Are you a data scientist? Have you pondered whether you’re really a growth hacker? Well…get over yourself! Picking up on a debate that started onstage at eMetrics, Michael, Jim, and Tim discuss whether a fundamental shift in the role (and requisite skills) of the web analyst are changing. You know, getting more “science-y” (if “science” is “more technical and more maths”). all in 2,852 seconds (each second of which can be pulled into R and used to build a predictive model showing the expected ROI of listening to future episodes; at least, we assume that’s what a data scientist could do).
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.”
[00:00:25] Hi everyone. Welcome to the digital analytics power hour.
[00:00:30] This is episode 35. Here we go once more into the breach. All of us digital analysts are looking over our shoulders and we’re seeing the rise of the data scientist and a lot of people would say that in five years that’s our future. All of us will be doing data science or not doing this whole digital analytics thing anymore and I think maybe there’s some merit to that maybe there isn’t. And so this show is all about data science the future of digital analytics and of course this topic was sort of inspired by somebody who asked a question on our last show at metrics as boys velocity. He asked kind of this question. We covered it really loosely and we liked it so much we wanted to come back around for another go.
[00:01:23] So joining me on this journey is your very good friend and neighbor Tim Wilson taking taking in position of Yes is the answer. What’s the debate. What are the debates. We’ll get to that later. So you have Cozen Kyengera have warned against position. No let me introduce our other host Jim Kane. Hey Jim and you are awesome and I’m Michael hellbound.
[00:01:49] All right so guys this is an interesting topic. Data scientist is that what we’re all going to become. Is that the future of the analyst. Go and maybe do we want to start with a definition.
[00:02:03] Now we don’t want to start with the definition but it’s just it’s been an hour and an hour and a half arguing about the definition of a.
[00:02:10] I don’t think he can be clearly outlined when everyone listening. Go ahead and insert Episode 2 right here. Now we’ve emphasized that. Right. So let’s get into it. Tim I feel like maybe you are are heading up the camp that says yes this is the future. Do you want to put some meat on the bones. It’s the affirmative.
[00:02:34] Camp that’s what it is. I never did debate but OK well so I don’t think we can. I think some parameters of a definition.
[00:02:44] You know there’s one school of thought that’s like a cartoon that was you know Wessner seen a data analyst and the data scientist and it says about 30 grand a year which is hey looking for a job but they the scientist in your title and linked in. So I get that there is all of the what is this it’s a fancy term. I think even Nate Silver sort of took it took a dig at it and was like like data scientist is just somebody who’s using statistics effectively. So given all that that you could spend an hour and a half of just arguing about what a data scientist is and recognize that no one is right because there is no authoritative place that can define it. I think we can draw some boundaries that it is kind of a more it is more technical it is more statistics it is more data more putting things into production from a models perspective than what I feel like I have primarily seen in my career when it comes to Web analytics which is adding value but is more limited to hey we understand this core dataset and we’re answering specific questions with this data set but a lot of times we’re working with aggregate numbers. Yes we have to segment and we need to slice it. So I just feel like there is increasing maturity and sophistication in the world of analytics or web analytics and it’s just on a march towards that as the data gets more complicated and the tools get more sophisticated and the storage gets cheaper and more robust.
[00:04:20] It’s kind of inevitable that we’re going to kind of cross this boundary to where what we’re doing is more sophisticated more science.
[00:04:29] Yeah but I I don’t know that that means we’re changing in terms of what we should be called or we’re just embracing what’s always been there.
[00:05:20] I mean if we go back to what we were originally talking about the Florida metrics you know again my definition of a data scientist is more so when it takes an engineer or a software developer approach to problem solving and currently they’re being given problems that live outside the current boundaries of the existing tooling and infrastructure. So if you can’t do it and google analytics or it’s very complicated to do easily and Tablo is some basic data exports then a data scientist goes out and they bring everything together they use bad statistics the software development skills and they try and answer a complicated question in a complicated way. Does that mean that’s the future of the industry or is that just mean at this particular moment certain business requirements have eclipsed the ability of the tools to answer them.
