#042: Data Storytelling with Brent Dykes

August 2, 2016

Once upon a time, in an industry near and dear, lived an analyst. And that analyst needed to present the results of her analysis to a big, scary, business user. This is not a tale for the faint of heart, dear listener. We’re talking the Brothers Grimm before Disney got their sugar-tipped screenwriting pens on the stories! Actually, this isn’t a fairy tale at all. It’s a practical reality of the analyst’s role: effectively communicating the results of our work out to the business. Join Michael and Tim and special guest, Storytelling Maven Brent Dykes, as they look for a happy ending to The Tale of the Analyst with Data to Be Conveyed.

Tangential tales referenced in this episode include:

Show Transcript

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. We apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

[00:00:28] This is Episode 42. No big ole buzz word in our industry right now is storytelling. It’s really important. Further using data to tell your story it’s a critical skill for the analyst. Seems pretty easy but so few people do it well. On this episode of The Power Hour we’re going to talk about it. So take a seat by the fire and listen to us spin you a yarn and who you might ask could help. TIM WILSON My cohost I hate him. Hey Michael. And of course me Michael Houweling hold forth on such a meaty topic. You might know him as the author of web analytics action hero for 12 years he was a leader in the consulting organization and analytics evangelist that Omniture slash Adobe. And today he’s the director of data strategy at Doumar where he hopes customers with Domar adoption and data usage and here also writes for this little magazine called Forbes Yeah it’s Brint dykes.

[00:01:31] Hey welcome to the podcast. We’re excited to have you. Thank you. So this topic around data storytelling.

[00:01:38] Right. It’s I think it’s near and dear to all of us certainly you’ve spent so many years championing this cause but maybe to start us off. Let’s talk about sort of some foundational or definitions around data storytelling. What it is and what it is.

[00:01:54] So I don’t find that storytelling as a structured approach for communicating data insights more effectively to an audience using narrative elements and data visualizations. Well that’s awfully succinct.

[00:02:06] Yeah great show. We did it. So

[00:02:10] if you just write that down I think that’s probably what we should go with. But how do you feel like I feel like there are people who say data storytelling but aren’t really thinking through I mean that I guess maybe the narrative piece is where a lot of times I feel like people said oh I had 22 slides so therefore they were in a sequence and a narrative is a sequence of things therefore it was a narrative in a very much was not. I mean to me it seems like storytelling is one of those things that is just really difficult to really have a narrative thread that is things linked together and yet most people just label stuff as sure I’m new and better storytelling.

[00:02:49] Yeah I would say I would say a lot of people kind of look at data storytelling. I mean a lot of work’s gone into it from the data journalism side. And so you’ll see a lot of that where a data visualization may be called a data story. And I think in some cases a data visualization could qualify as a data story but not always and in many cases it won’t. And there’s like five key elements that I see you need to have in order to have a data story. So first of all you’ve got to have a Mooncoin right. You’ve got to have a destination you’re taking your audience to and sometimes as analysts what we like to do is sometimes we find these really cool things and we start like just you know as often as Chicasa like to say day to Putin. And I really like that generally I don’t like thinking of vomit all the time that I have. But you just basically load up on all these really cool insights but really you’ve got to have it. I mean when you’re trying to convey to your audience and I think that’s the first thing the next thing is you know the story itself has to take not a descriptive approach but a explanatory approach.

[00:03:55] And so you know when we’re describing things we might say well you know we’re really focused on the who what and when but when we’re explaining something we’re going to a little bit deeper and we’re trying to help the audience understand what’s happening in the data and why how. And I think that’s a key differentiator between you know I did a story and maybe just a presentation of the visualization. So that’s that’s the key thing. And then the third thing you kind of mentioned there already where it’s got to have you know a story has kind of a linear sequence you know so if you think it’s like the Wizard of Oz. Right. So there’s like first you have like this tornado touches down and then Dorothy gets caught up in that the House lands on the things the Wicked Witch of the east and then she gets the shoes and then she goes on this yellow brick road calf which she makes. She meets lots of interesting characters. She goes seeking the Wizard of Oz and then he says you know if you want to go back to Kansas you don’t have to go kill the witch. Now there’s a sequence of things that occur. And I also think that you know as we’re telling this story as we’re at our data and trying to get to our key points or our main point. There are some data points along the way that we need to share to either set the context or to connect the dots for the audience and then the fourth element is all these narrative elements.

