LISTEN TO THE EPISODE
Introducing Aaron Maass of Hero Digital
Aaron Maass knows that data visualization is imperative in the marketing and analytics industries and attaining data is as easy as finding a cup of coffee.
The struggle companies and stakeholders are having is in interpretation and application. The data is there but knowing what it means and what to do with it is the real challenge.
And that’s where Aaron, founder of MaassMedia, independent digital analytics consulting firm, which was recently acquired by customer experience agency Hero Digital, comes in. Aaron’s experience and wisdom make him a guru in understanding and interpreting digital analytics data.
Finding solutions is his stronghold and he’s here to share his philosophies on the discovery process of learning what the stakeholders want, the data available, and how to apply it to solve business challenges.
Aaron has nearly 20 years of online marketing industry experience, has led web analytics initiatives in senior internet marketing management roles at many big companies, and was elected to the DAA’s Board of Directors.
In the nineties, he co-founded a website traffic tracking and reporting software service called Sitegauge, which he sold to Kohlberg, Kravis and Roberts in 2000. His experience has helped him develop a mind for the stakeholder and a keen focus on the importance of viewing data from every possible perspective.
In this episode, Aaron breaks down the most essential questions data analysts must ask to ensure their data presentations meet their stakeholders’ needs. He explains the importance of collaborative experiences with a diverse array of people and shares the “3 what’s” his mentor taught him for thorough data interpretation.
In This Episode, You’ll Learn…
- How Aaron fell into the data world on accident, like many of us, and his early business endeavors.
- His views on the massive amount of data available and the real struggles companies are facing.
- Where he sees data viz fit in as an important part of the insight discovery process.
- His belief that the most impactful insights come from a collaborative process and how his company ensures working together.
- åThe 3 What’s that he learned from his mentor and how he uses those questions to find the insight needle in the data haystack.
- How he keeps stakeholder mindset at the forefront of everything he does.
Aaron’s Upgrade:
- “Try to look at the data through the lens of the consumer of that data and consider how they will interpret it. And, title your slides with the conclusion you want people to come to.”
How to Keep Up with Aaron:
Thanks for Listening!
Thanks so much for joining me. Have some feedback you’d like to share or a question for Aaron? Leave a note in the comments below, and we’ll get back to you!
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A very, very special thank you to Aaron for joining me this week. And as always, viz responsibly, my friends.
Do you have a burning question for Aaron about interpreting data, stakeholder mindsets, or how to build a data-driven culture?
Lea Pica: [00:00:00] What's up guys. Lea Pica here. Today's guest is one of the early pioneers of tag based analytics and is here to help you transform your stakeholder conversations. Stay tuned to find out who's making waves on the present PR measure Show Episode 42.
Lea Pica: [00:02:26] Alright, so I am super excited about today's guest. He has over 20 years of online marketing industry experience and he's led web analytics initiatives and senior Internet marketing management roles and now he is the founder and CEO of a pretty kick ass analytics agency. Let's go.
Lea Pica: [00:05:03] Today's guest is one of the early pioneers of tag based web analytics. As the founder and CEO of Maass media, today he oversees the development of some of the e-marketing industry's most notable digital analytics programs and professionals. Since launching Maass media his team has helped numerous Fortune 500 clients develop and analyze e-marketing analytics programs that deliver measurable and immediate ROI. He's been recognized for his achievements by the digital analytics association the Interactive Media Council and Web Marketing Association. He teaches a course on web analytics for the online marketing Institute and he's just around the corner as a fellow Philly resident. So with that I'd like to introduce you to Aaron Maass.
Lea Pica: [00:05:47] Welcome.
Aaron Maass: [00:05:49] Thanks, Lea. It's a pleasure to be on your show.
[00:05:52] Pleasure to have you. So we have bumped into each other from time to time at various industry events and we thought it'd be a great idea to have you on the show since you work so closely with analysts and clients and data which makes you perfect. So first everyone's gonna want to know your origin story. Tell us a little bit about how you fell into the world of measure those kind of by accident.
Aaron Maass: [00:06:18] It was way back in the late 1990s during the Internet bubble. A friend of mine had invented an ad serving technology and asked if I'd like to start a business with him. So I looked into it and within a couple weeks I had quit my job moved to Boston and filed the paperwork to incorporate. That early technology soon became a website traffic tracking and reporting tool.
