Data Visualization and Dashboard Design Questions

Direct answer
Use a sequential palette (one hue, increasing intensity) for a single quantity that runs low-to-high, a diverging palette (two hues meeting at a neutral midpoint) when there's a meaningful zero or target to diverge from, and a categorical palette (distinct, non-ordered hues) for unordered groups; keep any multi-series legend to the smallest set of colors a viewer can hold in memory.

Structured elaboration

• Sequential: for a quantity like "revenue per region" where only magnitude matters, one hue from light to dark (or low to high saturation) preserves the sense of order.
• Diverging: for a quantity like "percent change vs. last quarter" or "actual minus target," use two contrasting hues (e.g. blue-to-red) meeting at white/gray at zero, so the viewer instantly sees direction as well as magnitude.
• Categorical: for genuinely unordered groups (product lines, regions, channels), pick 4-6 maximally distinguishable hues; going past 8-10 categorical colors makes the legend unreadable regardless of how distinct the hues are.
• Continuous vs. discrete color scales: a continuous scale blends smoothly across the full range of values (e.g. a choropleth, a map where each region is shaded according to its value, shaded on a smooth gradient), which best conveys fine-grained magnitude differences; a discrete/binned scale groups values into a small number of steps (e.g. 5 quantile buckets), which trades precision for a legend a viewer can actually name and remember, and is usually the better choice once the audience needs to talk about "the top bucket" rather than an exact value.
• Multi-series legend design: order series in the legend to match their visual order in the chart (e.g. top-to-bottom matching the lines' end positions), use consistent colors for the same entity across every chart in a dashboard, and give each series a clear, descriptive name rather than a generic label like "Series 3", vary line style (solid/dashed/dotted) alongside color so series stay distinguishable in grayscale or for colorblind viewers, and make the legend interactive (click to isolate/hide a series) when there are more than 4-5 series.

Worked example
A dashboard showing "gross margin by product line" for three product lines, Alpha at 22% margin, Beta at 18% margin, and Gamma at 9% margin (unordered categories), should use a 3-color categorical palette (blue for Alpha, orange for Beta, green for Gamma) so each line reads as a distinct entity rather than an ordered scale; a companion tile showing "margin vs. target" for the same three lines, Alpha +4pts, Beta +1pt, Gamma -3pts, should instead use a diverging red-white-green palette centered at 0, so Gamma's bar renders in red, Beta's in near-white, and Alpha's in green, letting a viewer read direction (over or under target) at a glance without reading the numbers.

Trade-offs and pitfalls
The most common error is using a rainbow (unordered, high-saturation) palette for genuinely ordered/quantitative data, which implies false category boundaries where there are none; a second common error is reusing the same hue for a different entity across two different charts on the same dashboard, which silently breaks the color-to-entity mapping a viewer has learned.

Direct answer
Make a chart accessible by pairing every visual encoding with a non-visual equivalent: a colorblind-safe, high-contrast palette plus redundant encoding (pattern, position, or direct labels) for anyone who can't distinguish the colors, and a structured, readable alternative (a data table or text summary) plus keyboard navigation for anyone using a screen reader.

Structured elaboration

1. Choropleth map: color alone should never be the only way to read the value; add a legend with explicit numeric bins, and provide an accessible data table (region -> value) as an alternative view, since a screen reader cannot interpret map shading or a keyboard user cannot easily probe a map's regions.
2. Stacked bar chart: use a colorblind-safe categorical palette (avoiding red-green as the sole distinguishing pair) plus direct labels or hatch patterns for the segments; provide an accessible table with the same category-by-value breakdown, and ensure each bar segment is keyboard-focusable with a text description ("Region A, Channel 1: $40K").
3. Interactive network diagram: the hardest case; provide a structured textual summary of the key relationships ("Node A connects to 5 nodes, most strongly to Node B") and an accessible adjacency table, since a screen reader cannot meaningfully narrate a force-directed layout (a network-diagram arrangement where connected nodes are pulled together and unconnected ones pushed apart like magnets, so the same data can settle into a different-looking arrangement each time it's drawn).
4. General WCAG practice: verify contrast ratios (4.5:1 minimum for text, higher for small elements), ensure every interactive element (filter, tooltip trigger) is reachable and operable via keyboard alone, and validate with an automated contrast/ARIA checker (ARIA: markup attributes that tell a screen reader what an interactive element is and how to use it, e.g. that a clickable icon is actually a "button" that toggles a filter) plus a manual screen-reader pass (not automated tooling alone, since automated tools miss narrative/logical-order issues).

Worked example
For the choropleth, a colorblind-safe blue-to-orange diverging scale plus a companion sortable table ("County, % change, rank") lets a screen-reader user get the same insight (which counties changed most) that a sighted user gets from the map's darkest regions.

Trade-offs and pitfalls
Building a full accessible-table alternative for every complex chart adds real engineering and design time; prioritize it for the charts that carry a decision-relevant insight, not for purely decorative or low-stakes visuals, and treat accessibility as a release-gating checklist item rather than a stretch goal.