Help Centre

Map Column Headers

The
Map column headers
operation takes source files with differing header schemas and maps them to a unified header schema. This allows the datasets to be unioned.
For example, you may have three source files, each with a column of policy numbers, but named differently in each dataset: Policy Number, Pol. #, Policy Num. These column names can be mapped to a common ‘Policy Number’ field.

A typical workflow incorporating column mapping would be:
  1. Import datasets
  2. Define the data area within each dataset
  3. Map the column headers to a single schema
  4. Union the datasets together
Additionally, columns in the output dataset can be added, removed or re-ordered, and empty or missing columns in the source data populated with text or numbers.

Workspace

Master Schema

List of columns in master schema.

Source Schemas

Lists of columns in source schemas. Horizontal position of mapped items indicates their mapping to the master.

Mapped items

The layout shows each column mapping across the source schemas and the master schema horizontally, making it very quick to visually deduce the mapping structure across all your sources.

Toggle mapped/unmapped

Toggle between mapped and unmapped items.

Enforce schema

Switch between a mode which enforces the exact master schema, or a mode which only maps column header names. See Enforcing the master schema.

Mapping suggestions

Clicking on an unmapped item suggests mappings in the master schema, alongside suggestions for new mappings.

Initial view

On opening the operation, the initial view shows the distinct column structures across the input files to the operation, with no master schema or mapping defined.
If there are multiple input files that have identical column structures, then Quantemplate will stack these together, so that only distinct columns structures are displayed. To see which input files to the operation are stacked in each distinct column structure, hover over the file name at the top of the column.

Defining the master schema

Define a source schema as a master


The most common scenario is to use one of the displayed file schemas as the baseline for the master schema and then to map any remaining column headers from the other source schemas to the master.
Click on
Define master from this dataset
for the file schema that you wish to use as a starting point for the master schema template. This will instantly:
Define the selected source schema as the master

Map all of the column headers in that schema to the corresponding columns in the master schema.

Maps columns in the other source schemas that have the same name as the columns in the master schema. The column name is case-insensitive in the mapping.
Tip
Keep a dataset which represents your organisation’s target mappings in your data repo, and reuse this when you wish to create new pipelines.

Map individual fields


Rather than taking a whole source schema as a starting point, individual fields can be mapped in from one or more source schemas. See Mapping in source files below.

Manually enter fields


Alternatively a master schema can be created from scratch by manually entering fields. To add a new field, click the
New field
button at the bottom of the master schema, enter the field name and hit return (↵).

Mapping in source files

To view unmapped fields in a source schema click on the
Unmapped toggle
. Clicking on an unmapped item reveals mapping suggestions.
Map to master
will map the source field to an existing column header in the master schema.
New mapping
appears under suggestions that are not in the master schema, and will create a field in the master schema and map the source item to it.
Add to master
will take the exact name of the source column and add it to the master schema as a new field.
The
strength rating
indicates Quantemplate’s statistical confidence level in the suggested mapping.
About suggested mappings
Quantemplate uses machine learning to provide mapping suggestions. These combine suggestions from items in your master schema with suggestions from your other pipelines and from organisations who have chosen to share their mappings with you. Quantemplate learns from the mapping decisions you make to continuously improve the quality of its suggestions. Mapping data can also be supplied directly to train the mapping model – contact Quantemplate support for more information.

Changing mappings

You can unmap, remap or replace currently mapped column headers.
To see the column headers that are currently mapped for a given schema, click on the Mapped toggle. Clicking on a
Mapped
toggle. Clicking on a mapped item reveals mapping suggestions.

Unmap


Unmap the item from the master schema. Item returns to the unmapped column.

Remap


Remap the item to a different available item in the master schema. Master items that have items from this source schema already mapped to them are not available.

Replace


Replace the item with one of the unmapped items in the source.

Populate


If Enforce schema is enabled, fields in the source can be populated with a data value or text. See Populating source fields below.

Enforcing the master schema

Column mapping has two modes,
enforcing the master schema
and
column name mapping only
. When the Enforce toggle is on, enforcing the master schema is enabled. This is the default setting.

Enforce enabled


Schemas of output datasets will match the master schema exactly.
Mapped source column names will be changed.