Aaron Maass: [00:06:58] We found that there was quite a bit of demand for tracking web behavior and determining ROI on campaign spend. And we found that the technology that was serving ads could also be used to track web behavior. And this was 1998. So I sort of fell into that a little bit by accident. My partner got into law school a couple of years later and I thought I couldn't continue without him. And so we decided to sell the business in 2000.
Aaron Maass: [00:07:37] But ever since I have spent my career in digital advertising and analytics.
Lea Pica: [00:07:45] I think so many of us can relate to the idea that we fell into it by accident and no one trained us in analysts school or presenter school. So I think it's so interesting how people's pasts evolve like that. So fast forwarding to today you know if we were locked in a room with a team of Maass media what would you be able to help me do by the time we come out?
Aaron Maass: [00:08:11] I think where a lot of lot of people in a lot of companies struggle is not with getting data because I think there's just tons of it out there and more and more of it every day. But trying to figure out what to do with it and that comes from people with experience in how to use the tools that are available and collecting and analyzing that data but also experience interpreting it and applying it to solving business challenges. So I think that's probably where we would help the most is hey you got all this data on your businesses data on your customers now what does it mean to us and how can we use it?
Lea Pica: [00:08:58] That's great. And what role does data visualization and storytelling play in terms of how you present your findings?
Aaron Maass: [00:09:07] Data visualization is a means to an end. For us, it's part of the discovery process. So we we we start with what are the what are the business goals and business challenges. Now what data do you have available and then let's look at that data and see if we can find any patterns in that data that might be able to reveal to us some answers to those questions. But it's not until you start digging into the data that we will know whether we have something on our hands or not or something that's valuable and that's where data visualization comes in because know good data visualization allows you to see those patterns in ways that you can't see. Just looking at a table for instance.
Lea Pica: [00:10:01] That is an excellent point. It is so true you can get close to the data and you might be able to read in a certain way by conveying that in a really clear way. A table is simply not going to do the same as a graph and a well executed graph, I would add to that. So in working closely with your analysts and marketers what are what are they getting right in terms of communicating the insights they're finding?
Aaron Maass: [00:10:29] I think what they're getting right is that finally data has come to the forefront in terms of being accepted as a way to prove an hypothesis or a way to find answers or a way to optimize spend or a way to increase revenue. So that's what I think is right. But how that happens I think there's still a lot of there's still a lot of room for improvement.
Lea Pica: [00:11:02] I see. OK so tell me more about the room for improvement that you see. What would you love to see being done better?
Aaron Maass: [00:11:11] So I was interviewed recently by the Digital Analytics Association and this sort of came up in the interview. It wasn't too long ago when I felt like we, as some practitioners in the digital analytics industry, had to twist arms just to get attention for using data in effective ways to answer questions or optimize benefit or use it in ways that marketing can take advantage of it. I mean just to get a seat at the table was not easy. And I feel like the pendulum has swung almost in the complete opposite direction to the point where now it's not a problem getting attention for data but it's almost like the perception is that data can be some sort of silver bullet to solve problems. And you see this manifested in commercials and ads for business intelligence software and and and even in job descriptions, exclaiming that you know you can you can get all the insights you need with the push of a button. Or, hiring a Data Officer to help us uncover groundbreaking insights to revolutionize our business. I mean sounds great but it's not realistic. And does the industry a little bit of a disservice because in my experience the reality is that analysis and using data in effective ways isn't easy. I mean it takes time, it takes people, it takes resources, it takes investment, and it's not something that happens overnight. That's where that's where I think that there's room for improvement.
Lea Pica: [00:13:33] Can you give us an example where an analysis by someone on your team kind of stood. I know I'm putting you on the spot here but was there anything that really stood out in terms of what you were talking about being that it wasn't easy but in a way for me presentation of analysis should make like it looks easy. If that makes sense like the presenter should almost make it look like it was easy. And in that way I feel it allows the audience to really focus on the actual, like the more clear the insights are presented and the more actionable they feel. It feels more effortless I guess in my view. So is there anything that stands out to you in terms of analysis or a case study that you have that really stood out as solving for the challenges that you're talking about.