[00:06:09] But can I you sort of two things one was kind of the technical getting getting and managing the data and that seems like a separate thing to me than applying the statistics or building a model not that you don’t use tools to do that but are you. It seems like you’re kind of lumping those together like the if we’re outpacing Adobe analytics or google analytics that they’re going to get to the point where they both handle those those more extreme cases that somebody is having to use Python or are something else to pull it and they’re going to be applying a layer of statistics on top of it that those are both kind of a engineering problem.
[00:06:49] So what you’re trying to say is that data capture shouldn’t be part of or getting the data shouldn’t be part of a data scientist Shabaz should just be manipulating data.
[00:06:58] No I’m saying that I think well one I think probably depends as depending on who you ask. One person is going to define it completely as the oh they’re just able to go out and write scripts to you know basically do well on the fly and get data you know manage the data not so much not so much the rock collection but the you know pulling it from resources like that engineering aspect of it. We’ve got to get the data and clean it up and get it transformed to do stuff with it. But when it comes to statistics and statistics I’ve taken I’ve taken like an intro to statistics class that like two major public universities and I still feel like when I head into something I’m never quite sure when I’m applying them right. There’s like there’s there’s math and there’s when is it appropriate based on the all the rules around it. Right. So that is not one rageous say yeah you know I can do a regression on anything but 90 percent of the time that’s the wrong thing to do. So there’s more knowledge around the math which and I think sort of both of those can get lumped under data science in some cases it’s the same person can do both and in some cases I think you’re I know you’re a python jockey and you know don’t know what an Unova table is but you’re call yourself a data scientist. And in another case you know what an Unova table is but you need the dataset handed to you so you can manipulate it and sacer SPSS.
[00:08:29] I’m not I’m not anti that but I also see those as like their own distinct areas of ability or profession. So it’s you know a statistician because to your point is the application of statistics at the right place at the right time based on the data that you have that makes statistics kind of really awesome. And when you apply it poorly which is you know that happens a lot you come up with weird results but that’s because you’ve got to have really smart statistics people statisticians who are able to like see why the data is shaped this way. So these methods apply better than those like that kind of thinking is I think it’s own profession. I don’t know that that’s that’s not a data scientist that’s just a statistician but it’s really awful.
[00:09:19] So Nate Silver and Nate Silver quote I think data scientist as a sexed up term for a statistician statistics is a branch of science data scientist is slightly redundant in some way and people shouldn’t berate the term statistician which is not to say that.
[00:09:33] Nate Silver is the be all end all definition but I think we are getting to that definitional part. I think that when talking about the future I think analysts. Have to get better statistics. I mean if I if I gathered a room of 50 analysts I am probably fairly a 50 kind of web analyst digital analyst I’m probably about average when it comes to statistics knowledge. And that scares me. I think that’s really dangerous. I think that’s like 1960s level of what people kind of Intuit about math and if you grat gather a room of 50 Web analysts and say let’s see what kind of our technical engineering ability is to do to grab the data and transform it and combine it and manipulate it. I think it’s the same thing. And I think both of those are going to I think the people who were in the eightieth percentile on either one of those fronts or what is are the ones who are actually going to grow and be successful I think the ones that are on the 30th percentile and who knows maybe I’m using the term percentile wrong are the ones who are going to what they’re going to drive the industry down because they’re going to keep training people that what we do is we automate reports using report we automate dashboards in Excel to show your trend lines which that stuff needs to happen.
[00:10:59] But a lot of times that’s going to be the only representation a business has. As to what digital analytics is and there is stuff ripe for the mining but it’s got to be a person with the skills. And there’s that. Not only do they have to have those skills. There’s the other piece of being able to identify what the right problems are. I I’ve worked with people who have been great really really solid statisticians and you just never felt like you could get a definitive answer out of them because everything was kind of couched in you know uncertainty and and they struggled to figure out which were the problems worth chasing. So I think it’s just the overall sophistication level that the bar is just going to kind of move up.
[00:11:41] So I think the only things that are different now or into the future from here into the future versus when we were doing the first few years of analysis that we were learning how to do is probably that there’s more data and it’s coming from more places and this needs more ECL to merge together into something meaningful for analysis. And there is a need for statistics and better statistics and that’s growing but the reality is it’s like and there’s more devices. Right. So those are the things that are changing our world like if you look at externalities are like just what it is that’s changing about what it is that we do.
