What if the truth was buried in a sea of misinformation and it was your job to find it? Paul Smith, Chief Information Officer of Amnesty International, joins us to explore how unstructured data drives advocacy in the fight for global human rights. Paul highlights the ethical and operational complexities of data in the nonprofit world, making this a must-listen for anyone navigating data integrity in high-stakes environments.

Key Takeaways:

(03:16) Unstructured data is key to documenting human rights abuses.

(06:43) Amnesty International verifies data using reverse image search, metadata and cross-referencing.

(12:34) SharePoint and Preservica help manage, archive and access multilingual data.

(16:35) AI adds speed and scale across Amnesty International’s research and evidence processes.

(20:21) Generative adversarial networks help mitigate AI risks like bias and censorship.

(24:18) Nonprofits need to guide people, from learning about the cause to becoming advocates.

(28:33) The future of advocacy depends on AI, crowdsourcing and protecting privacy.

(30:13) The flood of false information is an important area to watch out for in advocacy efforts.

Resources Mentioned:

Paul Smith

https://www.linkedin.com/in/pdsmithmba/

Amnesty International

https://www.linkedin.com/company/amnesty-international/

Preservica

https://preservica.com/

Microsoft Sentinel

https://azure.microsoft.com/en-us/products/microsoft-sentinel/

Microsoft Defender

https://www.microsoft.com/en-us/microsoft-365/microsoft-defender-for-individuals

SharePoint

https://www.microsoft.com/en/microsoft-365/sharepoint/collaboration

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Gut instinct alone isn’t enough in construction — data is now a game-changer. Annette Cooper, Director of Data and Analytics at Graham, sits down with us to explore how data is transforming the construction industry. She explains how Graham leverages analytics to manage risk, optimize efficiency and improve project execution. Annette emphasizes that success comes from solving real business problems rather than simply generating reports.

Key Takeaways:

(03:17) Graham is expanding rapidly through acquisitions, creating new data challenges.

(04:05) Risk management is a priority in an industry with long project timelines and tight margins.

(07:26) Many project managers are eager for data insights but lack access to the right tools.

(09:05) A standardized data strategy is essential for integrating multiple systems post-acquisition.

(12:45) Project estimations require a balance of historical data and real-world unpredictability.

(17:22) Effective data teams focus on solving problems, not just collecting data.

(20:01) Data should drive decisions, not just generate reports that frustrate users.

(25:42) Predictive models can highlight risks early, but human expertise remains crucial.

(31:55) The future of AI in construction lies in automating insights, not replacing workers.

(35:10) AI can streamline building code compliance by identifying design issues before construction begins.

Resources Mentioned:

Annette Cooper

https://www.linkedin.com/in/annette-cooper-2aa442bb/

Graham | LinkedIn

https://www.linkedin.com/company/grahambuilds/

Graham | Website

https://www.grahambuilds.com/

New Zealand Ministry of Business, Innovation & Employment

https://www.mbie.govt.nz/

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Joetta Gobell, Senior VP of Data Strategy and Insights at Dotdash Meredith, joins us to discuss strategies for reaching consumers in their moment of need. She explains how Dotdash Meredith leverages its vast network of brands to provide timely, relevant content. She highlights how D/Cipher helps advertisers connect with audiences based on their content interactions rather than invasive data collection. She emphasizes prioritizing user experience over ad revenue to ensure advertising enhances rather than disrupts content engagement.

Key Takeaways:

(02:31) Leveraging the power of brands to help people in their moment of need.

(04:14) Joetta’s background in math, psychology, and cognitive science shapes her approach to data.

(06:09) Publishing has constantly evolved, requiring adaptability.

(08:58) Human needs haven’t changed — only the way we deliver information.

(10:28) Prioritizing user experience leads to better long-term success.

(14:07) Targeting needs and behavior is more effective than using user data.

(19:25) Data is not the only source of insight — context is more powerful than identity-based targeting.

(24:50) Balancing AI and human teams by understanding each other’s strengths and weaknesses.

(31:05) Understanding content meaning is key to effective advertising.

(40:26) Interpreting data is more valuable than simply collecting it.

Resources Mentioned:

Joetta Gobell -

https://www.linkedin.com/in/joetta-gobell-9378832/

Dotdash Meredith | LinkedIn -

https://www.linkedin.com/company/dotdashmeredith/

Dotdash Meredith | Website -

https://www.dotdashmeredith.com/

D/Cipher by Dotdash Meredith -

https://www.dotdashmeredith.com/advertising

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Scott Taylor, “The Data Whisperer” and Principal Consultant of MetaMeta Consulting, shares techniques for communicating the value of data in a business context. He emphasizes simplifying complex data concepts to align with business goals, advocating for clear communication, the avoidance of technical jargon and the use of relatable analogies. From practical strategies for engaging C-suite leaders to insights on navigating AI-driven challenges, Scott offers actionable advice for data practitioners looking to bridge the gap between technical expertise and business outcomes.

