Podcasts about master data

In Episode 204 of Manufacturing Hub, we wrap up our month-long ERP series with Glenn from Waites. Glenn shares a practical framework for understanding ERP systems—perfect for engineers, plant managers, and anyone who's struggled with clunky ERP software. We break down key modules like Financials, Supply Chain, MES, and Asset Management, and explore how modern tools are reshaping ERP usability.

In this episode of The New Warehouse Podcast, Kevin welcomes Marinus Bouwman, Product Manager and Business Development Lead at KNAPP, to discuss the role of master data in warehouse automation. KNAPP, a global leader in intelligent automation solutions, has pioneered advancements in data-driven automation for industries like food retail, healthcare, and general logistics.Tune in to hear Bouwman share insights into how high-quality data fuels automation, why master data often gets overlooked, and how KNAPP's software solutions address these challenges. Learn more about Zebra Robotics here. Follow us on LinkedIn and YouTube.Support the show

The convergence of Master Data Management (MDM) and Artificial Intelligence (AI) is transforming how businesses harness data to drive innovation and efficiency. MDM provides the foundation by organising, standardising, and maintaining critical business data, ensuring consistency and accuracy across an organisation. When paired with AI, this clean and structured data becomes a powerful asset, enabling advanced analytics, predictive insights, and intelligent automation. MDM and AI help businesses uncover hidden patterns, streamline operations, and make more informed decisions in real-time. By integrating MDM with AI, organisations can move beyond simply managing data to actively leveraging it for competitive advantage. AI algorithms thrive on high-quality, well-structured data, and MDM ensures just that—minimising errors and redundancies that could compromise results. This synergy empowers companies to personalise customer experiences, optimise supply chains, and respond proactively to market changes. In this episode, Kevin Petrie, VP of Research at BARC US, speaks to Jesper Grode, Director of Product Innovation at Stibo Systems, about the intersection between AI and MDM. Key Takeaways: AI and master data management should be integrated for better outcomes.Master data improves the quality of inputs for AI models.Accurate data is crucial for training machine learning models.Generative AI can enhance product launch processes.Prompt engineering is essential for generating accurate AI responses.AI can optimise MDM processes and reduce operational costs.Fast prototyping is vital for successful AI implementation.Chapters: 00:00 - Introduction to AI and Master Data Management02:59 - The Synergy Between AI and Master Data05:49 - Generative AI and Master Data Management09:12 - Leveraging Master Data for Small Language Models11:58 - AI's Role in Optimizing Master Data Management14:53 - Best Practices for Implementing AI in MDM

This episode of Future Finance explores how organizations can better align their finance and data strategies to harness the power of emerging technologies like AI. With a focus on data governance, analytics, and the integration of advanced tools into financial workflows, hosts Paul Barnhurst and Glenn Hopper discuss both challenges and opportunities for finance professionals in the digital age.Nathan Bell, is a seasoned executive with over 20 years of experience in steering organizations through complex financial and digital transformations. His career spans roles in credit management, banking, venture capital, and corporate finance, with expertise in data governance, analytics, and strategy. Nathan's passion lies in helping finance leaders leverage data to drive decision-making and growth.In this episode, you will discover:The importance of getting your “data house” in order before implementing AI.How self-service BI tools can both empower teams and create challenges in organizations.Why data governance and stewardship should be a co-led initiative between finance and engineering.The role of finance leaders in ensuring data literacy and ownership across departments.Practical insights into scaling finance operations for mid-market companies looking to grow.As AI and automation tools continue to evolve, organizations must focus on integrating these technologies into their workflows in a way that complements their existing data strategies. Nathan emphasized the critical role finance leaders play in ensuring data quality and ownership across the organization.Follow Nathan:LinkedIn: https://www.linkedin.com/in/nathan-bell-1038662/Website: https://www.vai-consulting.com/Company: https://meetvirginia.io/Join hosts Glenn and Paul as they unravel the complexities of AI in finance:Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[01:58] - Generative AI and Finance: Myths vs. Reality[11:17] - AI's Dependency on Clean Data[12:36] - Guest Introduction: Nathan Bell[20:02] - Bridging Data, Finance, and Storytelling[25:25] - Foundations of Data Governance[29:54] - Challenges with BI and Metrics Ownership[32:33] - Scaling Finance Teams for Growth[42:38] - Fun Segment: Travel Stories[45:29] - Closing Thoughts and Call to Action

BLACK FRIDAY SALE

Maintaining assets in a cost-effective manner is essential to a company's profitability. To effectively and efficiently execute an Asset Management (Maintenance & Reliability - M&R) Strategy, an organization should have an effective Computerized Maintenance Management System (CMMS). The CMMS is the system of record that provides well organized and accurate data for continuous improvement. Effective utilization and execution of the three main elements of an Asset Management Strategy (Work Management, Asset Strategies, and Defect Elimination) requires accurate data and history in the CMMS.To do this, an organization must make the investment of effort to collect necessary data to create and possess an accurate Equipment Register. This investment provides an organization with agreed-to and standardized definitions, designs, processes, and guidelines for building the sub-elements of the Equipment Register which include a Master Equipment List (MEL), Equipment or Asset Hierarchy, and Master Data. Master Data is the foundation of Asset Management. It is a starting point for continuous improvement.This presentation will highlight the reasons for effective data collection and the do's and don't's associated with capturing good data in your CMMS in to allow for effective Asset Management.Speaker:Blake A. Baca, CMRP, CRL - Owner/Asset Management Coach, BDB Solutions LLCAsset Management professional with over 35 years of experience in industries including mining, refining, smelting, oil & gas, power generation, foundry, manufacturing, and material processing.Worked for Alcoa, Inc. for the first 20 years of careerAsset Management Consultant since 2009Bachelor of Science in Mechanical Engineering degree from Texas Tech UniversityCertified Maintenance and Reliability Professional (CMRP)Certified Reliability Leader (CRL)► Register for an upcoming webinar here: https://flukereliability.info/bpw-frr

Kelly talks to Joshua Kloke, Alan Cross and Dr. Adil Shamji. Learn more about your ad choices. Visit megaphone.fm/adchoices

Managing large volumes of data in the context of AI and machine learning applications presents challenges related to data quality, data preparation, and automation. The requirements of data management are changing with the advent of generative AI, requiring more flexibility and the ability to handle larger volumes of data. Pimcore leverages AI and machine learning to automate data utilization and improve data intelligence. By streamlining data management and integrating various data sources, Pimcore drives revenue growth for its customers. The platform combines data management and experience management to deliver personalized data across communication channels. Pimcore's MDM solution addresses the challenges of integrating data for both human and machine consumption. The choice between physical and virtual MDM hubs depends on the use case and industry. In this episode of the EM360 Podcast, Doug Laney, Data and Analytics Strategy Innovation Fellow at West Monroe speaks to Dietmar Rietsch, Managing Director and Co-Founder of Pimcore, to discuss: Data managementAIMachine learningData quality

Join Avery on the latest episode of the Data Career Podcast as he sits down with Brent Dykes, the genius behind 'Effective Data Storytelling'.

Episode 90: Die Leuphana-Alumna Isabel Lerch arbeitet als Datenjournalistin bei »NDR Data« und als Redakteurin bei »NDR Info«. Während der NDR-Info-Sender tägliche Radiosendungen anbietet, was in der redaktionellen Arbeit oft mit einen gewissen Zeitdruck verbunden ist, sieht das bei der datenjournalistischen Sparte etwas anders aus. Bei »NDR Data« arbeitet ein datenjournalistisches Team, das ohne festen Sendeplatz dafür sorgt, dass abstrakte Datenmengen für die Hörer*innen oder Leser*innen verständlich aufgearbeitet werden. So sorgte das Team beispielsweise während der Corona-Pandemie für Visualisierungen der Zahlen des Robert Koch-Instituts (RKI) für den Online-Auftritt der Rundfunkanstalt. Isabel Lerch nutzt zudem ihr Wissen rund um das Thema Datenjournalismus, um es als Trainerin an Volontär*innen oder Journalist*innen aus anderen Sparten weiterzugeben, wobei sie ihre eigenen, aktuellen Recherchen sehr gut einfließen lassen kann. Darüber spricht die Alumna in diesem »Karriere leupht - Studium und dann?«-Interview und gibt Antworten auf spannende Fragen wie: Wie unterscheidet sich ihr Arbeitsalltag als Redakteurin und Datenjournalistin konkret? Welche Rolle hat die Corona-Pandemie für das junge Feld des Datenjournalismus gespielt und wo steht es abgesehen davon? Welche Rolle spielen KI-Tools bei ihrer Arbeit? Was erhofft sie sich in Bezug auf KI für ihre berufliche Zukunft? Und was hat sie aus ihrer Hospitanz im Moskauer ARD-Studio mitgenommen? Zudem spricht die Alumna über ihr berufsbegleitendes Master-Studium »Data Science« an der Leuphana. Dieses begann Isabell Lerch 2020 nachdem sie bereits beim NDR als Datenjournalistin tätig war und beschloss, ihre Kenntnisse im Bereich der Informatik im Rahmen eines Studiums auszubauen. Wie ist der Studiengang »Data Science« aufgebaut? Was hatte Corona mit Isabel Lerchs Entscheidung für das Studium zu tun? In welchen Punkten fühlt sich Isabel Lerch mit der Gen Z verbunden? Und was bedeutet es für die Alumna, »Data Science« ohne Informatikhintergrund zu studieren? Das erfahrt ihr im heutigen Erfahrungsbericht von »Karriere leupht - Studium und dann?«! Viel Spaß beim Zuhören! Moderiert wird diese Episode von: Daniel Persiel Weitere Informationen und Links zur Episode findet Ihr hier: https://podcast.leuphana.de/karriere-leupht-als-datenjournalistin-und-redakteurin-beim-ndr/ Kapitelmarken: 00:00 Karriere leupht mit Isabel Lerch 00:46 Isabels Arbeitgeber»NDR Info« und »NDR Data« 03:25 Aufteilung zwischen den zwei Sparten 04:39 Hintergründe zum Datenjournalismus 07:34 Einsatz von KI-Tools im Datenjournalismus 09:32 Begeisterndes und Herausforderungen 12:54 Kürzliche Veränderungen und Ausblick des Datenjournalismus 17:42 Erlerntes weitergeben als Trainerin für Datenjournalismus 20:57 Hospitation in ARD-Studio in Moskau 22:46 Welche Fähigkeiten sollten Berufseinsteiger*innen mitbringen? 26:24 Was den berufsbegleitenden Master »Data Science« ausmacht 30:18 Warum die Leuphana? 33:15 Aufteilung zwischen Arbeit und Studium 35:48 Unterschied zu Isabels vorherigen Studienphasen 37:33 Der Background von Kommiliton*innen im »Data Science«-Master 39:40 Für wen ist der Studiengang etwas und für wen nicht? 44:18 Ausblick auf Isabels berufliche Zukunft 47:03 Tipps für den Einstieg ins Berufsleben

