Data is the starting material for a data story: raw values, records, or other facts that may already be cleaned but do not yet communicate a conclusion. In the DIKW pyramid, data is the base from which information, knowledge, and wisdom are progressively built. section 1.4
Move from raw data toward a useful story in deliberate stages:
- Start with the available values and decide which parts matter for the question. section 1.4 section 1.4.1
- Extract information by finding an insight that organizes the data into something understandable. section 1.4 section 6.5
- Add context to turn information into knowledge, then add an ethical or practical call to action to reach wisdom. section 1.4 section 1.4.3
A chart is not automatically information. First identify the audience, what it needs to know, and whether each field is relevant. Then emphasize a meaningful comparison, pattern, trend, anomaly, or relationship rather than merely redrawing a table. section 1.4.1 section 6.5
Pet-event participation
A pet-event table lists 1,138 cats, 130 dogs, and 17 other pets. A bar chart alone repeats those values. Removing the negligible Other category and calculating shares reveals a clearer message: about 9 participants out of 10 are cats and 1 out of 10 is a dog. That insight can support a discussion of dog-focused advertising. section 1.4.1 section 1.4.3
From dataset to comparison
For a training team, records and best times across disciplines become useful when the differences guide investment choices. For salmon aquaculture, exported value data can be filtered to world exports in U.S. dollars, given a date field, and plotted by commodity to compare sales trends. section 4.5 section 6.4
Listing 3.8 An example data section
"data": {
"values": [
{"x": 10, "y": 20},
{"x": 40, "y": 60},
{"x": 70, "y": 40},
{"x": 90, "y": 80}
]
},
A Vega-Lite data section makes the role concrete: it supplies the values that later encodings and marks transform into a visualization. section 3.1.2
