Key Insights in Building Effective BI Data Platforms

Creating a successful Business Intelligence (BI) data platform requires a strategic approach that centers around the end-user. Here are some critical insights and tips for building an efficient and user-centric data platform.

User-Centric Design in Data Platforms

A user-centric design ensures that every component of the data platform serves a specific purpose for the end user. This approach involves:

  • Identifying Key User Questions: Determine what questions users need answers to, such as the current status of projects, potential risks, and milestones.
  • Tailoring the Data Model: Align the data model with business processes and user needs. For example, project progress and health should be modeled separately from resource allocation and time tracking.

Using AI for Brainstorming

Leveraging AI tools like ChatGPT can help in brainstorming relevant business questions. This practice ensures that the data platform is equipped to provide the necessary insights. Some crucial questions include:

  • What is the current status of ongoing projects?
  • Are any projects at risk of missing their deadlines?
  • Which projects are on track, behind schedule, or ahead of schedule?

Practical Steps for Data Modeling

  1. Define Business Processes: Clearly define the business processes that the data platform will support. This clarity helps in creating focused and efficient data models.

  2. Create Fact and Dimension Tables: Separate fact tables for different types of data, such as project statuses and milestones, allow for more effective data analysis.

  3. Use Real Data for Prototyping: Export sample data from project management tools like Microsoft Project to build realistic data models.

Balancing Prototyping and Source Data Validation

When developing a data platform, it’s essential to balance prototyping with validating source data:

  • Prototyping: Create early versions of the data model and reports to visualize the final product and gather user feedback.
  • Source Data Validation: Ensure that the data model is feasible with the actual data available to prevent discrepancies between the prototype and the real-world data.

Implementing the Data Model

Once the data model is conceptualized, the next steps involve:
  • Creating a Data Lakehouse: Set up a storage location for the project data.
  • Importing and Transforming Data: Use data flows to bring data into the lakehouse and transform it as needed.
  • Building Reports: Develop Power BI reports based on the data model to provide actionable insights.

By following these steps and focusing on user-centric design, organizations can build effective BI data platforms that meet user needs and drive better decision-making.