Die Vision der Datenschmiede.ai ist es, Master Data Management Projekte durch den Einsatz von Künstlicher Intelligenz vollständig zu automatisieren und so Master Data Management zum Lieblingsthema im Unternehmen zu machen. Hier im Gespräch zwei der drei Gründer, Dr. Felix Kruse (CEO) und Raphael Holtmann (COO). ► Wir danken unserem Partner, der Public Cloud Group (PCG): https://pcg.io/ ► Das Video zum Podcast: https://youtu.be/xkyOe6A_4kw ► Datenschmiede.ai: https://www.datenschmiede.ai/ ► LinkedIn Felix: https://www.linkedin.com/in/dr-felix-kruse-38437414b/ ► LinkedIn Raphael: https://www.linkedin.com/in/raphael-holtmann-3a156121a/ ► LinkedIn Bernard: https://www.linkedin.com/in/bernardsonnenschein/ ► Wir freuen uns auf dich auf der data:unplugged: https://www.data-unplugged.de/

Planowanie i realizowanie wspólnych procesów obejmujących jednocześnie marketing i sprzedaż B2B wymaga spójnych i transparentnych danych na temat klientów. Trudno to osiągnąć bez danych podstawowych klientów (ang. Account Master Data), trudno też bez nich zbudować pełen widok wszystkich interakcji z klientem. To właśnie dane podstawowe są kluczem do odpowiedzi na pytania, takie jak: - Jak przełamać silosy danych i zapewnić spójność danych marketingowych i sprzedażowych? - Jak doprowadzić do stworzenia pełnego widoku 360' wszystkich interakcji z klientem? - Jak synchronizować procesy marketingowe i sprzedażowe? - Jak mierzyć penetrację rynku albo Marketing ROI? Zapraszam do podcastu, w którym opowiadam na te pytania oraz opowiadam jak zabrać się do wdrożenia danych podstawowych klientów (Account Master Data) w firmie B2B. Notatki do podcastu: Bizraport.pl - ciekawe i bezpłatne źródło nt. firm w Polsce: https://www.bizraport.pl/katalog-firm Taksonomie klasyfikacji branż: ISIC - The International Standard Industrial Classification of All Economic Activities: https://unstats.un.org/unsd/classifications/Econ/isic NACE - Statystyczna klasyfikacja działalności gospodarczej we Wspólnocie Europejskiej: https://ec.europa.eu/eurostat/web/nace/overview PKD - Polska Klasyfikacja Działalności: https://stat.gov.pl/Klasyfikacje/doc/pkd_07/pkd_07.htm Szymon Negacz na YouTube: Jak połączyć PROSPECTING, MARKETING i PROCES SPRZEDAŻY? https://www.youtube.com/watch?v=nr4N67VVlc4

In episode 164 of our SAP on Azure video podcast we talk about SAP Master Data updates from Teams. In previous episodes we had talked about Teams, leveraging AI services for a better chatbot experience and talked about how this could be connected to an SAP system. We even showed some demos -- but it was just that: proof of concepts! Today we have a very special guest: Steve Curtis from Mars. He has build an MDM Chatbot in Teams that allows mass updates to Master Data in SAP MDG using MS Teams -- which is actually live and running for some time now at Mars. Find all the links mentioned here: https://www.saponazurepodcast.de/episode164 Reach out to us for any feedback / questions: * Robert Boban: https://www.linkedin.com/in/rboban/ * Goran Condric: https://www.linkedin.com/in/gorancondric/ * Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/ #Microsoft #SAP #Azure #SAPonAzure #Teams #AI #MDM #MasterData #MDG #Excel ## Summary created by AI * Introduction of Steve Curtis and his MDM chatbot in Teams: Steve Curtis is an MDM lead at Mars who built a chatbot in Teams that allows users to update master data in SAP MDG using Excel templates and AI services. * Demo of the chatbot functionality and benefits: The chatbot can handle different scenarios such as creating new customers, updating existing customers, and validating data. It can also provide feedback, email notifications, and error handling. The chatbot reduces the need for tickets, training, and manual intervention, and improves the user experience and data quality. * Technical details and challenges of the chatbot development: The chatbot uses Azure Bot Framework, QnA Maker, LUIS, and Cognitive Services to interact with users and SAP systems. The chatbot faced some challenges such as security, compliance, and data mapping, which were solved by using onedrive, encryption, and lookup tables. * Future plans and enhancements for the chatbot: The chatbot is currently in production for one region and one data domain, but it will be extended to other regions and domains soon. The chatbot will also be improved by adding more intelligence, creativity, and humor to the conversations.

Hvordan får man 27 mio. årlige passagerer og mindst lige så mange stykker bagage til at glide let og elegant gennem 10 km baggagebånd? Hvordan sætter man data helt øverst på den strategiske agenda i ambitionen om at være en digital og datadrevet lufthavn? Og hvordan kan man helt konkret bruge AI og Machine Learning til at reducere ventetiden på udlevering af bagage? Det fortæller dagens gæster fra CPH Lufthavn, Tove Hejbøl Lindquist, PEX/Data & Digital Director, og Samuel Rude, Head of Airport Baggage Services meget mere om i dette afsnit. De fortæller også om, hvordan data-mindsettet har bredt sig til alle dele af organisationen – fra strategikontoret til driftscheferne for baggageanlægget – og om deres seneste konkrete initiativ: Et 10 ugers Master Data-program. Rigtig god fornøjelse i Dataklubben! 

In this enthralling episode of Legacy Leaders, we delve deep into the mind and journey of the multifaceted Ismael Chang Ghalimi.Holding the esteemed CEO position at STOIC, Ismael has magnanimously coached over 250 crème de la crème contributors from LinkedIn's top 500 Collaborative Articles. As a visionary, he pioneered the creation of the world's first intelligent data cloud. Today, he is known as an AI and data SME.But Ismael's prowess is not limited to the corporate arena. Skies acknowledge him as an instrument-rated private pilot, while the underwater world recognizes his expertise as a SCUBA assistant instructor. On land, his craftsmanship as a machinist is unparalleled.Born in France, he carries the heritage of Algeria, the heart of Japan, and the embrace of the Chinese union, all while proudly resonating with the spirit of America by choice.Ismael's thirst for knowledge is unquenchable. He is an insatiable learner, ceaselessly expanding the horizons of his intellect and skill set. Join us as we explore the riveting tapestry of Ismael's life and achievements.

In this enthralling episode of Legacy Leaders, we delve deep into the mind and journey of the multifaceted Ismael Chang Ghalimi.Holding the esteemed CEO position at STOIC, Ismael has magnanimously coached over 250 crème de la crème contributors from LinkedIn's top 500 Collaborative Articles. As a visionary, he pioneered the creation of the world's first intelligent data cloud. Today, he is known as an AI and data SME.But Ismael's prowess is not limited to the corporate arena. Skies acknowledge him as an instrument-rated private pilot, while the underwater world recognizes his expertise as a SCUBA assistant instructor. On land, his craftsmanship as a machinist is unparalleled.Born in France, he carries the heritage of Algeria, the heart of Japan, and the embrace of the Chinese union, all while proudly resonating with the spirit of America by choice.Ismael's thirst for knowledge is unquenchable. He is an insatiable learner, ceaselessly expanding the horizons of his intellect and skill set. Join us as we explore the riveting tapestry of Ismael's life and achievements.

Martin's guest is Andrew Good, CEO of Liberty Grove Software from Canada. Andrew is an engineer, project manager, analyst, manufacturing expert, and Microsoft Certified Trainer. His extensive knowledge and background have been built from personal experiences with many companies, working in various operational and management roles. Projects have ranged from new implementations to streamlining business operations. 21 years of Microsoft Dynamics 365 Business Central and Dynamics NAV experience mean that he can fall back on many different perspectives when working with customers.  He has helped clients get the most out of their Microsoft Dynamics 365 Business Central/NAV systems through integrations, upgrades, and extensions that allow them to deal with their changing business environments, regardless of whether financial operations, rentals, distribution, and manufacturing.  Being asked whether providing the master data that are so essential for a functioning planning process also means fun to him, Andrew admitted that, overall, from an organization's perspective, it is a painful process. Nevertheless, as he pointed out, the dividends that are paid to the organizations are huge both in the short and long term because, in the end, they are rewarded by getting a single system that provides them with trustworthy financial, inventory, and manufacturing information. And according to Andrew, the process of providing and maintaining master data is an ongoing one. The system needs to remain trustworthy and stable also after go-live, and for this, the data must be kept in good shape continuously. Depending on the organizations' size, there are a variety of techniques at hand for this.Martin then brought up the claim "Transform your business with human-friendly solutions to power progress" that he read on Andrew's website when preparing for the podcast and that he would like to investigate further. Starting with the "transform your business" part, Andrew outlined that when you look at the transformation necessity for the manufacturing companies, it is not primarily related to their "direct processes", meaning everything connected to manufacturing, but mainly concerns all their indirect processes, like engineering, purchase, finance, etc. where the day-to-day work can be significantly streamlined and improved to make people more efficient. Andrew sees a second area of transformation opportunity in leveraging the already massive data that are available in the organizations by using tools like Power BI to provide insights into how the factory is run. Of course, this also will impact production itself because Power BI can also be used to monitor real-time production data, thus finding out if production is running to its quote, finding fundamental issues with a certain part, etc.Discussing the "human-friendly solution" part of the claim, Andrew stressed that he is no fan of productivity initiatives being introduced by top-down directives. Not including the experience and opinions of all staff members will generate a lot of resentment and resistance to those initiatives. Andrew advocates for a more collaborative approach where also the view and the voices of the "direct" people in the project count. According to Andrew, it is always key to get people on the shop floor involved.Regarding the third part of the claim, "to power progress", Martin wanted to know whether and how Andrew measures and documents the progress his customers make. Andrew related that he encourages his customers to gather and analyze data and examine how it changes over time. They should also keep track of the activities having been executed during the same period to see the impact of these changes. He is a firm believer in constant measuring, monitoring, and then acting based on the monitoring. The typical metrics that get tracked in manufacturing companies, of course, depend on the companies' focus. 

