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5 core Data Collection Patterns

At AgileData, delivering our Fractional Data Service has revealed the diverse challenges of integrating data from varied organisations, industries, and systems. To scale effectively, we’ve adopted five core data collection patterns based on our “Define it Once, Reuse it Often” (DORO) principle:

1. Push
2. Pull
3. Stream
4. Share
5. File Drop

These patterns are supported by a toolkit of tested technologies like Dataddo, Meltano, and Google services, allowing us to solve new data challenges quickly. Our approach ensures flexibility and scalability, always starting with the question: Push, Pull, Stream, Share, or File Drop?

There are 3 strategic / macro data use cases
There are 3 strategic / macro data use cases

I often ask which of these three macro data use cases the Organisations believed were its priorities to achieve their business strategy:

Providing data to Customers
Supporting Internal Processes
Providing data to External Organisations

Each of these three strategic / macro data use cases come with specific data architectures, data work and also impact the context of how you would design your agile data ways of working.

Building the Data Plane while flying it
Building the Data Plane while flying it

In the data domain you typically have to balance between building the right thing and building the thing right.

The days of being able to spend 6 months or a year on “Sprint Zero” creating your data platform have gone.

One team I worked with called it “building the airplane as you fly it”

Here are 5 patterns I have seen data teams adopt to help them do this.

Dysfunction Mapping with Michael Lloyd
Dysfunction Mapping with Michael Lloyd

Join Murray Robinson and Shane Gibson as they chat with Michael Lloyd about dysfunction mapping.

Whether you are new to the agile world or a seasoned professional, you’ll find valuable insights as we delve into understanding team dynamics, honing the coaching process. And the essential principle of iterative hypothesis testing. Join us as we learned the power of mapping dysfunctions to unveil powerful solutions in agile environments.

2024 the year of the Intelligent Data Platform
2024 the year of the Intelligent Data Platform

AI was the buzzword for 2023 and it will continue to be the buzzword for 2024.

I have been thinking about our approach to AI in our product for a while and landed on 3 patterns that I use as a reference.

Ask AI
Assisted AI
Automated AI
Adopting these patterns moves a data platform from being a manual data platform, towards a data platform that can do some of the data work for you.

An Intelligent Data Platform.

Developing digital products in China with Bianca Grizhar
Developing digital products in China with Bianca Grizhar

In this episode, we talk to Bianca Grizhar about developing digital products in China. We discuss the cultural differences between Germany and China, and how Scrum improved team collaboration. We talk about the benefits of working directly with people offshore in cross functional, cross organisational teams. Whether you’re a product manager or a developer, this episode is packed with practical advice that will help you work more successfully with your offshore teams.

Knowledge Graphs with Juan Sequeda
Knowledge Graphs with Juan Sequeda

Dive deep into the world of knowledge graphs with Juan Sequeda on the Agile Data Podcast, hosted by Shane Gibson. Explore key insights from Juan’s journey in computer science, his pivotal role in semantic web development, and the transformative power of knowledge graphs in data integration. Discover how these technologies are reshaping the landscape of data management and the exciting future prospects with the advent of Large Language Models (LLMs). Tune in to understand the practical applications, challenges, and the future of knowledge graphs in enterprise data strategy.

Data Storytelling with Kat Greenbrook
Data Storytelling with Kat Greenbrook

Explore the Art of Data Storytelling with Kat Greenbrook on the Agile Data Podcast. Dive into Kat’s transformative journey from aspiring vet to a data storytelling expert, and discover the power of the ABT (And, But, Therefore) narrative framework in conveying compelling data insights. Uncover common pitfalls and learn crucial differences between data visualization and storytelling. Enhance your business communication skills with practical tips and insights from ‘The Data Storyteller’s Handbook.’ Perfect for professionals in data analytics, business intelligence, and anyone keen to master the art of turning data into impactful stories.

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What is Data Lineage?
What is Data Lineage?

TD:LR AgileData mission is to reduce the complexity of managing data. In the modern data world there are many capability categories, each with their own specialised terms, technologies and three letter acronyms. We...

Data Mesh 4.0.4
Data Mesh 4.0.4

TD:LR Data Mesh 4.0.4 is only available for a very short time. please ensure you scroll to the bottom of the article to understand the temporal nature of the Data Mesh 4.0.4 approach.This article was published on 1st...

Data Observability Uncovered: A Magical Lens for Data Magicians
Data Observability Uncovered: A Magical Lens for Data Magicians

Data observability provides comprehensive visibility into the health, quality, and reliability of your data ecosystem. It dives deeper than traditional monitoring, examining the actual data flowing through your pipelines. With tools like data lineage tracking, data quality metrics, and anomaly detection, data observability helps data magicians quickly detect and diagnose issues, ensuring accurate, reliable data-driven decisions.

Agile DataOps
Agile DataOps

TD:LR Agile DataOps is where we combine the processes and technologies from DataOps with a new agile way of working, to reduce the time taken and increase the value of the data we provide to our customers What's in a...