data-preparation

type	skill
lifecycle	stable
inheritance	inheritable
name	data-preparation
description	Data cleaning, profiling, transformation, and quality gates -- prepares raw data for visualization and analysis
tier	standard
applyTo	**/*data*,**/*clean*,**/*transform*,**/*prep*,**/*pivot*,**/*aggregate*
currency	"2026-05-02T00:00:00.000Z"
lastReviewed	"2026-05-02T00:00:00.000Z"

Data Preparation

Prepare raw data for visualization. This module fires after ingestion and before chart selection. It does not analyze or visualize; it makes data ready for those steps.

When to Use

• Raw data has nulls, duplicates, or inconsistent types
• Data needs aggregation, pivoting, or reshaping for the target visual
• The brief specifies quality concerns
• You are unsure whether the data is visualization-ready

When to Skip

• Data is already clean, typed, and shaped for the target visuals
• The dataset is small and you can verify quality by inspection
• The brief says "data is analysis-ready"

State why you are skipping. Silent skips hide data problems.

Step 1: Profile

Before cleaning, understand what you have. Run these checks on every column:

Check	What to look for
Row count	Is it what you expected? Zero rows = ingestion failed
Column types	Dates stored as strings? Numbers as text?
Null count	Which columns have nulls? What percentage?
Unique count	Cardinality: is this a dimension (low) or measure (high)?
Min / Max / Mean	For numeric columns: are ranges plausible?
Sample values	Eyeball 5 rows. Do they look right?

Report the profile as a table before proceeding. The user (or orchestrator) needs to see it to decide what cleaning is needed.

Step 2: Clean

Apply fixes in this order. Each fix is a decision; state what you chose and why.

2a. Duplicates

Signal	Action
Exact duplicate rows	Remove. Log count removed.
Near-duplicates (same key, different values)	Flag for user decision. Do not silently drop.

2b. Null handling

Strategy	When to use
Drop row	Null is in a must-have column and row is < 5% of data
Fill with default	Column has an obvious default (e.g., 0 for optional count)
Fill with median/mode	Numeric column, nulls are < 10%, distribution is stable
Leave as-is	The visual can handle nulls (e.g., gap in a line chart)

Never fill nulls silently. State the strategy and count affected.

2c. Type coercion

Problem	Fix
Date as string	Parse to date. State the format detected.
Number as string	Convert. Check for non-numeric values first.
Boolean as string	Map "yes/no", "true/false", "1/0" to boolean.
Mixed types in one column	Flag for user. This usually means the schema is wrong.

2d. Outliers

Context	Action
Outlier is a data error (negative revenue, future date)	Remove or fix. Log.
Outlier is real but extreme	Keep. Note it for the visual (it may need axis adjustment).
Unsure	Flag for user. Do not remove real data silently.

Step 3: Transform

Reshape the data to match what the target visual needs.

Aggregation

Aggregate when the raw data is too granular for the visual.

Visual need	Aggregation
Monthly trend	Group by month, sum or average the measure
Category comparison	Group by category, sum the measure
Proportion	Group by category, compute share of total
Distribution	Bin the values, count per bin

Always state the grain change: "Aggregated from daily to monthly by summing revenue."

Pivot / Unpivot

Need	Direction
One column per category (wide format)	Pivot
One row per observation (long format)	Unpivot

Most visualizations prefer long format. Pivot only when the delivery module explicitly needs wide format (e.g., side-by-side bar chart in ASCII).

Computed columns

Add columns only when the brief requires a metric that is not in the raw data:

• Margin: revenue - cost
• Margin %: (revenue - cost) / revenue
• Growth: (current - previous) / previous
• Share: value / total

Name computed columns clearly. Do not overwrite source columns.

Step 4: Quality Gates

Before passing data downstream, assert:

Gate	Assertion	Fail action
No nulls in key columns	Dimensions and must-have measures are null-free	Push back: data is not ready
Row count is plausible	Within expected range (not 0, not 10x expected)	Push back: ingestion may have failed
Types are correct	Every column has the expected type after coercion	Push back: type fix needed
Grain is correct	One row per expected unit (e.g., one per month per region)	Push back: dedup or aggregate needed

If any gate fails, push back to the orchestrator with a specific message: "Quality gate failed: 12 nulls in revenue column. Recommend: drop rows (< 1% of data)."

Output

Pass to the next module:

• The cleaned, transformed dataset (or a reference to it)
• A one-paragraph data profile summary
• A list of any decisions made (nulls filled, outliers flagged, columns added)

Relationship to Mall Plugins

Mall plugin	This module's role
data-analysis	Runs after this module; assumes clean data
data-quality-monitoring	Monitors ongoing quality; this module does initial prep
data-visualization	Designs the visual; needs correctly shaped data from this module

Anti-Patterns

Do not	Do instead
Clean silently	Log every change with count and reason
Remove outliers without context	Keep real data; flag for user if unsure
Aggregate without stating the grain change	"Aggregated from X to Y by Z"
Add computed columns the brief did not ask for	Only add what the questions require
Skip profiling because "the data looks fine"	Profile always; it costs one table and catches surprises