In this episode, the host interviews Peter Kapur, the Head of Data Governance and Data Quality at CarMax. They discuss the importance of data governance as an offensive strategy rather than just a defensive one. Peter explains his role in making data an asset that drives business and improves profitability, customer experience, and associate experience. They also touch on privacy as a component of data governance and the need to change the mindset around data governance from a defensive weapon to an offensive one. Highlights: 00:00:09 Data governance as an offensive enabler. 00:07:00 Shift from defense to offense. 00:12:40 Think outside the framework. 00:19:25 Data governance adds business value. Guest: Peter Kapur has more than 25 years in driving innovation in Data Management, with a focus on practical operationalization of Product Strategy, Data Strategy, Data Governance, Master Data, Data Quality, Metadata, and Data Stewardship. He is an Industry leader & Visionary, having led the operationalization of Data Analytics and Data Governance for several organizations, including Waste Management, JP Morgan, AIG, Depository Trust, Deutsche Bank, Goldman Sachs & Credit Suisse. An industry pioneer in driving a business-centric view in leveraging a component-based Data Strategy tied to specific business objectives International Speaker, including a keynote at FIMA conference, MDM/DQ, DGIQ, Data Summit Strategic Product & Industry Advisor to several Data vendors LinkedIn: https://www.linkedin.com/in/peterkapur --- Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at  https://www.linkedin.com/in/amirbormand (Amir Bormand)

Welcome to ILTA's Risk Management: Data Analytics & Intelligence series. Over the course of this program, we will provide access to experts in the legal industry to discuss challenges of adoption and the benefits of using cloud technologies and Data Analytics to enhance processes, leading to efficiency, cost-savings and secured compliance.   We will review the obstacles, challenges and successes of adoption focusing on matter intelligence. How are organizations leveraging data related to client/matter lifecycle to enhance processes, compliance, and security, build relationships (Business Development), and streamline cost saving efforts. Specific topics will include, Artificial Intelligence opportunities, adoption practices, security concerns and compliance.   Questions Elizabeth asked the speakers: 1)        What is the biggest challenge your organization faces today as you begin adopting Cloud Technologies and ensuring security compliance across the board? 2)        As new Cloud-Based technology is adopted by your organization, describe the security concerns your organization faced, how the organization was able to move forward given the concerns and the impact on people, processes and policy once adopted. 3)        What are the specific steps an organization can take to ensure a successful adoption, both from a people and system perspective? 4)        Data captured at client/matter inception is used throughout an organization. What were the key factors in joining differing areas | departments (Risk, Business Development, Finance, etc.) to develop a consistent “Master Data” foundation to leverage for reporting and intelligence organization wide?  Moderator: @Elizabeth Suehr - Director of Legal Risk Systems and Strategy, Jenner & Block Speakers: @Damien Riehl - VP, Litigation Workflow and Analytics Content, FastCase @James Hannigan - Director of Legal Project Management, Coblentz Patch Duffy & Bass, LLP Recorded on 04-25-2023

Hvem vinder tovtrækningen, når it trækker i retning af data governance, mens forretningen trækker mod demokratisering af data? Og hvad kan ML-modeller bygget til retssystemet i England gøre for master data i en dansk producent af høreapparater, gamingudstyr & avanceret software? Dagens gæst i Dataklubben har med stor succes fået data ud og leve hos de data-hungrende folk i GN Audio. Fundamentet for hele herligheden er høj datakvalitet på tværs af systemer - vedligeholdt af ML-modeller, kørt i Databricks og drevet af dygtige folk placeret både centralt og decentralt.  Byd velkommen til Dennis Thomsen, Global Data & Analytics Director i GN Audio. 

On today's episode of The Edge of Excellence, Matt chats with Milton Miyashiro, CFO of National Services Group. Milton Miyashiro is known for operationalizing accounting, finance, regulatory compliance, and is a multi-dimensional financial accountant.He will talk about his path to excellence and becoming a CFO. You will learn why academic excellence in high school does not always equate to success later in life. He will talk about why he chose to major in accounting at the University of Notre Dame and how it helped him build both his finance profession and his business career. You'll learn how switching from CPA firms to mortgage-backed securities launched his career on Wall Street. You will understand why you need to specialize in the accounting and finance world. You will discover why it is important to have a broader understanding of your area of expertise, including legal and sales aspects.You'll learn more about the collapse of Bear Stearns and its sale to J.P. Morgan Chase & Co. As head of regulatory compliance at Thomson Reuters Pricing Service, he'll talk about how he came to work at the firm and why it was a valuable learning experience. You'll learn about mortgage-backed securities and the different types of audits. You will learn how he came to his role as global head of Thomson Reuters Pricing Service Operations and why people were backing away from that position. He will also talk about his responsibilities as global head of Rights Management and Master Data. He will reveal the most exciting aspect of his position as CFO.Join Matt and Milton for a fascinating conversation about compartmentalizing, public accounting and expanding your network.Enjoy! What You Will Learn In This Show:Milton's definition of excellenceHow important it is to be aware of your abilities in order to excelWhy Milton branched out into mortgage-backed securitiesThe difference between public accounting and corporate accountingWhat caused the Great Recession An unqualified audit opinion versus a qualified audit opinionOn regulatory compliance, pricing services, rights management and master dataThe role of a CFOAnd so much more...Resources:The Edge of ExcellenceMilton's LinkedIn

Texas transplant Joe Forrester is featured in Episode #087 of Underserved. Joe wanted to be a pilot after watching the original "Top Gun", but colorblindness trumped his enthusiasm and determination. Instead, he cut his teeth in cellular billing software, which was followed by learning how to manage marketing data in the automotive industry.  This skill treated him well for several years across many verticals, ultimately leading to a gig bringing this marketing horsepower to small and medium-sized businesses. Also covered: turning recalls into a positive experience, conquest campaigns, and the Great Flood of '17.   Joe on LinkedIn: https://www.linkedin.com/in/joeforrester/ First Job: www.convergys.com Second Job: www.acxiom.com Third Job: www.choreograph.com Current Job: www.choozle.com Guitar Youtube sites mentioned: https://www.youtube.com/@MartyMusic and https://www.youtube.com/@BrettPapa

The Data Storytellers Podcast S03E04: Jason Jarrett - Director of Global Master Data Governance at AbbottWebsite: https://www.thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/0ec6LwomOkFD5OrCDvz1l4YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA 0:00:36 Introduction 07:34.7 Marketing and Creative Literature 10:45.8 Progress in Data Analytics 13:45.0 Communication Skills 18:56.3 Personal Branding 23:29.1 First Impression 26:56.5 Challenges in Engagement 29:24.1 Asking the Right Questions 34:36.0 Data Literacy 37:25.3 Selling the Concept 41:09.8 Importance of Story Telling 44:56.0 Data Leader Responsibility 48:30.6 Advice for Future Data Leaders

W 140 odcinku podcastu Business Marketer gościmy po raz kolejny Igora Bielobradka z Deloitte. Igor był niedawno naszym gościem i w 138 idcinku opowiadał o światowych trendach w content marketingu, które zostały przedstawione podczas konfernecji Content Marketing World.Dziś porozmawiamy o temacie wystapienia Igora podczas tej konferencji.A był to "Master Data Record". Igor pokazywał dlaczego warto dążyć do posiadania podstawowych danych o klientach w jednym miejscu i stale je uzupełniać.Wydaje się to bardzo proste, ale jeżeli weźmiemy pod uwagę fakt, że średni zespół marketingowyc korzysta z kilkunastu aplikacji, które zbierają i produkują dane o klientach, to okaże się, że utrzymanie tej spójności nie jest takie proste.Dlatego z tego odcinka dowiesz się między innymi: Co to jest Master Data RecordDlaczego warto dążyć do posiadania porządku w danych o klientachJakie są przykłady Master Data RecordJak w praktyce moża wykorzystać posiadanie tego typu danychDla mnie ten odcinek jest szczególnie ważny, ponieważ zajmuję się automatyzacją marketingu i bardzo często mam do czynienia z sytuacjami, kiedy klienci mają bardoz rozproszone dane. Często nawet nie są w stanie skutecznie wymieniać danych pomiędzy systemami marketingowymi i CRM, a to dopiero wierzchołek góry lodowej.Jeżeli zamierzasz zbierać więcej danych o klientach, koniecznie posłuchaj tego odcinka.Więcej publikacji Igora znajdziesz na jego blogu: https://b2b-marketing.pl/Odwiedź mój blog Business Marketer, gdzie znajdziesz artykuły, e-booki, webinary i podcasty na temat Marketingu B2BProwadzę konsultacje i szkolenia na temat: Strategii Marketingu B2B, Marketing Automation, Social Selling, Content Marketing Lead Generation lukasz.kosuniak@businessmarketer.pl

Fast kein Prozess in Unternehmen funktioniert ohne Stammdaten: Daten über Kunden, Mitarbeiter, Zulieferer, Materialien und Produkte in hoher Qualität. Der österreichische Automobilzulieferer Hirschmann Automotive hat in einem beeindruckenden Projekt zusammen mit IBsolution einen Stammdaten-Prozess exploriert und mit SAP Master Data Governance umgesetzt. Technology Evangelist Dr. Christian Michel spricht mit Nicole Niegelhell, Digitalization Manager und Master Data Governor bei Hirschmann Automotive, Michael Müller, Senior Sales Executive von IBsolution und Olaf Kexel, Solution Advisor für Stammdaten Management MEE bei SAP. Es wird spannend!

The long awaited live and in person event The Digital Intersect will take place in Detroit, Michigan on November 17th. Today Thom Singer and Craig Brown welcome back Digital Enterprise Society president and return podcast guest Adam Specht for a look at the details of this event, including speakers, breakout topics, and the benefits that come from meeting in person. Registration is now open!   On today's podcast, you will learn:   Meeting in person again The Digital Intersect will provide the opportunity to meet in person again.  There is an irreplaceable value in meeting in person to exchange ideas, data, and strategies.  Virtual events have served their purpose, but nothing virtual can replace the interactions that happen when people are physically together.    Overcoming the challenges of the virtual workplace This timely topic will be addressed at the conference.  How can you build a winning team culture from the home office?  We are a globally distributed workforce, which requires some virtual interaction.  The Digital Intersect will allow people to meet in person to share strategies for effective virtual connections.    Conference networking strategies  Your one-time attendance at a conference is not the only effort you have to make at networking.  Identify people you can develop long term, mutually beneficial relationships with.  Over a lifetime, career opportunities will come from the people in your network.    An overview of The Digital Intersect The Futurist Simon Anderson will provide a look at the future of the digital community.  Peter Bilello will offer the state of the industry.  Breakout groups will provide opportunities to address current challenges, including Collaborative Design, Digital Thread, and Team Culture.  Vendors will address collaboration and management in a mixed toolset environment.    Maximizing your time at the event Open your mouth, share your ideas and be open to conversation.  Recognize that everyone has a different comfort level post-pandemic and will respect others preferences.  Push yourself to meet one new person at every break or meal.    Continue the conversation with us within the Digital Enterprise Society Community at www.DigitalEnterpriseSociety.org.   Register for The Digital Intersect today   Digital Download: Virtual Round-Table Series

