Interview: Ashley Kramer, VP Product Management at Alteryx

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I recently caught up with Ashley Kramer, VP Product Management for Alteryx during the company’s Inspire 2018 conference in Anaheim, California, to get an update on her company’s direction. Ashley is responsible for driving the direction of the Alteryx platform. She brings tremendous knowledge to Alteryx to help scale its product organization, facilitate the development of cloud-based offerings and strengthen strategic technology partnerships. Prior to Alteryx, Ashley was Director of Product Management and Head of Cloud Strategy at Tableau Software, Inc. where she drove Tableau’s move to the cloud including overseeing the vision and development of its SaaS offering and managing their cloud partnership ecosystem. Ashley holds a Bachelor’s degree in Computer Science from Old Dominion University and a Master’s degree in Business Administration and Computer Information Systems from Colorado State University.

insideBIGDATA: As a form of introduction for our readers, how would you characterize Alteryx and its place in the marketplace?

Ashley Kramer: Years ago we were all about data prep and data blending, and over time we’ve added so much to the platform that our user base really spans anything from your typical business user to your citizen data scientist. Now with options like Alteryx Promote, Spark capabilities, and a new Python tool, what we’re really trying to do is have a platform where members of different groups can collaborate. The entry point can really be anywhere. It can be the data scientist that finds the model management solution and brings it back to the business, or it could be the business user that wants to integrate with the data science team. Our customer base actually exists over a pretty wide range because of all of the functionality we offer, especially over the last year considering what we’ve put into the product.

insideBIGDATA: Great! That leads into my next question. How do you address the diverse needs of various people in an enterprise – the IT folks, business analysts, and now data scientists? What’s the goal for your solution in addressing the needs of these diverse groups?

Ashley Kramer: Yes, we say bring them in a place together because what we typically see is everybody has their own point solution that they like to use. The problem is that perspective doesn’t help build a culture with analytics if you have your data scientist using something that they can’t integrate with the business. So what we’re trying to do is have entry points for all of them. Alteryx Designer is our core product that the analysts can use to do any of their modeling, reporting, analysis, and then they can promote those models through our Alteryx Promote product, which is where it gets its name, or our Alteryx Server product, and they all can collaborate together. So the entry point can be any number of pieces of the platform. That’s what I love about the platform. You don’t start in one place and then move through the stack. You can start at any point to be successful.

insideBIGDATA: That’s interesting because I attended an Inspire 2018 session this morning called “Citizen Gain: Citizen Data Science as the Catalyst for the Modern Analytics Strategy” presented by Heather Harris from Alaska Airlines. She had an interesting point of view because she said, “Well, I’m a formally trained data scientist and I had a sort of snobbery about tools like Alteryx. But when I start using it, I started drinking the Kool-Aid.” That’s how she put it. And she said, “Now, that’s all I do is drink the Kool-Aid.”

Ashley Kramer: Yes, Heather is amazing. For someone like a data scientist like Heather used to be– now she’s more of the player-coach building those relationships. But what’s interesting about her is what she would have realized once she was introduced a platform like Alteryx, if she wanted to stay a data scientist, she could go off and do cooler things because now the analysts can up-level their skills. They can do the base-level modeling, build the machine learning models, and then that data scientist can just go off and do even more impactful things with their code.

insideBIGDATA: Alteryx also just announced connectivity with Databricks for Apache Spark functionality. Our readers are very interested in Spark. Why would you be interested in interfacing with Spark, and how do you do it?

Ashley Kramer: We’re seeing a lot of interest from our larger enterprises that have Spark deployments, but they have no way to extract value out of it. Hadoop data lakes are all the rage but nobody knows how to get that information out. So what we did was we’ve always had native Spark capabilities via an ODBC driver. What we’ve just announced is a way to bypass that.

So again, the technical side of the house can come in and use our Spark code tool and put into the business process ways to extract data from Hadoop. Now the business user can just hit “run” on their workflow and they’re leveraging that data. This means they’re able to bring that data in without having to figure out how to go into Spark and extract that value. We had Hortonworks and Cloudera as our first implementation last quarter, and we just announced Databricks. We worked directly with them on that, so we tapped right into their API. We did joint work on it to make it successful.

insideBIGDATA: You also have a new Python SDK, with the ability to track and measure analytic model quality, and also Jupyter Notebook integration. Can you give me a quick overview?

Ashley Kramer: Sure! The Python SDK allows you to build your own connectors and tools within Alteryx. We have 250 native tools out of the box. They do everything from prep and blend your data, to reporting, to spatial, and then predictive include linear regression and forest model predictive preconfigured tools. But a lot of customers want to build their own. They want to connect to data that we don’t have a native connector for, and they want to build more robust tools, so they use the Python SDK for that.

