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Most of your data is rotten and it’s not your fault, but it is your problem
Data quality is an expectation, not an exception.
While data quality is crucial, it’s not always directly our fault when issues arise, nevertheless, it remains our problem to solve.
Data Contracts are one pattern that can help us solve this problem.
Are you delivering drills, holes or outcomes?
TD:LR Whether you're a Data Entrepreneur or an organisation looking for actionable insights, its the business outcome these insights help you achieve that is the most important thing. Yes you need a data platform and data to achieve these insights, but they are just...
Data Asset, Data Product, Data Service?
TD:LR Should we treat data as an Asset, a Product, a Service or a hybrid combination of all three? Data Asset, Data Product, Data Service? There has been a lot of discussions on LinkedIn, lots of podcasts, lots of webinars lately on the question of whether data should...
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.
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.
Upgrading Python: A Plumbing Adventure in the Google Stack
In the ever-evolving world of AgileData DataOps, it was time to upgrade the Python version that powers the AgileData Platform.
We utilise micro-services patterns throughout the AgileData Platform and a bunch of Google Cloud Services. The upgrade could have gone well, or caused no end of problems.
Read more on our exciting plumbing journey.
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
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!
The challenge of parsing files from the wild
In this instalment of the AgileData DataOps series, we’re exploring how we handle the challenges of parsing files from the wild. To ensure clean and well-structured data, each file goes through several checks and processes, similar to a water treatment plant. These steps include checking for previously seen files, looking for matching schema files, queuing the file, and parsing it. If a file fails to load, we have procedures in place to retry loading or notify errors for later resolution. This rigorous data processing ensures smooth and efficient data flow.
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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.
A Data Engineer an Agile Coach and a Fish walk into a bar…
This is the first of a series of articles detailing how we built a platform to make data fun and remove complexity for our users
Analysts can model democratising data modeling
In 2022 Shane Gibson was lucky enough to present “Analysts can model democratising data modeling” at the Knowledge Gap Conference
Watch the presentation.
Data Mesh Podcast – Finding useful and repeatable patterns for data
TD:LR I talk to Scott Hirleman on the Data Mesh Radio podcast on my thoughts on Data Mesh and the need for resuable patterns in the data & analytics domain. My opinion on Data Mesh I am not a fan of the current...
The Enchanting World of Data Magicians: Marketing Analytics vs. Product Analytics
Marketing Analytics involves analysing data from various channels, such as social media, email, and websites, to assess the performance of marketing efforts.
Product Analytics focuses on understanding and improving user experience and satisfaction with digital products or services.
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
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...
Abracadabra! Unravel the Mysteries of Data Catalog
Data catalogs are comprehensive inventories of an organisations data assets, helping data analysts and information consumers to quickly find, understand, and utilise relevant information. They foster collaboration, maintain data governance, and ensure compliance.
Catalog & Cocktails Podcast – agile in the data domain
Early in 2022 Shane Gibson was lucky enought to talk to the Catalog and Cocktails podcast crew about agile in the data domain. Watch or listen to the episode.
Whats the hottest new data thing in 2022 — Data Mesh or Metric Store
There is a lot of vendor washing going on A lot of data vendors are vendor washing their technologies to pretend they enable "Data Mesh" as they are punting on Data Mesh being the new thing for 2022. I think they are...
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.
AgileData >>> Modern Data Stack
TD:LR AgileData's mission is to reduce the complexity of managing data. A large part of modern data complexity is selecting, implementing and maintaining a raft of different technologies to provide your "Modern Data...
A selection of practical agile patterns when using Data Vault
In 2021 Shane Gibson was lucky enough to present “A selection of practical agile patterns when using Data Vault” at the Knowledge Gap Conference
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...
The “Killer” Feature
One feature to rule them all As product managers we are always looking for the next “killer feature” for our product. You know the one, that feature that will become the magical thing that will have customers flooding...
3 types of product features
Our UX/UI journey is accelerating We are currently full steam into the development of the initial User Interface for AgileData.io. The team have done some awesome work on the UX designs for a bunch of the core screens,...
Reducing Manual Effort, Everywhere, Every-time
Some tasks seem really small and only take minutes, but multiply that effort by completing that task a hundred times and you have found a task that should be automated. Collect your data In AgileData we automate the...
Why assumptions are just that
When we first sketched out our plans for AgileData we were pretty clear what AgileData would do and what it wouldn’t do. Those assumptions didn’t last long. Combine with magic We knew we wanted to focus on what we call...
Micro Actions, ensuring a little bit of Magic Happens Here everytime
We are currently doing some work on creating rule patterns that enable us to automagically find duplicate Concept values and create a master view of them. For example creating a master view of Customers, or a master...
Buy, Build or Lease
One of the (many) things we needed to decide when we started to build out the AgileData Minimal Magical Product (MMP), was which capabilities we would build vs which capabilities we would lease or buy. As part of our...
What problem(s) does AgileData solve?
This should be an easy one for me to answer right? Understand the customers problem first We get told in startup land that you need to understand the customers problem in-depth, before you start building your product....
Why we chose Google Cloud as the infrastructure platform for AgileData
Pick a few things that really matter, not thousands of “requirements” We when first started developing the core of the AgileData backend for the MVP, we knew we would need a cloud database to store...