[00:05:18] And if you think of the movie you know as an example I’d like to share at the beginning of the movie there’s this little segment and it’s only about four or five minutes long. Very little dialogue at all. But you know through this interaction between Carl the old man and his wife we kind of see this story. You know we learn something about him and we learn about his struggles and challenges that they went through and everything and then when his wife died. And so we cut that some of them are who we just think this guy is a jerk. You know we wouldn’t think of him as a lovable kind of character and understand some of the context. So if I take that to their story we as the storytellers of the data you know are inside we have to kind of set that context we have interviews the characters we have to set the plots and then setting them kind of set that up so that you know people that aren’t as familiar with the data and they come in and they can appreciate the story. They can jump in and we can give them exactly what they need to hear in order to make that decision and take that action. And then the last piece you know in the piece that gets all the attention is the visuals and you know the visuals are obviously a different story. Data is very complicated you know and it can be overwhelming for a lot of people that aren’t as familiar or aren’t in the data all the time.

[00:06:43] And so our job is to go in there and to take those data visualizations and then actually look for how can we convey an insight in a meaningful way that you know a lay person who maybe understands the business domain but doesn’t understand all the facets of the data. You quickly kind of come to a conclusion make a quick comparison and a data visualization and appreciate the point that we’re trying to make. And you know those five elements are present in a data story. And you know one of the questions you might have is you know does a dashboard convey a story in it. I think it’s curious that yes you could be curated dashboard so that does convey a story that an automated dashboard perhaps in many cases is just a collection of random data points is not a data story itself it could be a platform for something to construct or create a data story or identify data points in the data and then go and build a day.

[00:07:36] So if that’s what it feel like the dashboard is descriptive and it shouldn’t be you know random data points. I agree it’s not a story but a dashboard should be. How is the weather today. You know is it raining or is it sunny. It’s kind of a at a glance fact as opposed to a data story is more trained to get a level deeper into the what happened and what can we do about it. If I glance at a dashboard every day I’m not going to remember unless I’m some you know strange savant of some sort. I’m not a room with the dashboard said. 13 days ago. So it’s kind of meant to be at a glance. Are things good bad or indifferent. Do I need to take action to any to Duigan whereas to me part of with data storytelling is you’re trying to actually I think hook into other parts of the brain so that it gets retained so that yeah when you said hey that data’s story you were told 13 days ago or that presentation you saw 13 days ago. I can actually play back. Oh this was the takeaway I’m playing into the other parts of the brain to actually retain it. Yeah

[00:08:44] absolutely. You know story. You know one of the things I frequently say stories statistics you know there’s a couple of cases that are fairly well known out there there’s the one that Chip and Dan Heath the guys who both made to stick. Yeah maybe they didn’t study it. I think what Sampford where they had a bunch of punch students some data points and then told the students OK you got to argue a position on something like gun control or the number one topic it was that they had the students get up for five minutes they kind of threw their pitch using data. And then afterwards you know the students probably thought that the task at hand was to see who was the most persuasive. But actually what they did is they asked the students how many of you can remember any of the data points that were shared in only 5 percent of the students could actually remember any of the data points and then they asked them how many you could actually remember stories that were shared. And only 10 percent of the students actually shared a story as part of the five minute patch. And 63 percent of the students could actually remember one of the stories of a ship. So that’s from a memorability perspective.

[00:09:49] That story is going to outclass statistics on that from them from persuasion perspective. You may have heard of the study that Worton and I think the number a couple of other universities did where they it basically had the Save the Children charity basically funding African kids and trying to help them and they did a study where they they took one version and they took all this data on all the struggles and challenges that people in Africa had and then built kind of like infographics you know like a brochure that kind of highlighted you know people in Angola are struggling.