Unmapped source columns will be removed.

Column order of output files match the master.

Columns can be populated with values.

Enforce disabled (column name mapping only)


Output dataset headers are renamed, schema structure remains the same.
Mapped source column names will be changed to match the master.

Unmapped source columns will be output unchanged.

Column order of output files will be unchanged from the order in the source.

Enforcing the master is automatically enabled when defining a master schema from a source dataset.

Example

For a given source:
Source schema
Client name
Ref. no.
City
State
Country
Apply the following mappings:
Mappings
Client name
maps to
Client
Country
maps to
Territory
Ref. no.
maps to
Ref. no.
Outputs when enforce option is enabled / disabled:
Output options
Enforce on
Enforce off
Client
Client
Territory
Ref. no.
Ref. no.
City
State
Territory

Highlights
indicate renamed items.

Default column mapping

Where a master schema has been defined and a new input is added to the stage, the operation will apply the following default mapping rules:
  1. If the new input matches an existing schema, then the columns in the new input will be mapped identically to that schema.
  2. If the new input doesn’t match an existing schema, then each column will be mapped to a matching column name in the master schema. Note that column name matches ignore differences in capitalisation.
A warning will be shown in the stage panel if, after applying the above rules:
  1. There remain entries in the master schema without a mapped column from the new input.
  2. There remain entries in the new input that are unmapped.
Once the mappings have been reviewed in the column header mapping operation the warning will be removed

Example

For a given source:
Source schema
Client name
Ref. no.
City
State
Country
and the following mappings:
Mappings
Client name
maps to
Client
Country
maps to
Territory
Ref. no.
maps to
Ref. no.

Scenario 1

Adding another input with the same schema as the initial source will apply the same set of mappings to that new input.

Scenario 2

Adding another input with a different set of column names will apply the default mapping rules, matching column names ignoring capitalisation

For a new source schema:
New schema
client
ref
territory
will result in the mappings:
Mappings
client
maps to
Client
territory
maps to
Territory

Populating source fields

When
Enforce schema
is enabled and a source file has no data for a master field, that field will be output as an empty column by default. This column can be populated with homogenous data if required. For example, if your master schema contains the field currency, yet your source data has no currency column, then the empty fields can be populated with a currency value.

Using the populate function

To populate a source column:
  1. Click on an empty mapped item.
  2. Select populate.
  3. Enter text or numbers then hit return (↵).
  4. The data you have entered is shown in the field, alongside the populate symbol.
To remove populated data
  1. Click on an empty mapped item
  2. Select populate
  3. Delete the data from the the populate field then hit return (↵)

Populate all unmapped fields at once

Sometimes source datasets may not contain all the columns in your master schema. It’s often useful to populate these blank columns with a single value, such as ‘Unmapped’, so when the data is used in Analyse, these datapoints can be identified and filtered out.
To populate all unmapped fields
  1. Click on the cog next to ‘enforce schema’.
  2. Enter a value for ‘Populate unmapped’. The unmapped fields update live as you type.
Auto-populated unmapped fields can be overridden with an individual populated value. If the value for the auto-populated field changes, the overridden value is retained.

Automap

The Automap function automates the column header mapping process by applying all suggested mappings above a user-specified strength threshold.
The Automap preview mode shows the effect of adjusting the strength threshold on the applied mappings.
To Automap:
  1. Click the Automap button to reveal the automap preview.
  2. Select the desired automap settings via the settings cog.
  3. Drag the slider to the desired threshold.
  4. Click the ‘Apply mappings’ button to apply the mappings and leave the preview, or click
    ‘Exit preview’ to disregard the mappings.
Note that for any given schema mapping suggestions and strength ratings may change over time as the ML-powered decision-making model learns from your mapping choices.

Automap settings

Overwrite existing mappings

Mapped items will be overridden by suggestions. This is a ‘destructive’ mapping option since any mappings you have made previously will be overwritten. Off by default.

Overwrite populated fields

Manually populated fields will be overridden by suggestions. This is a ‘destructive’ mapping option since any populated fields you have made previously will be overwritten. Off by default.

Create new master fields

Suggestions not in the master schema will be added to it. Off by default.