Inside Analysis

Insight as a Service: Analytics in the Cloud

Eric Kavanagh, Bloor Group CEO, interviewed Jeff Morris, GoodData’s Vice President of Product Marketing, on February 5, 2015. You’ll enjoy this great conversation that ranges from the evolution of analytics to the current ability to deliver insight as an asset.

 

Eric Kavanagh: Ladies and gentlemen, hello and welcome back once again to Inside Analysis. My name is Eric Kavanagh and I’ll be talking with Jeff Morris today, VP of Product Marketing at a really cool company called GoodData. I’m very interested to learn more about what these folks are doing because they’re aligned with what we’re trying to do here at Inside Analysis which is train people to be analysts, to help them understand the whole analytical process and to learn how to use data to derive insights and then change their business for the better.

Jeff, welcome to the show.

Jeff Morris: Thanks very much Eric, it’s great to be here.

Eric: Why don’t you start off and give us a high-level view of what GoodData does because it’s a fairly interesting approach that you’ve taken, and you’ve been at it for a while now?

Jeff: Sure. GoodData is casting ourselves as an Insight-as-a-Service vendor. We’re an analytic vendor in the Cloud. We cover the entire end-to-end exercise of performing analysis, all the way from data preparation and loading it into our environment, to data warehousing so it stays there persistently and you develop that single version of the truth, to a very rich analytic experience powered by what we call our Insights Engine that delivers analytic information, dashboards and insights ultimately to our user community. We offer this as an asset service vendor. We’re rather unique there. We’re actually exclusively built in the Cloud.

We’ve been at this since 2010. We’ve had product in the market since about 2009, and we have a great deal of experience in helping organizations become better at how they do analysis, what they look at and what actually an insight is.

To us an insight is really that human experience of taking data, recognizing something about it, having a discovery of course and then taking action upon it or sharing it or doing something that helps drive the business forward.

One of the interesting things that we’ve seen as the market has evolved, and many of our team members here have been in the business intelligence space for pushing 20 years, and what we’ve noticed most recently is that we continue to not effectively address the issues in becoming good analysts. The issues are that the people don’t really know how to analyze data, the data is not available to them when they need it or where they need it, the technology that they’re using to house the data is not evolving fast enough or what they happen to have themselves doesn’t handle the overall volumes of data that they require for analysis.

The bummer is that each one of these three things that technology, the data itself and the people and the insights they seek are all changing at different rates, and it’s very difficult to synchronize them.

Eric:  That’s a really good point. What I love is that you have focused on abstracting out a lot of the difficulties and the pain points that invariably lead to, if not, hindrances, then outright hurdles that cannot be overcome by a lot of organizations. Namely the technical side of things, having to purchase the hardware and going through that process. That’s relatively painful stuff. By focusing on the Cloud you have really stripped away a big chunk of the pain that prevents people from getting insights, right?

Jeff: Oh, absolutely. Even just the buying cycle for acquiring new technology, it’s ridiculous. It takes forever. If you’re a marketing guy and you’re waiting for somebody to determine what their favorite analytic tool is going to be or what their ETL tool is going to be, that’s really approaching the problem in the wrong way and you’re delaying the value that analytics can provide to that marketing individual.

You’re right, we took that entire problem space. We’re removing from our customers’ management the need to manage the entire exercise. What happens is they are given more time to focus on analyzing the data that they have. As they do that, they get richer and more comfortable in performing analysis and their business transforms in a number of different ways that we’ll talk about in a second. They’re able to gather these insights and evolve in their analysis using these insights at a rate that you just don’t see in traditional business intelligence or analytics.

This is one of the reasons why we cast ourselves at start on insights. When you come on board with GoodData, we get you up and running within the same quarter that you start. We’ve got a track record rate now that is really giving us a great launching pad to get our existing customers more and more successful as they’re going along.

Eric: It’s really that speed to analysis on both a macro and a micro level that makes a huge difference in traction within the organization. Because I think oftentimes what happens with many business users in more of a traditional business intelligence type environment, there’s so much static between them and the insight they want to get or people they have to work with to get things set up. There are people they have to work with to get the data; there are people they have to work with to overcome bottlenecks and so forth. You wind up with this latency that I frankly think just kills the whole analytical drive, right?

