The Shifting Landscape of Analytics and Business Intelligence - A Spotlight Q&A with George Mathew of Alteryx

Originally published 9 January 2012

BeyeNETWORK Spotlights focus on news, events and products in the business intelligence ecosystem that are poised to have a significant impact on the industry as a whole; on the enterprises that rely on business intelligence, analytics, performance management, data warehousing and/or data governance products to understand and act on the vital information that can be gleaned from their data; or on the providers of these mission-critical products.

Presented as Q&A-style articles, these interviews conducted by the BeyeNETWORK present the behind-the-scene view that you won’t read in press releases.

This BeyeNETWORK spotlight features Ron Powell's interview with George Mathew, President and COO of Alteryx. Ron and George discuss the evolution of strategic analytics that enable organizations to make decisions with confidence.

George, as I reviewed the Alteryx website, I was intrigued by the approach that you take with your agile business intelligence platform for analytics. Could you give us a high-level overview of Alteryx and explain how you define agile business intelligence?

George Mathew: Ron, I'm seeing the change and shift in the overall analytics and business intelligence (BI) market being pretty significant today. There's more and more need to really get the ability to design great analytic applications into the hands of a broader set of users – the people that are on the business side that are designing these applications and the folks that are consuming them – without a huge amount of project-based effort that's typically required with a traditional IT staff. This is where I see the agile activities surrounding BI and analytics continue to evolve. We see quite a few of the new entrants into this market are largely being able to get things done in a significantly faster way than how analytics projects and BI initiatives were delivered in the past.

Alteryx is largely focused on getting our users to really drive Strategic Analytics inside their organizations. They really need the ability to make decisions with confidence. They are responsible for growing their business and being able to do better marketing, better merchandising, and better product strategy. The need to do this has to be separated from a Ph.D. in statistics or someone who's got a team and a project that's in the IT organization delivering all of these capabilities. The business line and the business owner really need to be driving the success of these initiatives, and that's where I see this agile movement really impacting BI and analytics moving forward.

Well, George, I agree totally. Many would say that when business intelligence involves both the business and IT, agility just seems to get lost. Today's business users want the ability to create their own analytic applications so they can make decisions quickly with limited IT involvement. How is Alteryx helping companies meet those types of business demands for these users?

George Mathew: This is a core of what we think of everyday in terms of how analytic apps have to be designed. We think of our solution and Alteryx Strategic Analytics largely as a desktop-to-cloud answer, Ron. The idea with the desktop-to-cloud is that a certain user class – the data artisans – is largely delivering and designing those analytics experiences. The desktop makes it easier to merge unstructured/semi-structured forms of information together with traditional data that exists inside a relational database and be able to compose an analytic app. What has been successful for publishing that app is publishing it to internal servers that are populating in a private cloud service. This is where the core of Alteryx and our success has been with our customers, particularly for the past three or four years.

As we see this evolve, clearly there's a need to provide desktop-to-cloud to the public cloud too. Much of our product strategy and direction at this point is focused on delivering that solution, both desktop-to-public and to desktop-to-private cloud.

You mentioned the term “data artisan.” Could you define what you mean by data artisan?

George Mathew: There has been a lot of discussion in the overall market about a new type of user that's emerging, particularly in the last few months. There's a lot of information that's being passed around about this emerging class of people called data scientists. I think I'm evolving that notion a little further to say that the data artisan is an individual that resides typically in the business line and is responsible for how he or she is pulling sources and experts that are related to data inside of an organization and driving insight out of it. So oftentimes, what we've seen in traditional solutions in the past, is that the business user has been defining requirements to a more technical individual, perhaps in an IT organization. That becomes a stated project that takes a series of iterations and efforts to develop an analytic application or a BI output, usually in months and quarters.

What we see now is these in-line users of analytics, these data artisans, largely need to be able to respond almost instantaneously – within hours, minutes or even seconds – to deliver answers. I'll give you a good example of this. If you're a retailer and you're looking at a merchandising optimization situation where you have to make a decision on how much inventory quantity you bring in from your supply chain, or you have a jet plane gassed up right next to your offices and that plane is taking off for the next four locations where you're considering the opening of a new store, all of these analytics are much, much more responsive and instantaneous. They are required right when there’s a need for a decision to be made. These situations are not necessarily something where you can rely on a longer project to deliver the answer. So these individuals that are creating analytic applications out of those data sources are the ones that I call the class of user as data artisan.