[00:12:22] We’re not web analysts anymore we’re digital analysts because we are trying to merge multiple devices and multiple different data sources and things like that into a sort of a I’m going to say a single view and I hate that I’m saying that because it’s so great to me not a 360 degree view and 360 degree you know 357 degree view of the customer in it. OK go ahead again because the thing of it is is that the need for us to be distinctly savvy has not changed one bit.
[00:12:59] It’s just that we are more able to get to the data to apply our own sense of statistics to it than we have historically been in the field of web analytics specifically as driven by a group of vendors who did a lot of the statistical slicing and dicing before they ever handed us a report and then we were interpreting those reports.
[00:13:22] I don’t think they were doing statistical. I mean if you really go back there they’re not doing statistics. I’m kidding about that part but they are doing things like session ization or doing things like sampling occasionally. You know those kinds of things are happening in those data sets and if you go and talk to that like the Big Data guys are the quote unquote today’s data scientists right they scoff at those tools. It’s like I need the raw data or else I can’t do anything with this and that’s great. I completely understand that but there’s plenty you can do with a rolled up set of reports if you know what you’re doing. We’ve all lived that.
[00:13:58] Yeah but I think going back if you go back 10 years there was genuine actionable information to come out of a top pages report. What are my top pages. And let me look at entries and balances because there were I would say the web was newer. There were some horrible experiences or even just finding why are people actually coming to my site. There was kind of a a simpler yes a list of the top or the bottom or maybe even making kind of a calculated metric where I kind of weight the you know weights some some values. But to me I see it much more as getting to where there is some some level of a machine right.
[00:14:40] I mean me and throw machine Orting into it throw in an ability to say I actually want to work for. I don’t want to just keep guessing and trending two lines together and see when which one seemed to move and in tandem right over Adobe rolled out Adobe social and that was one of the big things they showed is look you can trend your Facebook reach on the same chart as your Web site traffic and there’s something to be said for that.
[00:15:09] But I think it’s much better to say wait a minute I’ve got device type I’ve got new versus returning I’ve got this maybe RFM data about some users I’ve got voice a customer that’s feeding into that and really what I want to do is say Here’s my outcome I want to get I want to drive more revenue.
[00:15:29] Now I want to have a tool or some smart some informed. Damn it or Schoff gave me a line I’m going to have to find it that I now want to actually crank through way more than I can do with Excel or the web interface or one of these tools and see where where are things that start to become more correlative that I can then say is there causality can be affected. And that to me is fundamentally different thing.
[00:15:59] I don’t disagree with you Tim. I think there is. I remember doing analysis and getting to the end of my sort of my own math skills if you will. And like I know there’s something here I just don’t know the right ways to to find it. And it was you know Hadaway tear this apart in a way that I can see what’s going on. And so to that extent I think just like having technical skills as a digital analyst you’re never going to be upset for having some of those skills and developing your skill set as a statistical analyst as somebody who can manipulate data sets. But I also don’t I don’t think that means a successful analyst has to be a data scientist because I mean that’s the title right.
[00:16:44] Is data science the future.
[00:16:45] Marong is the title to the future with Antolin maybe we’ll you know 10 you will a good look at you pretending that you were that we were bandying about the final title more than 12 seconds after the show ended.
[00:17:25] I do know that there is never in the history of it has been a place where you don’t need to actually have a good intimate knowledge of what is actually generating the data and how it’s being captured. But this is where we’re getting all wrapped up in the fucking title again.
[00:17:40] I mean this is no Blyde or like there’s a difference between a data scientist and an analyst. And I think that difference will persist into the future.
[00:17:50] It’s great the analyst can go and kind of have dead end careers being cranking out repetitive stuff. I mean even remotely through your sound you’re saying there is no content. It is not a continuum from whatever whatever. We have no definition for a analyst and we have no definition for a data scientist. I think there is a spectrum from what most analysts that I see and work with and do myself today and what. From an aspirational of what I’ve picked up in the last couple of years some other Slyke people are doing that.
[00:18:24] I think that’s going to flip. It is 95 percent doing 1 and 5 percent doing the other. And I think it’s going to pivot around to the people drawn into the field are the ones who have the skills to be in what that 5 percent is now and they’re going to automate the crap out of some of the stuff that analysts do. They certainly do a lot faster.
