The Data Edge: Data Quality & AI Readiness

Stephanie Wiechers & Erwin de Werd
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Welcome to the Pearstop podcast series on data management, where experts Stephanie Wiechers and Erwin de Werd dive into the world of data quality, standardization, and the real-world value of information in technical industries. From procurement and facility management to hard services and large-scale manufacturing, we explore how 'messy' data can cost organizations millions—and how to fix it. Join us as we break down complex topics like enterprise-level standardization and Microsoft Fabric into concrete, actionable steps. Whether you're a CEO, an asset manager, or a bid specialist, this series provides the insights you need to turn your data into a fuel for smart decision-making and AI readiness. Don't let your data work against you—learn how to make it your greatest competitive advantage.

  1. AI for Asset Management & Data Quality

    APR 30

    AI for Asset Management & Data Quality

    In conversation with SPIE Netherlands about how they improved the quality of their asset data. Our guest is the amazing Martijn van Balkom – passionate, smart, and guaranteed to teach you something new today! This episode provides insights into the challenges and opportunities surrounding asset data management. It highlights the importance of data quality, the impact of AI technology, and the role of asset data in advising and optimising maintenance strategies.

    18 min
  2. Data Quality in Manufacturing and Procurement

    APR 23

    Data Quality in Manufacturing and Procurement

    Unlocking Data Quality in Manufacturing and Procurement with Jonas Hauswurz"In this episode, Stephanie Wiechers chats with Jonas Hauswurz, founder of Neomir, about the real issues behind data quality problems in manufacturing and procurement. Discover practical use cases, how to identify critical data leaks, and the importance of focusing on operational teams rather than just executives.Key Topics: The distinction between data problems and data quality issuesHow Neomir's software detects bad data at scale and automates resolutionsThe significance of rules in data validation, e.g., ensuring cars always have four wheelsPractical applications in manufacturing, procurement, and asset managementThe challenge of tying data quality to measurable ROI and cost savingsThe different approaches to data quality: identification vs. pre-filling and machine learningWhy operational teams often detect data issues before management doesStrategies for engaging ground-level staff to improve data quality Timestamps: 00:00 - The difference between data issues and data quality flaws 00:25 - Why data quality is often misunderstood in organizations 00:55 - How Neomir's software identifies and automates fixing bad data 01:20 - Practical examples in manufacturing and asset management 03:12 - Creating rules for data validation with AI assistance 04:29 - Common data errors in manufacturing, such as bill of materials inaccuracies 05:15 - Propagation of data checks through entire supply chains 07:28 - The role of data quality in cost savings and strategic decision-making 08:23 - Indirect effects of data quality on financial performance 09:58 - Approaches to measuring ROI for data quality initiatives 11:03 - Advantages of transparency and rough ROI estimates over precise calculations 13:23 - Engaging operational teams for better data insights 15:45 - How management often underestimates data issues until front-line staff reveal them 16:24 - The importance of targeting conversations at data specialists and operational staff 18:05 - Closing thoughts and how to connect with Jonas for manufacturing and procurement data challenges Resources & Links: Stephanie Wiechers - LinkedInJonas Hauswurz - LinkedIn

