March 2023 - Agile Mag Newsletter
Here we are with a new edition of our Agile MAG. We hope you'll enjoy it!
This edition's highlights:
📊 Journey into the lifecycle of a computational policy: a new White Paper to understand why computational policies are essential to scale efficiently
📌Data Mesh Learning Community: Agile Lab is one of the founding sponsors
▶️ Governance Shift Left: a new approach to Data Governance
📚Collaborative Modelling and EventStorming: a book about software engineering suggested by our Book Club
📣Open Positions: new opportunities to join our team
Insider
Journey into the lifecycle of a computational policy - White Paper
Why is a computational policy better than a governance policy? What types of computational policies can be used within an organization? Does it need a platform to work?
Computational policies have become the norm for large organizations that want to scale their governance practices. Manual checks become too many and difficult to execute, essentially bottlenecking scale. Enter our Governance Shift Left framework combined with computational policies. Read our latest white paper: Journey into the lifecycle of a computational policy.
In it you will:
- Understand why computational policies are essential to scale efficiently
- Enable the complete management of computational policies from their creation to their retirement
- Improve the transparency and visibility of policies organization-wide
- Check data quality, privacy/compliance, security, contracts, lineage and many more
- Remove technology and vendor lock-in by applying technology-agnostic principles to its computational policies.
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On Stage
Data Mesh Learning Community
Elite Data Engineering
Governance Shift Left
Data Governance is traditionally defined as a process. But what if it weren't?
In our view, Data Engineering is a process with the clear goal to produce data that is available, useable, and secure, among other things.
Data Governance, on the other hand, is a set of policies that must be enforced within the Data Engineering process. The Governance team is responsible to define and enforce their policies.
These are documented sets of guidelines that ensure an organization's data and information assets are managed consistently and used properly.
- They are hard to evangelize
- People forget about them Because they are busy
- They can be by-passed in case of emergency
Enter our Governance Shift Left framework, where the 4 pillars we have identified make these policies non-bypassable. This enforces them throughout the entire organization. Check out our example of the concept and its 4 pillars applied to a Data Lake:
1. Metadata as Code
2. You build it, you Govern it
3. No guidelines
4. Context-aware
A book to read: "Collaborative Modelling and EventStorming"
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Understanding what problems users face in a given domain. This is one of the keys to build effective technology solutions.
Yet, domain knowledge may not be something you just receive from above - rather, you may need to be actively involved and piece it together.
In the Agile Lab Book Club, we've been reading and discussing EventStorming - what it is and how it can help with this.
Why would we ever need to model collaboratively in software engineering?
In terms of collaborative modelling, are there any alternatives to EventStorming?
Suggested readings:
- Chapter 12, Learning Domain-Driven Design. Aligning Software Architecture with Business Strategy, V. Khononov. O'Reilly, 2021.
- Chapter 2, Collaborative Software Design. How to facilitate domain modelling decisions. E. van Kelle, G. Verschatse, K. Baas-Schwegler. Manning, 2023 (early preview).
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