Digital Analytics and the Keys to Trusting the Data with John Lovett and Bryan Eisenberg

I was HONORED to sit down with the two more foremost thought leaders around analytics at the IBM Global Summit in Nashville with Jon Lovett, Senior Partner at Web Analytics Demystified, and Bryan Eisenberg, Keynote Speaker, Consultant and Author, Eisenberg Holdings, LLC. In this interview we have a healthy debate around big data, digital analytics and the keys to trusting the data.

The following interview has also been transcribed below:

Bryan Kramer: Hi this is Bryan Kramer, I’m the President/CEO of PureMatter. Welcome back to another session here with us at the IBM Smarter Commerce Summit. I’m really excited to be here sitting on a panel with two analytics rockstars. I’ve got John Lovett, the managing Director of “Web Analytics Demystified” also author. And Bryan Eisenberg from “Use The Data” and also author. So I’m surrounded by authors and managing directors and also data guys and that’s kind of the subject here.  We’ve heard about data data data data all week here at the conference so now I’m finally getting the chance to talk to you guys. So why don’t we start with one thing that I think that’s kind of in conjunction with what you guys have worked on before which is the Association that you put together, and now that you’re President of. Maybe we can talk about that a little bit and then we’ll roll into some of the context around the conference.

Bryan Eisenberg: Yeah there’s no question that data has been the big theme of the conference, no pun intended there.

Bryan Kramer: Big data.

Bryan Eisenberg: Big data as well yeah. So I joined with my friend Jim Stern, and Andrew Edwards after attending a number of conferences where the analysts gathered together in Santa Barbara at Emetrics. And we talked about this idea of forming this association because there’s been a bunch of us for a long time out there trying to evangelize the use of web analytics among businesses and among analysts, and there weren’t that many of us back then. And so one day I call Jim and I get Andrew on the phone and we say we’re going to start the Association. That was in 2005. Since then I’ll let John kind of talk about where we are now and how far we’ve grown since then. But we’ve got a few founding companies to help put it together, a number of analysts to put it together. The main purpose was there just wasn’t enough people knowing what to do with the data, how to use the data. That was our main purpose in getting it started and getting established and getting that evangelism going.

Bryan Kramer: Great. You want to talk about where it’s heading?

John Lovett: Yeah. So since 2005 we’ve actually made a lot of progress. Where we started as the Web Analytics Association, we made a shift two years ago to the digital Analytics Association because we recognize that it’s not just about websites today. Everything is going digital, and we did that to encompass mobile, social, all the different ways we’re collecting and using data. But right now, our main charge…and I should say that right now from inception, it’s a nonprofit organization. We’re member driven and volunteer powered. So it’s really the strength of our members that makes the Digital Analytics Association what it is today. And now our focus is on professional development. So we’re really looking to be able to show our corporate members as well as our individuals, what does it mean to be in the digital analytics industry? How do you get involved? What are the steps, what are the resources that you can take advantage of? We offer things like certification and training, lots of education and networking events. But really it’s about developing professionals in this field, and that’s where we think our strengths are.

Bryan Kramer: What a great resource. That sounds great. I’m going to talk to you guys afterwards about how I can get involved. One of the things I think we should also talk about with you guys both being here is the difference between how you use the data, and using it both as a marketer versus a statistician. What is the difference? Maybe you can take a minute, and then you can take a minute.

Bryan Eisenberg: Yeah so that’s been my pet peeve. So when I originally started my evangelism it was focused in…I was trying to speak to marketers, marketers didn’t get it but obviously the analysts were interested in what I was saying in terms of how you can use the data. And it’s now been more than fifteen years I’ve been trying to promote and educate people on how to use the data. And what we found is in order to get an organization to really get the full buy in, it’s not really up to the analyst. I also understand the analyst might not be around in the same way in a couple of years, but we can talk about that afterwards. But I think what happens in order to get that nervous system that’s tied into data and into the analytics, you basically need to kind of you know, incorporate within the whole organization. So it’s not just the analyst, it’s obviously senior management, it’s the people on the front lines… everybody’s got to understand how the data plays into the global strategy, and to understanding the customers, and getting the most from them.

John Lovett: And I’ll just take a little bit of a different direction on that. You know in terms of the data, the statistician versus the business marketer, a lot of what we do is really complicated with analytics. I mean we can easily confuse an audience of business marketers by throwing out metrics and KPI’s and all our little numbers that we use to describe things. But if you can’t speak the language of the business, you’re gonna lose. And we can create the most complex engagement calculations that have seven different indices that come up with the metric, but unless a business person can understand that, there’s no value there. So one of the things that we advocate for is, kind of going beyond…we hear a lot of vanity metrics. Social media in particular, that’s what the topic of my book was. You hear lots about fans and followers and facebook likes and all these things and those are vanity metrics. They are good for quantifying volumes, so telling you things like how big your audience is, potentially how many people see your content, but it’s not good for determining is this good for my business.  And that’s why there’s some thought and collaboration across the company that needs to happen to be able to say what does this really mean to us and what can we discern as metrics that are gonna show us whether or not we’re making progress towards what our business goals are. To me that’s what it’s about.

Bryan Kramer: Great. I’d love to hear from both of you in terms of defining what analytics means, because analytics is everything. Analytics nowadays is business intelligence, it’s like you said web analytics, there’s all kinds of analytics that play into this. Define for me, and I know this is a big question, because it could be you know a two hour conversation we could have here. But why don’t you start?