    19 min
  3. AI & Data Standards

    APR 9

    AI & Data Standards

    𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿: 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 "𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗘𝗱𝗴𝗲" 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 In this episode of The Data Edge, Erwin de Werd and guest Stephanie Wiechers explore the critical aspects of data quality, standardization, and data movement for organizations aiming to leverage AI and advanced analytics effectively. They discuss practical challenges and strategic considerations for companies of all sizes seeking to build trustworthy, scalable data infrastructure. 
𝗠𝗮𝗶𝗻 𝗧𝗼𝗽𝗶𝗰𝘀: ✔ The increasing importance of data quality and reliability in AI applications ✔ Challenges in creating and trusting dashboards due to data flaws ✔ How data movement between systems influences decision-making and analytics ✔ The role of standardization in cross-entity data sharing and efficiency ✔ Trends and best practices for adopting data standards and improving data governance ✔ The impact of AI tools like Copilot on data analysis and development ✔ Strategies for smaller businesses to align with industry standards despite resource constraints 
𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀: 00:00 - Introduction and overview of data quality challenges in AI development 00:30 - The surge in democratized data analysis and its responsibilities 01:34 - Risks of trusting dashboards with potential data flaws 03:07 - The importance of data reliability for decision-making 04:13 - Moving data across systems to enable advanced analytics 05:18 - The significance of data standardization in different industries 06:34 - How data lakes and recent platforms support data integration 07:45 - The role of data quality as a foundation for dashboards and AI models 08:26 - Standardization trends and industry-specific norms 09:13 - Cost considerations and strategic choices in implementing standards 10:27 - Challenges and strategies for smaller companies adopting standards 11:48 - Practical steps for transitioning from non-standard to standardized data 12:18 - Industry standards like UNSPSC and industry-specific frameworks 13:25 - The strategic value of standardization for cost savings and operational efficiency 14:09 - Use cases in procurement and spend analysis 15:13 - The growing importance of data quality and standardization in analytics 16:02 - Final thoughts and future topics 
𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 & 𝗟𝗶𝗻𝗸𝘀: • UNSPSC (United Nations Standard Products and Services Code) – Industry-standard classification for products and services

    16 min
  4. Succesfactors for AI

    APR 2

    Succesfactors for AI

    𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗮𝗻𝗱 𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 In this episode of The Data Edge, Erwin de Werd and Stephanie Wiechers explore how AI can transform data management from a headache into a strategic advantage — if used wisely. They discuss the pitfalls of overhyped AI solutions, the importance of building robust systems, and practical steps to improve data quality. 𝗞𝗲𝘆 𝗧𝗼𝗽𝗶𝗰𝘀: The proliferation of AI "skills" and why over 90% are ineffective How automation, when done properly, enhances data quality and operational efficiency The challenge of discerning quality in AI tools and avoiding superficial solutions Practical examples of AI in lead generation (Dream 100 strategy) and content creation How to build trust in AI-driven data solutions amidst industry hype The importance of authentic, human-centered communication in AI content The distinction between front-end conversation and back-end automation in data management Planning for a future where AI and data quality ensure better decision-making 𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀: 00:00 - Introduction: Transforming data management with AI 00:30 - Why most AI skills are ineffective and what they entail 01:25 - Explanation of skills as standard operating procedures (SOPs) 02:24 - The explosion of AI skills on platforms like Instagram and their usability 03:20 - The common problem of people not doing the work when using AI tools 03:50 - Strategic laziness: automating repetitive tasks with quality checks 04:32 - Pitfalls of trusting AI outputs without proper validation 04:57 - Challenges in training AI models to produce accurate, high-quality content 05:44 - Limitations of custom GPTs in professional tasks like LinkedIn content 06:22 - The importance of investing effort upfront to create effective automation systems 06:47 - Why cost savings lead to underinvestment in AI automation 07:34 - Challenges of relying on incomplete or careless prompts 07:45 - The habit of short-input prompts and the impact on output quality 08:13 - Building outreach strategies with AI: the Dream 100 example 08:51 - Automating research and outreach to generate leads efficiently 09:35 - Using AI to identify influencers and industry events for strategic networking 10:58 - The need for consistency and authenticity in AI-generated content 12:04 - How good copywriters leverage AI as a starting point, not a replacement 12:51 - Authenticity remains crucial despite the efficiency gains from AI 13:17 - Connecting AI automation in data management with operational layers of business 14:09 - The importance of backend automation for data quality and integrity 15:14 - Trust issues in procurement and other industries regarding AI promises 16:26 - The hype versus reality of AI solutions, and the upcoming industry shakeout 17:08 - Final thoughts: Deepening the conversation in future episodes