Key Takeaways:

(03:24) Two types of data storytelling: one for analytics and one for data management.

(05:30) Truth in data must come before deriving meaning from it.

(08:10) Avoid buzzwords and focus on vocabulary the business understands.

(12:21) Listen to earnings calls or strategy meetings to learn your company’s objectives.

(17:23) Master data is foundational to delivering business value at scale.

(22:17) Use industry-specific analogies to clarify data’s impact.

(26:42) Poor data management can amplify errors in AI-driven systems.

(28:04) Master data challenges exist in every industry and era.

(33:19) Developing storytelling skills is essential for career growth.

Resources Mentioned:

Scott Taylor -

https://www.linkedin.com/in/scottmztaylor/

MetaMeta Consulting -

https://www.linkedin.com/company/metametaconsulting/

"Telling Your Data Story" by Scott Taylor -

https://www.metametaconsulting.com/the-book

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Natarajan Subbiah, Chief Product Officer of Uber Freight, joins us to discuss how data science is reshaping the logistics industry. He explains Uber Freight’s innovative approach to optimizing supply chains by combining human expertise, digitalized workflows and large language models to automate pricing, load tracking and delivery management. Natarajan also shares insights on balancing centralized and decentralized teams, the role of data in ensuring marketplace liquidity and strategies for building high-performing, collaborative teams.

Key Takeaways:

(02:30) Marketplaces thrive on data to balance supply and demand efficiently.

(03:30) Uber Freight”s introduction of upfront pricing to simplify logistics.

(07:20) Liquidity is the key metric for marketplace success.

(07:35) The easiest way to grow a marketplace is to constrain it on multiple dimensions. 

(12:10) 80 to 90% of complete goods or supply parts for goods get moved on trucks.

(17:22) What scaling automatization means for the freight industry.

(22:14) Over time, about 80% of freight will be moved in a highly automated way. 

(26:59) Centralized vs de-centralized teams, pros and cons.

Resources Mentioned:

Natarajan Subbiah -

https://www.linkedin.com/in/natarajan-subbiah-b82ba3a/

Uber Freight | LinkedIn -

https://www.linkedin.com/company/uber-com/

Uber Freight | Website -

https://www.uberfreight.com/

Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.

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On this episode, we are joined by Will LaForest, Global Field CTO of Confluent, to discuss the transformative impact of data streaming on businesses and platforms. He explains the shift from traditional monolithic big data models to data streaming systems, moving from data analysis to continuously bringing new data to the same questions, enabling hyper-personalized customer experiences and operational efficiency.

Key Takeaways:

(02:59) Will’s early career sparked a passion for large-scale data systems.

(05:25) Data streaming shifts the paradigm by delivering a constant stream of data to the questions.

(08:23) Data streaming helps decouple data producers and deliver data to the consumers in real time.

(17:05) Real-time data streams are used across multiple industries, including financial services and cybersecurity.

(19:36) Ride-sharing exemplifies the indispensable role of data streaming.

(22:49) Reliability, decoupling and speed are some of the reasons data streaming has taken off.

(27:00) Streaming supports hyper-personalized experiences.

(39:36) Confluent simplifies streaming adoption for businesses of all sizes.

Resources Mentioned:

Will LaForest -

https://www.linkedin.com/in/willlaforest/

Confluent | LinkedIn-

https://www.linkedin.com/company/confluent/

Confluent Platform -

https://www.confluent.io/

“Data Mesh” by Zhamak Dehghani -

https://www.thoughtworks.com/insights/books/data-mesh

Apache Kafka -

https://kafka.apache.org/

Apache Flink -

https://kafka.apache.org/

MongoDB -

https://www.mongodb.com/

Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.

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Rouzbeh Gerami, Senior Vice President of Data Science at Nielsen, joins us on this episode to discuss the complexities of measuring audiences and defining their characteristics across diverse media and platforms. He explains how the shift toward digital privacy impacts audience measurement and highlights the crucial role of high-quality ground truth data in teaching machine learning models to correct massive raw data.

Key Takeaways:

(03:08) How physics equips data scientists with unique skills.

(08:43) Nielsen’s history and mission.

(09:23) The evolution of audience measurement.

(11:46) The challenge of measuring fragmented digital media consumption.

(16:12) Nielsen’s efforts to stay relevant in the privacy-protected world.

(20:20) Ground truths help AI learn and correct massive data. 

(23:40) Privacy’s impact on smaller publishers and media outlets.

(32:40) The need for continuous learning in a rapidly evolving industry.