Jeff: Right. When that happens, and it’s such a regular occurrence, the individuals who are waiting on everything to evolve for them or others to come through for them go back to Excel or whatever they happened to have before because it’s not only comfortable but it’s expedient. That’s absolutely the case.

Consider this one. I have a customer here in San Francisco. They are an online retailer or a marketplace for artwork, they’re called redbubble.com. What Redbubble does is they are actually both a broker and a store for artists to present their work and then deliver their work to market, either in lithographs or posters or t-shirts, coffee mugs. They have a variety of product types but they help bring these modern artists to market.

What’s interesting about what Redbubble had done is they used GoodData first in their sales and marketing efforts to figure out what web traffic was happening in their organization. Then they moved it into finance and operation. Then they moved it into customer support They’re now operating in the entirety of the organization using Good Data.

Look what they do to their customers or for their customers. The artists that are supplying them their wares: Redbubble knows who the best artists are in the world based right now on who’s selling and who’s not.

They’re able to benchmark and analyze the popularity and the presence of the artist’s brand, if you will, on their behalf. It’s all because they actually happen to have, pardon the pun, GoodData.

Eric: That’s a really interesting point. You know as you were talking, it just dawned on me what’s so compelling about your story. Of course, a lot of organizations will develop what’s called a center of excellence within their company. They get some people who really understand the business side and the data side and they’re on hand to train people how to set up applications or how to analyze data, how to do various things. It’s like you are creating your own center of excellence which you can then leverage for all your different customers, right?

Jeff: Yes, for every single one of them. Oftentimes when we go and visit prospects, one of the first questions we pursue is, “How much experience does every member sitting across the table have in deploying a BI project?”

Some have years of experience but they probably only worked on one or two projects. Some might say, “I was a consultant and I worked on 20 projects.” Compare that to the collective of GoodData. As you said, we are our customers’ entire center of excellence. We worked on thousands of projects, and we continue to learn from every single one of them.

What’s great about the GoodData notion is that all of the information that has gone on, the successes and the mistakes that occurred in any and every GoodData project happens to be still available for us to evaluate and decide what was the good decision that got the customer moving forward and what was the bad decision and the mistake that made this situation not as successful?

We call this ourselves, it’s operating against our own institutional knowledge. We call it looking at the collective learning that we can impart upon our customers.

Now we’re starting to actually build into the product a number of capabilities that will help steer the customer into being a really effective analyst, simply based on the behaviors that we’ve seen successful users do in the past.

Eric:  That’s a good point and you’re reminding me of something that one of my good friends in the business, Dave Wells, who for years was the education director at The Data Warehousing Institute. He used to say and he told me this back in 2004 I think, “Look. Sooner or later Data Warehousing and BI, it’s going to be under- the-hood technology,” meaning the user doesn’t need to understand how the engine is working, they just need to drive the car.

At the time I thought, “Wow! That’s pretty forward thinking.” because in 2004 we were still pretty far back in terms of where we are today. I think that what’s happening now Is that you are focusing on handling all that engineering under the covers such that some will just come see you and obviously they have to give you access to their data and all that usual stuff.

Still, you’ve taken out a huge chuck of the process and as you suggest let people start at the insight or near insight phase. I have to think those quick wins that gets the customers excited, that’s what gets traction within the organization, that’s what gets more people at the table, which gets more data involved. It’s a virtuous circle, right?

Jeff: It absolutely is. One of the things we’ve certainly noticed about analytic exercises as we deploy them anywhere and, I’m sure you will recognize this too, is that your project doesn’t always have an ending. It’s something that always has to perpetuate itself. Really, it’s a living entity. If you don’t continuously feed it with either new ideas or new data or fresh perspective then it will ultimately atrophy and die away.

We have to treat the exercise of analysis as something that is indeed the lifeline of an organization. To date, I think it’s been very difficult for many organizations to overcome that inertia and that initial deployment success you just mentioned to start to enjoy the benefit. When you do it’s really incredible.