    17 min
  5. AI & Human Collaboration

    MAR 26

    AI & Human Collaboration

    🎙️ 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗲𝗱𝗴𝗲 — 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝗻 𝗮𝗶 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 In this episode, Erwin and Stephanie delve into the complexities of data quality in AI projects, emphasizing that messy data often leads to costly mistakes. They explore how human-AI collaboration and understanding the limitations of models like LLMs are crucial for success. 🔑 𝗞𝗘𝗬 𝗧𝗢𝗣𝗜𝗖𝗦 The common misconception that first data categorization is 100% accurate — and why errors are part of the processThe reality of achieving high data quality and near automation (up to 95%) in data processingExpectations vs. reality: Why clients sometimes expect AI to be a 'magic bullet' and how to set realistic goalsThe importance of contextual knowledge and communication to improve model accuracyMethodologies for training AI models as 'new employees', including leveraging human expertise and internal knowledgeA real-world construction project: data categorization challenges, including language issues (tablets as lozenges)Differentiating LLMs like ChatGPT from specialized machine learning modelsThe role of human-AI cooperation in improving data quality and operational efficiencyCreating a knowledge center for clients through ongoing data training and model refinementThe value of building IP within organizations by developing tailored data solutions and models ⏱️ 𝗧𝗜𝗠𝗘𝗦𝗧𝗔𝗠𝗣𝗦 00:00 Introduction: The impact of messy data on industry costs 00:30 Setting the stage: From data quality to correction hiccups 01:14 Why initial categorization often isn't perfect — and it's normal 02:02 The misconception of AI producing perfect results immediately 02:50 Achieving high data quality and near automation possibilities 03:17 Managing client expectations around AI and data processing 04:05 Importance of communication about processes and contextual insights 05:14 When models don't perform as expected: Training methodologies 05:45 Example project in construction: Data categorization challenges 06:47 Using dashboards to identify and fix misclassified data 08:11 Language nuances affecting classification (e.g., tablets as lozenges) 08:58 Differences between LLMs like ChatGPT and task-specific ML models 10:16 The core distinction: General language models vs. specialized models 12:11 Why consistency and rule-based training are vital 13:24 Human-AI collaboration enhancing data accuracy 14:02 Implementing biases and industry knowledge to improve models 15:19 Building an organization's IP through data and model development 16:21 Potential for transparency: Sharing system rules with clients 17:05 Recap: Differentiating AI types and combining human expertise 18:18 Closing: Key takeaways on data, AI, and IP in projects

    19 min
  6. Insights from the field

    MAR 19

    Insights from the field

    Turning Data Quality into Strategic Power: Insights from Procurement and Data Management In this episode, Erwin de Werd and Stephanie Wiechers dive into the complexities of data quality in procurement and data management. They share actionable insights from a detailed conversation with a seasoned procurement professional, highlighting how data quality impacts savings, efficiency, and strategic decision-making.Main Topics: The role of data quality in procurement and its impact on savingsCategory management in both predictable and project-based industriesThe importance of data accuracy at different organizational scalesHow large enterprises handle demand unpredictability and data complexitiesNavigating risk aversion and innovation adoption in data-driven projectsSmall pilots and iterative approaches for embracing AI and new technologies In this episode: Stephanie shares her interview with a former head of procurement from a billion-dollar enterpriseThe discussion on category management illustrates how scale and industry type influence data practicesThe conversation highlights the critical role of data quality in unlocking procurement savingsInsights on managing organizational change and technology adoption speed, including AI• Practical advice: start small with pilot projects to manage risks and learn quickly