«Think of data availability as online vs. offline.»What much of the discussions around data products, data catalogs, self-service boil down to is data discoverability, observability and availability.I talked to Ivan Karlovic, Director of Data Analytics and Master Data at Norwegian about these topics and gained some fantastic insights. Ivan always loved analytics and using data to improve the business and started his dat journey with a course in data mining and with «Pure curiosity on how we can use data!»Here are my key takeaways:The Airline sectorAirline industry is reliant on partner «A Flight is just a subset of an end-2-end journey»Data privacy and ethics are important topics for Norwegian and are faced with a systematic approach with an aim for automation.Norwegian is building a cloud based analytical platform to ensure a greater visibility of the data analytics setup.The first improvement should be on data discoverability, closely connected to data observability.«A Data Catalog will raise awareness of what we have of data assets.»There is a clear goal to ensure an automated observation of all data assets in Real time.A central team needs to be able to deliver cross-domain use cases, also across domains with different data maturity.«With this crisis-domain approach we are putting away some of the legacy discussions.» We can engage with each domain.It is ok to have specific crawlers on local data, but you need to synchronize it into the central data catalog.The organization needs to have a way to stay aware of everything that is produced.Data AvailabilityExcept for sensitive data, everyone in a domain should be able to see all domain data. Outside the domain, people should be aware of what kind of data each domain maintains.«If you work with analytics or machine learning you always what to talk to the domain people, because you can easily misinterpret if you don't have that domain experience.»Domain data products that are domain spesific without a use case outside the domain, do not have to adhere to central strandards. But if they can have a use cases outside the domain, they need to be fed into the central data catalog.Communication and understanding intentions from data producers to data users is really important. You have to continuously work with understanding.There is lost out business potential in not having data discoverable, no matter the quality.Most effort is wasted in rework of data products that where just not discoverable.Self-Service«When it comes to self-service we need to set up technology in a way that the end-user does not have to think about the data, only the problem to solve»Even if only 70% of use cases can be solved by self service, we need to strive for 100% to ensure that we offload the expert data analytics team as much as possible to work on the tough cases.«Data Catalog: You can buy a monster that gives you 95% of things you don't need, or cutting edge super-niche start ups. But you have some interesting players somewhere in the midle.»«Can we do data engineering on a meta level, without seeing the underlying data? Eg. For PII?»DocumentationHow can you retain knowledge in a distributed architecture?Ensure domain knowledge is fostered in the domains. Build a documentation repositoryInfrastructure as code. Technical knowledge supplied with contextDomain knowledge is the most tricky and most difficult to replaceGreat documentation can be both motivating and time saving. Motivating to reach a higher standard and time saving for problem finding, onboarding, etc.

CDQ ermöglicht es großen Unternehmen, Datenqualität und Datenpflege kooperativ zu bewältigen. Das Ergebnis ist eine hohe Stammdatenqualität mit geringem manuellen Aufwand. Hier im Gespräch dazu Dr. Kai Hüner (Co-founder & CTO) und Dr. Tobias Pentek (Head of Community & Innovation): 0:00 - Kai und Tobias stellen sich vor. 5:30 - Worum geht's bei CDQ? 11:39 - Warum sollten Unternehmen über Daten kooperieren? 23:18 - Inwiefern Netzwerkeffekte? 31:29 - CDQ Suite — was steckt technologisch dahinter? 43:39 - Konkrete Beispiele / Use-Cases. 50:40 - Zum Geschäftsmodell. 1:04:50 - Die nächsten Milestones. ------------------------------------------------------------------------------- Weiterführende Informationen: ► CDQ: https://www.cdq.com/ ► Evonik's Journey to a Touchless First-Time-Right Data Life Cycle Process: https://www.cdq.com/events-insights/webinars/evoniks-journey-touchless-first-time-right-data-life-cycle-process ► Bayers Cinderella Project on Automated Decision-Making: https://hub.cdq.com/summerreads2021/bayers-cinderella-project-on-automated-decision-making-in-vendor-master-data-management ► Data Sharing Positioning Paper: https://drive.google.com/file/d/199sL4kXBxw11g0TnkzAmpOymGHD3oFE3/view ► LinkedIn Kai: https://www.linkedin.com/in/kaihuener/ ► LinkedIn Tobias: https://www.linkedin.com/in/tobias-pentek-24b4a9140/ ► LinkedIn Bernard: https://www.linkedin.com/in/bernardsonnenschein/ ► Feedback: bernard.sonnenschein@datenbusiness.de

Watch this episode on YouTube: https://youtu.be/zVOqI2lm6I8 This Week's Guest is Master Data Management Expert, Malcolm Hawker As the Head of Data Strategy for Profisee, Malcolm’s mission is to raise the awareness of the value of MDM and Data Governance to senior leaders at companies across the globe. His focus is to help Profisee’s prospects and clients […]

IT Best Practices: Master data is defined by Garner as “the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise.” Intel uses vast amounts of master data every day for many reasons, spanning transactions, reporting and advanced analytics. But often, Intel’s master data exists in silos, presenting […]

IT Best Practices: Master data is defined by Garner as “the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise.” Intel uses vast amounts of master data every day for many reasons, spanning transactions, reporting and advanced analytics. But often, Intel’s master data exists in silos, presenting […]

IT Best Practices: Master data is defined by Garner as “the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise.” Intel uses vast amounts of master data every day for many reasons, spanning transactions, reporting and advanced analytics. But often, Intel’s master data exists in silos, presenting […]

This week, we're going to be talking again about that little devil that keeps cropping up - data.  I'm speaking to Costas Xyloyiannis, CEO of HICX, a company that takes a slightly different approach to data. Today we're talking about what he thinks is the safest way to get clean data in your organisation. Before we dive into the specifics of what makes his approach to data so different, I start off by asking Costas to explain Hicx in a nutshell. HICX - A different way to handle data Costas explains that HICX is in the business of supplier experience management. Supplier experience, for Costas, equals data. The end state isn't just that data - there are value drivers after that too - but data is the foundation of supplier experience management: Better data means better experience, and a better experience in turn yields better data. It's a flywheel value effect for both sides. I ask how this differs from other solutions that take the approach of automatically gathering data using scraping techniques or AI.  Costas gives a few differences here. Number one, what is the customer trying to solve? A lot of his customers want the right data in their systems. What you tend to find is that when you pull data out, clean it, change it, what you find is that you can never put it back into those systems. The data will have changed. So this is a very high risk, unsustainable approach. But this is the way most people have done it traditionally. HICX puts processes in place which control how data is entered into those systems. The supplier is the source of truth, so why not optimise the process of collecting data from suppliers? Only then does HICX apply automation to enrich that data. Customer experience and strategic sourcing  I ask Costas to clarify that he is using some degree of automation, but the fundamental difference is that he's relying on the supplier to provide the core master data. He says this is correct. He then goes on to speak to customer demand. Customers need a very granular view of their suppliers. If you're looking at, for example, a process around manufacturing that has to take place at your supplier's facility, most other sources don't explain the things customers want to know: What is the parent legal entity, for example? These factors change how data and business processes are managed. I bring up vendor master data. We often think about this wrongly: “What data do we need in the system to pay the supplier?”. But in something like manufacturing, food or automotive where you've got health and safety requirements that are important to the qualification process, there has to be some way of distinguishing the supplier experience and on-boarding process. If you're using a tool that automatically cleanses data, it's not going to know how strategically critical a supplier will be to you. Costas agrees. He also says that context of how you use data is important. It could be the address of a supplier, it could be a payment address… When you're cleansing you don't know these things. Costas thinks that this is where customers need help: Who is the parent entity? Is this supplier part of the same legal entity? What is this address? How does it all fit together? This is what customers need to understand to a high degree of accuracy. He goes on to talk about the importance of being clear on your use case. If your use case is highly analytical, then using an outside source to cleanse your data makes sense, because your goal is to process a lot of data. If you actually want to change the data in your operational systems, this does not work. You need context to meaningfully make those changes. What does each data point mean to your organisation, and depending on context. The HICX Solution  I mention HICX's clients, who are largely enterprise-level organisations. If the suppliers are HICX's source of truth, then what typically does Costas see from his customers when it comes to managing or changing...

Dr. Matteo Crotti has got his PhD from the Liverpool John Moores University. He has been involved in various research projects concerning children's physical activity, motor skills and health. Furthermore, he conducted a study concerning the relationship between play behaviours and motor skills in preschool children. His early career researcher was in the field of Sports Sciences and his key research focus on physical activity promotion, physical activity assessment, physical education and coaching. ----------------------------------------- This podcast episode is sponsored by Fibion Inc. | The New Gold Standard for Sedentary Behaviour and Physical Activity Monitoring Learn more about Fibion: fibion.com/research --- Collect, store and manage SB and PA data easily and remotely - Discover new Fibion SENS Motion: https://sens.fibion.com/

Insparis flagskibsevent, Data Day '22, har været den perfekte anledning til endnu en gang at invitere gæster ind i Dataklubben. Optaget i foyeren på Tivoli Hotel og Congress møder vi episodens gæster. Master Data er for alle i WSA Micala Gotfredsen, Director, Commercial Data & Analytics, kom forbi til en snak om et emne, der ligger hendes hjerte nært: Master Data Management. Micalas afdeling har formået at tænke Master Data-løsninger ind i alle initiativer, og derfor forbedrer de også deres datagrundlag hver gang, de leverer kundeindsigt til forretningen. Det kan man vist kalde en win-win.  Vi skal stoppe med at symptombehandle, turde tage de udfordringer, som ingen andre vil have, kommunikere undervejs og skabe synlighed i processen for resten af organisationen.  Micala fortæller også om nogle af WSA's konkrete Master Data use cases, såsom NPS-målinger og one business-platform, og om implementeringen af Master Data i WSA.

DB Schenker ist ein weltweit führender Anbieter von globalen Logistikdienstleistungen. Heute zu Gast ist Eugen Tissen, Vice President Global Steering and Master Data Management. Die Themen unter anderem: 0:18 - Eugen stellt sich vor. 6:20 - Logistik 1mal1. 8:03 - Worum geht's bei DB Schenker? 17:58 - Was sind Stammdaten und warum ist die Verwaltung dieser so wichtig? 27:54 - Data Science oder KI mit Stammdaten? 33:42 - We Love Data. 42:51 - Startups. 48:45 - Nachhaltigkeit. 59:26 - Gestörte Lieferketten. 1:06:06 - LinkedIn. 1:09:22 - Konzernkarriere --- Weiterführende Informationen: DB Schenker: https://www.dbschenker.com/de-de --- LinkedIn Eugen: https://www.linkedin.com/in/eugentissen/ LinkedIn Bernard: https://www.linkedin.com/in/bernardsonnenschein/ --- Ich freue mich über Austausch: bernard.sonnenschein@datenbusiness.de

Yolanda Redmond, VUMC and Terrie Reed, Symmetric Health Solutions describe the challenges of matching data sources and public information. Learn how VUMC's database cleanse and implementation of both GTIN and other key elements allowed for close alignment and the ability to provide clinical teams with a deeper level of insight, leading to improved value analysis outcomes.