I’ll give you another example of a customer of mine, they’re called SpareFoot. They’re the Expedia for Dell service storage locations across the country. They know what these data storage units are. They know what the driest ones are. They know what the closest ones are to you and how quickly and how soon you can move in.

They started their GoodData deployment analyzing their call center information and realized that when they map call center with website data, if they didn’t get on the horn with the customer who was actually investigating storage units at that moment or within the hour, then the likelihood that that customer would actually book something and then move in dropped by incredible percentages.

What they did is they built out and improved their personalization and their human interaction, meaning they added call center employees to respond to customer inquiries. That was all based on their analyses that they performed with their GoodData. Like I said before, this company too had become a benchmarking mechanism for the movements and migrations of families and of people simply by what their storage movement patterns happen to be.

Another one of those successes where becoming an absolute expert in your own particular market is indeed possible when all the pieces fall into place.

Eric: This is really cool stuff because what you’re talking about is a mechanism for organizations to grow in a very natural and organic fashion. Again, you’re using the data, you’re finding insights. The insights you find lead you to recognize new opportunities, not just for optimizing a current process but for new lines of business, right? That’s one of the best ways that you can get more money to identify an area where it turns out you have great data, you can use this data to things beyond what you’re already doing. That’s, I have to think, one of the best kinds of growth you can get, right?

Jeff: It absolutely is. Certainly, in our space about half of our revenue comes from the ISV market, the software market, and we sell to companies that embed our product into their product for analysis.

What we find there is exactly as you just described. Once they start rolling out the super easy-to-use GoodData environment to their customers, I’ll use Zendesk as an example, which is the market maker for Good Data. They recognize that the number-one reason why their customers move from their free product to their premium product was the analytics that were embedded inside of Zendesk. Happily, they did not white label their environment. They left the branding for ourselves in there so everybody recognized that’s actually GoodData powering Zendesk.

Even now, they introduced a product called Insights last year and Insights as well is a brand new revenue generating product for themselves. As you said, cracking this brand-new market and moving themselves forward using GoodData as the vehicle for that.

We see that same situation happening in digital marketing agencies who always have to be proving their value to their customers especially when they’re creating and designing digital imagery and digital artwork and digital programs and campaigns. You have to justify whether the campaigns are worth what you’re putting into them or delivering any kind of ROI. We’ve got a number of customers who are building data products around that.

My favorite one is an organization called ServiceChannel. ServiceChannel operates in a $500-billion market of maintenance spend within the retailer space. What ServiceChannel does is they integrate the facility managers for all of your favorite brick-and-mortar stores and restaurants, all of your favorites are their customers. They bring together the facility managers and the service providers, the cleaning crews and the plumbers and the electricians and everyone who’s maintaining those sites during off hours to make them look really good.

What ServiceChannel not only does by creating this network of their own customers and suppliers, but they also evaluate and grade the most efficient suppliers and the most cost-effective ones. So that for their customers, the facilities people, they gain great economies of scale and can actually decide which service providers are indeed the ones they want servicing their site.

Then ServiceChannel turns around the other side and grades the retailers on how much investment they make in maintaining their in-store branding. When you walk into a store and a light bulb is out, that’s an issue for your brand.

They grade the vendors themselves that way and create a scorecard. Organizations such as Apple get exceptional grades for their branding presence. Other retailers who are not faring as well get terrible grades for their branding present based on their maintenance presence.

ServiceChannel too is becoming this economic indicator for their market. Again, it’s a sizable market simply based on what kind of spend is going on in the retail space. I think it’s another really incredible situation where they’re generating not only revenue but their own market share and their own differentiation using their analytics.

Eric: That’s great stuff. It occurs to me as well, the ultimate benefit goes out to the consumers because what we get as a result of these analytical solutions are better prices, faster delivery. Really, if you get down to it, the companies that are the most agile and that really understand their market the best are the ones that continue to excel, and they’re the ones that stay ahead of the competition.