Another market need where you have a very unique approach is customer analytics. Could you share your insights in that area and tell us how organizations can incorporate social media data and unstructured data?

George Mathew: One of the things that we've witnessed, particularly for the last three or four years, is how much the forms of customer information are just not held in a traditional data warehouse anymore. In a lot of ways, the BI tools that were in the market for the last decade or two have largely been built to directly point against a data warehouse and be able to pull reporting and ad hoc analysis out of that, including perhaps a dashboard or a mobile experience to front end the ad hoc analysis.

When we look at how customer analytics has evolved, we now are seeing a lot of information coming from outside the enterprise such as demographic information from Experian, firmographic data from Dun & Bradstreet, population data from the U.S. Census Bureau – that we happen to be packaging as the sole provider, weather patterns, and information from social streams like Twitter. The world has become a much more structured/unstructured/semi-structured world, not necessarily centered around a single information source being the enterprise data warehouse. It has become about being able to consume and rapidly ingest all of these information sources effectively to make better decisions in the organization. So our belief is those kinds of customer analytics really need to be delivered in a solution that has the ability to consume that information, digest it and be able to create an analytic experience out of it much more seamlessly. This is where we've highlighted our ability to drive strategic analytics inside of an organization and effectively see the way the world is going around analytic development.

When I look at Alteryx and agile BI, we've all heard a lot about software as a service, data as a service, but you talk about platform as a service. Could you tell our audience what you mean by platform as a service?

George Mathew: It is very similar to what has done. The beauty of Salesforce is that it started as a company to deliver CRM, but at the end of the day, they delivered a very complete transactional application platform in a cloud-based service. That's where we see ourselves as a company. As much as Salesforce has become a beautiful transaction-processing platform for building transactional applications like CRM, we see ourselves being the analytic platform. Those analytics are ones that we are predefining and pre-baking around, for instance, churn analytics or trade area promotions. They are being used effectively by retailers, telecoms and other organizations that are leveraging Alteryx. The underlying capabilities in that desktop-to-cloud experience is for us an analytic platform. We see this largely being delivered as a service. One hundred percent of our revenue comes from a subscription-based model to deliver solutions and success to our customers. That platform effectively emerging for us is very key to our long-term growth and success. We see our customers starting with some initial use cases of leveraging Alteryx for very specific problems that they need analytically solved and then leveraging us more broadly as an analytic platform as a service.

George, geographic business intelligence is of very high interest to our audience recently, especially to the retailers. Could you explain Alteryx’s approach to geographic business intelligence, and then tell us how your customers are benefiting from it?

George Mathew: It's an area where we've seen some pretty interesting changes, particularly in the last few years. I think even a decade ago when people were looking at geographic intelligence, or call it spatial data or spatial analytics, it was largely a separate science and a separate practice, similar to how predictive analytics was largely kept separate from the rest of the BI experience from a reporting and analysis perspective. There was a separation of concerns with geographic or spatial data.

But here's where the world has converged quite dramatically. Analytics is analytics. You need to be able to take the information that's spatially oriented and the information that you need from a predictive forecasting standpoint – information that's fairly sizable from a point-of-sales perspective – and be able to bring it all into one integrated view of how the business is operating. So for us, geographic business intelligence is not viewed as a separate entity. It's just a natural way for us to create better analytic applications. We see our customers benefiting dramatically, not by the fact that we happen to have all of this very smart capability around spatial processing and geographic business intelligence, but rather as a natural thing that all of our customers need to do in order to make better decisions inside their organizations. Clearly, this is a very important piece of how retailers operate. For example a retailer looking at their merchandising assortment needs to be able to figure out, based on the point-of-sale data and the customers that have come in and purchased certain SKUs of products in the last 90 days, how they can create the next best prospect list by locational area and be able to create a merchandising assortment that's optimized for that specific region, location, or storefront.

We have customers like Walmart who are very actively using our solution to identify the next best prospect from a merchandising standpoint directly using our analytic engine.