[00:18:44] Sure. That’s all great but I think you’re you might be falling victim to sort of a hey a bunch of people I’ve met in my career who call themselves analysts have not been really really smart and a bunch people I have met who call themselves data scientists are really really smart. Therefore the future is data science.
[00:19:04] That’s a load of crap. It is not. I don’t even I don’t know who just said though I don’t know a single I don’t know a single person who calls himself a data scientist actually so.
[00:19:14] So that’s Chris Bheriya Chris. He calls himself a data scientist.
[00:19:18] Yeah. There you go. Chris Barrie. OK so maybe Michael Healy does he call himself a data scientist. He’s a scientist I call him one.
[00:19:28] So I mean this is it’s a little I have looked at what the discussion of of what data scientists are and it’s all over the map. So when you start saying I mean no definitional a Yes I am saying there is a level of sophistication there is a reason that I am trying to learn are I’m trying to figure out ways to get better statistics.
[00:19:50] All of this stuff and I don’t see it as well I’m just dabbling in I’m thinking about shifting my career to something else. I think I’ve looked around and said I’m looking at company after company that has analysts and they have data sets and they’re not doing dick with them and you know why that is because the analyst doesn’t have the capability and the analyst if they did it’s not like they’re going to be leaving behind some wonder for this glorious high value stuff they’re doing.
[00:20:18] If you could swap them out for somebody who had the higher level of statistical knowledge the higher level of technical chops. It’s been like two months automating a bunch of stuff and then they would just be off to the races actually finding things and I guess the third thing is this. The other thing is that it’s not somebody who’s coming with a pure math degree who can just crank through this stuff because you still have to have the business knowledge and the understanding which is why a lot of the data aside I think it’s fair to say it’s a little bit of a unicorn. All we need is somebody who can program and do advanced statistics and work with the business and have deep business knowledge. Oh you know what they need to do storytelling and communication as well.
[00:20:59] And that’s going to be one of my other points which was there’s so much that goes into taking analysis from the data through its formative stages from hypothesis to proving it out to then putting it into a place that people can consume it and then driving it through an organization to make the organization react and change to it. I think the way that I define an analyst they have a part to play through that process. The way I define a data scientist they don’t necessarily go through and hit every part of that process.
[00:21:32] And now because they don’t have value they have extreme value and to your point before there’s parts that analysts have been trying to do that needs to be the realm of the data scientist and analysts have done a poor job frankly because they just don’t have those skills of trying to cover or find the statistics and the hard math the real math that goes on. But I think there can be a division of labor too. So I just don’t know that if you’re an analyst today you need to become a data scientist. I don’t know that I would be 100 percent onboard telling somebody that about themselves for their career. I think I would say look at it. I think you’ll I would go back to you if you learn our Python or any of those skills or any of those tools you will never regret that. If you’re an analyst and if you get better statistics you will never regret that. And I’ve certainly regretted not having some of those skills in my career at various points in time because I kind of hit a wall and that I need to go get help. But what I do around that is I go build teams that have those set of expertise so that I can continue to go forward but I’m personally not. Can I try to go out learn every single piece of that.
[00:22:41] I’m I’m not well I’m with the analyst. What does the analyst not the analyst manager you know just the analysts the analyst what does the analyst. So I think the analyst is the bridge between the business and the data.
[00:22:55] So I. And as we talked about this on an early episode. AG I agree that there is a role of somebody who has a an ability to do some level of kind of data manipulation and data crunching but really it had but has the relationship building the communication skills maybe even the ability to set up reports and automate their somewhat experts on the data.
[00:23:20] And I had this model at one spot and we we called them program managers which we knew was not a good title but we can distinguish those people from the analysts and the analysts were the ones who were more down in digging into the data. So there’s a whole definition of like well if the analyst is kind of the generalist where they’re really trying to scope the business understand the business problems understand the data is there a broad brush level can say this is how we would want to solve that. Hey we need to use this is something we’re a predictive model as needed or this is something where we need to do some sort of weather out of that cluster analysis or correlation or regression but something is that what an analyst like Bay they would stop at that point and say Now I need the expert who I can draw that box. This is what we’re trying to solve.
