The Data Edge: AI, Procurement and FM

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  AI in hard FM – a real story of how to do it well 13.08.2026 18min

  One number. Nearly a month to find it.The problem wasn’t missing data. It was data that didn’t agree with itself.In this episode of The Data Edge, Stephanie Wiechers breaks down why messy supplier and invoice data quietly undermines procurement decisions, and how to build a cost baseline you can actually trust.Because better decisions don’t start with another dashboard. They start with better data.#TheDataEdge #Procurement #DataManagement #FacilitiesManagement #AI
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  Find the REAL Money Leak 23.07.2026 9min

  Somewhere in your contract, money may be hiding—and even the person managing it might not know where to find it.In this episode of The Data Edge, PearStop founder and managing director Stephanie Wiechers explores how poor invoice visibility can quietly reduce contract profitability across facilities management, construction and infrastructure businesses.Stephanie introduces “Mark,” an experienced contract manager overseeing a cleaning contract across 40 sites. Although his supplier totals and monthly reports appeared correct, they failed to reveal the real problem: identical consumables being purchased from different suppliers at different prices for more than a year.The issue was not fraud, negligence or bad management. It was simply hidden inside thousands of invoice line items that nobody had the time or tools to compare properly.This episode explains why supplier-level reporting is often insufficient, how invoice line-item data can expose cost leakage, and why recurring inefficiencies can be more damaging than one visibly bad month.Stephanie also shares a practical three-step invoice audit that contract managers, procurement teams and FM professionals can carry out immediately.Discover how better spend visibility, invoice data management and supplier price comparison can help protect margins and improve contract performance.Learn more about PearStop and access free resources through the links below.
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  No duplicate records - One number! 16.07.2026 17min

  A supplier calls ahead of a contract renewal and asks for a price increase. Before responding, the procurement team needs one simple figure: how much are we currently spending with this supplier across every site, service and contract?It takes them almost a month to find the answer.In this episode of The Data Edge, Stephanie Wiechers, CEO of Pearstop, explains why procurement teams across facilities management, construction and infrastructure struggle to produce reliable spend data, even when all the information technically exists.The problem is rarely a lack of data. It is that supplier names, invoice descriptions, contracts, quotes and spreadsheets do not agree with one another.Stephanie breaks down a practical four-step process for turning fragmented procurement information into a trusted cost baseline:1. Clean and consolidate duplicate supplier records.2. Build a practical procurement spend taxonomy.3. Categorise spend from the top down.4. Use contextual data to interpret unclear invoice lines.You will also learn why dashboards cannot solve poor data quality, why excessive categorisation causes projects to collapse, and how organising 95% of spend confidently is often more valuable than chasing unreliable perfection.With clean procurement data, contract renewals become faster, supplier negotiations become more informed, and hidden costs such as fuel surcharges, duplicate suppliers and inconsistent pricing become easier to identify.Pearstop helps procurement teams transform messy invoices, quotes and spreadsheets into structured, decision-ready spend intelligence.Subscribe to The Data Edge for practical insights on procurement data, spend analysis, supplier management and AI-powered data transformation.#Procurement #SpendAnalysis #SupplierManagement #FacilitiesManagement #ConstructionData #ProcurementTechnology #DataQuality #CostReduction #SpendManagement #Pearstop
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  What's Hiding in Your Data? 02.07.2026 12min

  In this episode of The Data Edge by Pearstop, Stephanie Wiechers takes facilities management, construction and infrastructure teams inside the hidden world of procurement data. From supplier totals to invoice lines, this episode breaks down why “clean data” is rarely as clean as it looks, and how line-by-line spend visibility can help businesses protect margins, reduce waste and make smarter procurement decisions.Using a real-world facilities example, Stephanie explains how everyday site purchases like toilet cleaner, paper products, PPE, transport costs, packaging and fuel surcharges can reveal major gaps in spend control. For CFOs, procurement leaders and operations teams, this episode shows how structured procurement data can support supplier benchmarking, contract compliance, tender preparation and better cost management across the UK, Netherlands, France and global facilities markets.If your organisation manages multiple sites, suppliers, invoices or cost centres, this episode will help you understand what is really happening inside your procurement data and why better data quality creates better commercial decisions.Learn more about Pearstop and how better procurement data can give your business its edge.procurement data, facilities management, construction procurement, infrastructure data, invoice line analysis, spend visibility, supplier benchmarking, data quality, fuel surcharges, procurement software, Pearstop, The Data Edge podcast.
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  100k£ secretly billed on packaging 25.06.2026 9min

  We started Pearstop because we were fed up.This episode talks about another very relevant and difficult hurdle in the world of procurement and data - secret costs and hidden billings. Listen to the Data Edge every Thursday to stay informed on the latest data updates and more!
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  Dead Whales and Data 18.06.2026 23min

