AI Impact - adding sparklines

Release note

Introducing Sparklines: Enhancing AI Impact Data Visualization

We are excited to announce a significant improvement to our AI Impact analytics with the introduction of sparklines!

Sparklines, which are small, simple graphs embedded within data tables, are significantly enhance the readability and accessibility of AI Impact data. By transforming numerical values into visual representations, the new sparklines make it easier to identify trends over time, allowing for quick spotting of upward or downward movements. This new visual approach also streamlines the process of comparing trends across multiple metrics, reducing the time and effort required when relying solely on numbers. Additionally, sparklines provide a clearer insight into patterns, seasonality, and anomalies within the data, offering a more intuitive understanding of complex datasets. You can read more in this link(tbd)

Overview

Following the release of AI Impact, we need to complete the work on the AI Impact table.

Problem

AI Impact data is currently displayed as numerical values in a table, making it difficult to find insights from it:

1. Difficulty in identifying trends to quickly spot upward or downward trends in the past 6 months of the metrcis.
2. Time-consuming comparisons of trends across multiple metrics when relying only on numbers.
3. Limited insight into patterns, seasonality or anomalies.

Proposal

Similar to #382063 (closed), adding sparklines into the AI Impact tables.

1. Add a new column "Past 6 Months" near the change % with sparklines line charts for each metrics.
2. The sparkline will display the metrics value trends (not change rate %), total of 6 data points, each for every month.
3. The sparkline will display the past 6 months data, aggregated monthly.
4. Data order from left to right.
5. Y-Axis start at 0.
6. To improve performance use lazy loading of the sparklines.
7. Tooltip content - for each data point display month_name and value.
8. Default sparkline color is blue but it should gradient to green when the change is good.
9. Ensures that there is no data falloff on the last point when the final month is incomplete.