# Data Orchestration

After ingesting your data into Treasure Data, there are a number of features and tools that allow you to clean, normalize, enrich, and unify your data before you begin creating parent segments.

## Data Queries

For information about querying your data, see [Data Queries](/products/customer-data-platform/data-workbench/queries).

## Data Cleaning and Normalization

After importing data into Treasure Data, you may see unexpected or incorrect data in the results. Sometimes this can be corrected by modifying your import settings, but sometimes you will need to perform additional processing to clean up, or normalize, your data. Some common cleanup activities are:

* Deduplication: removing duplicate columns or rows in the Treasure Data instances of your databases.
* Data Normalization
  * standardizing how similar data types are represented in Treasure Data. For example, a telephone number might be variously imported as 555-567-8911 or 5555678911 or 00 1 555 567 8911. Consequently, you will want to standardize the representation of telephone numbers across all your databases especially if you're planning to use it as key or ID unification.


## Data Transformation

Generally, businesses want to transform data for the following reasons:

* make it compatible with other data
* move it to another system
* join it with other data
* aggregate information in the data


As organizations bring in data from various sources, data transformation is used to unify the data into a single database. Here are some example use cases:

* You are moving your data to a new data store; for example, you are moving to a cloud data warehouse and you need to change the data types.
* You want to join unstructured data or streaming data with structured data so you can analyze the data together.
* You want to add information to your data to enrich it, such as performing lookups, adding geolocation data, or adding timestamps.
* You want to perform aggregations, such as comparing sales data from different regions or totaling sales from different regions.
* You want to mask data to protect privacy. Filter functions allow you to mask sensitive data as you bring it from one data source into another.


Related topics:

* [About Filter Functions](/products/customer-data-platform/integration-hub/batch/import/filter/about-filter-functions)
* [Data Science and SQL Tools](/tools/cli-and-sdks/data-science-and-sql-tools)


## Data Segmenting

Segmentation is the activity of filtering a collection of customer or account profiles. Typically this is the last activity in Data Orchestration, and it takes place after your data has been cleaned, transformed, and enriched.

This section describes the process of creating parent segments (or master segments) which are a collection of customer account profiles with similar likes, dislikes, or demographic data.