Direct answer: A chart can be mathematically correct and still fail its audience. Give the reader one obvious starting point, reduce competing visual weight, and make the label explain the decision rather than merely naming the field.
The common failure is not bad data. It is equal emphasis. Every line is saturated, every axis label is the same size, every annotation asks for attention, and the takeaway is buried inside a chart that technically contains it. When someone asks, “What am I supposed to notice here?”, the chart needs a stronger reading path.
Start with the intended conclusion. If the story is that retention improved after a product change, visually emphasize the period after the change and make the pre-change comparison quiet. If the story is a single segment that diverged from the rest, highlight that segment and mute the baseline series. Do not ask color alone to carry the explanation; pair it with an annotation that says what changed and why it matters.
Next, remove work from the reader. Round labels when exact decimals do not change the decision. Put direct labels near lines instead of forcing a reader to shuttle between plot and legend. Use grid lines sparingly, and reserve strong contrast for the data that deserves a decision. A chart should have one focal point before it has decorative polish.
Finally, test the chart in the context where it will actually be used. A dashboard card, a meeting-room slide, and a mobile report do not give the reader the same amount of attention. If the point disappears at the expected size, simplify before adding another series. Good visualization is not about showing every fact; it is about helping someone see the next useful fact quickly.