Your Decade in Analytics -- Share your thoughts!

Question

On the brink of 2020, we thought it would be fun to hear from the Alteryx Community on your past decade in analytics -- and what you think is coming next.I included several questions here to help get the conversation going.* In the world of data science and analytics, what do you think the most memorable change has been in the past decade, and why?
* What’s the biggest challenge data professionals have faced — and overcome — over the past ten years?
* What has been your biggest personal analytics accomplishment in last ten years?
* What's a favorite (funny, meaningful, etc.) moment in your career in the last decade?
* What are your predictions on topics like Culture and the Industry; Technology; Equality and Diversity; Data Literacy; Analytic Teams and the Way We Work; How Orgs Use Data?
* Ten years from now what's the one thing we, collectively as human beings, will look back on in shock? For example... I can't believe we used to store data on hard drives or I can't believe we used to use plastic!
* If you could solve a pressing world problem in the next decade with data science and analytics, what would it be?
* What area of life do you wish you had more data on?
* Is there anything else you'd like to share? Please do!

Reply to this thread to answer any or as many of the above questions as you like. All thoughts and opinions are welcomed and encouraged!We're planning on featuring some of the answers in upcoming blog posts both on Community and Alteryx.com.We look forward to hearing your perspective!

SeanAdams · Answer

Hey @LeahK  - happy 2020 to you!

The biggest change that I've seen over the last 10 years is a gradual progression towards a world where Analysis has truly become the domain of operational folk.    The workforce is becoming more technical with coding skills becoming much more common in people in roles across the firm (from HR to Finance to customer outreach - we're seeing coding skills become common and pervasive).

We have also seen an explosion of innovation - 20 years ago we saw the early days of user-friendly data prep; and we started to see companies like Crystal Reports beginning to bring reporting to end users but at that time you still needed very technical skills to configure the back-end.    10 years ago, reporting was more of an end-user capability but the data preparation was still a Technology role largely.

Now we're seeing a world where there are dozens of companies who realise that data preparation and user-friendly ETL is the secret sauce to unlock the value in organizational data - and we're seeing billions of dollars of investment in this industry (especially with the large industry moves this year like Tableau).

In terms of big analytical accomplishments in my own work - I wish I could point to something like the Higgs Boson, or being able to assemble multiple streams of radio telescope data into our first picture of a black hole.   I wish that I could claim credit for the data analytics which now allows space companies to land and re-use rockets; or the fact that our technology is quietly getting smarter (like Google's inbox which has learned how to filter out much of the obvious spam; and order my inbox in a way that reduces the noise).

But no - these were accomplished by people far more capable than me.

Our achievement over the last decade has been much quieter - we are very proud to have trained several thousand people in using data to solve their own problems, to bring their own sense of agency back to their work.   We look at our work as a force-multiplier effect, and the dramatic improvement in the capability of analytical tooling over the last 10 years has allowed us to bring thousands more hands to the pump to help us solve little AND large business problems by giving existing folks new skills and tools.

My sense of the challenges for the next decade are:

* Acceleration of data: Organisations have not yet even scratched the surface of understanding the richness in their own data; never mind the data that's in partner systems or in the relationship between them and their customers.   We will need to continue to invest with laser focus in user-centric data-prep / ETL / data quality - and be ruthless in driving down the cost and effort to assemble and transform data.   Yes, this is not glamorous but it's necessary and this is not going to change.
* Blurring the lines: Where does the cloud stop and start?   We're seeing an acceleration of innovation around using the cloud as a way to deal with infra and data.    We're seeing this in gaming (NVidia; Google and Microsoft all announced big gaming cloud streaming services); data storage (companies like Snowflake; Azure; and Google) and software as service explosion.   I don't believe that we'll see a complete transformation of the landscape in 10 years (we always expect that things will change faster than they actually do) but we're already seeing a blurring of the lines of what tech / data would be on-prem vs. on-cloud.
* Finally - I think that the biggest challenge in the next decade is going to be signal vs. noise.   We are bombarded with e-mails; Instagram; twitter; breaking news; SMS - and many folk are expected to be available all the time.   Data volumes continue to increase (including derived data as we understand more about our data landscape and assets) - and the competitive landscape is not slowing down (in fact, with cloud reducing the barriers to entry; it's speeding up).   Against this backdrop - we still have 24 hrs in a day; a few thousand calories of energy to use; and a limited attention budget which we need to protect (which companies are working hard to compete for).   * The key challenge for all of us will be finding better ways and tools to focus on the important; and get rid of the noise
* What's data vs insight.   
* What's important in my inbox vs. just more attention grabbing notifications
* Less vs. more - but it has to be the right items 🙂