John Lovett: I’ve got a simple answer. This is the way I explain it. Metrics are the data. It’s the core, the raw data. Analytics is when you put a human mind into it. When you put the thinking behind the metrics and the data to be able to interpret and understand what that means, and put the data into context. That’s analytics to me.

Bryan Eisenberg: Yeah, I won’t take it too far but I’ll argue about the human part. The fact of the matter is, and we see all the research, you know Mckenzie’s predicting we’re going to need about a hundred and fifty to a hundred and ninety thousand new data scientists or analysts in the next few years. They’re not going to magically appear, as much as IBM is investing in the education programs and doing some great stuff in that realm, as much as we’ve done with the Digital Analytics Association. We’re not going to magically find all these college graduates and people who know this stuff. What I think is going to end up happening, and we’re already seeing it, is there’s a number of developments, a number of programs out there…everything from what we’re seeing, what’s happening with like the google now type functionality where I can talk into this huge data set that google collects, and ask it questions. And I think what will eventually happen is, the business people will say okay you know, show me all my customers who have purchased over a hundred dollars over the last 90 days and have bought something that’s pink. And it will just spit it right out to us. And it will be more of the language of common every day business because the systems will be intelligent enough to gather that information in a way that’s accessible to them. Because I think if we limit it just to the analysts, I’m not saying the analysts won’t have a role, they’ll always have a role right? There’s deeper stuff that’s beyond what the traditional business people can necessarily do. But what they need at the day to day and be able to react to things in as fast as business needs it today. I think we’re going to have to change the whole paradigm of how we’re looking at analytics.

Bryan Kramer: Interesting. So it’s going through a shift alongside everything else.

John Lovett: Yeah so I love the promise of that, but my challenge, or what I get a little bit the shivers about that is…technology has made big promises with regards to analytics for a long time. And you know one of the things is, we’ve learned, technology is an absolute imperative. You’ve got to have it; you’ve got to have good technology. But it take somebody to explain it. It takes a human mind to be able to do the analysis. What we find is that most business users don’t even know how to ask the right questions. And there’s a level of training that goes along with that where somebody has to say, here’s how you form this syntax, here’s how you form your queries. That can come along with time but I think that’s a teaching thing. That is a cultural thing that an organization needs to be able to understand to really become data driven.

Bryan Eisenberg: Well I’m going to argue that.

Bryan Kramer: Go for it, this is what we’re here for.

Bryan Eisenberg: This is not a New Yorker going against this Boston red sox fan. We could go there…but you know what, I’ll argue because I’ve seen this in so many different organizations. The organizations whose cultural DNA starts from a measurement point of view – if you take a look at the Amazons, if you take a look at smaller businesses that we’ve worked at years that have grown this and have just incorporated into their DNA that these are the metrics we follow and they’re intelligently done from the top down very early on, you don’t necessarily need that same level that you’re talking about in other organizations. You don’t need that same level of education. The business people just know they’ve got a few core simple metrics, the same way of driving a car right? It looks very complicated but when you’re driving a car you’re basically looking at one or two little dials and you know what to do, as opposed to driving a big plane, right? Where they have so many different dials. If you’re focusing on the right metrics very early on, everyone understands this is it, all of a sudden the business doesn’t need that greasing.

John Lovett: I love the analogy, I’m going to stand to the counterpoint because I agree. To operate a car you need to know the speedometer, you need to look at the gas gauge, and when you run out of fuel you need to know when to go to a gas station, but when that car breaks you need a mechanic to open the hood and to fix it and to be able to understand what makes that car run. And I think to a great extent, in analytics, those are the analysts and those are the people that are actually making the operations happen. So it’s both sides.

Bryan Kramer: Let me just ask you guys real quickly before we move on because we can spend a bit of time on this and I’m enjoying it so we’re going to have this conversation after the interview is over as well but how do you learn to trust the data?

Bryan Eisenberg: Oh that’s a loaded, loaded question. So the nature of data, period, is going to be dirty. That’s a reality. And the one lesson I’ve actually taken away, and it literally occurred to me this past year when I was at Emetrics, and I was going through and thinking of all the companies that have become successful…the secret to being ultra successful with data is number one being focused on the customer. It’s got to be centric on the customer. But I think the key, and again the lesson learned back from something like an, is that they’re able to adjust and they’re able to adjust real quickly. They’re able to adjust in real time. And so the problem is if you choose a data point and it was dirty and you made a big business decision, you were going to roll out something that’s months into the roll out and it was completely based on wrong assumptions, you could just wave all that money goodbye. Right? But if you’re able to be nimble and agile and react to the data quickly, I think that’s one of the key missing ingredients in most, especially in large organizations today, and being able to access the data and use it intelligently.

John Lovett: Yeah I agree with that but I also think it starts even earlier in the process. Data governance is a huge factor for ensuring or building confidence in that data. So being able to prevent skepticism by saying here’s all the checks and balances we have in place. We audit our tags regularly using these technologies. We look at all of our data. We verify it. Before we put any number out there in the business, it’s been sanctioned by our center of excellence, our digital analytics team, to be able to say that this is the number. That’s one of the reasons why I like when analytics teams report up to the CFO because in those cases finance sanctions the numbers. That’s the group that says these are the numbers we use and when an analytics team is in that department, it actually – okay well those are the numbers. You know, the CFO said that’s what was good to go.

Bryan Kramer: Well guys we could sit here and talk for a while but unfortunately we’re out of time and so thank you very much for taking the time to do this. How can they find you online?

John Lovett: You can find me on twitter @johnlovett or

Bryan Kramer: And yourself?

Bryan Eisenberg: You can find me at or as well and on twitter @thegrok.