Plauti Context Reference Guide

1. All Field Types

1.1 Fill Rate

• What it does
  Fill Rate measures, per field, what percentage of records actually contain a value (i.e., are non-empty). Results are color-coded Red, Amber, or Green, based on thresholds you configure at the object or field level.
• Best used for
  Any field type, Fill Rate works across all field types and is a great starting point when analyzing a new object. It is especially useful for fields that are supposed to be required, or fields that feed into reports, dashboards, or AI models.

1.2 Uniqueness Detection

• What it does
  Uniqueness Detections checks whether all filled values in a field are distinct. It returns a simple boolean result per field: Unique = TRUE (no duplicate values found) or Unique = FALSE (at least one value appears more than once).
• Best used for
  Fields that could serve as identifiers or keys, such as external IDs, contract numbers, customer codes, or partner IDs; anywhere you want to know if a field is safe to use for matching, upserts, or integrations.

1.3 Duplicate Value Rate

• What it does
  Duplicate Value Rate measures what percentage of records share their field value with at least one other record. Per field, it shows the number of distinct values, the number of records in duplicate value groups, and the resulting percentage.
• Best used for
  Any field where you want to understand how widespread duplication is, not just whether it exists (that is Uniqueness Detection), but how many records are affected. Useful for evaluating fields as potential matching keys or for quantifying the impact of cleanup efforts.

1.4 Value Distribution

• What it does
  Value Distribution shows how often each distinct value occurs in a field. It groups records by exact value and calculates the count and percentage per value. You can configure a minimum occurrence threshold, for example, only track values that appear on at least 100 records, to control which values are included in the results.
• Best used for
  Text, picklist, and code fields where you want to understand which values dominate, and whether the distribution is healthy or skewed by placeholders, defaults, or test data.

1.5 Default Value Usage

• What it does
  Default Value Usage compares how many records keep the configured default value versus how many have been actively overridden. Per field, it shows the count and percentage of records with the default and with a non-default value.
• Best used for
  Picklist, checkbox, text, and number fields that have a configured default. Useful for understanding whether the default reflects reality (most records keep it because it is correct) or whether it is rarely appropriate and users are consistently changing it.

2. Text & Phone Fields

2.1 Format Pattern Detection

• What it does
  Format Pattern Detection converts each text value into a normalized pattern (letters become X, digits become 0, special characters are kept as-is) and groups records by the resulting pattern. It shows how many records follow each pattern and what percentage of the field they represent.
• Best used for
  Text and phone fields that are expected to follow a structural format, such as phone numbers, postal codes, invoice IDs, or custom codes. Not suited for free-text fields like First Name or fields where values are naturally very diverse.

2.2 Character Length

• What it does
  Character Length analyzes the length of text values in a field and calculates the minimum, maximum, and average character count. It also runs outlier detection using standard deviations from the mean, flagging values that are unusually short or long compared to the rest of the field.
• Best used for
  Text, email, URL, phone, and other text-based fields, especially where consistent formatting or content depth is expected, such as description fields, subject lines, or structured code fields.

2.3. Character Length Distribution

• What it does
  Character Length Distribution groups text field values into fixed-length buckets (0-50, 51-100, 101-255, 256-4,000, 4,001+ characters) and shows the count and percentage of records in each bucket. It also compares the average actual length to the configured maximum field length to flag over-provisioned or under-provisioned fields.
• Best used for
  Text-based fields where you want to understand overall usage patterns, whether a field is being used for short labels, medium notes, or long narratives, and to identify fields where the configured max length is far larger than needed.

3. Date/Date & Time Fields

3.1 Future Data

• What it does
  Future Date scans date and date-time fields and flags any value that falls after today. Per field, it aggregates results into an OK percentage and a Future Date percentage, color-coded by configurable thresholds.
• Best used for
  Date fields that should only contain past or present values, such as birthdays, contract signed dates, or creation dates. Helps surface typos, broken integrations, and form issues that accidentally write future dates where they do not belong.

3.2 Past Date Thresholds

• What it does
  Past Date Threshold scans date and date-time fields for values that exceed a configurable age limit (for example, more than 5 years in the past). Per field, it shows the percentage of records that are within the acceptable range versus those that are beyond the threshold.
• Best used for
  Date fields where unrealistically old values indicate stale, migrated, or incorrectly backdated data, such as close dates on open opportunities, case creation dates, or activity dates on records that should have been updated.

3.3 Date Range

• What it does
  Date Range checks whether data and date-time field values fall within a defined start and end window that you configure. Per field, it reports the percentage of records that are in range versus out of range, color-coded by thresholds.
• Best used for
  Date fields where only a specific time window is relevant to your current process, for example: only records from the past two years, or only dates between your system go-live and today. Useful for separating active data from historical clutter.

3.4 Time Component

• What it does
  Time Component analyzes date-time fields and checks whether the time portion of each value is exactly midnight (00:00:00) or an actual time of day. Per field, it shows the percentage of records at midnight versus those with a meaningful time value.
• Best used for
  Date-time fields where you want to understand the time portion carries real information or is simply a default placeholder. For example: to decide whether a field truly needs to be a date-time type or could be simplified to a date-only field.

