Explore the impact of GenAI on industries and challenges in scaling, data quality hindrances, and non-value-added tasks. Delve into the evolving role of data engineers, LLM integration, and GenAI tools for automation and data handling. Discuss risks with LLM models in AI applications, emphasizing data privacy, compliance, and decision-making strategies.
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Quick takeaways
Effective data management is crucial for scaling GenAI impact.
Documentation maintenance ensures accurate data quality and integrity.
Implementing LLMs requires strategic risk assessment and privacy measures.
Deep dives
Data Engineering Challenges with Text-to-Sequence Models
Despite advancements in text-to-sequence models, data engineering remains challenging due to practical examples where JNI is utilized but faces obstacles transitioning from MVP to production. Challenges like ETL of unstructured data persist, especially in assessing data quality from sources like PDFs versus structured data.
Upgrading Documentation and Data Quality Measure
Maintaining up-to-date documentation and ensuring accurate data quality poses significant hurdles, especially when dealing with evolving policies like HR updates or extracting and verifying data from changing document sources. Failure to update documentation can lead to crucial errors and outdated information affecting database integrity and user interactions.
Impact of Unstructured Data on Data Engineering
Managing unstructured data entails significant complexities, such as dealing with varied data sources like PDFs, requiring novel approaches for handling data ingestion, transformation, storage, and quality assessment. Organizations transitioning to leverage data lakes for unstructured data face challenges due to the need for new technologies and enhanced data management strategies.
Privacy Concerns and LLMs Implementation Strategy
Addressing privacy and data protection concerns with large language models (LLMs) demands a strategic approach focused on risk assessment, cost evaluation, and feasible implementation to balance innovation with data safeguards. Prioritizing high-impact, low-risk LLM use cases internally and enforcing strict privacy measures while gradually expanding usage to mitigate external threats and compliance risks.
Value Assessment and Risks in LLM Deployment
Quantifying the value and feasibility of LLM deployment empowers organizations to optimize costs, assess technology mastery, and prioritize use cases for value-driven outcomes while considering compliance and data privacy factors. Efficient LLM implementation hinges on gauging the potential benefits against associated risks, training costs, and ensuring scalability of high-value, low-risk initiatives.
Strategic Approach to Incorporating LLMs
Strategically incorporating LLMs involves evaluating use case potential, technology maturity, and financial implications to ascertain the value proposition and address scalability, innovation, and risk mitigation. Balancing innovation with data protection requires a phased approach of piloting, testing with limited audiences, and progressively expanding functionality while maintaining stringent privacy checks and human oversight for quality assurance.
Anass Bensrhir is the Associate Partner of McKinsey & Company Casablanca. Anu Arora is the
Principal Data Engineering at McKinsey & Company.
Check out
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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// Related Links
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
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