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Bad Data Product, Bad AI - Now What?
When a line-of-business data product causes a downstream AI model to produce bad results, who owns the problem, and what are the recommended steps to fix it?
Sapphire 2026 Key Take Aways for Data Leaders - What did you take away?
Here are some of the take-aways from SAP Sapphire Madrid in May 2026, for us data professionals who are keen to learn more about how SAP is elevating data to its strategic pinnacle. We’ve always known the importance of data so it was rewarding to he...
The future of data is conversational and agentic AI — Are you ready?
Hey Data Professional Community As AI assistants become more embedded in data workflows, many of us are starting to rethink how day-to-day work gets done. We are curious your thoughts on:How AI will or can impact your role as a data professional?W...
SAP Sapphire for Data Pros: What Changes for You in the Next 12 Months
SAP Sapphire just wrapped, and what a firehose that was! Alongside the big visions for the Autonomous Enterprise and the Business AI Platform, there were also many new capabilities launched and highlighted that can help data teams today and in the ne...
What SAP Sapphire 2026 sessions are we excited to see?
SAP Sapphire is here! Whether you’re attending virtually or in person, there’s no shortage of great sessions on the agenda. As a #DataPro, what are you most looking forward to learning about? Over the past few weeks, several community members have p...
A shout out to data governance professionals...
Check out this podcast from Coca-Cola data stewards. Does this resonate with you? Give us your thoughts on the your day to day!A Day in the Life of a Data Steward — Behind the Scenes of Trusted Data
NLQ in the real world: what metadata setup made it successful for you?
I recently shared a demo of natural language query (NLQ) and conversational analytics showing the just ask feature in SAP Analytics Cloud and how Joule takes NLQ further into a conversational experience. We also highlighted before that success depend...
Monday morning data problems - lessons learned
Share one story where a dashboard, model, pipeline, or report looked correct on Friday but broke by Monday. What was the hidden root cause (master data mismatch, late arriving data, access change, schema change, governance gap)? What rule or check di...
What makes an employee-churn model usable in the real world?
Related post:DEMO: SAP HANA Cloud - Analyzing Employee Churn with Data ScienceFor those who have built or tested attrition models, which mattered more in practice: better feature engineering or better label definition?Interested in practical lessons ...
Say it straight
As a data professional, if you had to describe what you do in one sentence, what would that be?
