General Discussions

Discuss a wide range of topics! Questions about the Alteryx Platform should be directed to the appropriate Product discussion forum.
The Expert Exam is now live online! Read about the specifics and what it took to bring it to life in the blog by our very own Elizabeth Bonnell!

Community Q&A Session with Dean Stoecker - Share your questions here!

Highlighted
10 - Fireball

I think this is a great event that you guys have planned!

 

I've been using Alteryx as part of the Adapt program since late May, and I absolutely love it!

 

Here is a bit of background before I get into my question. My family runs a greenhouse farm back in Nigeria, and Alteryx would be a gamechanger for our analytics endeavors. However, being a small family-owned business in a developing country, we do not have the finances to get an Alteryx license. We do not qualify for Alteryx for Good since we are a for-profit business. I am also using Alteyx for an independent stock market predictive algorithm project, and I do not have the financial backing of a firm. 

 

I was thinking that it could be mutually beneficial if Alteryx offered a pay-per-tool subscription plan. This would allow for smaller businesses to access Alteryx powerhouse analytics services while sticking to a budget they can afford. Alteryx would gain more customers with this as well. I don't believe market cannibalization would be much of an issue, as bigger corporations would probably use numerous tools, and it might be more expensive for them to go for a pay-per-tool model.

 

So my question is, will Alteryx consider providing a pay-per-tool subscription model in the future? 

 

The relevance to the current theme is that access to cutting edge software such as Alteryx will help those in analytics solve social problems, such as food insecurity. Reducing financial barriers for all kinds of businesses helps. 

 

Thanks!

Highlighted
7 - Meteor

I am excited to participate in this event!

 

I am looking for guidance on the next best steps to potentially pursue a career with data and data analytics. I am currently enrolled in the ADAPT Program, and I am about to start the Capstone Project. What would you recommend for someone like me who is just beginning their journey?

Highlighted
7 - Meteor

Thanks for this opportunity! I'm curious to know what Dean thinks of the current competitive landscape and how it might take shape in the future. I think it's easy for analysts on the front lines to see the value add of Alteryx because they have direct experience with the day to day work that goes into ETL processes. However, upper management doesn't always see the back end work and they're often juggling other considerations (e.g. cost, company infrastructure) in making their decisions on what software to invest in. How do you see the strategic direction of Alteryx taking shape over the next few years to convince key stakeholders of the added value? Is capturing greater market share part of that vision?

Highlighted
8 - Asteroid

Thanks @WillM & @DeanS for giving as this opportunity to discuss and see the world data from you point of view.

 

As a learner and a person interested in education and the influence of technology and data on the global education systems I am wondering how in your opinion the big data and data analytics will help the post-pandemic education module which I believe it's heading to be a hybrid on-campus / online module?

Here I asking about the education from KG to universities. How big the role of data analytics will be to shape this system?

 

Thanks again for the opportunity.

 

AliAS

Highlighted
Alteryx Partner
First, thank you for offering the ADAPT program. It has been very valuable to me.
 
Is Alteryx considering a longer-term program, like ADAPT, that would support on-going skill development, and most importantly, a Designer license, for those of us who don’t have the “luxury” of a organization-provided license? And, what might that program look like?
 
Thank you!
Highlighted
6 - Meteoroid

Hi, 

 

When I started my self-place learning journey with the videos on each tool, it was difficult to memorize things even though I took notes along the way. However, after I finished the Academy Summer Camp where I had the opportunity to see how certain tools applied in solving business questions and get my questions answered through live Q&A, I feel I suddenly understand and remember the tools better. 

 

My question is : there are enormous learning resources in the community, would you make them more alive, more relevant, more meaningful and more actionable to the beginners? This is a low-hanging fruit that can increase your user market. 

For example, there are different Alteryx starter kits. Would you hold a series of guided learning webinars on those starter kits? I would be inspired even if I am not in that specific sector. 

How about learning series on how to install and use popular tools from the Alteryx Gallery or community-support macros?

 

Thank you

Highlighted
15 - Aurora

Very cool, and I'm kinda late but FWIW... my question is how do you stay ahead of Open Source. It seems that our data science community wants to be right on top of things: they want packages from GitHub that aren't even in PyPi or CRAN yet... they want features that even the most cutting edge vendor cannot possibly have until months after the fact.  I'm interested in your thoughts on this... it seems that hooking into that at some formal level, with guard rails, is needed.

 

Also, how soon before our ML engines spot our problems before we even ask them too?