Resources Mentioned:

Rouzbeh Gerami -

https://www.linkedin.com/in/rouzbeh-gerami-69b0b77/

Nielsen One Ads -

https://www.nielsen.com/news-center/2023/nielsen-one-launches-globally/

“The Innovator’s Dilemma” by Clayton Christensen -

https://www.hbs.edu/faculty/Pages/item.aspx?num=46

Thanks for listening to the Data Masters Podcast. If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.

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In this episode, Lori Bieda, Chief Data and Analytics Officer of BMO North American Personal and Business Bank, explains how BMO leverages data and AI to drive business efficiency, personalize and enhance customer experiences, and mitigate risk. Lori highlights the importance of balancing centralized data governance with data democratization to make fast, smart and effective decisions across business units. She also shares how BMO is using AI-powered tools to make its home-buying process more transparent and personalized.

Key Takeaways:

(02:07) BMO centralizes data governance and distributes analytics across business units.

(04:50) Centralized data governance ensures regulatory compliance and reduces costly data issues.

(07:59) Decentralized analytics empower teams to make fast, data-driven business decisions.

(09:06) The importance of democratizing data and making it easily consumable for business units.

(12:23) Personalized customer experiences and fraud prevention.

(17:52) The risk of data security breaches has grown significantly, necessitating a strong focus on AI-driven fraud prevention.

(19:48) Convincing organizations to invest in data and AI requires showing both the risks of inaction and the benefits of investment.

(24:00) BMO is using AI to streamline its home-buying process, creating a more transparent and personalized experience.

Resources Mentioned:

Lori Bieda -

https://www.linkedin.com/in/loribieda/

BMO North American Personal and Business Bank -

https://www.linkedin.com/company/bank-of-montreal/

BMO | Website -

https://www.bmo.com/

Thanks for listening to the Data Masters Podcast. If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.

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On today’s episode, we are joined by Tom Andriola, Vice Chancellor, Information, Technology and Data, and Chief Digital Officer of UC Irvine. Tom discusses the growing impact of AI and technology on education and healthcare. From AI assisting in patient diagnosis to unbundling traditional degree programs, Tom reveals how institutions like UCI are adapting to the changing digital and workforce landscape. He explores how AI, data analysis and creative innovation can revolutionize the future of both learning and healthcare, making them more efficient, accessible and personalized.

Key Takeaways:

(02:47) UCI operates across two distinct sectors: healthcare and education.

(03:27) Healthcare and education are becoming more data-driven, from teaching to patient care.

(05:09) Effective AI depends on having strong data foundations.

(08:33) The traditional four-year degree model is evolving due to online and AI-assisted learning.

(10:30) Unbundling education focuses on mastering specific skills, not completing full degrees.

(12:19) UCI offers both online and in-person options for lower-level courses.

(16:30) Wearable tech is reshaping healthcare by providing continuous health data.

(20:57) UCI’s student and patient-centered strategy uses data to enhance outcomes and personalize care.

(24:15) The future of data is moving toward conversational, query-based, AI-driven analytics.

(28:04) Data analysts may evolve into expert prompters, helping businesses get what they need faster.

(34:47) Institutions need to challenge whether data aligns with their values. 

Resources Mentioned:

Tom Andriola -

https://www.linkedin.com/in/tomandriola/

UC Irvine -

https://www.linkedin.com/school/university-of-california-irvine/

Digital Squared -

https://digitalsquared.buzzsprout.com/

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On today’s episode, we’re joined by Austin Tidmore, Vice President of Data Innovation at ASGN Incorporated. Austin shares his vision of innovation as an intentional data-powered process developed to achieve business goals. He emphasizes the importance of augmenting human capabilities with AI rather than replacing them and highlights the critical intersection of innovation and data in shaping enterprise-level strategies.

Key Takeaways:

(02:05) Unify businesses with data and AI to operate more efficiently and add value for clients.

(03:10) Innovation is linked to maximizing operational efficiency.

(06:35) Consulting accelerates career growth through diverse exposure.

(12:33) The difference between innovation and disruption.

(13:47) The talent and staffing industry is ripe for innovation.

(15:00) AI augments human decisions, it doesn’t replace them.

(18:30) The role of AI in both crafting résumés and mining through the noise.

(23:47) The challenges for LLM and enterprises posed by unstructured data.

(26:40) Graph databases can help structure and navigate data.

(29:46) Innovation is intentional and needs to align with business value.

Resources Mentioned:

Austin Tidmore -

https://www.linkedin.com/in/austintidmore/

ASGN Incorporated | LinkedIn -

https://www.linkedin.com/company/asgn-incorporated/

ASGN Incorporated | Website -

https://www.asgn.com/

University of North Texas -

https://www.unt.edu/

Clayton Christensen's Disruptive Innovation -

https://hbr.org/1995/01/disruptive-technologies-catching-the-wave

Hadoop and Big Data -

https://hadoop.apache.org/

Spark Clusters -

https://spark.apache.org/

Thanks for listening to the Data Masters Podcast. If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.

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