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We are in the final phase of building a new product, AgileData Disco, aimed at efficiently discovering and documenting data platforms. We are exploring various Go-to-Market strategies like SLG and PLG. Pricing strategies include options like pay per output or subscription models. We are building in public to gather feedback and refine their approach.
We review feedback, highlight emerging use cases like legacy data understanding, data governance, and automated data migration. New patterns are needed for moving from prototype to MVP. Challenges include managing tokens, logging responses, and secure data handling. The GTM strategy focuses on Partner/Channel Led Growth.
We focus on developing features such as secure sign-in, file upload, data security, and access to Google’s LLM. Challenges include improving the menu system and separating outputs into distinct screens for clarity. Feedback drives their iterative improvements.
We discuss the ongoing development of a new product idea, emphasising feasibility and viability through internal research (“McSpikeys”). Initial tests using LLMs have been promising, but strategic decisions lie ahead regarding its integration. The team grapples with market validation and adjusting their workflow for optimal experimentation.
We are tackling challenges in migrating legacy data platforms by automating data discovery and migration to reduce costs significantly. Our approach includes using core data patterns and employing tools like Google Gemini for comparative analysis. The aim is to streamline data handling and enable collaborative governance in organisations. Follow their public build journey for updates.
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.
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.
Having AI embedded in your product have become table stakes it seems.
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
TD:LR Patterns like DocOps provide massive value by increasing collaboration across team members and automating manual tasks. But it still requires a high level of technical skills to work in a DocOps way. For the...
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.
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...