The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases engagement with underlying content. How might your organization benefit from more targeted access to insights? What valuable information might be buried in your existing research that AI could help surface? With over 30 years of experience in marketing, media, and technology, Dan Coates is the President and co-founder of YPulse, the leading authority on Gen Z and Millennials. YPulse helps brands like Apple, Netflix, and Xbox understand and communicate with consumers aged 13–39, using data and insights from over 400,000 interviews conducted annually across seven countries. Prior to founding YPulse, Dan co-founded SurveyU, an online community and insights platform targeting youth, which merged with YPulse in 2009. He also led the introduction of Globalpark’s SAAS platform into the North American market, until its acquisition by QuestBack in 2011. In addition, Dan has held senior roles at Polimetrix, SPSS, PlanetFeedback, and Burke, where he developed cutting-edge practices and products for online marketing insights and transitioned several ventures from early stages to high-value acquisitions. In the episode, Richie and Dan explore the creation of an AI chatbot for market research, addressing customer engagement challenges, the integration of AI in content consumption, the impact of AI on business strategies, and the future of AI in market research, and much more. Links Mentioned in the Show: YPulseConnect with DanHaystack by DeepsetUnmanaged: Master the Magic of Creating Empowered and Happy Organizations by Jack SkeelsSkill Track: AI FundamentalsRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYURewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Summary Every data project, whether it’s analytics, machine learning, or AI, starts with the work of data cleaning. This is a critical step and benefits from being accessible to the domain experts. Trifacta is a platform for managing your data engineering workflow to make curating, cleaning, and preparing your information more approachable for everyone in the business. In this episode CEO Adam Wilson shares the story behind the business, discusses the myriad ways that data wrangling is performed across the business, and how the platform is architected to adapt to the ever-changing landscape of data management tools. This is a great conversation about how deliberate user experience and platform design can make a drastic difference in the amount of value that a business can provide to their customers.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Adam Wilson about Trifacta, a platform for modern data workers to assess quality, transform, and automate data pipelines

Interview

Introduction How did you get involved in the area of data management? Can you describe what Trifacta is and the story behind it? Across your site and material you focus on using the term "data wrangling". What is your personal definition of that term, and in what ways do you differentiate from ETL/ELT?

How does the deliberate use of that terminology influence the way that you think about the design and features of the Trifacta platform?

What is Trifacta’s role in the overall data platform/data lifecycle for an organization?

What are some examples of tools that Trifacta might replace? What tools or systems does Trifacta integrate with?

Who are the target end-users of the Trifacta platform and how do those personas direct the design and functionality? Can you describe how Trifacta is architected?

How have the goals and design of the system changed or evolved since you first began working on it?

Can you talk through the workflow and lifecycle of data as it traverses your platform, and the user interactions that drive it? How can data engineers share and encourage proper patterns for working with data assets with end-users across the organization? What are the limits of scale for volume and complexity of data assets that users are able to manage through Trifacta’s visual tools?

What are some strategies that you and your customers have found useful for pre-processing the information that enters your platform to increase the accessibility for end-users to self-serve?

What are the most interesting, innovative, or unexpected ways that you have seen Trifacta used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Trifacata? When is Trifacta the wrong choice? What do you have planned for the future of Trifacta?

Contact Info

LinkedIn @a_adam_wilson on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Trifacta Informatica UC Berkeley Stanford University Citadel

Podcast Episode

Stanford Data Wrangler DBT

Podcast Episode

Pig Databricks Sqoop Flume SPSS Tableau SDLC == Software Delivery Life-Cycle

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Send us a text  This week's guest is Jorge Castanon, a Sr. Data Scientist for Watson Studio at IBM. Host Al Martin and Jorge discuss some typical data problems currently plaguing the industry, and how Watson Studio makes dealing with those problems that much easier. Get ready for an in-depth, technical conversation with two industry experts.

Show Note 00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter.  00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter.  00:20 - Connect with Producer Rachit Sharma on LinkedIn. 00:25 - Connect with Host Al Martin on LinkedIn and Twitter.  00:41 - Connect with Jorge Castanon on LinkedIn and Twitter 05:42 - Check out the machine learning hub here. 09:53 - Unsure what customer churn is? Find out in this article. 20:00 - AI is not magic. Read an article discussion the topic here. 24:34 - Learn about SPSS Modeler here. 35:46 - Check out coursera here. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

What IS customer intelligence? What is a customer? Is the customer best understood by breaking the word down into its component parts: "cuss" and "tumor?" Would that be an intelligent thing to do? Will these and related questions some day be answered by self-aware machines? Will any of these questions be answered on this episode? Give it a listen and find out! The mish-mash of companies, products, and miscellany mentioned on this show include: Adobe, Oracle/ATG, SAS Customer Intelligence, Salesforce.com, Scott Brinker (Chief Martec), Domo, Data Studio 360, Tableau, iJento, Netezza, SPSS, Unfrozen Caveman Lawyer, Eight Is Enough, Legend of the Plaid Dragon (and the Slack version), Office Vibe, p-value article on fivethirtyeight.com (and the p-hacking app), and the "AI, Deep Learning, and Machine Learning" video.