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Defining self-service data

Everybody wants self service data, but what do they really mean when they say that.

If we gave them access to a set of highly nested JSON data, and say “help your self”, would that be what they expect?

Or do they expect self service to be able to get information without asking a person to get it for them.

Or are they expecting something in between.

I ask them which of the five simple self service patterns they want to find, which form of self service they are after.

Demystifying the Semantic Layer
Demystifying the Semantic Layer

The semantic layer is your mystical bridge between complex data and meaningful business insights. It acts as a translator, converting technical data into a language you understand. It works through metadata, simplifying queries, promoting consistency, and enabling self-service analytics. This layer fosters collaboration, empowers customization, and adapts to changes seamlessly. With the semantic layer’s power, you can decipher data mysteries, conjure insights, and make decisions with wizard-like precision. Embrace this enchanting tool and let it elevate your data sorcery to new heights.

Attribution Model Patterns with Yorgos Moschovis
Attribution Model Patterns with Yorgos Moschovis

Explore the intricacies of marketing attribution with Yorgos Moschovis on the Agile Data Podcast. Dive into the complexities of tracking customer behavior across various channels and the challenges of connecting online and offline data. Learn about Yorgos’s journey from the Office of the Auditor General to a leader in data analytics, navigating through companies like Spark New Zealand and Silicon Graphics. Understand the evolution of attribution modeling, from basic first and last touch to sophisticated, multi-touch approaches. Discover the impact of third-party cookie demise on tracking and the emergence of identity management solutions. Gain insights into how different industries, like retail and insurance, approach customer journey mapping and the significance of continuous touches versus deeper histories. Hear Yorgos’s perspective on the diminishing role of static demographics in favor of digital behavior analysis. The conversation also delves into operational improvements through actionable insights, emphasising real-world applications and AB testing over theoretical model complexities.

Understanding Concepts, Details, and Events: The Fundamental Building Blocks of AgileData Design
Understanding Concepts, Details, and Events: The Fundamental Building Blocks of AgileData Design

Reducing the complexity and effort to manage data is at the core of what we do.  We love bringing magical UX to the data domain as we do this.

Every time we add a new capability or feature to the AgileData App or AgileData Platform, we think how could we just remove the need for a Data Magician to do that task at all?

That magic is not always possible in the first, or even the third iteration of those features.

Our AgileData App UX Capability Maturity Model helps us to keep that “magic sorting hat” goal at the top of our mind, every time we add a new thing.

This post outlines what that maturity model is and how we apply it.

Building a vibrant community with Scott Hirleman
Building a vibrant community with Scott Hirleman

Explore the art of building vibrant communities with Scott Hirleman on the Agile Data Podcast. Uncover key insights into community growth, operational strategies, and sustainability. Discover how Scott transitioned from a stock market enthusiast to a community management expert, specifically in tech and data spaces. Learn about the rapid expansion of the Data Mesh Learning Community, the importance of engaging members, and the role of timely content and responses. Delve into the operational nuances of community building, including automated onboarding, managing different fluency levels, and establishing community vibes. Understand the critical need for investment, sustainability, and how to overcome founder dependence. Gain insights into ideal team structures for community management and the significance of adaptability and realism in community growth. These comprehensive strategies and experiences offer valuable lessons for anyone looking to nurture and grow a successful community in the tech and data sectors.

AgileData App UX Capability Maturity Model
AgileData App UX Capability Maturity Model

Reducing the complexity and effort to manage data is at the core of what we do.  We love bringing magical UX to the data domain as we do this.

Every time we add a new capability or feature to the AgileData App or AgileData Platform, we think how could we just remove the need for a Data Magician to do that task at all?

That magic is not always possible in the first, or even the third iteration of those features.

Our AgileData App UX Capability Maturity Model helps us to keep that “magic sorting hat” goal at the top of our mind, every time we add a new thing.

This post outlines what that maturity model is and how we apply it.

