Home
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
Data Quality datasets
A dataset defines a data source and characterizes data to be loaded and transformed by pipelines.
How to
This section contains tasks for using datasets in Precisely DQ.
Edit a dataset
After you create a dataset, edit settings to match file content. You can also edit field names, data types, and semantic types that identify and characterize data.

Welcome
The Precisely Data Integrity Suite is designed to deliver trusted data with accuracy, consistency, and context – built on the core capabilities of data integration, data quality, data governance, location intelligence, and data enrichment.
Data Integration
Replicate data from mainframe, IBM i, and relational databases to cloud analytics platforms.
Data Observability
Proactively monitor and manage the health of your data. Receive actionable insights through alerts that drive business decisions.
Data Governance
Manage data policy and processes with greater insight into your data's meaning, lineage, and impact.
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
- Data Quality Catalogs
  The Data Integrity Suite Data Catalog lets users discover and understand connected data sources.
- Data Quality datasets
  A dataset defines a data source and characterizes data to be loaded and transformed by pipelines.
  - About datasets
  - Using datasets
    Datasets are created, edited, and deleted on the Datasets page of the Data Quality application. You can also create a pipeline that uses a selected dataset on this page.
  - How to
    This section contains tasks for using datasets in Precisely DQ.
    - Create a dataset
      This procedure describes how to upload and create a dataset from a text file.
    - Edit a dataset
      After you create a dataset, edit settings to match file content. You can also edit field names, data types, and semantic types that identify and characterize data.
    - Delete a dataset
      You delete a dataset from the Datasets page.
    - Edit data type formats for a dataset
      Perform this procedure to determine how data types are characterized for date, time, and numeric fields in a dataset.
    - Create a pipeline from a dataset
      Create a Data Quality pipeline to import and transform data from a dataset.
  - Reference
    This section contains reference information for dataset pages in Precisely Data Quality.
- Data Quality pipelines
  Pipelines define and apply transforms to data loaded by a dataset.
- Data Quality jobs
- Data Connections
  Connecting to data is the first step in processing, analyzing, and storing information.
Administration
Manage your subscription, your users and their roles, and other system tasks.

Edit a dataset

After you create a dataset, edit settings to match file content. You can also edit field names, data types, and semantic types that identify and characterize data.

On the Precisely Quality page, expand the secondary navigation panel, and click Datasets .
Find the dataset that you want to edit in the datasets table.
In the Search keyword box, you can enter any portion of a dataset name to only show matching names.
Click the context menu button , then click Edit
This displays the Data Settings page. The Preview table displays sample data as it would be defined by settings on this page.

Make any necessary changes in the Data Settings.

Option	Description
Character encoding	Specifies how data is encoded in the dataset file.
Field delimiter	Specifies the character that delimits field values in each record.
Text qualifier	Specifies the character used to enclose text values. A text qualifier is always required when the field delimiter character appears in field values.
Line separator	Specifies whether rows are terminated by Unix (single linefeed `'\n'`) or Windows (carriage return-linefeed `'\r\n'` ) characters.

You can select or clear the First row is header row check box to specify whether the first row contains column labels or data.
Field Name labels in step 6 are highlighted in red if this option is selected and the first row appears to contain data instead of field names.

Edit values in the field definition table to define field characteristics.

Option	Description
Field Name	Specifies the field name for imported data.
Data Type	Specifies the primitive data type for data in a field.
Semantic Type	Describes the kind of information data in a field represents, such as name, email, or postal code.

Data Type and Semantic Type selections affect transforms that are recommended for a field.

Click Refresh Preview to view changes in the Preview section.
The Preview section displays data correctly when options in the Data Settings correspond to the source data.
Click the Save button.