[00:24:13] I don’t know if I’ll get it back. I don’t know that I see the boundaries as that distinct because I feel like there is it’s a collaborative thing. I think there should be ideation from both sides. And you know there’s every person is a little bit unique in their ability to kind of hit on various aspects of the have. You’re the one who keeps telling me exactly how I’m defining how you define data scientist and that is the way to define it and so I will draw the boxes here. I think about titles but it’s well it’s there’s functions there’s functions that have to be filled in a business for analytics to work. Would you agree with that. No there there are no functions that need to be filled. Like guess further on than when I thought businesses should not exist for separate enterprises. Numbers and just use their gut instinct. They trust your intuition people are expert to expert intuition that the phrase that macher SCIAF gave me which I think yeah no but that’s what I mean there’s no data scientists have expert intuition and great facility with understanding how a business should respond to various things and things like that. The role of the way that I see them interacting and maybe it’s the way it’s interacting today and in the future it won’t be this way. But there is. Well I think one thing we all agree on digital analytics is more complex today and the possibilities require greater management mathematics technical and statistical skills to really plumb the depths and capture as much value as possible. That happen.
[00:25:56] I think that’s that’s something we can all agree on. The other thing I think we could probably agree on is while marketers I think are getting more sophisticated in digital they’re not that sophisticated right. So there’s always going to be a gap between the brainy guy in the corner and the marketers who want to go you know five years ago that Groupon was amazing not to but you think I’m insulting someone’s intelligence and I’m sorry because that’s not my intent. Well but don’t you think.
[00:26:27] Don’t you think though that marketers when you take the tools and this is kind of maybe to just point earlier when you’ve got Tablo when you’ve got Domo when you’ve got sweet spot when you’ve got inside Rakin when you’ve got these various platforms that are allowing kind of more sophisticated more robust little sandboxes of sorts to plan that marketers the marketer of tomorrow is probably getting more exposure to data in school and as they’re coming out they’re getting internships are being told to go go dig in our Google Analytics and figure out what we can change on the site. So I think marketers are coming out are increasingly comfortable with data still agreeing that they they are not going to be at the point of a of an analyst. But I think what many marketers are doing now when they talk about bounce rate on landing pages in segments and hypotheses that they are kind of moving up the maturity chain into the to the World Junior and that is absolutely happening.
[00:27:31] But but one of the things that you just described is that people are creating sandboxes where those marketers can work and they’re creating smaller versions of the problem more more closed systems so they can get their arms around what’s really meaningful to the business. I would call that the creation of a data product whether that be merging data sets together into a place where you can analyze it simply like Tableau or Damiao. But that is I think that is the role of the data scientist is to create those spaces by thinking overarching everything well what will it take for us to meaningfully analyze within put a course you know there’s communication but the build of those particular things that’s what’s going to make or break a business in the future is busy with making data products simple for marketers to use with great complexity and thought that goes into the the architecture and underpinning of those so that when marketers pull the levers they pull to run the business. Good things happen because someone has thought through all the implications and all the statistics about how that all flows through to the underlying data.
[00:28:38] So the funny thing is describing that and I’ve got a couple of cases where now I’m doing it again and again and it’s becoming expected of them and I’m doing it with Excel and report builder for the most part. But it’s an automated weekly or monthly thing. And it’s a file. So you know those out there who are going to flip out about Excel spread Martes but it has a decent level of interactivity. You know these are the these are these five metrics pick which ones you want to sort by pick whether you want to sort ascending or descending. You’ve got ERT’s got slicers in it so they can actually explore and I can because I say look I’m not I don’t know your business well enough. You’re going to want to see this is how you want to optimize. You say you want to inspect your content. You know you’re all it is is the waning pages and this is what we’ve set up. I never would have defined that as being Hakki. Early early early stage data science although if I built something like that in a Web environment where it was real time wasn’t being delivered in an excel file. Maybe so. I mean as a data scientist building cars and what are they called pages in domo. That can’t be right.
[00:29:43] No but they’re building data flows and architecting how those how the dot dot the data for today Data Scientist congratulations on let’s say dialect out. Right now there’s so much more to it too. And like we should get a data scientist to tell me I’m wrong I’m probably wrong but I also think that’s probably why so many data scientists I’ve encountered have such a low opinion of Axelle because it does such a bad job of what a data scientist is trying to do for a business. You know it’s not giving a business a way to really interact and decide the date with the data in a meaningful way. I mean and and or the ability to manipulate the data in that environment is poor historically whereas I’m pleased as punch to kick around all day long with pivot tables and we look up some I’ll go as far as my little legs can carry me well and maybe maybe there’s some indication that Microsoft doesn’t have.