[00:10:24] You know these kids don’t have clean water.

[00:10:26] And then in Mali you know other kids are you know 37 percent of the kids. Whatever the data points were. And then what they did on another group they created a different kind of test for they just took one kid and I think her name was Rocchia.

[00:10:40] She’s a little 11 year old than Molly and told her story.

[00:10:45] And they told her struggles and what she went through. And so they had two groups of students dividing them up had them take these two or exposed them to these two formats and then in the end they asked the students you know you’ve heard about this charity. You know we’re paying five dollars you know five dollar bills for for doing this survey. Would you like to contribute to the charity and then they found that the ones that had the info graphic version they don’t need a dollar forty three. But the the ones that saw the story of Rocchia they almost doubled the amount they contributed. It was two dollars 38 cents.

[00:11:21] So there was another group that just had Sally Struthers doing a voiceover and those people demanded that they get more than five dollars after getting more money back a bit more.

[00:11:29] Yes so some memorability in a persuasion perspective. Storytelling is really powerful. And so if we just leave statistics on there I’m not going to work as well. When we combine the statistics with a story that’s we’re going to have an impact now.

[00:11:45] And actually you see this played out even in the political arena right on the politicians giving speeches will often pull one story out as an example of like their broader policy or let them know. It’s pretty pretty common. You know it’s interesting because it’s it’s hope for all of us artistic types trait that that’s such an important part of analytics. You know and you think oh yeah the data is so important in your ability to manipulate the data. But you know if you’re listening and you went to a liberal arts school that you can be a great analyst because of those things that you were just discussing.

[00:12:21] But when we talk about a story of it tutor friend two different sort of things that I that I think challenge me one is when you’re telling a story and the Save the Children is a good example where what you’re doing is using an anecdote and with the data that could be a real customer or it could be a an archetype of a customer persona or however you want to make them tangible just like when you know you’re developing personas. I mean that’s kind of one way to go which is the data supports this and now I’m going to kind of weave in a very personal not personal sharing your feelings with personal trying to make it very tangible and real with the example. Is there another type of story though that isn’t necessarily the it’s not the individual it’s not the customer it’s not the anecdote it’s it’s more. I don’t know it can be larger portions of data. You know this this thing happened then because that happened you know what are you going to use a maybe a horrible example you know revenues down.

[00:13:23] That’s that’s kind of the the crisis well we dug into the data and we realized that average order values down. And we’ve dug in further and realized that our lines per order is down. Our revenue per line is fine. So it’s classic analysis where you’re kind of trying to break down and that’s not. That’s certainly not how you tell the story because that’s in reverse. But then can you tell a story where you say look let’s talk about what goes into revenue well it’s how many orders. You know what we’re getting the same number of orders. Is that a valid type of story as well. Or is that still kind of too clinical and data or is it really kind of in the presentation if done well with appropriate visuals you can kind of help people understand the business better because you’re illustrating the model and putting numbers behind it.

[00:14:07] Yeah I mean I don’t think there is necessarily one approach you know and I would say the first rule of data storytelling is understand the audience right. So it’s you know that your audience would respond to one type of story format and not to another go with what works for your audience that’s that’s pretty important. But yeah I mean there’s different elements of storytelling. One one is the classical has to have a hero or somebody who represents you know some Rocchia was the hero story essentially and Gore Karte abandoned their person that you build a data driven persona around could be that hero of your story. But that I don’t think you necessarily have to be locked into always having somebody represented as a hero in your story. I think there are elements of storytelling like surprise and suspense and different things you can incorporate into a story you know and to get them engaged in the data. You know it might be more like good good to go back to your example. You know how do you think Rezvani really comes together now we see a drop in revenue or all the different ways that can happen. And you know and maybe it’s you know it’s part of your audience like how deep do they know the data. Now the one thing I think we have to be cautious about is what I call the analyst’s journey.

[00:15:30] So that’s that’s going down the path of well first I looked here and then I took the data and I live your aggression and then that could reveal anything. And then I got this other data. When I combine that with this one then that’s when it’s like who cares.

