analyst guy. His name is Manuel Coello and he want to set up 30 minutes with you to meet." So I set up the-- so my first month what I did is set up a meeting with those 20 people and I put a page together and shared it with them. And say, "Hey, I have a dream [laughter]. I don't have a plan but I have a dream." And we told them how we wanted to leverage their analysts and technology to go the next level in terms of analytics.

MANUEL: 24:18

And having that sponsorship that was key. And half of them they actually said, "Oh, I didn't realize the internal audit wasn't using analytics. Everyone is using it. You are just about to use it." So half of them they were surprised that we were asking and the other half was like, "Hey, whatever you need don't send me an email. Just stop by or give me a call even for the smallest detail." The sponsorship was incredible. And once you get that sponsorship from the top everything is easy because when we get some pushbacks either from a specific business or for a specific business partner and-- many different challenges. Could start with the data, trying to get their time, or trying to follow-up in certain things. As soon as we cannot hit a wall we always said, "Okay. Sounds like we may need to tap into the executive to give us some resources." And things happened. So that was key. And then you have the internal partners, kind of the peers in your team. And kind of keep them in the loop and kind of let's start a movement together. It's not an old initiative, it's not something that we knew doing something new. Let's start just start a movement together.

MANUEL: 25:42

When you talk about the movement people get excited because now they're a part of that movement. And the gamification that I was sharing with you that was key for us because that gave us data on who was using it. And once we were able to quantify, we were able to do things in a scorecard and we have a dashboard that people can do it. I use that input to share with the old leaders and all my peers, all my fellow senior directors. And when I share with them it is amazing because bring the visibility and transparency, involve the activity and they are the ones driving the change. So sponsorship is key. And one rule that we have when we do specific projects is right up front we go to the executive and say, "Hey, we are planning to use analytics for his so we're going to need a lot of data. We're going to need a champion and we may need to tap into your experience." And the rule that we have is if this person says no that's it. We don't do anything. We don't do analytics. If we cannot get this sponsorship right up front we just don't move a finger because we know we're going to fail. The good news is in my four years here I have not had a single case of an executive saying, "No, Manuel. Don't use analytics. That's dumb." Not even one. Everyone again is very useful. Especially you bring them up front. And it's difficult, it's painful, it is uncomfortable but once you get the sponsorship everything kind of flows through.

MADDIE: 27:22

And one thing that you pointed out when you went to the executives when your boss pulled them all together and you said, "I have this dream but I don't really have a plan." I think that that's really important too because in my experience I feel if I have an idea and I bring it to somebody and I say, "Hey, I want to do this." To your point, people typically want to help you and they want to step up and provide whatever assistance or sponsorship that they can. So with your point of you've never had an exec tell you 'no, don't use analytics' I think that's very important to shed light on too. A lot of people have great ideas. They might not have the full plan yet because they need help formulating the plan or they know it's going to be this big organizational transformation or maybe it's going to light the fire to something bigger. And I think it's super important to speak up when you have an idea even if it seems too big for you to take on and maybe the execs will say, "Okay. Yeah. You take it on." And you don't really have all that buy-in but then people will get excited for and then they'll start allocating resources for it and let it kind of grow on its own and have it be this, like you said, grassroots end-user type transformation that it just kind of bubbles into.

MADDIE: 28:48

So if you could point to any sort of failure that you've had-- I know that might be kind of a tough question but I'm curious to know if you've ever tried something and it didn't quite work out. What was the thing that you tried out? And how did you pivot and maybe turn it into a success?

MANUEL: 29:07

Boy, I have many [laughter]. Let me start with-- oh! I have so many failures especially early in my career. The biggest one I can point out and it's still haunting me is I really didn't understand the concept of value and that really hurt me in many different ways. Let me ask you, who defines value?

MADDIE: 29:33

The end-user.

MANUEL: 29:35

Exactly. You got it. The end-user or the person that we're doing the analytics for defines the value. When we start partnering in any project I was too hung up with the data or with the technology or how am I going to do these resources. The focus was never the value. And because of that, I did a lot of rework. I presented a lot of things that they didn't want. A lot of things I did that they were not used because I was always missing the mark. Yeah, sometimes I got it right but I wasn't consistent performer because I didn't understand the value. And because I didn't understand the value I honestly didn't get the recognition that I thought I deserved because I was just not generating the value. So halfway through I learned my lesson because I wasn't promoted. I was bypassed for a promotion and that really hurt me. And that was the wake up to say, "Okay. I need to learn what is meaningful for the people that are receiving - the consumers of my analytics. And I became very obsessive to understanding, "Why are we doing? What are you trying to do?" And in some cases, I was able to deliver things that they didn't know they needed. But because I understood the value, I was delivering something they didn't ask for but they needed. They just didn't know that they needed it because I understood the value. The concept of value. And a lot of iterations happened based on that concept of the value.