So they can use the Python SDK with an HTML SDK to make it look really nice. The Python SDK is used to build tools and extensions into Alteryx. The Python tool and the Jupyter Notebook integration is so you can take what the data scientist has been doing natively and put it into the business process. They are two separate concepts. One’s to build their own tools. One’s to integrate into the business process with their native code.

Then there is our new Alteryx Promote product which was from our Yhat acquisition out of Brooklyn from last year. They had a product called ScienceOps. So this is ScienceOps where we’ve taken their method of taking our Python and Scala code and wrapping it in a service and making it instantly available for real-time usage. Outside of the technology, which is great, we also got a phenomenal Yhat team that is one of the strongest– it’s a phenomenal team. We’ve had fun integrating them. A lot of them even moved to Colorado to our dev office to be part of the team. So it’s been really fun.

insideBIGDATA: Another distinction Alteryx is providing centers around a “code-free and code-friendly” environment. Now, those are two different things. How are you reconciling them?

Ashley Kramer: Exactly! “Code-free” is where you can use drag and drop tools along with parameters that we’ve pre-built in, or you can make your own in order to be doing everything you need to do from analysis to modeling. We just added the interactive Visualytics tool into the landscape as well. So that’s the code-free perspective. You don’t need to know anything as we’re writing the code for you underneath. But on the flip side, there’s all of these developers and data scientists in the organization. They have code. And so the “code-friendly” features allow them to integrate with an R code tool, the Spark code tool, and the new Python tool that we just announced in beta. Now this group is able to come in and do their code, do their SQL queries, do their R and Python code directly within the platform.

So when it comes to advanced analytics, you see two groups of users. One is the coders, and of course, they can integrate into tools like the Python and R code tool. But then you also see the analyst that wants to up-level their skills, and start becoming a citizen data scientist. So we have pre-configured predictive tools and we have ways to walk them through the process on how to be successful building a model without writing code. We have a lot of customers taking advantage of both these orientations.

I have pretty extensive products career behind me and this is the first company that actually provides the landscape where you can be putting code in, but also writing code and not even knowing it. So that’s why we say code-free and code-friendly.

insideBIGDATA: You mentioned data sources earlier. There are so many data sources in an enterprise. How do you enable users to find and connect to diverse data within the organization? How is that a priority?

Ashley Kramer: We had another acquisition last year before Yhat, called SEMANTA, a Czech data governance firm out of Prague that has a data-cataloging solution with social collaboration. The technology has been in our platform since last year, called Alteryx Connect. It can pull in metadata from any source, whether it’s an Alteryx source or whether it’s Oracle, Excel, Tableau, Power BI. You can pull in the metadata, you can search in a Google-like search experience, and understand all of the assets that exist. So to your IT point, you can certify it, you can see the lineage, comment and collaborate around it. That’s our first-mile challenge of understanding what even exists before you get into the analysis and model-building process.

insideBIGDATA: Can you say something about collaboration at scale? Say you have a large enterprise, many locations, potentially in different countries. How can they use your tools to collaborate on such projects?

Ashley Kramer: Yes, that’s the Alteryx Server piece of our platform that allows you to use any work you’ve done locally to build out models. You can build analytic apps. You can publish and share with others in a governed way on Alteryx Server, and that allows you to scale both throughout the organization, globally, however you need. So that would be our scaling and sharing solution and we have a full governance model around that. And our customers love all this. It’s a great customer base. I hear often, “I’ll quit my job if they take Alteryx away from me.” And I say, “Well, we’ll keep developing and making sure it’s better and better for you.”

insideBIGDATA: How do you compete with the Tableaus and the Qliks of the world?

Ashley Kramer: Well, we partner with them. We’re partners with Tableau, Qlik and Power BI. So a lot of times at the end of your analytic process, you want to build a dashboard and publish it, and so for those customers, we provide that through our partners. We also have Visualytics built within our platform, so we always want it to be customer choice. So we partner with all of those other solutions. I don’t consider any of them a competitor at all.

Actually, I was head of cloud at Tableau for four years. I started at Tableau the exact same time I started here, just right after IPO, and I was with them for four years as they grew and scaled. And so I’m back here to do it again in a code-free and code-friendly way!


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  1. Thanks for sharing this!

    As a product manager growing into his career, i find this very helpful to better understand the roles and responsibilities as a VP and where the progression of this career can grow.