AI for Asset Management & Data Quality

AI in FM: today's steps for tomorrow's decisions

The conversation explores the use of data in facilities management and the challenges of collecting, managing, and utilizing data effectively. It delves into the role of AI in data analysis and decision-making, as well as the importance of clean and structured data for creating measurable value. The conversation also highlights the need to empower people through technology and attract the younger generation to the facilities management industry. Takeaways * Data collection and management are crucial for effective decision-making and creating measurable value. * Empowering people through technology and attracting the younger generation to the facilities management industry are key focus areas. Chapters * 00:00 The Role of Data in Facilities Management * 11:31 Types of Data and Business Impact * 17:04 Empowering People and Attracting the Younger Generation

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.

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 issues * How Neomir's software detects bad data at scale and automates resolutions * The significance of rules in data validation, e.g., ensuring cars always have four wheels * Practical applications in manufacturing, procurement, and asset management * The challenge of tying data quality to measurable ROI and cost savings * The different approaches to data quality: identification vs. pre-filling and machine learning * Why operational teams often detect data issues before management does * Strategies 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 - LinkedIn [https://www.linkedin.com/in/stephanie-wiechers/] * Jonas Hauswurz - LinkedIn [https://www.linkedin.com/in/jonas-hauswurz/]

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) [https://www.unspsc.org/] – Industry-standard classification for products and services

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