Chairman and Chief Executive Officer of Alteryx, Dean Stoecker in an interview with Techseen talks about how self-service data blending and analytics is changing the landscape of business analytics and processes around the world. He also talks about how Alteryx, which is a data blending and advance data analytics company, is striving to change the notion that there are limited data scientists in the world. Stoecker claims that the company is making BI analytics simpler and smarter and at the same time giving the power of analytics to normal people, making them citizen analysts and fostering the ‘quantified self’. Excerpts:
Techseen: What is the difference between a self-service corporate data analytics and self service business intelligence, can these terms be used interchangeably?
There is a big shift that is going on in the enterprise today, over the last decade or so the stack vendors have had their way with IT, putting in heavy bare-metal in big implementations such as BOBJ and Hyperion. I think what has happened in the last decade has really ramped up their multi-million dollar implementations. I feel that he market has moved on and the entire tech stack has modernized in a serious way. Part of it is just the natural shifts that have come through in the technology world. A lot of it because of the user’s desire to take their career back and take charge of the data in the world around them.
What we are seeing is the rise of the self-service data analyst, the quantified self, the individual who is not only creating big data in social media, cloud services and mobility but they are also the consumers of data analytics. They are a savvy audience and want to be challenged about the world around them. This is creating a change in IT that is unstoppable at this time.
Techseen: You talked about data literacy. How does it fit with self-service; as one does not need a background in business intelligence, statistical knowledge or data mining capabilities per-se?
I would say that the general trend is towards code-free technologies that allow users to get answers to questions about any data that they want. To be able to see insights that not only brings delight to their personal world and their job but also drive operational efficiencies for their employers. We see thousands of analysts who log onto this product, they are living in a complex view of an excel or dabbling a little bit in sequel coding, but it is the 21st century, you shouldn’t have to write sequel coding even though you know how to write sequel and you shouldn’t have to turn your data set to a statistician or tools that were used in the 1970s like SPSS. The users want to take their lives back; they want to take control.
IT is a little befuddled by this because they spent millions of dollars over the years on the enterprise stack which is built for IT, managed by IT, bought by IT, but line of business never really got what they wanted. We are seeing a sea change in this generational shift and a lot of it is, technology like ours that reduces the friction between man and machine, making it easy for people to become the quantified self, to be citizen data scientists. I think it is changing the new age data worker, people don’t have to go to 4 years of university anymore, you don’t have to have a degree in business analytics. You can get a Nano-degree for data scientists, it teaches you how to and why to build a model for any kind of data. We are changing the notion that there are only limited data scientists in the world.
Techseen: Speaking about the Nano-degree for data scientists, your focus has always been towards making people aware, educating them and then going ahead and using your product. Were there any challenges that you faced which led you to personalize the process?
No, I think the biggest challenge is the competition that we faced. It was not the traditional software products that we compete with, but corporate cultures, and so we did not have to wait for people to get smarter, we had to wait for corporate cultures to be willing to change knowing that data worker is not going to sit and wait for IT to modernize the stack.
We have seen this before in the tech world, we saw it with the first wave of cloud services, where Salesforce made CRM easy, what it did was destroy Siebel, because it was an IT centric CRM that required massive amounts of coding and multi-million dollar implementation and months to implement. Salesforce on the other hand was a light-weight easy to use system, anybody could log onto it in 30 seconds and start feeding data. We saw the same thing with Workday disrupting PeopleSoft, I think something similar is going to happen in the BI analytics space too. I think the stack vendors are battle complacent, where they still sell to the IT, we sell to the analysts, we have a subscription model, they have a perpetual model, we have almost instantaneous ROI for anyone who tries out and downloads our product. Hence, we are seeing disruption in the BI analytics space and I feel the whole stack is getting modernized and it’s our turn now.
Techseen: By using Alteryx, can we say that you are making BI analytics personalized?
