Transforming & Selecting Data
DataRight IQ reshapes, converts, and selectively processes your data so the output fits exactly the structure and scope you need.
Search-and-replace
A flexible search-and-replace capability lets you convert and clean values as they move through processing.
| Operation | What it does |
|---|---|
| Convert coded data | Translate codes into meaningful values - for example, turning prename or income codes into readable text. |
| Remove unwanted data | Find and delete content that doesn't belong - for example, stripping a stray date out of a name field. |
| Search-and-replace | Replace one value with another - for example, changing "Occupant" to "Current Resident." |
| Search-and-put | Look up a value in one field and place the result in a different field, leaving the original intact. |
Conversion tables can be maintained inside a job or kept externally and shared across jobs, whichever suits your workflow.
Splitting & restructuring records
Split combined names
When a record contains combined names - most often couples or family members - DataRight IQ can split them. You can keep both names in one record or create a separate output record for each person.
Scan-and-split
For fields that mix several kinds of information - names, firms, account numbers, special designations, or dates - scan-and-split precisely pulls the pieces apart into separate fields. For example, a designation like "Trustee for" can be recognized and repositioned relative to the name it applies to.
Selecting records
You decide which records are processed and which appear in each output target - independently, on both input and output.
- Select records by specific criteria such as demographics or geography.
- Take a representative random sample - for example, 50,000 records drawn from across a large file.
- Apply different selections to different output targets from the same run.
File, format & date conversion
DataRight IQ is well suited to migrating and consolidating data from disparate sources into a single, consistent house format.
- Read many input sources at once and consolidate them into one target format.
- Convert free-form, floating data into clean, fielded data.
- Convert dates between formats, expand 2-digit years to 4-digit years, and parse dates into fields.