The legendary "Data Whisperer", Scott Taylor, joins the Monday Morning Data Chat to discuss master data - what it is, and why you should care. Scott pulls no punches, and this is a fun and candid conversation. Streamed live on LinkedIn and YouTube #data #masterdata #dataengineering --------------------------------- TERNARY DATA We are Matt and Joe, and we're "recovering data scientists". Together, we run a data architecture company called Ternary Data. Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small. Ternary Data Site - https://ternarydata.com LinkedIn - https://www.linkedin.com/company/ternary-data/ YouTube - https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg

Last week on the Land.MBA Podcast, we talked about important skills that you need to succeed in the land business. In this episode, we are building on those fundamental principals and taking a deep dive into Data, the most important part of land business.  Are you looking to purchase land? Is your land not selling? Understanding the numbers and details about a property is key in determining if the deal will make you money or cost you big time. Today, we talk about the difference between number and data, how you can use data to find the best deals, how to clean your data for accuracy, and how you can use it to get the attention of motivated buyers.    Let's Connect  For coaching and courses go here - https://www.land.mba Instagram - https://www.instagram.com/land.mba/ Facebook - https://www.facebook.com/mylandmba   Transcript: Speaker1: [00:00:00] They say the data are like people getting interrogated, tortured hard enough and will tell you whatever you want to hear. Seriously, though, and our last episode, Dave and I introduced the five skills you need to pay the bills as a land investor. It's fitting that the first one is data. Data sits at the core of land investing. Do it well and you can make a lot of money with a lot less effort. Do it poorly and you can struggle to cover your mailing budget. In this episode of the Land MBA podcast, Dave and I are going deep on data what it is and how you master it to succeed in land. Speaker2: [00:00:57] Welcome to the Land NBA podcast on your host David  along with my co-host. How on earth are you doing today, buddy? Speaker1: [00:01:08] I'm I'm good. It's getting cold here. Got a sweaters and sweatshirts, and it's all is breaking out fast. [00:01:18] I know. Fire pit out in with some of my my men's Speaker2: [00:01:26] Group last night, and yeah, I brought a light rain jacket and it wasn't warm enough. It's the chill is starting to set in here too. Speaker1: [00:01:37] Yes, sir. Speaker2: [00:01:39] But we've got a hot topic today near and dear to my heart. The warm you up, especially, you're a data geek. Speaker1: [00:01:49] Exactly. Well, last time we we talked about the five skills. What did you say? It was the five skills you need to bill or something pay the bills. Five skills to pay the bills. So, yeah, so the first one of those is data. So data, it is. That's what we're going to talk about. And I can't think of anything more important in this business. I mean, there are five skills, but this is without this. The other ones just don't matter. Speaker2: [00:02:19] Right. It all starts, it all starts with data. Speaker1: [00:02:22] Exactly. Speaker2: [00:02:23] Being able to analyze it, being able to use it to your advantage. Speaker1: [00:02:30] Exactly. But before we get into the meat and potatoes of data, if you are enjoying this podcast and I hope that you are in getting some good value out of it, whether you're watching or whether you're listening to it on podcasts, on on iTunes or Google Play or Stitcher or wherever you get your podcasts, or if you're watching it on YouTube, please like subscribe. Leave a comment. Let us know what you think. Let us know what you want to hear from us. It really, really helps us to deliver great content to you guys. And even if you don't love it, give us a five star review anyway. Just because we're nice guys. Help us out. All right. And with that, you will improve our data. So let's get to it. Speaker2: [00:03:16] Awesome. Awesome. So, Howard. Biggest question, what is data? Speaker1: [00:03:23] Data, what is data? Well, it's different kinds of information that are formatted in a particular manner, which then we can subsequently analyze and report on at some point. Speaker2: [00:03:37] So already? Come on. Speaker1: [00:03:40] That's the technical definition. I think that within our business, the way that most people immediately think of data is we get a list from the county, from data tree wherever we get it from. And and then we mail to that list. Well, that list represents data. It is made up of a set of records which have a number of fields of information, and we use all that to send mail and to connect with people without that data. We're going nowhere fast, so it starts with data even before we mail. We're using data because we're analyzing counties, and so we're collecting information about those counties in order to say, I want to mail to this one, but not to that one. That is a form of data analysis. Speaker2: [00:04:37] Very good. Unclear. Speaker1: [00:04:43] Clears wood, but it should be saying that in software, you've got two different things, you've got software programs and then you've got data. So the software programs are things that store, manipulate and use data, things like CRM systems, pricing systems, mapping systems, whatever they are. But in order for them to have any value, we have to feed in that data, which is the raw information that we're then going to manipulate with those systems, CRM or whatever, so that we can, you know, make use of it for our business. I mean, people like to call the land speed, for example, a CRM system, and to some sense, it is. But what one of the things that it really does, you know, at the most fundamental level, it's a place where we store or collect, store, manipulate and use data. That's fundamentally what it is. The rest is just icing on the cake. All right. Speaker2: [00:05:43] So. Why is this important and how to a land investor and how would they use it? Speaker1: [00:05:52] So I think a good way to think about it is something really relevant in today's world is with with with the whole all the emotion around COVID, you've got everybody saying, follow the science. Well, what is the science? The science is somebody. Hopefully somebody credible is doing a study. Hopefully, they're building a model for that study that would be credible. And then they are collecting data. And that's what science is. We observe and collect data and then analyze it so that we can understand the world around us. And it's it's no different here. So it helps us, whereas that helps us understand everything from the spread of a disease all as well as what different therapies and whatnot will help mitigate the spread of the disease. And our case, we're collecting data. And again, we're understanding markets or understanding pricing. We're able to, you know, comp and figure out what market values are. So it really is. It is the entire foundation of our business and it's used across everything. It starts with the county, you know, with county selection, it gets into the mailing, but it goes all the way through to sales. Before I do a sale, a final sale, I've got a pricing calculator and I'm putting all the numbers in and it's telling me, OK, if I if I, this is particularly true, if I'm selling it with seller financing, if I price it with this price at this interest rate, with this term, it's going to give me this ROI, this margin and this number of months to break even. And so I'm analyzing all the way through every stage of the business based on data. Speaker2: [00:07:42] Gotcha. So we use it to calm properties to prep our mailers. We're going to use it to validate the deals that come in to look at what kind of margins we need to make and price it accordingly based on what's out there, et cetera. So there's different. Stages, types and stages of data. Talk about that a little bit in regards to from, you know, acquisition and the analysis, cleaning your data, things like that. Sure. Speaker1: [00:08:19] So first, types of data, there's two different types. There's what they call quantitative data, and there's qualitative data. Quantitative data are things that can be measured. So for example, if you're looking at a property, you can measure the sides of the property a number of feet and then you can multiply that. You can figure out what the what the acreage is. You can figure out the elevation from the lowest point to the highest point. Those are all quantitative numerical facts that you can use to evaluate something qualitative would be its attributes. So that would be things like does it have trees on it? Is it hilly or is it flat? Does it have any water features? What is the zoning? And the interesting in both are very important both and in every aspect of the business, because what it allows us to do using that qualitative information is to get a better understanding of how a set of different properties might have something in common. And some of those factors, some of those qualitative factors are referred to, sometimes referred to as metadata. So for example, if I had a still shot picture of David here, that would be a picture. But the metadata around it might say that the picture was taken at such and such a time on such and such a date in Colorado, and it represents a picture of a bald man. Speaker1: [00:09:48] It's so then if I collect thousands and thousands of pictures, I can say, you know what, I just want to see a subset of pictures that are from people who are in Colorado bald and where the picture was taken in the last month. And then I get a subset of those pictures of of what puts them in common. Well, we can do the same thing in land. We can we can show a set of attributes that we think make a number of properties in common. Now it may be that they're in the same subdivision. It may mean that they're in the same zip code, location, whatever. But aside from those sort of government metadata pieces, we might do it based on attributes. So we might say, actually, what I'm looking for is I'm looking for land between this acreage and that acreage with trees on it with at least 50 percent flat. And you know, and a water feature, whatever, and then collect a set of properties like that. And that would be a cohort of properties. And those are the ones I would use to comp a property because they have the most attributes in common. So qualitative and quantitative. Speaker2: [00:11:00] And I would say that that is more of a quantity. It's, you know, the difference between qualitative and quantitative sometimes is also between the art and the science. We talk about this business being a lot of both. You know, sometimes it's more art than science because you talk about attributes and, you know, trees, flat part, you know, things like that. View's location may be valuable to one person and not the other as far as a buyer goes. And or, you know, they may wait it a little bit differently than than others. You know, how many times have you sold a property where you know you had a few people look at it and they didn't like it, and then the right person came along and loved it? It's it's very qualitative. It's art and science. So there's no there's no magic bullets, no matter how much you analyze stuff, but you've got to do is you've got to be as quantitative as you can when, especially when you're buying it. Speaker1: [00:12:04] So you'd like to be, you know, it's funny, my son is so into of all things metalwork. I mean, go figure, how that happened certainly apparently did not come from me. In fact, we went to a whole weekend of blacksmithing last weekend. He was so into it. So anyway, there's a new show on TV called Metal Smith Masters or something like that. And so we're watching it, and two of the contestants on this show had to work together in a team. And one is a guy that designs everything on a computer and then has, like a water cutter, cut exact pieces based upon the design he's created on a computer. He's really good with the numbers and everything else. The other gal is just an artist. She's like, Yeah, I think it should look like that. She just starts working on it. So completely right brain, left brain. But usually the best work comes from a combination of the two. I mean, if you are, it's sort of it's the same thing in business like, you know, you've got financial people. Everything is driven by the numbers and they can't sort of get their head out of the numbers to say, OK, well, what about relationships and what about, you know, sort of the the softer the goodwill and, you know, the softer aspects of value? You really need both. And that's that's true of data, too, and it gets back to this idea of quantitative versus qualitative. Speaker3: [00:13:19] These are some crazy times in the real estate field. Demand is high, inventory is low. If you're a realtor, a wholesaler or house flipper, you've probably noticed how hard it's become to find quality deals. This is why so many in our industry are looking at land as an outstanding way to add new revenue stream to their portfolio. Speaker4: [00:13:40] If you're listening to this podcast, you already know that land is a relatively unknown niche of the multibillion dollar real estate market with huge profit potential. Seriously, what other business delivers 200 300 a thousand percent return on investment deal after deal? Speaker3: [00:13:57] It seems hard to believe, but land really returns 100 to 300 percent commonly and sometimes over a thousand percent deal after deal and in the age of COVID. Demand for land has never been higher. Speaker4: [00:14:11] Many of our students have already created new revenue streams with land and added six figures to their incomes. Speaker3: [00:14:18] We've had clients who have achieved multiple six figures in their first year of business. Another pay for all his coaching and pocketed 15 grand on his first deal. Now, not everybody has these kinds of results, but they're certainly possible if you have the right instruction, the right support and highly experienced mentors. Speaker4: [00:14:40] You don't need another course that promises the moon and then delivers an elementary school education. You need a proven program suitable to your experience and ambition. You need a land MBA. The Land MBA is everything you need to blow it out in the land, business, courseware, mentorship, tools, community and even deal funding. Look, because you're here listening to me, you know that Dave and I don't hold anything back. That's a founding principle we've had from the beginning with the land MBA. You get everything we have to offer. There are no upsells, and now through popular demand, we have transformed our highly regarded one to one coaching program into a small group format at a fraction of the price. If you're ready to build a six figure income with the freedom of being your own boss, go right now to WW W Land NBA fortune. That's WW W Land, NBA fortune. Let us help you create your next path to wealth. Speaker2: [00:15:44] So talk about the types. Let's talk about the stages of data, and this, I think, gets into a little bit of the meat for the land investor because it's more about the process. Speaker1: [00:15:56] Yeah. So this is definitely not going to be a discussion of pure data science. I, I think I might have mentioned once before that when in my last corporate job, the first person I hired was a PhD data scientist and utterly brilliant. I'm still quite the novice in this area, but everything I know she taught me, so I probably will not be getting, you know, it won't be perfectly complete in my definition is maybe a little bit wrong. But what I tried, what I'm trying to do here is pull out those things that are meaningful to the in community and using words that I think would be meaningful to the land community. So the first thing that we want to do with data is acquire it. We have to get it from somewhere and we're probably not going out and getting it ourselves. So it's usually it starts with some kind of government data or some kind of an aggregated data, whether it's from a data tree or Zillow or from wherever. The next thing that we want to do is we want to clean that data and I'll go into all these in a little bit more detail. But the next thing we want to do is clean the data and that is so important. It's the one thing that I think a lot of land investors may not take quite as seriously as they should. It's definitely an opportunity for improvement. Speaker1: [00:17:20] And then the next thing we want to do is once we have clean data is we want to analyze it so that we can understand it. And that's where this whole qualitative and quantitative analysis comes in. And then we want to report on it or use it. So acquisition really starts with where are we getting this stuff from? So let's just say we're talking about a mailing list, right? We create a mailing list in data tree, right? I create this list for people all the time. At the end of the day, the next question you want to ask is worse data tree getting the data from. Well, they're getting it from the county. Well, how good's the data? That really depends on the county because they're they're actually the ones that are collecting it. So what is the data? The data represents their records of who owns what. So hopefully they're maintaining good records. But, you know, sometimes they're not. And and some, you know, a lot of times people pass away, people move. How well is that data maintained over time? You know, are they are they capturing that information? Sometimes good, sometimes bad. When we've, you know, for those of us that have been in enough counties and enough states, you know, we know that there are some places that just have great data like Florida's data is amazing, probably the best data in the entire country. Speaker1: [00:18:42] But there are other places where it's just not so good. I did. I was working on a deal in Costa County, Colorado, and give me an example of how data can really trip you up. Is what I if you want to know, the size of the property in Castilla. It's kind of a manual process. You have to go look into the legal description to find it. So I did that, and so I had and I had a V.A. do that, and so I sent my mailers out based upon that. Well, then I had an opportunity come in person, was all excited. And I went to the GIS system and I actually put a polygon on the property and the size of the property was an order of magnitude different than what was in the legal description when I pulled it up on the GIS system. So I called the county and I'm like, What's going on with this? It's like, Oh yeah, those two systems don't talk to each other. I think it's so I basically had priced the property on an incorrect size because that was what was in the data. So data is very imperfect and the more we clean it, the better it's going to be. So we need to be conscious of where we're getting it. I'll give you another example is I generally would never provide lists to people in the Carolinas and probably Georgia as well, because no matter when I when I click the buttons and say, look at I only want vacant land and then I'll say, Look, I want it where the living square foot footage is zero and I do all kinds of little things in there to try to make sure I'm only getting vacant land. Speaker1: [00:20:22] But invariably a lot of the data that comes back has a building on it. And that's just because the nature of their data and how it's presented, so it's so hard that I don't even provide lists there anymore. You know, if I was going to invest there myself, I would have to figure out how to overcome that. But just using like a data tree, it's very, very difficult. So that's acquisition. You want to know where where are your sources of data are? What it's going to cost, how you're going to get it, what format it's going to come in? Is it going to come in as an Excel spreadsheet? Is it going to come in as a CSV? Is it going to come as a PDF, as a word document data? You know, I've gotten a delinquent tax list and they're literally photographic PDFs. And the only way to get make anything value about it, you've got to give it to somebody like a VA and have them transcribing, which then leads to potential human error. Speaker2: [00:21:15] Right, right, you know, I had one of those same Castilla County when I was first starting maybe the third or fourth property I did and I missed that acreage thing and I actually so my offer was based on 40 acres. And after I bought it, I discovered it was about 20 acres. Speaker1: [00:21:35] Oops. Speaker2: [00:21:37] Yeah. Luckily, I bought it cheaply enough to where I was able to, you know, bail out of it and still make a little bit of money. But yeah, it could have been bad. Speaker1: [00:21:45] Yeah, so. So when you collect that data, if you just use it and you don't like, clean it up or check it, you could get yourself into trouble. And that's where that great little acronym comes in. Guy, go. Garbage in, garbage out. So all data, I don't care how good it is, needs to be looked at, needs to be cleaned up. And so that gets to the next step, which is cleaning the data. Sometimes in our business, we just call it scrubbing, scrubbing, but scrubbing is not perfectly. It's not the perfect word for it because a lot of times what we mean by scrubbing is I want to eliminate records that are not of interest to me. It's not my strategy. It's too expensive. It's the size is wrong, whatever it is. So I want to just have the columns. Yeah. Well, so formatting columns, that would be an example of cleaning, but refer Speaker2: [00:22:37] To the scrubbing. Speaker1: [00:22:39] Yeah, exactly. No, it's true. So it kind of scrubbing encompasses all of that. But normally cleaning would just mean getting the data in good shape. Right. So an example would be maybe you pulled the data down and has the entire mailing address as one field. So you've got this this the street city state zip, all is one field. But when you want to get to the mailing, the way you're mailing set up is you really want those in separate. You also I want one for street address, one for city, one for state, one for ZIP. So how do you break that up? And that becomes a whole kind of Excel formula exercise. But that's an example of of cleaning the data. The other the other. Another aspect of cleaning is is augmentation. Sometimes when you get the data, it doesn't have everything you need in it. And so you've got to find another source of data that has the information you need and then find a common key. So like maybe based on the APM, if they if these two data sets have the same APN, then I can take my first data set, create some new fields and bring in the extra data from the