[00:15:44] Is the story of me it’s a story yeah nobody cares about the story if you don’t give them the book give them the movie right. Yeah but take the Wizard of Oz. I feel like there are times yeah.

[00:15:56] Everybody’s seen that movie a few times where it feels like you are really just bouncing along with a bunch of adventure and excitement and events. But the real I mean you’re you’re really kind of waiting for them to realize that oh people are getting self realisation about themselves. But the real kind of climax is that the wizard behind the curtain. Sorry I didn’t say spoiler alert for anyone who hasn’t seen The Wizard of Oz boy.

[00:16:22] We’re going to get letters.

[00:16:25] The tension between you know when do you want to have the reveal. I feel like that maybe it’s it goes hand in hand with giving the analyst journey and telling yourself that’s the story if you have an hour scheduled and you’re 45 minutes in. You either have to really have them hooked and interested if you haven’t actually given them the answer. So how do you balance that. You want to tell a story you want it to resonate you want to have some emotion and tapped his parts. The brain because you don’t want to not give them the answer so far into the meaning they might have gotten up and left for one reason or another.

[00:16:59] So again it goes back to knowing your audience and I think it’s a psychosexual head maybe a year or so ago where what do you do with that executive is just like give me the numbers you know give me the data give me the insight and there they’re not necessarily as patient for that story to unfold into the big reveal. So in those cases you know with a lot of executives their time compressed their give it to me kind of mentality. And I think that’s where you shift to more of a trailer as a movie trailer instead of the movie. So you give them the teaser of the trailer you give them the information. You know like I’ve done this analysis and I found that I’ve discovered a two point two million dollar actually for marketing campaigns. You know how we can optimize them and give them a little bit of a setting a little bit the set up and that’s where I kind of say you know what you’re asking for that one is permission to tell a story. And if they want to hear that story then they’re like Tell me more. You know what. How do you how did you get to that number. Oh OK well let me show you the number you know. Now you’ve got their attention. They give you permission to to take more of a story approach and then you know you haven’t got engaged audience at that point.

[00:18:10] Of course that’s that’s a tall order for somebody who’s two or three years into their career and they finally get an audience with the executive and they’ve sweated bullets and they have put together 75 slides and what you’re. I mean I agree with you that it’s what you need to be flexible and you need to be. You need to have your strongest opening and you need to have a couple of different ways that it can go like I know I’ve found myself doing that and saying well this is fundamentally going to go one of two or three different ways and let me kind of think through how I’d want to run with it because I sort of know what the destination should be regardless as opposed to you know no dammit I’m going to tell you my story. No I gave you the punchline and you didn’t. You were supposed to ask the Tell Me More question and you didn’t so screwed. I don’t know is it just come with with practice if you go in thinking you’re going to try to do that and then it’s just basically critiquing after the fact looking into what you could have done differently.

[00:19:04] Yeah it is if I was in that situation where I had that bit know my first fake moment a big opportunity with that executive there’s no way I would go in there guessing on anything I would be talking to there you know trying to get an audience for their direct reports to kind of say hey I’m going to be meeting with Nancy. And Nancy you know it was my first big presentation and you know she asked me to do this analysis and how you know so you’re going to be talking to her reports and saying you know how do you think she’d want to see this or how does she typically take data. You know I mean maybe even crafting here’s what I was thinking of showing here the data and then and then her reports might say Yeah now she’s going to want to see this and that you know I really did rest a lot of a lot to rest on that one opportunity and you know I’m not the best of a lot of work. I wouldn’t want to just you know screw it up. So you know I think the key thing is to understand your audience. You know and I think you know I had a recent experience where I had an executive telling me how this you know our CMO would want to see things and got his advice on how to structure the presentation and how to handle things and the presentation went great. So you’re getting that insider information is really critical because you know I’ve also screwed up. I’ve also found into presentations and I’ve taken the wrong approach.

