4#8 - Shuang Wu - Service Platform: From Analytics to AI-Driven Success (Eng)

MetaDAMA - Data Management in the Nordics

«We want to make data actionable.»

Join us for an engaging conversation with Shuang Wu, Mesta's lead data engineer. We delve into the concept of platforms and explore how they empower autonomous delivery teams, making data-driven decisions a central part of their strategy.
Shuang discusses the intricate process of evolving from a mere data platform to a comprehensive service platform, especially within organizations that aren't IT-centric. Her insights emphasize a lean, agile approach to prioritize use cases, focusing on quick iterations and prototypes that foster self-service and data democratization. We explore the potential shift towards a decentralized data structure where domain teams leverage data more effectively, driving operational changes and tangible business value in their pursuit of efficiency and impact.
My key learnings:

• It’s not just about gaining insights, but also about harmonizing and understanding data in context.
• Find your SMEs and involve them closely - you need insight knowledge about the data and pair that with engineering capabilities.
• Over time the SMEs and the central data team share experiences and knowledge. This creates a productive ground for working together.
• The more understanding business users gain on data, the more they want to build themselves.
• Central team delivers core data assets in a robust and stable manner. Business teams can build on that.

The Data

• You can integrate and combine internal data with external sources (like weather data, or road network data) to create valuable insights.
• Utilizing external data can save you efforts, since it often is structured and API ready.
• Dont over-engineer solutions - find you what your user-requirements are and provide data that match the requirements, not more.
• Use an agile approach to prioritize use cases together with your business users.
• Ensure you have a clear picture of potential value, but also investment and cost.
• Work in short iterations, to provide value quickly and constantly.
• Understand your platform constrains and limitations, also related to quality.
• Find your WHY! Why am I doing the work and what does that mean when it comes to prioritization?
• What is the value, impact and effort needed?

Service Platform:

• Is about offering self-service functionality.
• Due to the size of Mesta it made sense to take ownership for many data products centrally, closely aligned with the platform.
• Build it as a foundation, that can give rise to different digitalization initiatives.
• If you want to make data actionable they need to be discoverable first.
• The modular approach to data platform allows you to scale up required functionality when needed, but also to scale to zero if not.
• Verify requirements as early as you can.

Working with business use cases

• Visibility and discoverability of data stays a top priority.
• Make data and AI Literacy use case based, hands-on programs
• You need to understand constrains when selecting and working with a business use case.
• Start with a time-bound requirements analysis process, that also analyses constraints within the data.
• Once data is gathered and available on the platform, business case validity is much easier to verify.
• Gather the most relevant data first, and then see how you can utilize it further once it is structured accordingly.
• Quite often ideas originate in the business, and then the central data team is validating if the data can support the use case.

Kapitler

1. From Data to Service Platform (00:00:00)

2. Data Ecosystem for Actionable Insights (00:16:04)

3. Utilizing Data for Efficiency and Impact (00:30:50)

4. Efficient Infrastructure Design for Organizations (00:40:16)