Aaron Maass: [00:14:36] Yeah. I can give you a few examples but I'm going to start with a moment that was an epiphany for me and it was and we were working for one of our clients who is a large computer manufacturer and the the task was we have all this survey data. This customer satisfaction survey data on our site that's been running for years and it's not tied to any of the behavioral web data that we have. They were using Adobe Analytics and I forget the survey platform they were using but essentially they had the behavioral data and attitudinal data and they wanted to merge that data and see if there were any insights that we could uncover. And it was a pretty broad ask you know typically we were with our clients to define a little bit more clearly what is it that they're looking for. In general we knew what the business was about and what they were trying to do. But this is sort of like a blue sky type project. So I mean I have to admit I'm not a Tableau expert but I know enough about Tableau to know what is capable of.
Aaron Maass: [00:16:11] And so I work with one of our analysts who is a Tableau expert but didn't necessarily have the kind of experience where she could ask the bigger picture type questions and so as she was putting together a deck for this client on the results of this analysis using Tableau to build the charts I stood over her shoulder and we were looking at the data together and she was showing me the results of the merge of these two different types of data we merged on the survey complete I.D. for the survey series were being passed into Adobe Analytics that allowed us to merge the two datasets and she was putting the data together into some charts and I asked well what is it..If you were to look at page views which is probably the most boring web metric all, and and customer satisfaction what comes up? And so she said Well I don't know, let's see. So she dragged her Web metrics into the demand that that dimension into the charts and also the customer satisfaction results into the same chart. And what appeared was OK we've got a section of this company site that has the highest satisfaction but the lowest number of page views for that section which in and of itself isn't that interesting. But then the section of the site that had the lowest satisfaction had the highest number of page views and that was like on almost too coincidental.
Aaron Maass: [00:18:07] Was there a relationship? So the next thing we did was we went to actually look at what the user experience was on the site. The section that had the highest customer satisfaction was the consumer section of the site. They sell like consumer laptops. And so it should not be a surprise that that section of the site was designed with the consumer in mind. Here's a product configurator a comparison of different features and benefits of these products. There was a very clear where to buy or how to buy Call to Action. So you know the things that you would expect to see in a consumer oriented website. It turns out the section with the lowest satisfaction and the highest number of page views was like the server section or the tailored to more commercial applications and ad section the site had none of those features. Instead had very technical PDF like downloads like text backs for the different products. There was no there was no clear Call to Action on how to buy those products. There was no tool that helped you compare the benefits of each to those products. And so you can infer from the results that it was such a poor experience that people who were looking to buy products for that end use, the commercial products were clicking around looking for more information and not finding it and driving up the number of page views.
Aaron Maass: [00:19:50] So I mean if you were to step back and just look at the user experience it's sort of like a no brainer. This is an ugly site and I can't find anything. It should be obvious that it needs work. But in this day you've got big companies with distributed departments and different budgets and global organizations. There's not a lot of time, you know resources are stretched thinly so stuff like that can slip through the cracks. It takes an analysis like that to reveal where the opportunities are, to reveal where the problems are that need fixing. And the moment for me that was the epiphany was combining this the subject matter expertise of the analyst who knew how to use Tableau. And and my bigger picture knowledge of the company and the website and the products and what might be happening or explaining certain behaviors that we were able as a team with this insight that then we could turn around and deliver to the client.
Lea Pica: [00:21:06] Wow that is a really fascinating case study. And it's interesting how you talked about the role of a team bringing that together. What are you what do you think are the different complementary skills of each teammate that allowed that synthesis to happen?
Aaron Maass: [00:21:24] I think that the best insights come out of collaborative experiences, collaborative projects where you have a diverse array of people in the room with different expertise and experience and skill sets. I think it's not realistic to think that one analyst sitting in a queue by themselves can somehow be expected to pore through mountains and mountains of data and come up with groundbreaking insights that are going to be game changers for a global organization in two hours or whatever it is the time that they given to find you know find some insights know in my experience like magic happens when you have people with the right skill sets pulling the data and visualizing it in a way that allows more people in the organization to see what's going on and give them an opportunity to think about what's going on understand what's going on. I think it's a job of the analyst not just to create these visuals and present it but also to help stakeholders understand where the data comes from and what it might mean. Maybe not the I don't think they could be expected to come up with the insights themselves but at least explain enough about the data to stakeholders so that maybe together as a team insights can happen and insights will be discovered.