4. Picklist Fields

4.1 Unused Active Picklist Values

• What it does
  Unused Active Picklist Values scans picklist fields and identifies which active picklist options are never used on any record. Per field, it shows the count and percentage of active values with zero usage, along with a list of the specific unused values.
• Best used for
  Picklist fields on any object, especially useful for identifying UI clutter, outdated options that were added but never adopted, and safe cleanup candidates that can be deactivated without affecting any data.

4.2 Inactive Picklist Value Usage

• What it does
  Inactive Picklist Value Usage scans picklist fields and identifies which active picklist options are never used on any record. Per field, it shows the count and percentage of active values with zero usage, along with a list of the specific unused values.
• Best used for
  Picklist fields on any object, especially useful for identifying UI clutter, outdated options that were added but never adopted, and safe cleanup candidates that can be deactivated without affecting any data.

4.3 Active Picklist Value Distribution

• What it does
  Active Picklist Value Distribution shows how often each active picklist value is used across records. Per field, it calculates the count and percentage share of each active value, highlighting skewed distributions where one or a few options dominate.
• Best used for
  Picklist fields where you want to understand whether usage is meaningfully spread across options or concentrated on a single value like "Other" or "Unknown". Useful for improving picklist design, reporting, and AI feature quality.

4.4 Multi-Select Average Count

• What it does
  Multi-Select Average Count analyzes how many values are selected per record on multi-select picklist fields. Per field, it shows the average, minimum, and maximum number of selected values, along with a distribution of records across selection counts (0, 1, 2, 3+).
• Best used for
  Multi-select picklist fields, particularly to identify whether users are selecting nothing (field is ignored), always selecting one value (better modeled as a regular picklist), or selecting everything (field used as a dumping ground).

5. Number, Currency & Percent Fields

5.1 Number Analysis

• What it does
  Number Analysis calculates the minimum, maximum, average, and median for numeric fields. It highlights outliers, records whose values are significantly above or below the typical range, to help distinguish normal values from anomalies.
• Best used for
  Currency, number, and percent fields. Useful for detecting data entry errors (extra zeros, wrong units, placeholder values like 999,999), and for understanding what a realistic value range looks like for a given field.

5.2 Negative Value

• What it does
  Negative Value scans numeric fields and counts how many records contain a negative value. Per field, it shows the number and percentage of records with a negative value, making it easy to spot fields where negatives are unexpected or violate business rules.
• Best used for
  Currency, number, and percent fields where negative values should not logically occur, such as price, quantity, or license count fields. Also useful for fields like margins or deltas where negatives are valid, but extreme or unexpected values may indicate errors.

5.3 Standard Deviation

• What it does
  Standard Deviation Analysis calculates how spread out the values are in a numeric field. Per field, it computes the Mean, Standard Deviation, and Coefficient of Variation (CV = SD / Mean), giving you a normalized view of variability that allows comparison across fields.
• Best used for
  Currency, number, and percent fields, especially when you want to identify fields with extreme variability, potential outliers, or values that are suspiciously uniform.

6. Email Fields

6.1 Email Regex

• What it does
  Email Regex validates email field values against a built-in regex pattern to determine whether each value is syntactically valid. Per field, it shows the percentage of valid versus invalid email addresses, color-coded by thresholds.
• Best used for
  Email fields on any object, particularly useful because Salesforce's native validation only checks for the presence of an @-symbol, leaving many malformed addresses undetected. A good first step before running mailbox-level verification with Plauti Verify.

6.2. Email Domain

• What it does
  Email Domain extracts the domain portion (everything after the @) from email field values and ranks the top domains by frequency. Per field, it shows the domains that meet a configurable minimum occurrence threshold and their percentage share of all email records.
• Best used for
  Email fields where you want to understand the mix of corporate versus personal versus test/disposable domains. Useful for B2B data quality checks, campaign preparation, and identifying integration or entry-point issues.

7. Lookup, Hierarchy & Master-Detail Fields

7.2 Orphaned Records Detection

• What it does
  Orphaned Record Detection measures, per lookup, hierarchy, or master-detail field, what percentage of records have no value (i.e., are not linked to a parent record). Results are color-coded Red, Amber, or Green based on configurable thresholds.
• Best used for
  Lookup, hierarchy, and master-detail fields. Useful for identifying records that are "floating" without a required parent relationship, such as Contacts without an Account or Opportunities without an owner.

7.2. Many-to-One Distribution

• What it does
  Many-to-One Distribution analyzes lookup fields and counts how many child records reference each parent record. It buckets parents by child count (1, 2-10, 11-100, 101-1,000, 1,000+) and shows the average and maximum number of children per parent.
• Best used for
  Lookup fields such as AccountId on Opportunity or OwnerId on Case. Useful for identifying hotspot parents with disproportionately large numbers of child records, data model imbalances, and performance or UX risks from oversized related lists.

8. Checkbox Fields

8.1 Checkbox Distribution

• What it does
  Checkbox Distribution calculates the percentage of records where a checkbox field is TRUE or FALSE. It highlights fields that are constant or near-constant, meaning they almost never vary from one value.
• Best used for
  Checkbox fields, particularly to identify flags that are always the same and therefore carry no analytical or process value, making them candidates for deprecation or redesign.

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