Systems | Development | Analytics | API | Testing

Using Set Analysis P() to Improve Your Marketing Spend

On the Next Do More with Qlik Tips and Tricks edition: Using Set Analysis P() to improve your marketing spend. By focusing only on those customers tied to key product purchases, you can better analyze what’s driving real revenue—and adjust your marketing budget accordingly. Target smarter, spend better.

Ep 5 - The Secret to Data Streaming Success: Speaking the Same Language

Want your real-time data streaming initiative to stick? Success hinges on more than pipelines—it’s about people, governance, and business impacts. Jeffrey Johnathon Jennings (J3), managing principal at signalRoom, shares how to bring it all together. In this episode, J3 shares how he’s used impactful proofs of concepts to demonstrate value early, then scaled effectively through shift left with governance and stronger cross-team collaboration.

Cloud Cost Control: Strategies for Scaling Data Operations Without Breaking the Budget on Databricks

As data operations grow, so do cloud costs—but it doesn't have to be a one-to-one relationship. Join us for this enlightening session in our Weekly Walkthrough series, "Controlling Cloud Costs," where we'll explore how to scale your Databricks operations efficiently. You'll gain invaluable insights into balancing performance and cost for scaling your data operations. With Unravel's Data Actionability Platform, you can take immediate, impactful action for transformative results in cost management.

Risk & Reward: A Balancing Act for Success

Adopting AI? Risky. Not adopting AI? Riskier… It’s an interesting conundrum illustrating a broader truth: playing it safe is sometimes risky. To succeed, companies need to take smart risks—and those who do often achieve the greatest rewards. But a risk that is smart for one organization may not be as smart for another, so what risk is right for you?

Break Data Silos: Build, Deploy and Serve Models at Scale with Snowflake ML

Despite the best efforts of many ML teams, most models still never make it to production due to disparate tooling, which often leads to fragmented data and ML pipelines and complex infrastructure management. Snowflake has continuously focused on making it easier and faster for customers to bring advanced models into production.