Highlighted
14 - Magnetar
14 - Magnetar

Looking forward to tomorrow's Q&A session! 

 

I'd love to hear your thoughts on balancing the "latest & greatest" with the "tried and true" - when it comes to Alteryx's strategy as a company and leader in the analytics space, as well as for us users, as we try to balance the battle between maintenance vs. creation. How do you personally balance the two? When you are faced with choices about investing in new technology/solutions, or putting resources into improving the "core" functionality, how do you juggle & make choices? How does this translate to the analyst or data scientist - i.e. what are some things to keep in mind when I am trying to decide whether to spend time refining an existing workflow or process, or diving into learning new tools & solutions? I think this is a major decision factor for a lot of folks who WANT to learn the cool, new, flashy features... but they also need to make sure the things they've built in the past are maintained and continuously streamlined & improved. How do you encourage people/teams to not lose sight of taking care of the foundation, even if not's quite as exciting as all the icing on the top layers of the cake??

 

Another topic I think is really interesting in today's current environment is how to make sure you are using data responsibly. We are SO inundated with facts and figures and charts and graphs and numbers that used to look really big and scary but now are, in many cases, becoming desensitized... how do you make sure that you are not only providing the right information, but the right AMOUNT of information, so that the consumers of data are not overwhelmed by the numbers to the point where they cease to have the same meaning? 

 

Love having the opportunity to have these honest and transparent conversations with you and the Alteryx family, Dean - thank you for being open to sharing your views!!

 

Cheers, and see you tomorrow!!

NJ

Highlighted
6 - Meteoroid

Hi Dean,

 

I introduced Alteryx to my team in Warner Bros as an alternative to SAS. We saved weeks of work into just an hour of a workflow.

The data size we were dealing with at that time was only about tens of millions of rows and it worked fine. But currently in large companies the data size is increasing every year and is literally exploding into tens of billions of rows. Alteryx is not able to process data at such high volumes. Alteryx designer's In-db tools seem to help a bit as it offloads the compute to the db. But it does have the following drawbacks:

 

1) In-db tools are limited in variety and capability compared to the suite of regular Designer tools. We can only do simple joins and transformations with in-db.

2) The in-db tools constructs a SQL query but is highly inefficient due to multiple nesting and subqueries. I find that the queries run well if I just write the SQL efficiently instead of using in-db designer tools.

 

As a result, when dealing with billions of rows of data, in-db leads to poor performance and limited functionality, while non-db regular workflows do not even complete.

 

I am a big fan of Alteryx and I don't think there is any product that has such mature features and flexibility like Alteryx. But, with growing data volume, even after your parallel processing updates, there needs to be a server offering that can process massive volumes of data.

 

Do you plan to re-design your compute engine with your Alteryx Server offering that can handle the kind of volume that is synonymous with Spark or massive cloud db computes? 

 

While this is not a problem for most of your customers, with the growing data volume, this will inevitably become a bottleneck to using Alteryx at large organizations that produce massive data.

 

 

 

 

Highlighted
6 - Meteoroid

Hi Dean,

 

I introduced Alteryx to my team in Warner Bros as an alternative to SAS. We saved weeks of work into just an hour of a workflow.

The data size we were dealing with at that time was only about tens of millions of rows and it worked fine. But currently in large companies the data size is increasing every year and is literally exploding into tens of billions of rows. Alteryx is not able to process data at such high volumes. Alteryx designer's In-db tools seem to help a bit as it offloads the compute to the db. But it does have the following drawbacks:

 

1) In-db tools are limited in variety and capability compared to the suite of regular Designer tools. We can only do simple joins and transformations with in-db.

2) The in-db tools constructs a SQL query but is highly inefficient due to multiple nesting and subqueries. I find that the queries run well if I just write the SQL efficiently instead of using in-db designer tools.

 

As a result, when dealing with billions of rows of data, in-db leads to poor performance and limited functionality, while non-db regular workflows do not even complete.

 

I am a big fan of Alteryx and I don't think there is any product that has such mature features and flexibility like Alteryx. But, with growing data volume, even after your parallel processing updates, there needs to be a server offering that can process massive volumes of data.

 

Do you plan to re-design your compute engine with your Alteryx Server offering that can handle the kind of volume that is synonymous with Spark or massive cloud db computes? 

 

While this is not a problem for most of your customers, with the growing data volume, this will inevitably become a bottleneck to using Alteryx at large organizations that produce massive data.