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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.
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.
When you work in a data team you have to split your time between building and delivering new Information Products and maintaining the ones you have already delivered.
DataOps patterns can help reduce the time you spend on the maintenance work.
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.
Data teams struggle to not “boil the ocean” when doing data work.
Use milestones as a pattern to help the data team to focus on what really needs to be built and manage the trade-off decisions for what doesn’t.
Modern data teams are transient, often staying less than 5 years, unlike past decades of long-term loyalty.
Companies should adapt by defining robust Ways of Working (WoW) that endure beyond individual tenures.
Balancing in-house teams with reliable data vendors for continuity and efficiency may also be a useful pattern as part of your WoW.
TD:LR Having a shared language is important to help a data team create their shared ways of working. When we talk about self-service, we should always highlight which self-service pattern we are talking about.I'm...
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.
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...
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...
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.
TD:LR In mid 2023 I was lucky enough to present at The Knowledge Gap on the Information Product Canvas. Watch The Information Product Canvas, is an innovative pattern designed to capture data requirements visually and...
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...
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.
TD:LR Late in 2022 I was lucky enough to talk to Tobias Macey on the Data Engineering podcast about combining agile patterns and practises with those from the data domain. Listen to the episode or read the transcript....
TD:LR To get data tasks done involves a lot more than just bashing out a few lines of code to get the data into a format that you can give it to your stakeholder/customer. Unless of course it really is a one off and...
Early in 2022 Shane Gibson was lucky enough to talk to the Focus podcast crew about agile governance in the data domain. Watch or listen to the episode.
In 2022 Shane Gibson was lucky enough to present “Analysts can model democratising data modeling” at the Knowledge Gap Conference
Watch the presentation.
In 2021 Shane Gibson was lucky enough to present “A selection of practical agile patterns when using Data Vault” at the Knowledge Gap Conference
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