Bridging the Platform Empathy Gap - [Devoxx 2024] [Lightning].pdf
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As a Product Manager, I often questioned why more developers weren’t using my Platform. I thought I was building for everyone, but I was lucky if I had 1 or 2 enthusiastic users. The theory states that Platform Engineering allows DevOps to scale across an organisation, but in my implementations, it didn’t. As we shifted from the DevOps mindset to Platforms, the landscape of Platform Engineering evolved from being embedded within or adjacent to individual application teams to forming a central Platform team that serves multiple teams simultaneously. However, this transition created a widening gap between the Platform and its users—an empathy gap. The longer this separation persists, the bigger the empathy gap becomes. I believe we can close this gap by using Product Management techniques and treating our Platform as a Product. In this talk, Cat, a Product Manager with five years of experience working alongside Platform teams, will share practical Product Management techniques you can use when building your Platforms to close the Empathy Gap.
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- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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- 1. Bridging the Platform Empathy Gap Applying Product Management to Platform Engineering Icons made by Freepik from www.flaticon.com
- 4. “Things were good before we started scaling. The devs now have no idea what they are doing.” “Developers need clear guard rails. Otherwise they break things.” “They just ignore the platform and do whatever they want, whenever they want” “I have to help our developers with everything. It’s so tiring”
- 5. Why are Platform Engineers so frustrated by their users?
- 6. Hypothesis 1 Moving from DevOps to Platform Engineering created an Empathy Gap
- 12. Hypothesis 2 The larger the Empathy Gap, the lower your platform adoption.
- 13. “Software usage is simply logging on to a platform and engaging with it, whereas, software adoption is leveraging and using the platform to reap its benefits.” apty.io
- 14. ● Infra-driven development ● Big Gap: Slooooooow ● Low Adoption: Used as little as possible ● Frequent, universal black-outs ● Big Gap: Unreliable ● Low Adoption: They built their own ● Half-hearted automation ● Big Gap: Not feature complete enough ● Low Adoption: Gaming the system
- 15. Hypothesis 3 Platforms with low adoption will fail
- 16. What does a successful Platform look like?
- 18. Cheaper. Quicker. Safer. Developers use the Platform as intended.
- 19. Without developers adopting your platform you do not have a successful platform
- 20. Hypothesis 1 Moving from DevOps to Platform Engineering created an Empathy Gap Hypothesis 2 The larger the Empathy Gap, the lower your platform adoption Hypothesis 3 Platforms with low adoption will fail
- 21. Conclusion Platforms with a large Empathy Gap will also fail.
- 22. Define a North Star metric
- 23. “[A north star metric] condenses down everything your product is really aiming to do above all other functions” Ellen Merryweather for Product School
- 24. Make it related to adoption
- 25. Understand why your developers do not adopt your platform
- 26. 1. It isn’t valuable 2. It isn’t usable 3. It isn’t valuable or usable
- 27. ● You are frustrated due to the platform empathy gap ● Large empathy gap means low adoption ● Bridge the gap and make adoption your north star metric ● Fix the reasons why developers don’t adopt your platform
- 28. Thank you! cat@syntasso.io • www.linkedin.com/in/catmo/