Anass Bensrhir is the Associate Partner of McKinsey & Company Casablanca. Anu Arora is the
Principal Data Engineering at McKinsey & Company.

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mckinsey.com/quantumblack

MLOps podcast #214 with QuantumBlack AI by McKinsey's Principal Data Engineer, Anu Arora and Associate Partner, Anass Bensrhir, Managing Data for Effective GenAI Application brought to you by our Premium Brand Partner QuantumBlack AI by  @McKinsey .

// Abstract
Generative AI is poised to bring impact across all industries and business functions across industries

While many companies pilot GenAI, only a few have deployed GenAI use cases, e.g., retailers are producing videos to answer common customer questions using ChatGPT. A majority of organizations are facing challenges to industrialize and scale, with data being one of the biggest inhibitors.

Organizations need to strengthen their data foundations given that among leading organizations, 72% noted managing data among the top challenges preventing them from scaling impact. Furthermore, leaders noticed that +31% of their staff's time is spent on non-value-added tasks due to poor data quality and availability issues.

// Bio
Anu Arora
Data architect(~12 years) and have experience in Big data technologies, API development, building scalable data pipelines including DevOps and DataOps, and building GenAI solutions.

Anass Bensrhir
Anass Leads QuantumBlack in Africa, he specializes in the Financial sector and helps organizations deliver successful large Data transformation programs.

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Website: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients

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Connect with Anass on LinkedIn: https://www.linkedin.com/in/abensrhir/

Timestamps:
[00:00] Anass and Anu's preferred coffee
[00:35] Takeaways
[04:02] Please like, share, leave a review, and subscribe to our MLOps channels!
[04:09] Huge shout out to our sponsor QuantumBlack!
[04:29] Anu's tech background
[06:31] Anass tech background
[07:28] The landscape of data
[10:37] Dealing with unstructured data
[15:51] Data lakes and ETL processes
[22:19] Data Engineers' Heavy Workload
[29:49] Data privacy and PII in the new LLMs paradigm
[36:13] Balancing LLM Adoption Risk
[44:06] Effective LMS Implementation Strategy
[49:00] Decisions: Create or Wait
[50:39] Wrap up