[00:30:41] They’re not they’re easy punching bag but you know as they have as their products have gotten more mature Excel has more pivot table slicers gives you a crap ton of interactivity that they used and that they have any idea they’re just clicking on all the boxes they want to filter the data well and and the tools are emerging now inside the tools like analysis workspace with Adobe and design.
[00:31:05] Was it called the 360 version of the same visual data studio that is Studio 60 360. So don’t worry. Cool I will learn the name of that just so you know.
[00:31:19] I’m going to get slightly better 360 357 357 if you of the customer. 357 mag.
[00:31:35] So but don’t you love my view of it. Isn’t that sound so great. I want to buy into it.
[00:31:41] I was ready to completely blow off the product. Building Building data products. But as we’re talking I’m realizing an analysis workspace is another great example.
[00:31:51] I mean I sat on a call with a client late last week where I built her a little analysis workspace project and then she kind of took and ran with it and then she was like Can we have back on a call and here’s how I want to use this and I’ve got you know these five panels I think only three of them. So I was kind of coaching her but ultimately she is she is not. She is an analytical minded user. Absolutely. She’s not in a role of an analyst. And I guess I was helping empower the business by by building better than a Sam but not a sandbox where I’m pulling my bag of sand over and then saying Go and play with it. She’s working with it in the live environment which is what Kodomo does with somebody with shiny and are with python. So it’s kind of a level of how much technical. Yeah I mean Domo Domos data flows are my shit. I can connect to two things and join it and need to have enough snap to make sure I’m not screwing something up.
[00:32:45] Yeah and of course the level of complexity goes way through the roof and you need really high end skills to be able to really take advantage of some of those things. But just on the level of some of the base products today which again sort of goes back to your point Tim which is there is an increased sophistication and a need for increased Fisty occasion.
[00:33:03] But the people who put together that cohort or define that segment and make sure that segment pulls the right visitors from the data set. That’s that’s the function data scientists. Well it’s that is headed towards that direction and I would say the analyst is sort of the data scientist when they’re doing that.
[00:33:23] Okay. I just think that’s empowering the business right to me on that bridging gap is saying you know what rather than giving your crappy static Excel file I’m going to figure out a way and it could be you’re using some teams of analysts.
[00:33:35] We’ll work with the sandbox tools that they’re given by data scientists to go and solve business problems. I mean the reality is is why we’re all moving outside of our traditional set of web analytics tools because a there are not comprehensive and covering everything that we need to cover as a business right because there’s external data as well as multiple device data and so on and so forth and they’re aggregated and so there’s certain analysis you can’t do on the reporting itself until you get the raw data out of those tools which is why big Querrey exists for analytics 360 customers and why Adobe premium exists and you can dump all your raw data into that and do all kinds of analysis on it. I think there’s it’s been a long time coming but we’re starting to see the need to desegregate or aggregate. I
[00:34:25] don’t know which one is the right word out of our standard reporting tools a new layer of reporting tool is taking its place in the form of tableau or the form of Domo or the former sweet spot intelligence or something of that kind. Jim
[00:34:42] I’m enjoying watching that Ping-Pong game with you guys and I hadn’t thought about the concept of data products being an output of a data scientist. You know I always see them kind of like I said at the very beginning and they’re on the cusp of where the UI driven tools stop. And I think it’s you know a valuable product of the data scientist to build things for other people to use that have their own kind of user experiences that don’t need to dicker our statistics and our and big data tools. That said I still think it’s it’s to a large extent a level up on being an analyst. You know it said a direction to get into a business stream for people that are more technical but you know I think the value of someone who is an expert at listening to someone ask a question and bringing an answer out of the preexisting tools isn’t going away because the tools are getting better and better and better all the time so well and that’s why I think there’s actually more demarcation between and maybe it’s not called an analyst in the future and maybe it’s not called a data scientist in the future.