Because these days you have to stay on top of what everyone else is doing, and if you’re not agile, if you’re not able to identify these new opportunities, before you know it you’re going to be taken over by someone. I’ve seen it now time and time and time again and these analytical apps are very, very helpful in giving companies the confidence they need to try new ideas and put them in motion and then be able to measure what happens, right?

Jeff: Yes, it’s absolutely the case. Now consider this the evolution that we’re seeing. I’ll only talk about Cloud data itself or Cloud-based applications. As I said, we’re a great provider to this application-installed base, the Zendesks of the world.

What makes them interesting as a data source for us — trying to integrate and weave in data from them, data from your CRM system, data from your favorite marketing automation system or Google Analytics — is they’re all API driven.

Being API driven you have to know not only today’s version of the API but tomorrow’s version of the API as well when that vendor changes it. Leaving that responsibility to each and every customer just seems like an absolutely unwieldy problem.

Let’s make the problem worse by adding in all of your favorite social channels. Now, when your customers are not only wanting to evaluate their traditional drip e-mail campaign ROI or their webinar ROI, but they also want to see what kind of region effect their social programs are having. Does it really work to be advertising in Twitter or to be creating special pages in Facebook or Pinterest and evaluating your spend not only within one channel, but across them all so you know where to place your marketing bets.

We’ve developed an expertise practice in that social analytic exercise as well and we’re finding a number of highly visible brands are adopting us for exactly that reason, to help them monitor their social activity. That’s going very well.

Now let’s make the problem even more worse. What happens as real Internet of Things data starts to come in to play as well? Even the Fitbit example I just used of what kind of data does your Fitbit or your iPhone generate about you? Because that will start a blending that info in with your social behavior, with your online behavior and how marketers or how organizations can best come up with ways to serve you and make your life easier.

Certainly, the goal for us is to use the data from all of these to make things more efficient, make things more cost-effective for businesses, give them new business opportunities. Ultimately too, improve the lives that every consumer was enjoying it.

Eric: That’s wonderful stuff. Let’s close with what’s coming down the pipe. What do you have planned for later this year? Any big innovations or announcements coming out?

Jeff: We just recently announced our Insights-as-a-Service offering which is really a recasting of how we want to be perceived in the market once we realized our insights are delivered more quickly and more effectively to our customers than what we see happening in the regular BI space.

We’ve also introduced in the product, our Analytical Designer, which is a guided data discovery interface based on the activity and the collective intelligence that we have developed as best practice within the GoodData environment. We teach users what the next best move is when they’re performing analysis.

Perhaps you are looking at an opportunity and you’re looking at the opportunity amount. The most common practice is to take that opportunity amount and look at it quarter over quarter. Then probably the next best action for you is to do one of two things. Either zero it in on a particular sales territory or look at it year over year and compare each period against same time last year.

Our system is making suggestions, our Insights Engine is making those suggestions for the user and, ultimately, teaching them how to perform better analysis.

What’s going to happen as we look ahead this year, we will roll out what we call a Data Explorer which will allow that same user to add in their own data elements from either their agile data warehouse or perhaps from their favorite Excel or Google spreadsheet and continue to be productive there.

We’ll continue to advance ourselves in these suggestive or recommending BI activities, making them more analytically rich, more market focused and topically rich based on the kinds of information that the user is actually looking at because we’re becoming very good experts in CRM and sales automation data. We’re becoming experts in marketing automation data and systems there.

Those are a couple of things that are moving ahead. I think you’ll see us expand globally as we look ahead this year. We’re working on our European data center right now that will be in the UK. That would give us a much larger reach into the market as we look at it.

Eric: I just love that you are helping people learn how to analyze because that is a big part of the process. If you can have someone shepherding your education and your exploration of the data, it just expedites that much more significantly how quickly someone can go from a blank stare to a really good idea, to validating the idea to a new product. At the end of the day that’s good for all of us out there. This is great stuff.

Folks, we’ve been talking to Jeff Morris from GoodData. You can find more information about them at www.gooddata.com. Watch out for these guys, they’re rocking and rolling. Thanks so much for your time.

Jeff: Thanks a lot Eric, it’s been a blast to be here.

 

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