OECD_Dashboard

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  Charts 1-2

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  KPI Cards

For the hackathon, I decided to dive deep into the platform and leverage Data Studio’s AI capabilities. I focused on the given dataset and spent more time deriving insights and building the best possible dashboard, only diving into code when absolutely necessary.

What it does

This project is an interactive well-being dashboard that lets users explore country-level performance across multiple well-being domains over time. Users can:

Filter by country, year, domain, measure, and performance metrics.

View key insights such as the happiest country, most improved, highest inequality, and most challenged domain.

Compare a selected country against OECD averages.

See sparklines and radar charts to visualize trends across domains.

The goal is to turn complex, multi-year well-being data into actionable insights quickly and intuitively.

Challenges

Data consistency: Handling missing values and ensuring numeric calculations for percentage changes, scores, and percentiles.

Preprocessing: Preparing data outside Plotly Studio, including normalizing metrics, grouping by columns, and exploring the dataset.

AI slot behavior: Inconsistent behavior with remixes; radar chart could not always display all data.

Limited data: Dataset size constraints (75 MB) meant some country data was missing, limiting the insights that could be drawn.

Dynamic filtering: Ensuring filters (country, domain, year, measures) interact smoothly and update both data cards and charts correctly.

Visual clarity: Displaying multiple metrics (radar charts, sparklines, comparisons) without cluttering the UI.

What I learned

Techniques for handling missing and inconsistent data in pandas while maintaining accurate calculations.

Best practices for visualizing complex multivariate data, including radar charts, sparklines, and comparative analytics.

The importance of user-centric design to make dashboards intuitive for non-technical users.

How to make Plotly Data Studio follow design and layout instructions effectively.

Built With

• plotly