Webinar Recap: Data Quality Challenges and Conversational AI Solutions

A very thoughtful discussion on Data Quality and the use of Conversational AI with Igor Ikonnikov , Principal Advisory Director of Info-Tech Research Group and Dr Imad Syed Co-CEO and CIO of PiLog Group had a lot of takeaways and excellent questions from the audience.

Here are 4 key takeaways from the conversation:

• Conversational AI needs to be trained on relevant, high-quality data for accurate results. This ensures that the AI model can effectively understand and respond to user queries within the specific business context.
• Data quality is essential for AI to function effectively. Poor data quality can lead to inaccurate insights and hinder the overall effectiveness of AI-driven solutions.
• Conversational AI can significantly enhance data governance by simplifying data interaction, automating tasks, and providing decision support. This makes data governance more accessible and efficient for organizations.
• Conversational AI solutions offer benefits like simplicity, ease of use, and quick data management capabilities. These features can help organizations overcome the challenges associated with traditional data governance approaches.

A brief summary from the discussion

Key challenges associated with data quality in the modern business environment, and how is conversational AI being used to address these challenges

• Lack of Holistic Thinking: Many organizations do not think holistically about data quality management. They often only address quality issues when problems arise, such as discrepancies in reports.
• Manual Data Management: Traditional data management methods are too slow and imprecise for the speed of decision-making required in the modern business environment. There is no longer time for manual data quality management or in-house built solutions.
• Poor Data Organization: Organizing data poorly leads to confusing answers, especially when using AI, which requires clean and well-organized data to produce accurate insights.
• Negative Reputation: Data quality and master data management (MDM) projects have a negative reputation due to their complexity, reliance on subject matter expertise, and history of challenges.

How Conversational AI Solutions help with Data Quality

• Improved User Experience: Conversational AI makes it easier for users to interact with data and obtain accurate answers quickly using natural language, similar to Google Analytics.
• Process Automation: AI can automate and augment data management processes like data discovery, extraction, validation, and improvement. This saves time and reduces the need for manual intervention.
• Decision Support: Conversational AI can provide decision support by enabling users to quickly ask questions about their data and receive relevant insights. For example, a supply chain manager could ask about the procurement history of a material or the best source for a particular part.
• Simplicity and Agility: Conversational AI can be used to build lean and agile data governance solutions that are preconfigured with industry standards and taxonomies, making them easier to implement and use. This addresses the complexity often associated with traditional data governance projects.
• Data Quality Improvement: AI can help discover, extract, and validate data, recommend remediation steps, and automate data modeling and quality improvement processes. This reduces the burden on human experts and facilitates faster, more efficient data quality management.

Important Considerations:

• Training Data: It is crucial to train conversational AI on relevant, high-quality data to ensure accurate and reliable results. Training AI on general internet data may lead to incorrect assumptions and responses.
• Continuous Learning: AI models need to be continuously trained and updated to keep pace with changes in business data and processes. Knowledge bases, taxonomies, and repositories require ongoing attention and maintenance.
• Data Privacy and Governance: Data access controls are essential to protect sensitive information. It is important to implement a data access layer that restricts access to data based on user roles and permissions.

Conversational AI offers a powerful set of tools to address the challenges of data quality in the modern business environment. By simplifying data access, automating tasks, and providing decision support, conversational AI can help organizations achieve better data quality and make more informed decisions.

Benefits of PiLog's Conversational AI Solution "AI Lens"

PiLog's "AI Lens" is a conversational AI solution designed to address the challenges of data quality and governance in the modern business world. Here are some key benefits of using "AI Lens":

• Simplicity and Ease of Use: "AI Lens" is built on the principles of simplicity and agility, aiming to make data governance more accessible and less daunting for organizations. It features predefined models, industry standard content, and preconfigured processes, eliminating the need for complex custom development.
• Conversational AI for Data Interaction: Leverages conversational AI to facilitate user interaction with data using natural language, eliminating the need to learn complex query languages like SQL. This allows users to quickly obtain accurate answers, improving user experience and decision-making efficiency.
• Quick and Efficient Data Management: Automates and augments various data management tasks, including data discovery, extraction, validation, and improvement. For example, it can analyze a file, identify data elements, validate them against set rules, and recommend improvements, significantly speeding up data processing.
• Data Quality Enhancement: Provides a range of features designed to enhance data quality. It can perform automated matching, merging, and consolidation of data sets using machine learning techniques. Additionally, it can identify potential data quality issues and recommend remediation steps, supporting proactive data quality management.
• Configuration and Customization: Configurable and adaptable to different business contexts. Users can customize prompts, integrate with various data sources, and tailor the solution to meet their specific needs and industry requirements.
• Reduced Reliance on Expertise: By simplifying data access and automating tasks, AI Lens reduces the need for specialized expertise in data quality and governance. Users with functional knowledge can effectively utilize the solution through conversational interaction, making data governance more accessible to a wider range of users.
• Enhanced Decision Support: Can answer a wide range of business-specific questions, providing users with quick and relevant insights to support decision-making. For example, it can provide information on material sourcing, pricing, manufacturer details, spare parts availability, and more, enabling users to make informed decisions quickly.
• Continuous Learning and Improvement: "AI Lens" is trained on industry-specific contextual data, including taxonomies and repositories from various standards, ensuring its relevance and accuracy for specific business domains. Additionally, the solution can be continuously trained and updated with new data and patterns, ensuring it remains effective as the business evolves.

For more information visit https://piloggroup.com/

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