As the adoption of Generative AI (GenAI) continues to transform businesses, ensuring that your data is prepared to support this transformation is essential. During the upcoming Data, AI & Analytics Conference on Tuesday, October 15, 2024, Sherifat Akintunde-Shitu, UKI MDM Capability Lead, and Epco Maat, Data Governance Capability Lead, both from Accenture, will delve into the importance of a robust data foundation for successful GenAI implementation.
Here are some of the key insights from their interview, offering a preview of their much-anticipated session.
Building a Robust Data Foundation for GenAI
At Accenture, ensuring that an organisation’s data is discoverable, accessible, and trusted is crucial for leveraging GenAI to its fullest potential. According to Sherifat and Epco, there are three essential components to this robust data foundation:
- Data Discoverability: This involves actively managing metadata to tag, classify, and catalogue data with the necessary context for training large language models (LLMs). Making data easily searchable and understandable ensures that organisations can fully leverage their assets for AI initiatives.
- High-Quality Data: High-quality data fuels effective AI. Organisations must implement robust data quality management, including observability and Master Data Management (MDM), to ensure the accuracy, completeness, and reliability of the data used to develop AI models.
- Data Trust: Strong data governance is essential for maintaining data integrity and compliance. This includes establishing clear processes, policies, and roles for managing both structured and unstructured data, ensuring responsible AI development and deployment.
These elements form the backbone of any AI strategy, allowing businesses to trust and maximise the value of their data in the age of GenAI.
Overcoming Common Challenges in Aligning Data Strategies with AI Objectives
Sherifat and Epco have observed several common challenges organisations face when attempting to align their data strategies with AI objectives. These include:
- Prioritising investments in AI and determining which areas of the business will benefit the most.
- Assessing whether an organisation’s data and technology infrastructure is ready for GenAI.
- Upskilling the workforce to handle the technical demands of AI and automation.
- Balancing the value of AI adoption against potential risks, such as decision bias or IP infringement.
- Making ecosystem decisions, such as choosing between building AI solutions in-house or acquiring them externally.
To address these challenges, they recommend organisations take a value-driven approach, focusing on business capabilities that promise the most significant return on investment (ROI). Additionally, upskilling the workforce and implementing robust governance frameworks are vital for success.
Best Practices for Data Quality and Governance in GenAI
As AI becomes more integrated into enterprise systems, maintaining high standards of data quality and governance becomes even more critical. Sherifat and Epco recommend several best practices for scaling AI solutions while ensuring data quality and trust:
- Boost Data Quality and Observability: Establish processes to manage data quality, particularly from unstructured sources, and consistently monitor data pipelines to detect and fix anomalies in real time.
- Leverage Master Data Management (MDM): Ensure that LLMs have access to consistent and unified information by using mastered data, leading to better predictions and decision-making.
- Establish Robust Governance: Develop a strong data operating model that clearly defines roles and responsibilities across the organisation. This framework should be accompanied by agreed-upon processes, standards, and metrics for maintaining data integrity.
By implementing these practices, organisations can maintain a high level of reliability and transparency in their AI-driven processes, leading to more successful outcomes.
A Real-World Example: GenAI at BMW North America
One of the most compelling examples of a successful GenAI implementation comes from Accenture’s collaboration with BMW North America. Together, they developed a platform called EKHO (Enterprise Knowledge Harmonizer and Orchestrator), which uses GenAI to drive decisions, boost productivity, and improve customer experiences.
Some of the key factors behind EKHO’s success included:
- High-Quality Data: The platform was built on a solid foundation of well-managed, accurate, and reliable data, which was critical to its performance.
- Advanced Data Capabilities: EKHO leverages LLMs to answer complex questions across different business functions, allowing for real-time insights and decision-making.
- Collaborative Ecosystem: The project involved close collaboration between BMW’s data scientists and engineers and Accenture’s AI experts, ensuring that the AI solution was tailored to BMW’s specific business needs.
As a result, EKHO has led to a 30-40% productivity surge and significantly enhanced BMW’s customer experience by streamlining processes like car configuration and supply chain optimisation.
Scaling GenAI: The Path from Proof-of-Concept to Full Deployment
Transitioning from proof-of-concept to full-scale GenAI deployment requires careful planning and execution. Sherifat and Epco emphasise a value-led approach that prioritises business capabilities and aligns AI initiatives with the organisation’s broader strategic goals. They also stress the importance of robust infrastructure, governance, talent development, and ethical considerations to ensure a smooth and scalable transition.
By following these steps, companies can move beyond isolated AI use cases to achieve more widespread, transformative outcomes across their operations.
This insightful session will take place on Tuesday, October 15, 2024, from 2:55 PM to 3:35 PM, offering practical advice and examples from leading industry experts at Accenture. If you’re interested in accelerating your GenAI journey and building a strong data foundation, be sure to attend! Secure your spot today: Tickets