  Back to the Data Edge - a podcast for those interested in understanding the Data and AI world. In today's episode we talk about waste management, data and the world of procurement with an interesting and unexpected anecdote that will grip our listeners. Tune in for an educative and insightful conversation between Stephanie, CEO at Pearstop and Mike from SLS!
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  Use Data & Save 100k on Procurement 11.06.2026 14min

  The conversation delves into the challenges of reading and utilizing spend data, highlighting the importance of data structuring and classification for effective analysis. It emphasizes the shift in data classification and the benefits of structured data for procurement and finance teams.Whether you're a procurement manager, finance lead, or operations director dealing with messy invoice data, unstructured spend, or poor visibility across suppliers and categories — this episode covers why spend data classification is the foundation of any serious spend analytics programme.TakeawaysSpend data analysis requires structured and classified data for effective utilization.The shift in data classification has enabled scalable and accurate data processing.Clean, classified spend data gives procurement and finance teams the visibility to negotiate better, cut waste, and make faster decisions.Chapters00:00 Introduction to Spend Data Analysis09:33 Data Structuring and Classification15:41 Conclusion and Call to ActionKeywords: spend data analysis, procurement data classification, spend analytics, UNSPSC classification, invoice data, procurement visibility, spend management, facilities management procurement, construction procurement, finance data quality
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  AI in FM: today's steps for tomorrow's decisions 03.06.2026 14min

  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.TakeawaysData 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.Chapters00:00 The Role of Data in Facilities Management11:31 Types of Data and Business Impact17:04 Empowering People and Attracting the Younger Generation
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  AI for Asset Management & Data Quality 30.04.2026 18min

  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.
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  Data Quality in Manufacturing and Procurement 23.04.2026 18min

  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 qualityTimestamps:00:00 - The difference between data issues and data quality flaws00:25 - Why data quality is often misunderstood in organizations00:55 - How Neomir's software identifies and automates fixing bad data01:20 - Practical examples in manufacturing and asset management03:12 - Creating rules for data validation with AI assistance04:29 - Common data errors in manufacturing, such as bill of materials inaccuracies05:15 - Propagation of data checks through entire supply chains07:28 - The role of data quality in cost savings and strategic decision-making08:23 - Indirect effects of data quality on financial performance09:58 - Approaches to measuring ROI for data quality initiatives11:03 - Advantages of transparency and rough ROI estimates over precise calculations13:23 - Engaging operational teams for better data insights15:45 - How management often underestimates data issues until front-line staff reveal them16:24 - The importance of targeting conversations at data specialists and operational staff18:05 - Closing thoughts and how to connect with Jonas for manufacturing and procurement data challengesResources & Links:Stephanie Wiechers - LinkedInJonas Hauswurz - LinkedIn
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  AI & Data Standards 09.04.2026 16min

  𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿: 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 "𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗘𝗱𝗴𝗲" 𝗣𝗼𝗱𝗰𝗮𝘀𝘁In 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 development00:30 - The surge in democratized data analysis and its responsibilities01:34 - Risks of trusting dashboards with potential data flaws03:07 - The importance of data reliability for decision-making04:13 - Moving data across systems to enable advanced analytics05:18 - The significance of data standardization in different industries06:34 - How data lakes and recent platforms support data integration07:45 - The role of data quality as a foundation for dashboards and AI models08:26 - Standardization trends and industry-specific norms09:13 - Cost considerations and strategic choices in implementing standards10:27 - Challenges and strategies for smaller companies adopting standards11:48 - Practical steps for transitioning from non-standard to standardized data12:18 - Industry standards like UNSPSC and industry-specific frameworks13:25 - The strategic value of standardization for cost savings and operational efficiency14:09 - Use cases in procurement and spend analysis15:13 - The growing importance of data quality and standardization in analytics16:02 - Final thoughts and future topics
𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 & 𝗟𝗶𝗻𝗸𝘀:• UNSPSC (United Nations Standard Products and Services Code) – Industry-standard classification for products and services
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  Succesfactors for AI 02.04.2026 17min

  𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗮𝗻𝗱 𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁In 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 ineffectiveHow automation, when done properly, enhances data quality and operational efficiencyThe challenge of discerning quality in AI tools and avoiding superficial solutionsPractical examples of AI in lead generation (Dream 100 strategy) and content creationHow to build trust in AI-driven data solutions amidst industry hypeThe importance of authentic, human-centered communication in AI contentThe distinction between front-end conversation and back-end automation in data managementPlanning for a future where AI and data quality ensure better decision-making𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀:00:00 - Introduction: Transforming data management with AI00:30 - Why most AI skills are ineffective and what they entail01:25 - Explanation of skills as standard operating procedures (SOPs)02:24 - The explosion of AI skills on platforms like Instagram and their usability03:20 - The common problem of people not doing the work when using AI tools03:50 - Strategic laziness: automating repetitive tasks with quality checks04:32 - Pitfalls of trusting AI outputs without proper validation04:57 - Challenges in training AI models to produce accurate, high-quality content05:44 - Limitations of custom GPTs in professional tasks like LinkedIn content06:22 - The importance of investing effort upfront to create effective automation systems06:47 - Why cost savings lead to underinvestment in AI automation07:34 - Challenges of relying on incomplete or careless prompts07:45 - The habit of short-input prompts and the impact on output quality08:13 - Building outreach strategies with AI: the Dream 100 example08:51 - Automating research and outreach to generate leads efficiently09:35 - Using AI to identify influencers and industry events for strategic networking10:58 - The need for consistency and authenticity in AI-generated content12:04 - How good copywriters leverage AI as a starting point, not a replacement12:51 - Authenticity remains crucial despite the efficiency gains from AI13:17 - Connecting AI automation in data management with operational layers of business14:09 - The importance of backend automation for data quality and integrity15:14 - Trust issues in procurement and other industries regarding AI promises16:26 - The hype versus reality of AI solutions, and the upcoming industry shakeout17:08 - Final thoughts: Deepening the conversation in future episodes
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  AI & Human Collaboration 26.03.2026 18min

  🎙️ 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗲𝗱𝗴𝗲 — 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝗻 𝗮𝗶 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀In 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 𝗧𝗢𝗣𝗜𝗖𝗦The 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⏱️ 𝗧𝗜𝗠𝗘𝗦𝗧𝗔𝗠𝗣𝗦00:00 Introduction: The impact of messy data on industry costs00:30 Setting the stage: From data quality to correction hiccups01:14 Why initial categorization often isn't perfect — and it's normal02:02 The misconception of AI producing perfect results immediately02:50 Achieving high data quality and near automation possibilities03:17 Managing client expectations around AI and data processing04:05 Importance of communication about processes and contextual insights05:14 When models don't perform as expected: Training methodologies05:45 Example project in construction: Data categorization challenges06:47 Using dashboards to identify and fix misclassified data08:11 Language nuances affecting classification (e.g., tablets as lozenges)08:58 Differences between LLMs like ChatGPT and task-specific ML models10:16 The core distinction: General language models vs. specialized models12:11 Why consistency and rule-based training are vital13:24 Human-AI collaboration enhancing data accuracy14:02 Implementing biases and industry knowledge to improve models15:19 Building an organization's IP through data and model development16:21 Potential for transparency: Sharing system rules with clients17:05 Recap: Differentiating AI types and combining human expertise18:18 Closing: Key takeaways on data, AI, and IP in projects
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  Insights from the field 19.03.2026 18min

  Turning Data Quality into Strategic Power: Insights from Procurement and Data ManagementIn 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 technologiesIn 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
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  How to reach 95% Data Quality 13.03.2026 12min

  Ensuring Data Quality in AI Projects: A Conversation with Stephanie WiechersIn 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 AITimestamps: 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 projectsResources & Links:PeerStopConnect with Stephanie Wiechers:LinkedInNote: Stay tuned for our next episode featuring a special guest from the field discussing real-world data projects and best practices.
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  The Human Factor in AI-Driven Procurement Data Management 25.02.2026 16min

  The Human Factor in AI-Driven Procurement Data ManagementIn 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 TopicsThe 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 managementTimestamps00:00 - Introduction to the role of data quality in AI and enterprise decision-making00:42 - The importance of the human factor in AI projects01:37 - Case study: Procurement data integrity challenge in a large organization02:51 - Standardization challenges across multiple sites and teams03:44 - AI complexities in categorizing diverse invoice costs04:48 - Systemizing procurement data processes through AI and human insights05:42 - Combining rules and machine learning for improved categorization07:00 - Utilizing large language models for granular and flexible data classification08:54 - Automating validation and review processes within AI systems11:04 - Achieving high accuracy through training and feedback loops12:19 - Validation workflows involving multiple departmental reviews13:55 - Sharing and securing enterprise data in AI applications15:02 - The balance between data sharing and confidentiality in AI training16:16 - Ensuring compliance with corporate data policies and security policies17:01 - The evolving collaboration between humans and AI in procurement17:17 - Upcoming series: Field insights from client interviewsConnect with Stephanie Wiechers:LinkedIn
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  How Data Management Impact the Bottom Line 19.02.2026 13min