second data set Speaker2: [00:23:45] By skip tracing. Speaker1: [00:23:46] Skip tracing is a great example of that. You know, maybe I want to add email addresses and phone numbers into my data, right? Speaker2: [00:23:52] So maybe I want to do that because I want to do SMS campaigns in addition to to direct mail. Yeah, yeah, for sure. Very good. So we've talked about so we've got where where is data used in the land business? We talked about creating a mailer and scrubbing that data, cleaning that data. So. We want to use it for county selection area selection, pricing, property analysis, so in county selection, you know, state and county selection. What are some of the key pieces of data that we would use to decide on a place to invest? Speaker1: [00:24:43] I think that the answer to the number of answers to that question is equal to the number of land investors out there. I know how we do it and how we teach it. And I, because I like data, I like to get as comprehensive as I can. Some people think, you know, some people are, you know, a little bit more of let me throw a dart at the at the dart at the map and see where it lands. But I think that the two most important metrics I care about are how how active is the county with relationship to buying and selling raw land. And the second one is how fast are properties moving? Are they sitting for six months or are they selling rather quickly? Those two are head and shoulders above everything else. Everything else becomes qualitative. Right. So now I might say, what's the quality of the land there? What's going on? I shouldn't say it's not all qualitative, but what's going on in terms of job growth or unemployment. What's going on with the economy is what's going on with demographics are more one of the one of the metrics I always like is net migration, which would be the amount of people moving in versus the amount of people moving out of that county. Speaker2: [00:26:05] And let me let me just interject there because I think it's really important for the state as far as because I want to see sales growth in the county, right? Speaker1: [00:26:16] Absolutely. Activity, but Speaker2: [00:26:18] That county where people have bought a lot of recreational lots or lots that they want to do something, build something on some day, you could still have negative net migration in that county. But the county next door has or two counties over, which is kind of in that two to three hour perimeter for the vacation home for the weekend has migration growth. But the actual county that you're looking to buy in is could have negative migration. So that one is when you got to kind of take with a little bit of a grain of salt. Speaker1: [00:26:56] I think that's the interesting thing is we, you know, we didn't get into too much detail about how to do analysis and the reporting and all of that. There are many factors that can go into an analysis and then you can weight the factors and say this one's more important than that one and you can score. I tried to do this when I first got in the business. I'm like, Oh, I'm going to use data, I'm going to, I'm going to blow it out. And I created this massive, massive spreadsheet. It had every county in the country, all three thousand one hundred and fourteen of them. And then I collected I just pulled in all this information from the U.S. Census Bureau and Land Watch and from all these other sources. And I created this massive spreadsheet with all of these metrics, and then I waited each of the metrics so that I could give each county a score of what I thought would be like the perfect county for land. And then I could just fill. I could sort it, and then I can say OK in any given state. This is that this is the order of from the best of the worst. Or I could do it nationally and say this is from one all the way to three thousand one hundred and fourteen. And boy, that didn't work. Speaker1: [00:28:04] So but you know, you figure out what matters to you and sometimes, you know, and that's a really good point is, you know, I like net migration. My belief is that if a county has started out with the most important things, right, if it has a lot of activity, if properties are moving fast, then a positive net migration will only over time will only improve that. The other point that you made, which is which is great, is sales growth. You know, if you put those things together, then a positive net. If I have two counties that are kind of comparable in terms of activity and sales growth in the rest, and one has a positive net migration, when has a negative net migration, I probably would lean to the one that has the positive migration, but that's only if all things are equal. So you have to look at all of the different kind of factors and then in in the totality and then you can make a decision. And that's where that's where the human part comes in. It's about the analysis and you're looking at that data. Through the lens of your strategy. So two people could look at the same data based upon looking at five, six, seven different counties and come up with different conclusions based upon what their strategy is. Speaker2: [00:29:25] Hey, folks, people often talk about automating and outsourcing your land flipping business, but what does that really mean? Generic solutions leave it to you to figure out how to set up and maintain the automations. I've been running my land business on land speed for over three years because it's a total solution and allows me to focus on being a great land investor. Land speed was built specifically for land investing by land investor and with many of the most successful people in the business using it for years. It's evolved into one of the most feature rich solutions on the market. Some of the key benefits I get are being able to create and manage mail campaigns and neighbor letters. I'm able to automate tasks amongst my team, create contracts and deeds and email text or mail them within a few clicks. I can automatically capture sales leads from any lead source, including Facebook Messenger. Then it automatically pushes those leads into my sales funnel so that I can manually follow up, but they also go into my automated drip campaign. And since Lance Speed's a total cloud based solution, I can run my business from anywhere in the world with a phone, laptop or tablet. So if you want to. Turn your hobby into a professional, scalable business. Just go to land speed, techno forward slash Dave to receive one hundred and fifty dollars discount today. We've got that. Speaker2: [00:30:53] We've covered four county area selection, some of the some of the key metrics that we want to look at. And then, of course, obviously for pricing. And then once we get a deal in, we want to be able to look at so when you're pricing your your list, you're looking much more comprehensively at at the comp data. It. I mean, a lot of people will, you know, print a map and write down values on an area of map, a map and kind of figure out the average price per acre and go. And in those areas and then others will just, you know, price the whole county based on a price per acre. And, you know, throw enough dang on the wall and see what sticks and others will get down much more micro and price small areas on the map so they can get more accurate and more concise on their pricing. That's very time consuming, and I'm just going to plug a little tool that you make that I love to use for that. And it's called Price Boss, and it's very helpful for letting you capture a lot of data off of sites like Zillow and Lands of America and figuring out the median price per acre. Setting up your acreage splits and pricing it, and I find that tool invaluable really helps me nail down my pricing pretty accurately. Speaker1: [00:32:32] I appreciate that. You know, this is sort of the debate of neutral offers versus blind offers, you know? So in blind offers, you put forth a lot of effort before the mailing goes out. In Neutral offers, you put forth a lot of effort after the mailings go out. And but the trick is, you know, for me with pricing, when you're doing blind offers or even range to offers, the goal is to get as accurate as you can possibly get without killing yourself. Because you know that only half one at the match three percent of a list is going to convert. Most of them are not going to convert. So you don't want to kill yourself at the beginning. We've got to remember in our head what is the purpose of that letter? That letter, the purpose of that letter is to generate a conversation. Speaker2: [00:33:26] Yes, exactly. What you talking about is getting lost in analysis paralysis, right? You know, we can we can. We can over scrutinize and overanalyze the data. And I mean. It's something that you learn over time, because pricing Mailer's is is, you know, again, it's a lot of art mixed with science. And, you know, sometimes you've got areas where you've got lots of comparables and you can plug those in and feel pretty confident that you're in the ballpark. And that's just it. You're trying to get close enough to get people on the phone and have a conversation. And inevitably, I don't care whether you're going to piss some people off and it's just part of the business and you're going to price some way too high and get a bunch of calls like, Hell, yes, I'll take that offer. It's the best offer I've had in years, you know? And then you go and you look at it and you're like, Oh yeah, I screwed that one up, and then you try to walk it back. And you know, many times that's successful. Many times it's not. But you had a conversation. Speaker1: [00:34:38] Yeah, when I first got into the business, the guidance that I received was go find a couple of comps, you know, maybe five to 10 comps off of Craig's List, maybe LaneWatch, average them together. Figure out, you know, and then and then basically price your county based upon that, the whole county. And it was. And so you kind of had to. There was two primary methods back then. It was OK. Everybody in the county gets a $500 offer. Or everybody in the county gets a two hundred and fifty dollars per acre offer, and I just multiply it times the parcel size to get my actual offer. And in both those cases, it's just horrible. And what's horrible about it is it generates a ton of missed opportunity because what's going to happen in that situation is there will be a sliver of properties where your pricing is pretty close to being right. A sliver. And it was a reasonable chance you'll get a call from those people. Then there's going to be this huge group of people where the price is way too low. And they're either going to throw it in the trash or they're going to call you and tell you how much they hate your guts. Either case, you know, you're not getting deals out of it, and it kind of wastes some time and then you're going have this big group of properties that are way overpriced, in which case your phone's going to be ringing off the hook and you're going to realize you're not able to actually do those deals once you get into due diligence because those prices will not yield a profit. Speaker1: [00:36:10] So if you can get a very good ballpark right before you send your letters out, then fewer are going to end up in the trash can. And in the some of those that would have otherwise ended up with the trashcan may actually call you and you can get more deals out of it, and it'll save you a bunch of time because your phone's not going to be ringing with a bunch of deals that you're not going to be able to do anyway. So it saves time and it will get you more deals. The trick is to be able to do that without killing yourself, and that's why you need tools. There's there's a few different ones on the market right now. I think they're all pretty good. Obviously partial to to price boss. And the primary reason I'm kind of partial to price boss is because. Everything's everything is built on assumptions, right? So, for example, when you get a set of comps from Zillow or Lens of America or the county, wherever you're getting them from, some of those comps won't make any sense. Speaker1: [00:37:15] Right? Let's just say it was a family transaction. Ok, I sold this property for one hundred bucks. You know, it's worth $50000. That doesn't make any sense. Sometimes it goes the other way. Sometimes they're trying to shift for tax reasons. They move it to another entity, and so they sell it for some, for more than it's worth. That happens from time to time. Do, or sometimes a property sells just for more than it's worth. But it's it doesn't make any sense when you look at the larger market and those are called outliers, and you definitely want to pull outliers out before you do your full analysis and say, OK, this is the price per acre for this group of properties. What is an outlier and what isn't an outlier? Well, it's very difficult for machines to figure that out. It can be done, but it's built on assumptions. Sometimes those assumptions are right, and sometimes they're wrong. You brought what? You brought a real interesting up one time, Dave, about the situation where what happens if there are multiple properties on a single deed? It really screws everything up. Speaker2: [00:38:21] Yeah, yeah, exactly. So for example, you know, you'll see this on if you're looking at Zillow sold and if you have data tree up with with the sales and sales turned on, you may find a cluster of properties two or three or four or five properties I've seen that are sold very close to each other, and they have they all have the exact same price. Well, if you're just using an AI tool to scrape that data, you're going to let's say these, let's say you've got twenty five thousand dollars and you've got, you know, five properties that also for four, twenty five thousand, you're like, Oh, that's great. You scrape that in, well, the the the software is going to interpret that as, you know, twenty five thousand cops, twenty five thousand dollars each. And but if you if you click on them, for example, in data tree, you'll see that, oh, it was the same buyer for all of these. So they essentially bought all five of those properties for twenty five thousand. So your actual comp is five thousand. Right? And so that can that can fool you. And so that and I am not sure that some of these automated tools are taking that into account when it happens. You can do that in price plus, I mean, it still can slip by you, but it's something that you have an opportunity to manually manipulate that and delete those fields if it looks fishy, fishy to you. But it's something that I it fooled me. I didn't catch it until, you know, probably only about a year ago, and I've been in this business for six years. Speaker1: [00:40:12] So, yeah, I mean, when you talk about cleaning data, this is especially true in pricing because let's just say I've got two price two properties and they translate into the same price per acre. Well. If they are to properties and that's valid, what if they're the same property, it's just a duplicate data. It actually manipulates your number and makes it too high or too low because you're you're counting too many properties that that aren't real. And so that happens again, this it's important to understand where your data is coming from. So Land Watch Lands of America land a farmer, all the same company now and not all, but most properties that go into that system are syndicated across all the platforms. So if you pull data from Land Watch and from Lands of America, you're going to end up with a lot of duplicate data. Sometimes people just post multiple times. So what we did in price bosses, we set it up so that you can sort it, and it basically highlights where all your duplicates are. So you can look at that and say, Is this really a duplicate or is it not? If it is, you delete it again. You know, machines can do some of that, but it's built on assumptions and sometimes it's right and sometimes it's wrong. And if you don't have the ability to kind of go in there and put your own little decision making on it, you know, you get what you get. Speaker1: [00:41:32] I like I'm a bit of a control freak when it comes to data. I like to see it so. So I like to take that out. And then what? What what we do is we create a graph called a scatter plot in one on one access. It'll just have acreage. So it'll go anywhere, you know, can go from whatever range you put in there, but say zero to a thousand acres on on the x axis and on the y axis, it has price per acre. And so what you'll see is that across all the comps that you've collected, it tends to build clusters. But it also will show you on this on this scatter plot, those properties that are way, way up high or way way too low, and they're not part of any clusters. And those are most likely your outliers, so you can easily find them and make a decision, whether you keep them or or you get rid of them. And that's the cleaning part of it, and it takes a bit of time and effort. But the more you do it, the better you get at it and the less time it takes you. But once you've done it, boom, you know you're ready to get some very accurate pricing. And with that, hopefully you'll get more property, more deals and less wasted time. Speaker2: [00:42:40] Right. So I just want to bring up one more piece of data that I think is important that we for tracking and deciding where we spend our marketing dollars and in this business and that is, you know, tracking where your leads are coming from. If you are set up with the various sources, like if you have a Lancome account, you know you're going to have leads coming from the three different lands of America land and farm and land watch and then maybe use land flip. Maybe you do some Facebook and some Craigslist and any other source that you have. It's really good to measure how many leads are coming from from these sources and which ones have the highest rate of conversion as well. Like, we know that, for example, Facebook, you get a high number of leads, but the quality of lead may not be that great. And so it's really good to measure. You know, Facebook has become Facebook Marketplace and groups has become a very challenging place to market, and it now is very labor intensive. They flag you, they shut you down and and things like that. So, you know, I recently analyzed my my data on the number of leads, but not just the number of leads, the number of properties that we actually converted on Facebook and just decided it's not worth the effort anymore, at least not the way we were doing it. Speaker2: [00:44:16] And so I'd rather take the labor that I was paying, even though Facebook's free you get, even if you're, you know, paying a cheap VA, I was still spending, you know, two $300 a month on that VA for posting in Facebook. And so you have to take a look and go, Well, maybe it's more advantageous if I take that than a couple hundred bucks and and increase my exposure on land or land, flip and do some of their boosts there and things like that, or flat fee meals. So you want to be able to have a tool, a system that measures where those leads are coming from. I'm going to plug land speed again as that's all my leads come in and they automatically tick off the box. You know where, where the lead come from. And so I have a ledger on the left hand side that shows my my lead count. And then I can also show, you know, which which leads source converted. And that's a very good piece of data to track to know which is my best lead source, not just for leads but also converting them and where I spend my marketing money. Speaker1: [00:45:31] You know, it's a real it's just a really good point that you made, which is there's there's how many leads are generated and then how many convert. And so you collect the data. But when you get to the analysis and the reporting side of things it's about, we don't need to collect and analyze and report unless it's going to actually lead to decisions that improve our business. Otherwise you just doing it for the sake of doing it because you like data. That's all fine. But you know, we want to exert ourselves in ways that are going to improve our business. So, you know, it starts with asking the right questions. You know, if you start to say, OK, you know, what are the what channels are working best for me? What matters most? I mean, is it leads or is it is it conversions? While I would argue it's conversions. So that would, you know, that would be a great example of that. And yeah, so I mean, it all starts with asking the right questions. Speaker2: [00:46:30] Yeah. You know, because it all it all translates into labor as well, right? Because if I'm getting just like the Facebook example, if I'm getting a lot of leads, but none of them are converting, I mean, those leads got to be followed up, and some of them are just depends on how you handle your leads and how you rank them. You know, sometimes if they're just an email that came in and I just let it go to the auto responder if it's Facebook. But you know, if somebody gives me a phone number, they're getting a call, you know, so it's and they're getting a manual email on top of the auto responder. So, you know, if the lead quality is really crappy from a certain. Source, you either want to. Stop that source altogether or just, you know, put them on automation period and don't do a manual follow up. So all of these points of data are control how you operate your business and, you know, think about the parade or principle all the time, right? 80. 20. Yeah. Great point. All right, I think we beat the horse dead. Speaker1: [00:47:39] I love it. Well, this is an important horse. And if you don't master data and you don't, you know, if you've got kind of a background in this and you've played around on Excel spreadsheets and things like that, it's probably not going to be terribly hard for you to to master it within this business if you don't have that background. It's just one of those areas you want to make sure that you are. You are improving all the time because the better we are with data, the better this business is going to run. It's just there's just no way around that. It is, you know, one of the five most important skills to pay the bills. Speaker2: [00:48:19] All right. That was a great talk. We'll talk to you soon. Adios, everybody. Speaker4: [00:48:24] Yeah, we hope you enjoyed this episode. Had a bit of fun and walked away with some actionable insights that you can apply to your business. Dave and I have got some great content in interviews planned, so don't forget to rate and review. And of course, subscribe to this podcast on iTunes, Google Play, Stitcher or wherever you get your podcasts. If we mention any interesting links or tools, you'll find them in the show notes to learn more about landed MBA. Visit our web site at Wait for it landing MBA. See you next time on the Land MBA podcast.  