[00:20:24] And then I’m either backpedaling or maybe modifying on the fly or you know or doing the Homer Simpson. I just I just miss my my window of opportunity. And now for me that much harder the next time I trampers that something.

[00:20:37] Yeah and for situations where you’re may get surprised it’s always good to have sort of that elevator pitch or I was told people have a 30 second five minute and full version. Why. Because then you know if they’ve got oh hey what’s going on with this blah blah blah. Oh well do you have a couple of minutes and then you can give him the five minute version of there. You know we’re just in the elevator to get to the door opens to do the 30 second version. But yeah. So there’s a lot of times when the story that needs to be told isn’t just one person or one group’s data story to tell. In fact probably most large companies. That’s true. So what have you seen. Working well and if I’m accidentally teeing up like a softball for a Duomo pitch just just avoid that. What about when your story is only part of the bigger story. What have you seen or what would you advise people to do in those scenarios to craft sort of the the broader story or the. I don’t know. It’s a matter of narrative but the overarching narrative of the of the business and what should be done.

[00:21:46] Yeah I mean I think it’s important you know you may have found an interesting insight in the data that you have access to and if your spider senses are tingling that this is bigger than just you know the day that you’re currently accessing them then that would be where I think you got to be careful sometimes because you can get really excited about an insight that you see in the data which can then be easily countered or better explained in another dataset. So before I even crack the story at that point I’m really still exploring the story. Do I have a story. Can I stand on the story. Has this insight that I have really something that we can run with and it also goes back to action you know to think about you know if we want to take action on this insight is that across multiple teams working collaboratively on taking action on that insight or is it isolated to the one scene that the data that I have is influential on them. So I think those are some of the factors that I think of as do I have a sense that maybe this is bigger than just I or I need to confirm or bring in additional data to enrich the story I’m telling and then I’ll see what level of my telling the story and if I’m if I’m looking at marketing data and trying to influence marketers to do something then that may be sufficient.

[00:23:05] If I’m trying to change a process that the company has terms in how we operate which touches marketing but also maybe touch the sales or touches on customer service then obviously I’m going to need to get help maybe from other people where I don’t know that data set and I’m going have to go to an expert for the sales force there I manage to go to an expert for el-Khoury or some of these other systems where I don’t have the expertise. And at that point it may become more of a team effort than just me as the single data storyteller. Maybe it’s you know we’re going to have to collaborate on this and tell the unified story across these different datasets that hopefully then you know influences whatever change action.

[00:23:48] What are the upside of that is that you actually then probably wind up getting some cross functional collaboration and therefore probably you started turning the ship a little bit in some small way with people from different departments who are working together. The flip side is that may six months before you can actually get everybody on the boat to actually leave the dock it does take time. I used to actually run a weekly meeting in my analyst career called story time where I invited specific people from different groups and we would all bring our.

[00:24:23] Story or data. What were seeing and work together to sort of say what’s the overarching message.

[00:24:30] And it was it took a long time for us to realize that’s what we needed to do and that who the right people were from all the different teams was that it was a team of analysts who were kind of validating that you’re not chasing something prematurely you’re thinking it through and you’re collectively building a story or was this you trying to convince you weren’t telling the story.

[00:24:51] There’s a lot of things that can influence outcomes. Right. It could be our brilliant marketing campaign. It could be you know we just got a great product and frankly no matter how you market it it’s going to sell like hotcakes. It could be that unbeknownst to you it’s over there going viral on snapchat. So those kinds of things are the things that. You want to gather people together to let you know. Well what are you seeing from how you look at this and kind of incorporate that all into sort of the over overarching story. And so it just it worked really well because then when we would go into business review meetings were presentations we would have that perspective from that other team to sort of say and what we’re hearing from other people is this is also you know so on and so forth that we can bring a more holistic message together which I think a lot of times certainly in digital can be lacking. You know digital data and you know me Tim and probably most people who listen to this podcast. That’s that’s our specialty. But digital data always has the most for most companies has to be translated back in. And so our ability to be effective within sites has a lot to do with how we translate our stories back into the business metrics. I think those things are really important and useful.