Lea Pica: [00:23:15] I could not agree with you more on the aspect of collaboration. What you're talking about actually brought up a a fond memory of an analysis that I was asked to do and I chose to loop in one of my marketing cronies and I remember sitting there with her and we were combing through data together and finding all of these interesting nuggets that many of them were only possible because she had her unique lens and perspective that she was bringing to join with mine. And when we presented that final product together to our stakeholders it was one of the most talked about presentations for months after that. And what's interesting is that a lot of times I've done an analysis on my own and then presented to a group a finished product but some of the more interesting meetings that I've had are where we call them expeditions where we would just project Tableau on a screen and I would start with three major question points saying I wanted to know this this and this and I show them my visual. But I said now where where can we go with this? Where would we like to explore? And it really facilitated some fascinating dialogues. So I'm all about the collaboration, I love that.
Aaron Maass: [00:24:33] So that's a great I'm going to just piggyback off of that and in turn our conversation. I know we talked before before this recording that you'd like to talk a little bit about what stakeholders can ask of analysts to get more value from the data and from data visualizations.
Aaron Maass: [00:25:00] I would say that if you're a stakeholder and you get an email with a dashboard or a report as an attachment or you're in a room like you described, Lea. And someone's presenting data to you, if you don't fully understand it and I don't and I would argue that you kind of need to fully understand it in order to be able to take any kind of action or come up with any kind of insight from it, then I would highly recommend that you ask of the analyst what their process was in getting there and building the report. Where did the data come from? How is it collected? What tools did you use? What does it mean that this data is here and then now it's being visualized and then like if you understand the process and how it was collected and how the data was presented then that sort of provides a little bit more context on how you might be able to use it.
Lea Pica: [00:26:02] Yes. So I'm I'm glad we've segued here because just to take a step back you know what this show has covered mostly are what are the things analysts can do to present their information in a way stakeholders can extract the most value from. But like you said and for talking about collaboration, I think the most successful leveraging of insights comes when there's a collaboration between stakeholder and presenter analyst. So let's start with those three questions: What are the questions that stakeholders aren't asking analysts about the data that could really transform the value they're getting from it?
Aaron Maass: [00:26:42] So when I started Maass media 10 years ago my very first client was a bit of a mentor to me but also I can credit him with helping me float the company from the beginning by sending us work and sticking with us through many of those years.
Aaron Maass: [00:27:11] He taught me something that has stuck with me ever since. And that is because sometimes you know I have to admit even when I fell into the trap of you know finding data that I thought was interesting but didn't really mean anything or that you could do anything with. And and he would often challenge me with asking three questions “What what and what?” What's happening? Like what is this, what is going on in the data here. And that's sort of like what I just described you know and suggested that stakeholders do is to ask what. What does this data mean and how is it collected? And what was the process that used to get here? The next question once you understood that what's happening is to ask well what does it mean this data? Like why is it important? What what could I possibly do with this? You know, tell me how this is significant. And that really puts it back on the analyst to try to explain why are you why did you take the time to show me this data? And I think that analysts have an obligation to think about that question first before they present any data to stakeholders. But I do think that stakeholders should be challenging analysts with that question. And then the third question should be what do I do next? Now that you know that this is what happened and that this is more than just an interesting phenomenon but that it actually means something to my business potentially, what might I do next about this? What actually might I take as a result as little as three questions and I think you might get a lot more ahead of the data. And your analysts and the data visualizations they present to you.
Lea Pica: [00:29:06] Those are really interesting and what I would add almost to the end is addition to and now what is, as a stakeholder I want to know what am I leaving on the table if I don't do what you think is best or if I don't take any action. Has that ever been incorporated like looking at the possible opportunity cost of not acting?
Aaron Maass: [00:29:31] Right. Yeah no exactly. Data's can be essentially used in two ways one for a for-profit company to save money or save costs and time or to make money. So it could be either one. But you know if I'm being presented data then you know I want to know how I might use it in one of those two ways. And I will say so I've been in the stakeholders shoes before. I worked at Dupont in an e-marketing role and I worked at Comcast in an e-commerce role and I've worked with many agencies and have seen how you know there were times when I might sit on a two hour call and go through an 80 page deck of charts. And then at the end I'm still left wondering. OK like what did this mean for my business? Did I get any more sales out of it or more customers and and what can we do next about it? And that should never happened.