AI is a term that often scares anyone who is not familiar with the technology and the application. In this episode, we're going to cover some of the different use cases for AI in the procurement space, and then dive into master data as a specific case study. Adriano Garibotto, Co-Founder and Chief Sales & Marketing Officer of Italian procurement data management company Creactives is my guest on this week's show to break down this not-as-scary-as-it-sounds technology! Using AI to Clean Procurement Data Creactives originally set out as a cost reduction consultancy for indirect procurement back in the early 2000s, focusing specifically around optimisation of MRO spend in manufacturing businesses. The constant challenge of poor data, inaccurate or missing taxonomies and battling with free text PO descriptions is what ultimately led them down the path of creating a software business to solve this problem at scale. Origins of what Creactives is today comes from some of the early stage AI utilised by their consultancy around 15 years ago. Together with a collaboration with the University of Verona, they then doubled down on developing a software solution which can help to classify, clean and structure complex master data from multiple ERP systems and sources. Some examples of what AI can do in the Procurement space AI must be applied to specific fields to provide tangible solutions. The fundamentals of procurement can be broadly classified into the following 4 questions: What am I buying? From whom do I buy? What price am I paying? Who is doing the buying? Answering these questions is not easy, especially in large, enterprise level organisations with legacy systems i.e. different ERP systems, multiple languages and complex supply chains. AI can play a strategic role in the harmonisation of the data and helping to create a unique visibility. This is the fundamental building block which leads to other opportunities to use AI in the procurement space. How can AI be leveraged as a catalyst for positive change? Addressing resistance to technology is a change management issue, as Adriano explains. At its core, getting the right organisational structure in place is the key to success. Data preparation historically required a large amount of work from procurement professionals. There is the classic Pareto of 80% of the time being taken doing the preparation, and only 20% conducting the actual added value activity for which the clean data is necessary. If AI is able to do the lion's share of the 80%, this then allows strategic resources to be freed up to focus on more value-added activities which can actually implement the changes and the projects to drive the costs down, or reduce the supply chain vulnerability, or whatever the higher goal of the activity may be. Data itself on its own doesn't intrinsically have value - it is the enabling factor that facilitates the journey to be able to deliver the value. Is category management dead as an organisational structure? Adriano makes a controversial - but very valid - point that the category management architecture of procurement organisations which has been dominant for the past 20 or so years will be rendered irrelevant by AI. The way procurement teams operate in future will be beyond the category model, as a result of data being the driver of how organisations drive value in their business. Product launches and lean activities cut across numerous different categories, and the design of procurement departments must consequently adapt to this. To what extent can AI perform data classification? Adriano uses a nice example of comparing master data and AI solutions to motor vehicles. The more sophisticated the engine is, the more sophisticated that the fluid going into the engine needs to be. The same applies to procurement data and the AI solution you are using to clean it. If you have very complex data, then the process to...