[00:26:17] Any any opportunity to get additional contacts especially when you see something weird in the data that you can explain you know being able to reach out to other people who may have additional contacts that can really help.

[00:26:27] Here’s why. You know an example of this was I was working with a customer and they noticed a drop in their traffic on their site. And this is a high tech company and I went and I was able to isolate it down to search traffic back in the days when we had keywords.

[00:26:46] Oh a moment. Every day. Wait. Going to take a moment of silence or.

[00:26:50] Yeah we’re all pouring out a little bit of our drink.

[00:26:57] And so the interesting thing was this analyzing I was noticing that a lot of this traffic was coming from MSM. And so this MSM traffic had no keywords associated with it which was kind of weird. And so I was like oh OK well all of that dropping your traffic to your site is from this decrease in traffic coming from MSM with no search terms. And I thought that’s kind of weird. And then as soon as I brought that up to the customer they were like Oh. Oh now I remember. Yeah like MSF told us that we had a three trial promotional placement on their portal site for their small business center. And they you know we got some traffic from them from that article or that that placement or whatever and then that expired recently. So they had to contact some of their site. I was just the you know external analysts doing this but I didn’t have the full context for what was going on within their business and they did. And just you know sometimes we don’t win if we don’t have the full context made and maybe you’re at the company event and you didn’t know that PR is doing something on their site with some kind of initiative or page search just rolled out a new vendor and they’re trying some new things and you never know you know unless you talk to people and you’re maybe even sharing the insight. I don’t know why the spike there’s dip or why things change this month.

[00:28:21] You know any idea that you have you know and then once they tell you it’s like Oh now you’ve got a story. You know you’ve got more color to the story that you can share with your stakeholders.

[00:28:31] Yeah. No that’s absolutely right.

[00:28:33] Context is so important for analysis that it brings up the double edged sword if you tell a fantastic story the super memorable and then you realize Chloe related that you’re missing some context than out on unwinding that there’s like well you don’t want it to be you want to be memorable enough that if you reinforce it it sticks.

[00:28:54] But you know in case you have to walk it back well and actually though that’s another challenge that businesses face is influential people and good storytellers not necessarily great analysts. Grab a hold of data and decide they’re going to tell the story and they they start moving people in a certain direction and you’re like no stop. What do you what do you guys think about how do you manage that. Like it’s sort of a governance question but it’s sort of a soft one right. Who’s allowed to go in and give us insights and data stories.

[00:29:32] I mean I I mean mistakes can be made. And if somebody is running with data that not necessarily the right data you know it’s a tricky situation have to look again at who those individuals are what are the repercussions of changing or shedding the true light on something. I mean there was a recent example that I heard where a vendor was providing some metrics to one of our customers at Daramalan. And then we replicated essentially the rules or the way that they’re tracking that data in Douma. And the numbers weren’t the same. And and at that point it’s like oh well I guess Domos got it wrong and then they went double checked again verified. No no no. The way we’re tracking this information is correct. So the customer then went back to the vendor and said you know what we’re we’re seeing different numbers here. Why what’s going on and what happened was they had some rules in place that a few of those rules were actually not updated. And so actually the data that we saw in Tomoe actually shed light on this vendor hadn’t really updated their rules and actually they need to actually go and fix it on their site which is interesting. But I guess the thing you’ve got to do when you’re looking at the data is you know maybe share the insights that you have with that individual that he and see if you can have them you know like talk about the sources how are they getting the data. I mean at the end of the day hopefully these are data driven people and they respect the data and summarizes it stopped it.