  SummaryIn this episode of Data Edge, Erwin De Werd and Stephanie Wiechers discuss the critical role of data management and quality in driving business success. They explore how data quality impacts the bottom line, particularly for C-level executives who often overlook its foundational importance. Through real-world examples, including procurement and predictive maintenance, they illustrate how effective data management can lead to significant cost savings and strategic advantages in competitive markets.TakeawaysData management should empower businesses, not hinder them.Data quality is essential for creating actionable insights.C-level executives often overlook the importance of baseline data quality.Improving data quality can lead to significant cost savings.Predictive maintenance relies heavily on accurate data management.Data quality impacts both operational efficiency and strategic decision-making.Companies often sit on valuable data without realizing its potential.Effective data management can differentiate a company in the marketplace.Understanding margin risk is crucial for service providers.Data insights can enhance both service delivery and profitability.Sound Bites"Data management shouldn't be a headache, it should be your fuel.""Data quality is the foundation.""There are multiple ways to hit the bottom line."
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  Unlocking Predictive Maintenance: A Guide 12.02.2026 14min

  SummaryIn this conversation, Erwin De Werd and Stephanie Wiechers discuss the complexities and actionable steps involved in predictive maintenance. They explore how technology has evolved to enable predictive maintenance, the benefits it offers in terms of operational efficiency and cost reduction, and the challenges companies face in managing data quality. Stephanie emphasizes the importance of a clean database and the role of AI in improving data management practices, ultimately guiding companies towards effective predictive maintenance strategies.TakeawaysPredictive maintenance allows for smarter scheduling and planning.Technology advancements have made predictive maintenance more feasible.Data quality is crucial for effective predictive maintenance.Companies can reduce downtime by anticipating maintenance needs.A clean database is essential for accurate predictive maintenance.Quality assurance checks help maintain data integrity.AI can automate data cleaning and improve accuracy.Understanding asset lifecycle can optimize maintenance strategies.Predictive maintenance can lead to cost savings in parts procurement.Initial assessments are key to implementing predictive maintenance.Sound Bites"We wish it was that straightforward.""Reduce the amount of downtime.""Save hours on every service call."Chapters00:00Introduction to Predictive Maintenance02:59The Evolution of Predictive Maintenance05:56Benefits of Predictive Maintenance08:58Challenges in Data Management11:50Technological Solutions for Data Quality14:49Getting Started with Predictive Maintenance
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  C-Level findings on data management (3/3) 05.02.2026 9min

  Keywords:data management, data quality, technical industries, competitive advantage, talent shortages, commoditization, insights, case studies, strategy, digital transformationSummary:In this episode of The Data Edge, Erwin De Werd and Stephanie Wiechers discuss the critical importance of data management and quality in technical industries. They explore insights from CEO interviews, highlighting challenges such as talent shortages and pricing pressures. The conversation emphasizes how leveraging data can provide a competitive advantage and shares case studies demonstrating successful data management strategies.TakeawaysTechnical industries lose millions due to messy data.Data quality serves as the foundational layer for success.Talent shortages are a significant concern in technical fields.Pricing power is under pressure, risking commoditization.Data management can enhance service offerings.Breaking down silos leads to better insights.Organizations can extract best practices from data.C-level executives increasingly recognize data's value.Collaboration across teams improves operational efficiency.Data can transform into a strategic asset for companies.Sound Bites"messy data costs millions""talent is disappearing""data as a strategic asset"Chapters00:00Introduction to Data Management Challenges04:47Real-World Applications of Data Insights
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  Unlocking Data Potential (1/3) 28.01.2026 17min

  SummaryThis episode delves into the intricacies of data management and its pivotal role in driving business success. Experts Stephanie Wiechers and Erwin De Werd from Pearstop discuss the importance of data quality, the value it adds, and practical insights into managing data effectively. The conversation highlights the foundational layers necessary for data management and explores real-world examples of how data can be leveraged to achieve organizational goals.Keywordsdata management, data quality, business success, Pearstop, data insightsTakeawaysData management is crucial for business success.Understanding data quality can drive value.A good data baseline opens up numerous possibilities.Data is the fuel for many business operations.Effective data management requires foundational layers.Data quality ensures accurate and actionable insights.Real-world examples illustrate data's impact.Data management involves both technical and strategic aspects.Pearstop experts share practical insights on data.The series explores data management in depth.Title OptionsUnlocking the Power of DataMastering Data ManagementData Insights for SuccessThe Art of Data QualityDriving Value with DataData Management EssentialsExploring Data PotentialData Strategies for GrowthThe Future of Data ManagementData-Driven Business SuccessSound bitesData management is crucial.Unlocking data's potential.Data is the fuel.Quality data drives value.Foundational layers are key.Real-world data insights.Data management essentials.Strategic data use.Data quality matters.Driving success with data.Chapters00:00:20 Introduction to Data Management00:00:33 Importance of Data Quality00:01:16 Real-World Data Insights00:01:55 Foundational Layers of Data00:03:10 Data as Business Fuel• • 00:03:56 Series Overview and Future Topics