Lea Pica: [00:30:42] Right. Are you finding that clients are asking these questions or is this something that they themselves need to be trained on?
Aaron Maass: [00:30:52] Very good question, both.
Aaron Maass: [00:30:59] I do think that stakeholders and companies and executives ought to stop and take the time a little bit more to to ask those critical questions about the data and and what they're being presented with by their internal resources and their agencies.
[00:31:22] I know that it's there's so. Okay so as a practitioner and as a consultant we have an obligation, a responsibility to deliver value to our client. Right. And to me that doesn't always just mean doing what was asked of us and doing it well. It means going above and beyond, anticipating some of the questions that weren't asked because I mean at the end of the day we're the experts right. I mean we're the experts in the tools and the technologies and the data but were not experts in their business right, for instance. So that's where I think the whole teamwork and collaboration comes in is you know it's tell me stakeholder. Tell me what is it that you want to know. Tell me your pain points. Tell me what's a problem for you that you'd really love to solve or what would really add value here. Like what are your goals for this quarter or this year? And then now it's figure out how we might be able to solve those problems or answer those questions with the data. So it's just taking the time to stop and think about those questions. Ask those questions that I think needs to happen a little bit more.
Lea Pica: [00:32:51] I completely agree with you. And you know when I when I take my workshop students through a methodology called the Pica Protocol, it starts with questions always and some of those questions are kind of looking at your stakeholders more than just a person you report to. But it's someone where you're actually getting invested and what will make them successful. Like what is hot on their plate right now? What is keeping them up at night? What's their biggest hurdle right now and what would make their quarter be exceptional? What would make their bonus? What would max out their bonus? You know like in thinking of those ways and then you're acting as a support system to make them successful which is essentially how I see the role of a person delivering that information. I want to know your thoughts are on that,.
Aaron Maass: [00:33:45] Let me give you another example. So I was working for a large chemical manufacturer and there was a product that had a site and an important to selling this product was color. Color was very important and there were about 150-160 different colors that this product could be purchased in and there was one page on the site that had one inch by one inch picture of the color, a sample of the color, all of the colors appeared on this page in four columns. So you can imagine to put one hundred fifty colors on one page that was only four columns, you'd have to scroll pretty far to see the colors.
Aaron Maass: [00:34:42] Which you'd think doesn't present the best user experience so the so the thought was we should, you know let's redesign this page and and let's pull some of the data to see what is being clicked on and what colors are important and then let and which are most profitable and maybe put those colors above the fold. So we we pulled the full data on two years of click data on that page and we what was interesting is we found that the most clicked colors were actually pretty well below the fold which was interesting. So it's interesting right. But what can you do with that? You know that's one of the what's: What can you do with that information?
Aaron Maass: [00:35:29] Well for one we thought we'll hold on maybe maybe. Not sure whether we need to redesign this page now but the company was a six sigma driven company. And that's a business methodology for eliminating errors and using data to deliver results in a very scientific way. If you're not familiar with Six Sigma and a black belt was assigned actually and started digging into the data and we pulle, we decided that we were going to pull sales data for the same period and see if there was any correlation between clicks on colors and sales of material in those colors. And what we found was that there actually was a pretty high correlation between clicks and sales of those colors three months later. So now that the now that the data is in the hands of more people who are thinking more broadly about the business.
[00:36:39] It was determined that if clicks could be a leading indicator of demand, three months ahead of when a sale would actually occur, then maybe clicks could be used as a factor in helping to determine how much material to manufacturer. This company had a team of economists whose sole job was to develop models to forecast how much material to manufacture. And this new metric, which by the way is again one of the most basic metrics, flicks on images of ended up becoming a key factor in helping to fine tune this model and help the company save close to 5 million dollars that first day. I'm sorry year, first day. 5 million in the first year though which is very still.
Lea Pica: [00:37:40] Yes. That is really fascinating so it really always comes down to the context of the business.