[00:31:16] You know this is just an assumption An example would be you know there’s this customer in Japan and they were saying their revenue go down here every year and everybody in this the Japanese division of this e-commerce company were like oh it’s all because of the currency fluctuation that’s occurring you know and basically you know the executive it is in this for it was basically watching people wash their hands of this drop in revenue. And so he actually had an analyst go in and actually do the statistical analysis to then verify you know what is it the currency that’s the factor here. And the analyst was able to return. No it’s not. It’s got nothing to do with the currency. And once the executive had that information he was able to go to his team and say it is not about the currency at this point. And so. That that took away a. And maybe it was a mental blocker in their minds that they were powerless. You know you can’t influence currency to affect what you’re doing. But once he removes that then they were like oh OK well let’s see what’s going on and they were actually able to identify a significant enhancement to their to their marketing performance once they had removed the currency element from it. So I mean I think data has got to be the solution and then obviously you’re going to have different data sets and different opinions and things and hopefully you know you can influence change or Septmonts of the right data by showing you know revealing what’s going on. And it’s you know can be challenging.

[00:32:56] It’s hard to it’s hard for people you know obviously then it becomes personal where people feel embarrassed or maybe at fault for using or trusting or making decisions on bad data. I mean it can be a delicate situation. And so I would I would recommend that you don’t start wielding this sort of data and the truth of the data. You know it’s true that lots of people you know remember that budgets could be shifted based on the data you have. You know people can be embarrassed.

[00:33:26] Power can decrease and these are all interpersonal emotional responses that can be generated to them.

[00:33:34] So I would be careful. And again it goes back to your audience or your audience.

[00:33:39] It’s interesting as we’re talking in the whole I mean it is time consuming great to say untold good story. I’ve got to one be damn sure that that it’s an accurate story.

[00:33:50] I have to craft the story I have to know my audience. I may have to do some trial runs of telling the stories because I’ve edited. One of those things on that accuracy you know I’ve got my system. I mean the classic one take media display media and they’ve got their pixels and we’ve got our web analytics and we’re looking at it. Nobody is expecting them to match it up but the fact is to me I kind of want to collaborate and see if we can come up with a date if we’re off by 20 percent. That’s really not that bad if it’s turning in the same direction. These are totally different data collection mechanisms. I always feel much better when I have triangulated numbers that’s within the web analytics to say well I’m going to look at the data this way now in theory I can make a segment and look at something totally different and it should sort of show the same thing. OK. Once I’ve seen it twice and it’s showing directionally the same thing. I’ve got a lot more confidence. I tend to be the bigger the inside or the aha. The more I want to go find another person to find the same thing with their data. You know it’s kind of the opposite of a single version of the Ruth I want to find three different versions of the truth and say you know what they’re all close enough that we can get on the same page and that’s a good way I think to usually loop those people in and if they say well we’ve been we’re seeing the exact opposite.

[00:35:12] Well then you’re probably uncovering a data issue and there’s value in that too. I die a little bit like we are we can’t really count that as a business when at the end of the day that we found messed up data in our system and we’ve been making decisions on that data for a while. But the fact is finding it is a hell of a lot better than not finding it and proceeding for another year or two.

[00:35:30] So it just took me down a tangent of when people were like no my data is right. We’re not arguing about which data is right. We’re trying to figure out how do we reconcile it so that we’re comfortable that both are right enough that we’re all comfortable with the story that we’re telling.

[00:35:46] No I’ve found that even combining data from different places. So I mean the classic example is leveraging sort of like a voice of customer data set with your digital data to provide a qualitative backbone to a quantitative that is really helpful for kind of get a unity around that as well. Yeah this is a great discussion and I feel like we just don’t. I mean we never have enough time to really cover it but I feel like I could talk about this for about two more days but we can’t. So let’s wrap up before we do that there’s a segment we like to do on the show called last call where we go around and talk about something we’ve found that’s interesting or fun or cool that maybe people want to check out.

[00:36:30] Well if you’re interested in this topic of data storytelling I actually published a really comprehensive article on the subject on March 30 first Tom Forbes and the title of the article is data storytelling. The essential data science skill everyone eats but it goes into a lot of the ideas that I have around data storytelling and and how powerful it can be you know for for anyone really that uses data and people who are using today data today is expanding. You know it’s not just the analysts not just the data scientists but it’s everybody you know everybody down to frontline employees sometimes are trying to use data and tell a story.