[00:35:41] But I think there still continues to exist separate roles because it’s going to be too hard. It’s going to be too hard to find somebody with the math and science skills to really go and get and dig into all the data and then be able to turn right around and craft a narrative around that and present it to executives in a business. And there are people like that who existed probably the most the data scientist that we all know the best are probably some of the examples of the people who are covering that really really broad spectrum. But even analysts struggle with this right. I mean what’s the number one complaint of the analyst today as well.
[00:36:19] People don’t listen to me or I work in a silo and I can’t make anything happen. And it has everything to do with narrative creation and communication.
[00:36:28] So I had Ed shared with me as there’s a guy I found when I was goofing around earlier the Steven Geringer the data signs Venn Diagram 2.0 which is one where now he drew data science around the entire thing.
[00:36:44] But he sort of tried to draw a Venn diagram with computer science versus subject matter expertise versus math and statistics and said you know when computer science meets math and statistics you’ve got machine learning. You know when subject matter expertise meets computer science is traditional software and so on and so forth and kind of his I think part of the point was where all of those intersected a unicorn you know which is definitely a phrase we bandy about that there’s there’s probably an opport we’re moving towards more inherent specialization because it’s a narrower set of expertise is still as much as one person can can ramp up on. But there’s probably what may be critical is an awareness of what all of those roles are back back in the day. This would be five or six years ago or if I was if I was Jim Kane I’d say 15 years ago to make it longer and more grand.
[00:37:39] Ten years have on average ten years ago my Analiz skills coming out on average for people who were great in statistics and they just didn’t want to acknowledge the need for communication.
[00:37:53] They sucked at communication. They didn’t want to acknowledge that is needed to be a role. The classic the data speaks for itself. Why do I need to you know build relationships and communicate effectively. So I think maybe that’s maybe we’re getting to a point. You know the DA right now with their competencies. You know they sort of broke it down into two things. The the analysts and the implementer I came were with the two things Arbed it is basically one bucket’s analyst and one bucket is the operational side of things and maybe that’s where we’re heading is that we’re going to wind up with three or four or five sort of disciplines not the right word because that’s kind of defining an entire discipline but it’s kind of like these are your easier five things and you need to be expert at two of them and recognize the value and the importance of the other three and either figure out if you’re if you’re the sole analyst figure out how to get by in those and if you’re managing a team or part of a team. Figure out how you’ve got them covered but it’s probably gone to more more breadth all of which has increasing depth to to cover that.
[00:39:01] And maybe that’s the issue is that when you say data science we try to just throw everything into it that you know that we personally don’t do or that we look around and see some analyst not doing and maybe that term will just go away entirely and we’ll talk about we’ll just talk about statistics or we’ll talk about modeling or we’ll talk about machine more and all those things are necessary parts like there.
[00:39:24] We need to totally embrace them. And yeah there’s precious few unicorns out there. Like if I’m anything I’m probably like whoa devil donkey you are. Oh dear. Do I need to go to Urban Dictionary. Oh boy. Get up. Yeah I made that up. No I’m just like was I’m not I’m no unicorn when it comes to this stuff and nor I know my limits right I’ll never be that. But there are people out there who have those skills and the one thing I get concerned about as it pertains to this kind of a track is that I think there that schools and businesses and organizations are working hard to meet this need.
[00:40:08] The data science need and I don’t see an equal and opposite response to filling the other half of the equation. Mapping it into the business and making it work and being able to do that and maybe I’m wrong because maybe that’s what EMBAs are supposed to be doing now.
[00:40:25] I think I think you’re by the way there is a double darkey Twitter handle. Patrick Ormund well thanks Patrick. Following 23 people out of Abilene Texas I’m sure but I think I think there’s also I mean with academia I think there’s tends to be a presumption of cleaner data than is reality right. I mean I think that’s where they’re kind of marching towards the niche. It’s you have a big clean glorious dataset. Look we found this sample dataset that no one’s ever going to give two shits about. And that’s what you’re going to do all your assignments on. Oh no look now you’ve got web analytics data. It’s messy.
[00:41:01] Good luck. All right. I mean Gary Angell speaks a lot about the difference between sort of traditional B.I and digital analytics and very compellingly as per usual just says it’s all about the four V’s underway. He says it’s not about the four V’s not about the four movies. Yeah. Anyway. All right well we should probably wrap this up. I love where we ended up. Jim in stunned silence and Tim and I any sort of agreement as your moment. Jim I was stunned. All right well let’s wrap up. I don’t know if you want to go around the horn.