Lea Pica: [00:37:50] You know a North Star metric I'm sure you'd get laughed off the stage at an analytics conference saying that clicks are a North Star metric but in this particular case they were and I love that you're team kind of got to the bottom of that and wasn't afraid to focus on that and ended up putting something in place that really served your client. I think that's awesome. So one more question before we move on to the next segment. You know as an owner of an agency where you are hiring and training analysts, how is the dissemination of insights the communication of insights, how does that factor in to the culture? Are they getting trained for that. Are they kind of learning on their own. Are they learning by experience. Can you speak to that a little?
Aaron Maass: [00:38:41] That's a good question. But first just for clarification I don't own Maass media anymore. The firm was acquired by Hero Digital four months ago. Now I run the data you know parts of the data and insights practice for Hero Digital, which is based in San Francisco. We're still in Philadelphia. But as far as training analysts to ask those questions and deliver those insights. it's an ongoing process and it takes coaching. It takes persistence and the the model that I use is again a team approach. So on every project, on every engagement, on every client, we pair an analyst with a junior analyst generally, with a more senior person on the team. And that way we have some senior level supervision of the the junior analyst. Who then is coached on what to do with the data and how the data might be used in novel or innovative ways. And I found that without that pairing it's very like I said before we were talking about it earlier on. It's very hard to come up with insights in a vacuum. When done right, it's a lot easier to do and in a collaborative environment. And so I think it's very important to have at least two on every project someone a little bit more senior who can be asking questions.
Aaron Maass: [00:40:41] Ok you just pulled all that data you're about to send that report to the client or to your boss now before you do take a stab at interpreting it first. They could ask some of the questions that you think the stakeholder might ask and they should be asking questions, probing questions about what it means and what they might do with it. Ask those questions first and try to come up with an answer on your own and then deliver those insights, deliver your interpretation of the data along with the data itself or with the data visualization itself so that you know your stakeholders have something to go on.
Lea Pica: [00:41:22] That is such a fascinating strategy. I think that's so great. I mean I remember joining certain projects as sort of a budding analyst and feeling kind of stranded. Where I was hoping to just come up with some stuff on my own and hoped it flew with the client. But the really more successful ones were the ones where I did have intervention and collaboration with a senior person who was closer to that client and was able to act as a proxy for them essentially. So the finished product would go much further. I think that's fantastic.
Aaron Maass: [00:42:00] Yeah I'll give you just another example that sort of highlights how important that is, collaboration.
Aaron Maass: [00:42:07] Last November, Strava, the fitness tracking company. I don't know if you do biking or running and you have a Fitbit or fitness tracker you might use the Strava app to to to track your run or your bike ride and it maps, Strava will map your course and then there's an option to to publish your run. And millions and millions of these runs have been uploaded to Strava. And in November, Strava published all of their data publicly. I think it was something like 13 trillion G.P.S. data points were published online for everyone to see. That was last November and it and so all this data was out there for a couple months and it wasn't until January of this year, a couple of months later, when a graduate student studying international security came across this data and from his perspective of international security, he discovered that within the data, soldiers and military personnel around the world had been uploading the routes where they run and bike from their bases so you could almost literally trace the outlines of individual military bases around the world. And he tweeted this out. And so if you think about it.
Aaron Maass: [00:44:09] So I think the military got involved in and Strava said they that they would redact some of that data and take it down. But if you think about the U.S. military and all of the resources that it has at its disposal, intelligence and you know all that they didn't discover that. It wasn't them who you know who uncovered this major security risk. It was some graduate student who just happened across the data, who applied his own learnings in the right context and in many ways his kind of luck that he found this. And it wasn't until the data was published like that and this guy founded the doubt that that was discovered. It was like. If you don't if you're an organization and you're sitting on top of all this great information that you don't you might not know what to do with it. But someone else might. So it would you know I think it's in every organization's best interest to get that information out. Democratize it to as many people as possible and you might be surprised how many insights might come out of doing just that.
Lea Pica: [00:45:34] That is an incredible story. I'll have to look that up. But you're right, the more lenses are on something the more you see. That's just a fantastic example of that. Thank you for sharing that
Lea Pica: [00:45:54] So Aaron. I call the next segment the upgrade which is a power tip for doing our jobs of presenting data sharing insights more effectively. Is there a resource or a tip that comes to mind that you found really helpful in your journey of learning how to present information?
