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Data as a first-class citizen recognizes the value and importance of data in decision-making. It empowers data magicians by integrating data into the decision-making process, ensuring accessibility and availability, prioritising data quality and governance, and fostering a data-centric mindset.
In a compelling episode of the Agile Data Podcast, Shane Gibson invites Hans Hultgren to explore the intricacies of Data Vault modeling. Hans, with a substantial background in IT and data warehousing, brings his expertise to the table, discussing the evolution and benefits of Data Vault modeling in today’s complex data landscapes. Shane and Hans navigate through the foundations of Data Vault, including its core components like hubs, satellites, and links, and delve into more advanced concepts like Same-As Links (SALs) and Hierarchical Links (Hals). They highlight how Data Vault enables flexibility, agility, and incremental development in data modeling, ensuring scalability and adaptability to change.
Join Murray Robinson and Shane Gibson as they chat with Jim Highsmith, one of the authors of the Agile Manifesto. Take a journey through the history of software development, from the wild west to structured methods and the emergence of the agile movement.
Are you wrestling with the concept of whitelabelling your product? We at AgileData have been there. We discuss our journey through the decision-making process, where we grappled with the thought of our painstakingly crafted product being rebranded by another company.
Metadata-driven data pipelines are the secret behind seamless data flows, empowering data magicians to create adaptable, scalable, and evolving data management systems. Leveraging metadata, these pipelines are dynamic, flexible, and automated, allowing for easy handling of changing data sources, formats, and requirements without manual intervention.
Join Murray Robinson and Shane Gibson as they chat with Pawel Huryn about product management. What it is, and isn’t? How to motivate teams and what makes a product succeed. We discuss the difference between product management, which focuses on discovery and project management, which focuses on delivery. And how to do both at the same time in one empowered product team. We talk about how you can become a product manager and the best product management tools to use. Join us as we uncover valuable insights to help you succeed in this critical role.
Dive into the world of data consulting with Shane Gibson and Joe Reis on the Agile Data Podcast. Explore their journey from traditional employment to successful data consulting, covering client acquisition, business models, financial management, reputation, sales strategies, employee management, and work-life balance.
Data modeling is a crucial process that involves creating shared understanding of data and its relationships. The three primary data model patterns are conceptual, logical, and physical. The conceptual data model provides a high-level overview of the data landscape, the logical data model delves deeper into data structures and relationships, and the physical data model translates the logical model into a database-specific schema. Understanding and effectively using these data models is essential for business analysts and data analysts, create efficient, well-organised data ecosystems.
TD:LR Early in 2023 I was lucky enough to talk to Joe Reis on the Joe Reis Show to discuss how to make data modeling more accessible, why the world's moved past traditional data modeling and more. Listen to the episode...
AgileData reduces the cost of your data team and your data platform.
In this article we provide examples of those costs savings.
Join Murray Robinson and Shane Gibson as they chat with Stefan Wolpers about scrum anti-patterns. Explore common anti-patterns, such as scrum masters assigning tasks to disempowered teams, and discover the solutions...
Cloud Analytics Databases provide flexible, high-performance, cost-effective, and secure solution for storing and analysing large amounts of data. These databases promote collaboration and offer various choices, such as Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse Analytics, each with its unique features and ecosystem integrations.
The key components of a successful data warehouse technology capability include data sources, data integration, data storage, metadata, data marts, data query and reporting tools, data warehouse management, and data security.
In a nutshell, a data warehouse, as defined by Bill Inmon, is a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making processes. It helps data magicians, like business and data analysts, make better-informed decisions, save time, enhance collaboration, and improve business intelligence. To choose the right data warehouse technology, consider your data needs, budget, compatibility with existing tools, scalability, and real-world user experiences.
Explore the MarTech stack based on two different patterns: marketing application and data platform. The marketing application pattern focuses on tools for content management, email marketing, CRM, social media, and more, while the data platform pattern emphasises data collection, integration, storage, analytics, and advanced technologies. By understanding both perspectives, you can build a comprehensive martech stack that efficiently integrates marketing efforts and harnesses the power of data to drive better results.
A graphical overview of the components required for a Data Product
Join Murray Robinson and Shane Gibson as they converse with Brendan Marsh about his experience working at Spotify. Understand how Spotify's agile methodologies aim to achieve speed to market and learning, which their...
Data clean rooms are secure environments that enable organisations to process, analyse, and share sensitive data while maintaining privacy and security. They use data anonymization, access control, data usage policies, security measures, and auditing to ensure compliance with privacy regulations, making them indispensable for industries like healthcare, finance, and marketing.
In this episode of the AgileData Podcast, Shane Gibson has an insightful discussion with Tomas Kratky on the evolution and importance of data lineage, especially in large enterprises. Tomas Kratky, a traditional software engineer turned data enthusiast, shared his journey to founding Manta, a company focused on data lineage. The conversation highlighted the significance of data lineage, not just as an end in itself, but as a powerful tool for unlocking potential in large enterprises, enhancing visibility, and fostering agility.
TD:LR There is some great free course content to help you upskill in Google Analytics 4 (GA4) Here are the ones we recomend.Discover the Next Generation of Google Analytics Find out how the latest generation of Google...
Join Murray Robinson and Shane Gibson as they delve into the fundamentals of product management with Roman Pichler, renowned author of 'Agile Product Management with Scrum'. Topics we'll explore in this conversation...