Clearly we are, I think it does not separate value from the enterprise though. I have a board member who was the former chairman the tech investment banking practice at Morgan Stanley. He joined my board earlier this year, when I got him on board he said something about our software that I continue to talk about today, he said, “I get it Dean, you liberate the analyst in the line of business and in doing that you turn every data worker into the discoverer of marginal profitability.” He’s right, for example, the Ford Motor Company
, an old school organization, 110 years old, still making cars, modernized with robots today, said “We went to bed as an industrial giant and woke up as a data analytics company, we have to now drive value with data and analytics.”
So what they did is take a bunch of analysts, some of them were quants (quantitative analysts) most of them were business analysts who hated their job because they never reached the thinking stage and put Alteryx in their hand. In 9 months they drove $250 million in savings. It was not the money they found, it was a whole bunch of million dollar problems that they saw and solved. They were able to prosecute analytics with Alteryx that they could not do before.
Techseen: Do you think, your software reduces the enterprises’ dependency on professionals and companies that are in the core business line of BI analytics?
I think it changes the dependency, I don’t think it necessarily reduces the dependency. We see organizations, which will leverage Alteryx and reduce headcount, but usually it will change the dependency. For example if an organization has a PHD quant, why will the PHD quant build models and then hope that somebody deploys them? What they really should be doing is changing their ability and turning them into teachers of why an analyst should build a teaming model versus a regression model. Because doing the model is not hard anymore, we have proved that, a guy like me who is a baby bloomer can go in and check out ERPs, Salesforce stages, I can get the perfect data set and global analytical models, I still need help with knowing what that r-square means and the appropriate decision that has to be made. This means that there is a change in the reliance that we have on the quants, who become teachers as opposed to doers.
Techseen: Alteryx has been providing solutions to various industry sectors, starting from media and entertainment, restaurants, financial institutions to healthcare and travel and hospitality. According to you which is the upcoming sector that will accede to self-service data analytics rapidly?
When we first started with Alteryx, we built a space around geo-spatial analytics, we would talk to the organizations that were more spatially literate, which were usually telecommunications and real-estate companies. But we have expanded our platform to include VR, libraries, we build our own packages, we do financial analytics, a whole series of crowd connectors. We don’t see any concentration in a specific vertical.
We almost are equally involved in financial, insurance, healthcare and we do a whole lot of work with the government trying to get smarter and processing the human data to drive services. We do a ton of work in manufacturing, high tech media and I would say that the beauty of a platform and not a point solution is that it addresses a very wide variety of used cases in a given organization. The IT loves us, because they go from being gatekeepers to Air Traffic Controllers.
Techseen: What are your thoughts about AI? Are you thinking of building a solution around Artificial Intelligence?
I think that there is obviously a lot of interest in AI, and the things leading up to AI like machine learning. So we have connectors into Microsoft Azure so that the library can be leveraged into Alteryx, we have connectors into things like data-robots, so that you can push your data-set into cloud based optimization engine, which will figure out which model is best performing for the task at hand. We also believe that just AI in general is never going to replace logic coming from a human.
Techseen: Where do you see the company 2 years from now and how do you see the landscape of data analytics changing in the same period of time?
There is going to be a lot more changes particularly a lot of disruption in the BI analytics world. I think what we are going to see is more capabilities being delivered to lighter machines to more people who have fewer analytic skills, it wont keep them from striving to business performance for organizations. I think we will see data and analytics moving towards the cloud, we think its going to be a hybrid cloud world for a long time.
The legacy companies will still drive a major portion of the world economy and they are not going to move their data capabilities to the cloud anytime soon. I think there will be a move toward data analytics with mobile experiences and that is going to happen in the short run with with forward thinking analytic processes. I think we are going to see an expanded audience for sure, using smarter tools that allow analytics or business workers to ingest more data so the smart data services will clean and organize that data to make thinking faster.
I feel a lot of organizations are afraid of data analytics as they think it as a monolithic multi million-dollar effort, whereas the reality is that it’s a $4000 desktop solution for a $4000 problem to be solved. People want to become the quantified self, they want to have the same experience in their professional world as they do in their personal.