[00:41:36] Anybody learn anything new or it think anything new or I can say this is super helpful for reasons I’m at the metrics in Chicago in early June. This is I am quite sure there will be some elements of this discussion which was coming to my presentation where I will be making a trying to make the case for why analysts should be learning.
[00:41:57] Our goal is not something that put like my little icon on any slides that I helped you with there. No. And you know this is I think it’s really good because I don’t think this is the last time this conversation will be had and I certainly think a lot of definition is needed. And for me I always take a view of what does the organization need to have insight have going on and functions to define what then should the role be for each person and should there be overlap. And the beauty of it is is there’s such seamless transitions. Data scientists can be an amazing analyst and an amazing analyst can learn to become a great data scientist. I don’t think they’re mutually exclusive. And like I say there’s not. There’s not a generally accepted definition for either honestly how it’s wide open.
[00:42:47] I’m thinking in your log in your edit profile and then you can seamlessly change your role from analyst status.
[00:42:54] That’s right. Michael Helbig I’m doing that right now. Michael help Bling is a data doctorate. Wait no data. What’s better than a scientist a data surgeon. Tago Drew I’m a data Druid All right. And again it’s not a growth hacking because there’s fighting and Growth Hacking. Yeah. If we’re all becoming data scientists what will growth hackers do. Oh some people who argue attackers for real. Maybe if you wanted to drop us a line we’d love to hear from you.
[00:43:32] I got my entire quota of the word phrase growth hacker in one session at a recent conference I attended any closing.
[00:43:39] Jim Kane I just wonder if some existing analysts who don’t go data science just start to move into a more traditional business analysis stream and you guys are correct that the discipline is getting more technical.
[00:43:49] It’s quite possible you know that was the thing I realized after being an analyst for a few years is that really I was just a business person who was trying to use data although maybe I got burned.
[00:44:00] I feel like I worked with a lot of business analysts who are basically just gathering requirements for I.T. to build things like a business analyst. It wasn’t like that definitely what you would think the words analysis of the business was it was. So I think it would’ve been a great term for but somebody’s got to it before us and used it for something else for evil.
[00:44:23] Right. Well obviously we figured it out. But if you’re listening to us and you think we missed pieces or you’ve got it figured out better we’d love to hear from you.
[00:44:35] And we don’t care which side you’re coming from analyst or data scientist. If you’re in our every day you’re going to want to pop up on our Facebook and tell us how wrong we are. Tim Wilson is here for you.
[00:44:47] And if you’re in Adobe analytics all day long then I’m here for you for that. And if you’re an angel Google Analytics 360 and Jim Kane is here for you for that. So give us. Drop us a line. We’d love to hear from you on our Facebook or on Twitter or on the measure slack which is a great community of like minded people who are proudly both data scientists and analysts all at once in the happy unicorn Valley. That is our digital community that you should join.
[00:45:16] So drop us a line. Love to hear from you from my other two co hosts Tim and Jim. This is Michael Sand Key data scientist. Keep analyzing keep analyzing.
[00:45:32] Thanks for listening. And don’t forget to join the conversation on Facebook Twitter. We welcome your comments and questions. Facebook dot com slash on Facebook now or on Twitter.
[00:45:45] Mangahas. Wonder if we made up how it.
[00:45:51] Worked. Oh yeah let me just jump in here with a wacky opinion about a notable industry figure. I’ve gone back and forth between like weird metal and old school hip hop and jazz and shit like those Bieber if you want to hear some Beever. Or do you ever have music that makes you happy. What. What about my persona makes you think that makes me happy. Say. A show top the very idea that we should never write down and I would just chat about it and back and forget about the Dagley. Never mind to record just. Old Guildea. JACK BLACK Yeah. Did I get that without without doing what he did. You just got a 12 year old pop culture reference. And later. An analyst. You hate them so much. This scares you because it’s really hard. Even though it’s really powerful and you’d rather just mail it in and be a touchy feely relation to person. Know we’re going to be a little short on the all out. I think it’s going to be. You know just to put and go to search discovery. Dot com slash careers go work for Michael Holbein. Must have been a hard day for Jim. It was.
[00:47:26] Rock rag in Dayton and.
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