    18 min
  7. How to reach 95% Data Quality

    MAR 13

    How to reach 95% Data Quality

    Ensuring Data Quality in AI Projects: A Conversation with Stephanie Wiechers In this episode, Erwin de Werd and Stephanie Wiechers explore the crucial role of data quality in AI and data projects. They discuss practical approaches to maintain high accuracy, the challenges of testing AI with AI, and the importance of human oversight to achieve reliable results. Key Topics: The impact of messy data on AI output and decision-makingStrategies for achieving 95% data accuracy for automationThe process of data enhancement using AI and rule-based systemsTesting AI models: AI-to-AI vs. human review approachesCost and time considerations in data quality verificationThe ongoing progress: from 85% to over 95% accuracyThe collaborative role of humans and AI in data validationFuture outlook: the importance of human involvement for reliable AI Timestamps: 00:00 - Introduction: How messy data costs industries billions 00:41 - Importance of data quality in AI and reporting 01:25 - Common issues with data errors impacting insight generation 02:17 - Automating error detection and correction in databases 02:58 - Client quality expectations and the 95% accuracy benchmark 03:26 - Achieving and validating 95% accuracy in AI models 04:01 - Using AI and internal rules for data enhancement 04:41 - Challenges of testing AI with AI and the need for human validation 05:56 - The risk of relying solely on AI for quality checks 06:37 - Human review as a reliable fallback 07:03 - The four-step process for data validation 08:25 - The iterative role of human review and AI learning 09:06 - Balancing internal and outsourced validation efforts 10:17 - Outsourcing testing versus internal validation challenges 11:13 - Current progress: surpassing 85% accuracy 12:00 - Upcoming guest episode and future projects Resources & Links: PeerStop Connect with Stephanie Wiechers: LinkedIn Note: Stay tuned for our next episode featuring a special guest from the field discussing real-world data projects and best practices.

    13 min
  8. The Human Factor in AI-Driven Procurement Data Management

    FEB 25

    The Human Factor in AI-Driven Procurement Data Management

    The Human Factor in AI-Driven Procurement Data Management In this episode, Erwin de Werd and Stephanie Wiechers explore the critical interplay between human expertise and AI in ensuring data integrity and standardization within procurement processes. Discover how organizations leverage AI to enhance categorization accuracy, streamline validation, and safeguard sensitive information. Key Topics The importance of human input in AI-driven data categorizationChallenges of enterprise-level procurement data standardizationCombining rule-based systems with machine learning models for enhanced accuracyThe role of the validation process in ensuring data qualityLeveraging large language models (LLMs) for granular categorizationHow ongoing user feedback refines AI performance over timeData security policies and anonymization in AI trainingPractical steps for integrating AI with existing procurement workflowsThe future of collaborative man-machine approaches in enterprise data management Timestamps 00:00 - Introduction to the role of data quality in AI and enterprise decision-making 00:42 - The importance of the human factor in AI projects 01:37 - Case study: Procurement data integrity challenge in a large organization 02:51 - Standardization challenges across multiple sites and teams 03:44 - AI complexities in categorizing diverse invoice costs 04:48 - Systemizing procurement data processes through AI and human insights 05:42 - Combining rules and machine learning for improved categorization 07:00 - Utilizing large language models for granular and flexible data classification 08:54 - Automating validation and review processes within AI systems 11:04 - Achieving high accuracy through training and feedback loops 12:19 - Validation workflows involving multiple departmental reviews 13:55 - Sharing and securing enterprise data in AI applications 15:02 - The balance between data sharing and confidentiality in AI training 16:16 - Ensuring compliance with corporate data policies and security policies 17:01 - The evolving collaboration between humans and AI in procurement 17:17 - Upcoming series: Field insights from client interviews Connect with Stephanie Wiechers: LinkedIn

    17 min
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About

Welcome to the Pearstop podcast series on data management, where experts Stephanie Wiechers and Erwin de Werd dive into the world of data quality, standardization, and the real-world value of information in technical industries. From procurement and facility management to hard services and large-scale manufacturing, we explore how 'messy' data can cost organizations millions—and how to fix it. Join us as we break down complex topics like enterprise-level standardization and Microsoft Fabric into concrete, actionable steps. Whether you're a CEO, an asset manager, or a bid specialist, this series provides the insights you need to turn your data into a fuel for smart decision-making and AI readiness. Don't let your data work against you—learn how to make it your greatest competitive advantage.

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  • Creator
    Stephanie Wiechers & Erwin de Werd
  • Years Active
    2K
  • Episodes
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    © 2026 Stephanie Wiechers & Erwin de Werd