MANUEL: 31:19

The other kind of big failure that I had, and maybe it's linked with the value, is the delivery. When you finish something there are many ways that you deliver your work product. You can just send an email or you can have a conference call or you can put a PowerPoint presentation together. So I wasn't really putting too much effort in the package. Somehow I thought that the work I performed was good enough and that was sufficient. So when I share it with them, whoever was the consumer, the focus wasn't on the value that they wanted. The focus was in all the hard work that I did and look how much work I put together. And look what I did. So I lost my audience in many different deliverables because I was focused on my own work. And then I realized, "You know what? These people don't care what I did. They only care about the piece of information that they are asking for that will generate the value. Those outliers, those insights, those confirmation of the hypothesis." Whatever I was doing the analytics for. And once I understood the value of what they were receiving it was so easy to put everything together. So I got the analysis, I interpreted it, I can identify the value and I was able to put it in one page in PowerPoint. Now I was just in colors and visuals and very simplistic. In a very simplistic way, "Hey, this is what I found." And based on that kind of delivery the magic happened. Actions were taken, a lot of people saying how impactful was the work that we did. And everything was kind of derived from the value.

MANUEL: 33:08

The other thing that happened-- I'm still kind of going back linking to the value was because I identified the value I start sharing all the work that I did. All the value that I was about to deliver. So all of us have to justify our goals every year. Our performance. So that was easy to write because I was able to identify the value. And so I think everything kind of boils down to understand the value and once you understand the value can you adjust the work that you do to meet that value? Can you communicate and share all the value that you are generating? And can you package it in a way that the end-user will understand in a very simplistic way?

MADDIE: 33:57

I think that that's really great advice. How do you translate that for somebody who might be a team of one and they are super over-worked and they are focused on the work because they're putting in so much effort? How do you translate focusing on the value when maybe they're frustrated or jaded about their job just because they are super overworked and maybe their organization hasn't necessarily bought into that transformation yet?

MANUEL: 34:27

Good question. It is difficult because now you have one person that is jack of all trades. The same person gets the data, kind of clean up the data, maintains the data, performs the analytics, delivers the analytics, introduce all the new technologies. So it's very overwhelming work like you mentioned. The first thing that perhaps this person can do is have a very clear understanding of the expectations from the supervisors, from their boss so they can be aligned because they define how we win - our bosses. So whatever the supervisor expects that's perhaps the best thing you can do. And once you understand the expectations you can come back with a transparent picture of the things you can do with the resources that you have because we always need more resources, we always need more technology, we always need more funding. And either you increase the resources that you need in order to meet those expectations or you start saying no to some of the things that you needed. And one issue that we have when you have a one-person show is that you have to be very deliberate. Very deliberate in the things that you do because time is precious when you only 40 hours of quality work every week. You cannot do it all. So having that one.

MANUEL: 36:02

The good thing is you can tap into other resources. In our case, we have interns, we have people out of college, we have people off-shore, we have people that come and do a rotation with us. So there are many ways that you can get creative and have that discussion with your boss to say, "Hey, this is what I want to do. This is what you expect me to do. That's all I can do." And the issue is in most cases the executives do not know what it takes to do analytics. They don't know how difficult it is to connect to a database from a system. They don't know how difficult it is to reconcile the data and clean it up and enrich the data. And how difficult it is to do the analytics. Sometimes it takes days if not weeks to perform one analytic let alone communicate it in an impactful way. So having the ability to quantify all the effort and quantify all the value and use that to get the funding, the resources that you need I think is going to be key [music].

MADDIE: 37:11

Thanks for listening. For more on this episode check out our show notes at community.alteryx.com/podcast. And don't forget to subscribe on your favorite podcast app and tell all your friends that this is your favorite podcast. If you have a success story on how you got buy-in and sponsorship from your team's leadership tweet about it and use #altereverythingpodcast to enlighten the rest of our listeners. Oh, and spoiler alert, you're going to want to listen to our next episode coming out on March 3rd featuring the one and only Bingqian Gao. If you don't know who she is she's kind of a big deal. She was the first person to pass the Alteryx Certified Expert exam. It's a super hard exam and only a few people have passed it. So stay tuned.

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).