Unveiling the Magic of Change Data Collection Patterns: Exploring Full Snapshot, Delta, CDC, and Event-Based Approaches
Unveiling the Magic of Change Data Collection Patterns: Exploring Full Snapshot, Delta, CDC, and Event-Based Approaches

Change data collection patterns are like magical lenses that allow you to track data changes. The full snapshot pattern captures complete data at specific intervals for historical analysis. The delta pattern records only changes between snapshots to save storage. CDC captures real-time changes for data integration and synchronization. The event-based pattern tracks data changes triggered by specific events. Each pattern has unique benefits and use cases. Choose the right approach based on your data needs and become a data magician who stays up-to-date with real-time data insights!

Layered Data Architectures with Veronika Durgin
Layered Data Architectures with Veronika Durgin

Dive into the Agile Data Podcast with Shane Gibson and Veronika Durgin as they explore the intricacies of layered data architecture, data vault modeling, and the evolution of data management. Discover key insights on balancing data democratisation with governance, the role of ETL processes, and the impact of organisational structure on data strategy.

How can data teams use Generative AI with Shaun McGirr
How can data teams use Generative AI with Shaun McGirr

Discover the transformative impact of generative AI and large language models (LMS) in the world of data and analytics. This insightful podcast episode with Shane Gibson and Shaun McGirr delves into the evolution of data handling, from manual processes to advanced AI-driven automation. Uncover the vital role of AI in enhancing decision-making, business processes, and data democratization. Learn about the delicate balance between AI automation and human insight, the risks of over-reliance on AI, and the future of AI in data analytics. As the landscape of data analytics evolves rapidly, this episode is a must-listen for professionals seeking to adapt and thrive in an AI-driven future. Stay ahead of the curve in understanding how AI is reshaping the role of data professionals and transforming business strategies.

AgileData App

Explore AgileData features, updates, and tips

Network

Learn about consulting practises and good patterns for data focused consultancies

DataOps

Learn from our DataOps expertise, covering essential concepts, patterns, and tools

Data and Analytics

Unlock the power of data and analytics with expert guidance

Google Cloud

Imparting knowledge on Google Cloud's capabilities and its role in data-driven workflows

Journey

Explore real-life stories of our challenges, and lessons learned

Product Management

Enrich your product management skills with practical patterns

What Is

Describing data and analytics concepts, terms, and technologies to enable better understanding

Resources

Valuable resources to support your growth in the agile, and data and analytics domains

AgileData Podcast

Discussing combining agile, product and data patterns.

No Nonsense Agile Podcast

Discussing agile and product ways of working.

App Videos

Explore videos to better understand the AgileData App's features and capabilities.

Observability – Raj Joseph

Join Shane Gibson as he chats with Raj Joseph on his experience in defining data observability patterns.Guests Raj JosephShane GibsonResourcesSubscribe | Apple Podcast | Spotify | Google Podcast  | Amazon Audible |...

5E’s
5E’s

As Data Consultants your customers are buying and outcome based on one of these patterns – effort, expertise, experience or efficiency.

We outline what each of these are, how they are different to each other and how to charge for delivering them.

Agile-tecture Information Factory
Agile-tecture Information Factory

Defining a Data Architecture is a key pattern when working in the data domain.

Its always tempting to boil the ocean when defining yours, don’t!

And once you have defined your data architecture, find a way to articulate and share it with simplicity.

Here is how we articulate the AgileData Data Agile-tecture.

DataOps: The Magic Wand for Data Magicians
DataOps: The Magic Wand for Data Magicians

DataOps is a magical approach to data management, combining Agile, DevOps, and Lean Manufacturing principles. It fosters collaboration, agility, automation, continuous integration and delivery, and quality control. This empowers data magicians like you to work more efficiently, adapt to changing business requirements, and deliver high-quality, data-driven insights with confidence.

Information Product Canvas

Join Shane and Tammy Leahy as they discuss the Information Product Canvas, what each area of the canvas holds and why you would want to collect this information.

This is the second in a series of podcast episodes that deep dives into the Information Product pattern.

ELT without persisted watermarks ? not a problem
ELT without persisted watermarks ? not a problem

We no longer need to manually track the state of a table, when it was created, when it was updated, which data pipeline last touched it …. all these data points are available by doing a simple call to the logging and bigquery api. Under the covers the google cloud platform is already tracking everything we need … every insert, update, delete, create, load, drop, alter is being captured