We all start our careers and discover our passion with the First Job! In this special episode we're joined by Seoyeong Lee, Data Analyst, Holden Schaffer, Data Science Intern and Himanshu Singh, Data Analyst intern who have started their career off to an amazing start with ServiceNow.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

We all start our careers and discover our passion with the First Job! In this special episode we're joined by Seoyeong Lee, Data Analyst, Holden Schaffer, Data Science Intern and Himanshu Singh, Data Analyst intern who have started their career off to an amazing start with ServiceNow.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

Conversation with Padma Jaganathan, Sr. Manager of Master Data Management, brings you an in-depth overview of managing, enriching and continually monitoring health of the data for the entire enterprise, to ensure smooth operation across the organization.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

Conversation with Padma Jaganathan, Sr. Manager of Master Data Management, brings you an in-depth overview of managing, enriching and continually monitoring health of the data for the entire enterprise, to ensure smooth operation across the organization.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

Sarath Tamminani, Manager of Data Integrations, and Naveen Sanka, Staff Data Engineer joins this episode to break-down the evolution and the hype of latest and greatest data integrations and best-practices.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

Sarath Tamminani, Manager of Data Integrations, and Naveen Sanka, Staff Data Engineer joins this episode to break-down the evolution and the hype of latest and greatest data integrations and best-practices.  To learn more about Data & Analytics team, visit us at: https://www.servicenow.com/analyticsatnow   To explore other ServiceNow podcasts: https://www.servicenow.com/podcast  See omnystudio.com/listener for privacy information.

In this episode of the Data Exchange, our special correspondent and managing editor Jenn Webb organized a mini-panel composed of myself and Sonal Goyal, founder of Aficx, a startup that builds solutions to unify data silos for cross selling and upselling, fraud and risk management, compliance and regulatory reporting.Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

Talking about

In episode 114 of Financially Simple, Justin begins to look at the process of building Operations for Maximum Growth. To effect growth in your Business, having well managed Operating Systems is a must no matter how big or small the company - even the smallest improvement to the management of a process will yield positive results. Justin goes over the various elements of Operations management and their systems. Don't forget to subscribe, and let us know how we are doing by leaving a review. Thanks for listening! ARTICLE TRANSCRIPT: BLOG: Operational Steps Service-Based Businesses Should Take   TIME INDEX: 00:46 - The Operating Elements of a Service Business 01:14 - What is a Service Business? 01:56 - The Master Blueprint 03:29 - Design & Engineering 05:09 - Account and Order Management 06:10 - Project and Production Management 06:56 - Procurement 07:48 - Materials and Inventory Management 08:42 - Facilities Management 09:11 - Tools and Tech 09:45 - Resourcing 10:56 - Quality and Risk Management 11:59 - Field Operations 12:40 - Distribution 13:44 - Packaging 14:05 - Work on Your Business, Not in Your Business 15:43 - Managerial Systems 16:40 - Customer Service 19:11 - Management Reporting 19:49 - Master Data 20:23 - IT 21:05 - Talent Training 21:20 - Closing   USEFUL LINKS: Financially Simple Financially Simple on YouTube Financially Simple on Facebook Financially Simple on Twitter ________ BIO: Justin A. Goodbread, CFP®, CEPA, CVGA, is a nationally recognized financial planner, business educator, wealth manager, author, speaker, and entrepreneur. He has 20+ years of experience teaching small business owners how to start, buy, grow, and sell businesses. He is a multi-year recipient of the Investopedia Top 100 Advisor and 2018 Exit Planning Institute's Exit Planner Leader of the Year.DISCLOSURES:This podcast is distributed for informational purposes only. Statements made in the podcast are not to be construed as personalized investment or financial planning advice, may not be suitable for everyone, and should not be considered a solicitation to engage in any particular investment or planning strategy. Listeners should conduct their own review and exercise judgment or consult with their own professional financial advisor to see how the information contained in this podcast may apply to their own individual circumstances. All investing involves the risk of loss, including the possible loss of principal. Past performance does not guarantee future results and nothing in this podcast should be construed as a guarantee of any specific outcome or profit. All market indices discussed are unmanaged, do not incur management fees, costs and expenses, and cannot be invested into directly. Investment advisory services offered by WealthSource Partners, LLC. Neither WealthSource Partners, LLC nor its representatives provide legal or accounting advice. The content of this podcast represents the views and opinions of Justin Goodbread and/or the podcast's guests and do not necessarily represent the views and/or opinions of WealthSource Partners, LLC. Statements made in this podcast are subject to change without notice. Neither WealthSource Partners, LLC nor its representatives, the podcast's hosts, or its guests have an obligation to provide revised statements in the event of changed circumstances. Certified Financial Planner Board of Standards, Inc. (CFP Board) owns the CFP® certification mark, the CERTIFIED FINANCIAL PLANNER™ certification mark, and the CFP® certification mark (with plaque design) logo in the United States, which it authorizes the use of by individuals who successfully complete CFP Board's initial and ongoing certification requirements.   Advisors who wished to be ranked in Investopedia's Top 100 Financial Advisors list either self-submitted answers to questions compiled by Investopedia or were nominated by peers.  Rankings were determined based on the number of followers and engagement on social media, primary contribution to professional industry websites, and their focus on financial literacy.  Neither performance nor client experience, however, were considered.  No compensation was paid by WealthSource Partners, LLC or Justin Goodbread to secure placement on Investopedia's Top 100 Financial Advisors List.   The Exit Planning Institute's Leader of the Year is awarded to a nominee who is a CEPA credential holder who has made a significant impact or contribution to the exit planning profession or overall community through innovation and influence and is viewed by the Exit Planning Institute as a thought leader, risk-taker and specialist while showing characteristics of collaboration.   This podcast might recommend products or services that offer Financially Simple compensation when you use them. This compensation is used to help offset the cost of creating the content. We will, however, never suggest products/services solely for the compensation we receive.