It's going to be an interesting couple of years - looking forward to seeing how this all plays out!

Sean

MarqueeCrew · Answer

2010:  Broomfield

Snowpocalypse 🥶

2012: Alteryx gallery is launched!  I won this iPad for contributing one of the first macros.

2015: Boston with my friend Enrique Javier

2016: San Diego I came in second at the space invaders championship. That's closer than my 2012 Grand Prix finish.

2017: Las Vegas. What an Inspire!

2018:  Anaheim & Codechella

2018: London

2019:  Sydney

2019: Nashville

2019: London

Where I'm at now, celebrating the new year:

Cheers 🥂

JohnJPS · Answer

In the past decade, I went from software engineer to data architect with a focus on analytics (when I learned Alteryx), and for over a year now, I've been in charge of Analytics, Reporting, BI and ML platforms where I work. My biggest accomplishment is still in front of me as our team will be applying ML to a variety of problems. We're reading papers and applying cutting edge concepts almost daily. Other notables this decade were placing well in the Kaggle Higgs Boson challenge, and of course being an Alteryx ACE for a few years!

My funniest tidbit is... we installed an experimental chatbot, and I fed in "Haha, I will" as a feeder, in my mind thinking "Haha, I will fool you, robot!" but it completed the feeder with "Haha, I will kill you, you insolent bastadge!" (except it was the real word, not bastadge, but the real word got bleeped here, LOL)... anyway, so, yeah, not quite ready for production there, I don't think.

The biggest things in data science, for me, have been cloud infrastructure and the explosion of machine learning. Around 2014 I was struggling to get Theano to utilize a high-powered gaming laptop GPU. Now I can spin up a server in EC2 with a GPU roughly 10 times as powerful as that laptop, and pip install tensorflow-gpu and keras like nothing (which are easier to use than Theano by miles).

Next decade - a few things: first, I think back to the explosion of computers in the 70's and 80's, roughly around the time that they first made compilers that were good enough to compile a better compiler; that explosion continues to this very day... similarly, we are now at a place where we can use AI to construct a better AI. That rocket ship is just now taking off: no longer sci-fi: in ten years AI will be super smart and almost perfectly humanoid in its capabilities. Next, biotechnology... give everyone a built-in AI assistant: you talk to it and it talks back, you even tell it to build things for you and it knows (and/or can learn) how to do so. Finally, quantum computing will develop and become available as quantum-enabled cloud services, further expanding the power of computation.

Oh, and not to get away from data: this is ALL data driven. Alteryx will be there for all of it and will continue to thrive!

As computers get more powerful and automation becomes more robust in its capabilities, there will be plenty of concern with regards to joblessness. Data literacy might go backwards at some point, when the robots know all the data and we just ask questions and/or make demands of our super-human AI partners. I think these will be the biggest challenges eventually. Right now, it's still a smorgasbord of opportunity and will probably still be in ten years... but eventually we'll have to take stock of what were doing and the implications thereof.

Looking back in shock. I dunno, but I hope it will be with regards to politics or religion; these areas have been shocking for a while and I can only hope that in 10 years we're sensible enough to realize it from a common, broadly agreed-upon angle.

If I could solve any problem... probably finding cures for deadly diseases. Same for the area I wish I had more data on.

Anything else? Within 20 years I aim to be retired and perhaps author of a good sci-fi novel. Haha.