Aaron Maass: [00:46:12] Less is more is one I think, trying to look at the data through the lens of the consumer of that data how it might be interpreted is important. And to layer on top of any did it data visualization, your commentary, your opinion. And in terms of actually presenting the data on a slide, for instance, one of the tips I learned a long time ago that, I mean I could be wrong on this but, the titles of my slides are usually the conclusion that I want people to come to or at least lead with.
Lea Pica: [00:47:04] Fantastic. Yep I'm totally onboard with that one. I believe I heard it called a McKinsey Title. Where, instead of stating what the visual is, you're actually stating the conclusion that you've come to as a headline of sorts and I think that's that is an excellent tip. It bears repeating. So this is the final question. Think hard imagine this very plausible scenario: you're sailing the French Riviera when suddenly your boat gets caught in a whirlpool and it pulls you back to the moment you're about to walk into your first presentation. What would present day you say to you yesterday you?
Aaron Maass: [00:47:50] Present day me, first of all, I like your analogy. Can't say I've ever come across a whirlpool though. You know what, the present day me would say to stop and consider all of the available information first before drawing any conclusions. It's sort of like, you would never navigate a boat around the world with just one instrument with just a radar or G.P.S..You know you would always have multiple instruments supplying you with information simultaneously. Weather patterns and all that, plus you know observations that you make yourself on what's happening on the water. And in terms of like using data to come to conclusions and develop insights, stop and ask as many questions as you can from as many different perspectives, before presenting any kind of conclusion. First before you know as you see that time and time again this is you know it's drawing conclusions without having, it's like that parable with the with the elephant in the five blind men.
Lea Pica: [00:49:21] Yes. Yes that's a that's an excellent metaphor for that. Yeah. It's actually I love this tip in the third step of my protocol. It's all about context and asking all of those questions. Is there anything I'm missing that's going to not allow me to present a full picture or make a truly informed decision? And I don't know that we can ever possibly ask every question possible. But you are right. A lot of times I've found myself taking the first thing I saw at face value and creating a conclusion about it when in reality there was definitely probing that would have shown me a different story.
Lea Pica: [00:50:08] Well this was so interesting. Thank you so much Aaron for donating your time to this show. And fortunately now it has run out. So please tell listeners where they can keep up with you and anything they should look out for.
Aaron Maass: [00:50:26] Well first of all thank you Lea. This was this is great and I can't think of a better way to spend my time than be interviewed by you.
Aaron Maass: [00:50:38] To stay abreast of what I'm doing and what my my my company is doing, you can go to HeroDigital.com. I often write content that gets posted there and also you know social media channels. I'm not as active as I should be but I do post from time to time and then I attend many industry events as well.
Lea Pica: [00:51:32] Exactly I hope so. So thank you again Aaron. Really. I'm so glad that we were finally able to get you on this show and I think your perspective of being so close to the data itself and how it's being transmitted back and forth between analyst and stakeholder was really valuable. So thank you again. And I look forward to running into you very soon.
Aaron Maass: [00:51:56] Anytime. Thanks Lea.
Lea Pica: [00:52:02] Another awesome interview. Aaron certainly had so much to offer and his huge breadth of knowledge around presenting things to clients to help them get to act and helping to nurture analysts with exactly how to do that. So I hope this was valuable for you. And I'm really loving how the theme for this year is all around questions and curiosity. So to catch all of the links and resources mentioned in this episode. Visit the show notes page at leapica.com/042. I'd love for you to leave me or Aaron any questions or suggestions because we want to hear about the challenges you face when presenting information. And if you like what you've heard hop on over to iTunes to subscribe, leave a rating, and please, leave a review. They are so appreciated because they affect the rankings of the show and I'll be reading out my favorite ones on future episodes. The show is on Spotify as well so if you're a Spotify listener you can definitely find me there and today's inspiration is from Voltaire. And that is “Judge a man by his questions rather than his answers.” That might be my quote of the year. You know from this episode and from my previous one on the Conscious Critique where I strongly suggest staying curious through a critique process I think curiosity is the theme for this year for me.
Lea Pica: [00:53:39] Staying curious and asking questions you know your world changes when you remain in a place of curiosity and I can't wait to watch how your world changes as we go and navigate through this world of data storytelling together.
Lea Pica: [00:53:56] That's it for today. Wishing you an awesome rest of January. Namaste and Nama-go.
