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10 Key Takeaways from the DATAcated Conference 2021

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The DATAcated Conference 2021 brought together data lovers from a wide range of industries and expertise areas—everything from financial services to healthcare, energy, retail, and more. For those who aren’t already familiar with the conference, it provides an opportunity to explore trends, case studies and tools, and have discussions with data-loving peers that are likely facing similar challenges.

As you can imagine, the talks were packed with seasoned advice, crafty tips, and tangible strategies for professionals looking to leverage data more effectively and drive greater business impact. RapidMiner was pleased to be included in the impressive lineup of speakers, with our very own Martin Schmitz sharing how our research branch in Germany has been working with beer breweries to improve their workflows with data science and machine learning.

But Martin’s talk was far from the only bit of interesting data science discussed. Overall, the conference offered valuable advice for anyone looking to improve how they work with data. Because there were so many lessons from the conference (these certainly weren’t the only insightful moments!), we thought it would be helpful to summarize some of the big takeaways that popped up over the two days.

If you’d like to listen to the presentations in full, you can watch recordings of both days of the event here. And be sure to check out Martin’s talk, Excuse me, bartender, there’s AI in my beer.

Ten Takeaways from the DATAcated Conference 2021

So, without further ado, here are our top ten takeaways from DATAcated 2021—five from day one, and five from day two.

Day one

1. Regardless of what you’re trying to do with data—whether you want to provide personalized experiences or detect fraud—you need to be able to do it with data that’s happening right now and be able to do it quickly to maximize your ROI. (Jordan Tigani, SingleStore)

2. If you’re struggling to get organizational buy-in, leverage use cases. Don’t try to boil the ocean! Start with smaller pieces of the puzzle by coming up with tangible use cases where you’re using little pieces of data to illustrate the bigger picture learnings. (Tiffany Perkins-Munn, Ph.D., BlackRock)

3. When we try to establish a single source of truth, what we’re really trying to do is establish authenticity, making sure that the data you have is authentic to the folks that manufactured it as well as to the end users who are consuming it. (Debbie Williams, BNY Mellon)

4. We’re digitizing so many things in clinical medicine, which holds huge promise for the future. Digitization leads to demonetization. And demonetization leads to dematerialization. This digitization gives us a unique opportunity to actually democratize healthcare. (John Nosta, Nostalab and Professor Shafi Ahmed, The Royal London Hospital)

5. Explainability is key when you’re trying to put out a statistical model. You must be able to easily explain why the output is what is it, or you won’t be able to convince anyone that it’s worthwhile; if people don’t understand the model, there won’t be full trust in your process. (Kimberly Sorrell, MBA, MSCS, Southern Company)

Day two

6. Enabling quick access to as much data as possible (both external & internal) helps move a manufacturer from being just a supplier to being a partner who’s bringing new ideas & uncovering insights that allow both organizations to grow. (Katie Hermann, Master Lock Company)

7. It’s important to find the right champion in both sports analytics & analytics in general because having a champion helps you do things faster than competitors. This means being able to look at the data the right way & having the resources needed to work quickly. (Hyoun Park, Amalgam Insights)

8. You need to map out the business problem and use that to decide what kind of work you’re going to do. We don’t do data science for fun (mostly)—we do it to drive business revenue! Prioritize potential projects based on both impact and feasibility. (Martin Schmitz, RapidMiner)

9. When analyzing customer voice, companies should turn to social media. This feedback is raw, honest, and unsolicited, which allows you to gain insight from what customers are saying out in the wild. (Kendall Ruber, MSBA, Yum! Brands)

10. Companies already actively believe in the power of recommendations. This belief means there’s ample opportunity for the kind of innovation that data and associated technologies can provide in terms of helping to drive more personalized and, ultimately, successful recommendations. The whole process of exploration vs discovery is key. It might be unintuitive to some, but it speaks to the innovation that data science has to carry forward in any industry. (Enrique Olivo, McDonalds Canada)

Wrapping Up

So, there you have it! Our top ten takeaways from DATAcated 2021 to help you better think about and work with your data. We would like to give a special thanks to Kate Strachnyi, the host and Founder of DATAcated, for including us and hosting this top-notch virtual event!

Looking for more insight from leaders in the field about how to use data for maximum impact? Check out 50 Ways to Impact your Business with AI for examples of data science use cases from a range of industries.

Looking to drive real business impact with AI?

Sometimes the most difficult thing is simply knowing where to start. Identifying impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI.

Learn from these 50 use cases across all industries.

Additional Reading

Kristen M. Vaughn

Kristen M. Vaughn

Kristen Vaughn is a Digital Marketing Manager at RapidMiner. She develops, manages, and executes digital strategies to better reach audiences, provide the information that users are looking for and create engaging experiences across online channels.