[00:37:10] So you’re not by any I mean you’re don’t know the answer to this you’re not slated for any upcoming conferences where you’re going to be conferences or webinars where you’ll be talking about data storytelling. I feel like I’ve seen you speak on it a few times and it’s very compelling. And with visual and audio as opposed to just audio.

[00:37:28] Ray Yeah.

[00:37:28] No I don’t have anything planned but I am always open to people you know still looking for somebody to talk on the subject I really enjoy presenting on it.

[00:37:38] And I would love that opportunity call Tim. So I’ll go a little more whimsical a little older school so the story of mass I kind of stumbled across this on netflix and my youngest my daughter we watched them and she was 10 she’s now 11 and it’s kind of fascinating. It goes all the way back to the discovery of zero. You know how long we went without having zero and kind of early trigonometry and the need to be able to measure nons square areas to figure out land and taxes back you know way back when it’s a four episode series. If you’ve got a burgeoning analyst as a child it’s appropriate to watch with them. The guy who does it is a British mathematician. So is John Oliver would say he is British so it even sounds smarter and it’s fun to listen to but it’s a neat little nerd Netflix time. Nice but what’s your last call Mr. HL1.

[00:38:31] Well I ran across something and I feel like I’ve known about it but ran across it again recently and I just find it constantly amazing and useful.

[00:38:39] It’s a Web site where you can grab market data for anything. It’s called mockery Dotcom. But you just put in a couple simple things hit the button and spit out like you know a sci fi file or something with all that data which is super great for mocking stuff or you know getting some stuff because from time to time you want to do stuff like that. I mean you know work with work with real data.

[00:39:07] Most of the time if you’re making up a dashboard I mean you’re right it like you actually lose credibility when you say oh every one of my little spark lines is exactly the same and you try to tell them that no that is just the mockup if you can throw them.

[00:39:18] Some color. It’s cool fun little fun little tool the camera where you heard about it. Somebody told me about it.

[00:39:26] It’s a very Domar connector for macaroon. That would be I actually use mocker suffer mockups instead. Oh there’s a connector there might be well given. If you want to just share random data within your organization.

[00:39:41] That’s all it does look at this amazing chart. Yeah this is all Raynham. What’s the story of this data chaos. No but it does spit it out on DSV which would be very easy to import into a tool like Dovo or R. Or tabla or we allowed to say that or Excel or excel. Well just open anyway.

[00:40:11] So this has been great. Brent thank you so much for coming on the show. It’s been a great conversation as people been listening as you’ve been listening. If you’ve got comments you’ve got questions feel free to ask them or reach out to us on our Facebook page. Also on the measure slack we’d love to hear from you. And you know if you happen to be on iTunes near our page and you wanted to rate this podcast we would love that it does something for us.

[00:40:44] I think we will probably hear other podcasts really ask people to do that. So we feel like to put our big boy pants. We should ask that we should do the same.

[00:40:51] We copy those podcasts and asking for the same thing. So at some point there’s a winner.

[00:40:57] We just want to be in the running. All right. Thanks for listening for my cohost Tim Wilson. Thank you Brent dykes and remember keep telling data stories.

[00:41:12] Thanks for listening. And don’t forget to join the conversation on Twitter on measures like. Great. We welcome your comments and questions come forward slash analytics or analytics on Twitter. Smart guys wanted a made up.

[00:41:32] Word. Let. Me it. Hi everyone. I still have your background and. What. We’ve been waiting for. All right headphones you can’t hear any of that I can’t hear any of that. Well you did. Turn the fan down. So if you get too warm it’s going to take your shirt off. Yeah JR Smith style eliminated for the greater glory of analytics and some. I don’t know about you by the way. In fact we were actually met in person before that Woe to me because I did not. You know OK I’m sure will. Suffer for this. So Brent what is it. Like. Moderator it is not on his game tonight that get and I’m getting. One. I think there is also now. Quite angry. Well put. It’s rare thing for me to see where somebody is getting their hands on data. Well I take it back. I’ve seen people just like you know I walk that one back. I speak with pictures mostly Internet leaves. You can edit. Rock flag and data storytelling.


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