We see this not only in the context of retail. There are incredible examples now where churn analytics inside a telecom largely happen through better understanding of how customers are coming into a call center and reacting to what their experience has been. Based on negative and positive experiences with a call center, they decide whether they're going to churn out. There are a lot of churn models that have been built accordingly. It turns out the most interesting predictor of churn is not just the fact that someone has called in and had a good or bad experience. Dropped call volume and the information about signal strength from cell towers that are nearest to that person's home or office location also play a large role in predicting churn.

Being able to bring that kind of spatial awareness, that kind of spatial context, to the overall churn analytics inside a telecom or the retail merchandising optimization example that I mentioned, is just the way analytics are now going to be delivered. I see spatial processing and the geographic business intelligence that surrounds it being just a natural part of how analytics is delivered to customers and creating better success in their organizations.

Well George, in addition to Walmart, could you tell us about some of your other customers?

George Mathew: Our customer base is very extensive. Let me just highlight some of the key industries that we serve. We have a lot of customers that are retail in nature, like Walmart and Toys”R”Us. A derivative of retail market is, of course, restaurants, and we have captured quite a number of customers that are very actively working with us within the restaurant space. The holding company of Outback Steakhouse is a very large customer of Alteryx. Whenever any sort of analysis for the locational development of a new Outback store occurs, it’s Alteryx that's being used. McDonalds is also a customer, and Chipotle (which McDonalds divested from about four or five years ago) is also a pretty significant customer. Other retail customers include companies like Dick's Sporting Goods.

Interestingly, companies that have broader analytic use cases that need to drive demographic, psychographic, consumer-based information with their data management happen to use us pretty significantly as well. Most recently, we've been working with very closely with the Boy Scouts of America. They have been using Alteryx for all of their membership management to optimize their troop membership throughout the country.

Additionally, seven out of the top 10 telco wireless providers in the North American market are currently using Alteryx so that includes Verizon, Sprint, Cricket and AT&T.

That is quite an extensive customer list. We have discussed how Alteryx approaches agile business intelligence. We also talked about mobility and geospatial business intelligence. Looking to the future, what do you see as the next biggest paradigm shift that we'll witness in business intelligence over the course of the next few years?

George Mathew: Well, I think the shift is in process. Ray Kurzweil said the future is here but it's just unevenly distributed. All of the things that we're seeing right now are taking shape over time. What we're seeing the emergence of successful businesses that are delivering cloud-based analytics into this market. I think this has been an area that's been very challenging for companies who've gone after a very on-demand-only view of BI and analytics to date. Where we see this evolving, and what we fundamentally believe, is a desktop-to-cloud experience around delivering those analytic applications. That will take a few more years to mature and seed itself into the market. We see ourselves being the spearhead of that moving forward.

Clearly, the mobile topic is one that's becoming much, much more prevalent. What I see today is the emergence of HTML 5 as a very natural way that analytic applications are being delivered and consumed on a mobile interface. Of course, that starts with something as elegant as the iPad but also doing it to smaller form factors like the Android and iPhone devices. The prevalence of HTML 5 is certainly going to play a big role in how mobile analytics is effectively being consumed.

I would say that the next big area is how big data and just data in general are being consumed from an application standpoint. The Hadoop movement within big data circles is pretty significant. To be able to consume a Hadoop cluster, ingest even petabyte-scale information and bring that forward to meaningful analytics to an end user is a very natural set of applications that are going to come out of the infrastructure that's surrounding Hadoop.

That should give you an idea of where a lot of my time, attention and thinking is going as far as where I see the next generation of analytic application development. Hadoop and being able to leverage that clustered form of very unstructured information rapidly being consumed on petabyte-scale basis is certainly where the big data movement is headed, and we absolutely are planning to participate very broadly in that movement.

Well, it's definitely an evolving area, and I want to thank you for taking the time to tell BeyeNETWORK readers about Alteryx.

SOURCE: The Shifting Landscape of Analytics and Business Intelligence - A Spotlight Q&A with George Mathew of Alteryx

  • Ron PowellRon Powell
    Ron is an independent analyst, consultant and editorial expert with extensive knowledge and experience in business intelligence, big data, analytics and data warehousing. Currently president of Powell Interactive Media, which specializes in consulting and podcast services, he is also Executive Producer of The World Transformed Fast Forward series. In 2004, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). He maintains an expert channel and blog on the BeyeNETWORK and may be contacted by email at 

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel.

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