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Data validation is the process of ensuring that your data is accurate and clean. Data validation is critical at every point of a data project’s life—from application development to file transfer to data wrangling—in order to ensure correctness. Without data validation from inception to iteration, crucial errors could translate into inaccurate forecasts, increased costs and lost revenue. Effective data validation efforts ensure that no oversight becomes a larger issue throughout the data lifecycle.
What Is Data Validation?
Data validation is the process of ensuring that your data is accurate and clean. Validation rules—also known as check routines—are repetitive programming sequences that check for data accuracy, relevance, and security. The success of your efforts is dependent on how meticulous you are with implementing routines throughout the data lifecycle.
Types of Data Validation and Tools
Data Validation in Excel
Data validation in Excel is possible, but limited due to the selection of available features. The validation tool allows users to control what is entered into a cell by displaying a message, offering a drop-down menu, or preventing certain values. However, one of the major limitations of data validation in Excel is that a user can override the control by entering information in a non-validated cell and copying it into the controlled cell. This action can lead users to enter invalid information and can prevent the success of data validation.
Data Validation in Data Wrangling
Data validation is critical at every point of a data project’s life—from application development to file transfer to data wrangling—in order to ensure correctness. Without data validation from inception to iteration, crucial errors could translate into inaccurate forecasts, increased costs and lost revenue.
Validation is especially important to a data wrangler, who is often importing vast amounts of complex, unstructured, or semi-structured data from a myriad of disparate sources. The impact of improved data validation on the data wrangling process cannot be underestimated. Effective data validation efforts ensure that no oversight becomes a larger issue throughout the data lifecycle. By leveraging Designer Cloud data validation and data analysis tools, capabilities firms like PepsiCo have improved their bottom line through reduced time to analysis, faster predictive modeling, more correct forecasts, quicker response to market and sales trends, and increased revenues, while reducing costs.
Designer Cloud and Data Validation
Designer Cloud was created to enable data validation techniques and makes validation a breeze, so that users can get to the important work of analysis and decision making. Here’s how:
- Data Quality. Designer Cloud’s intelligence automatically classifies data quality issues. Designer Cloud uses an extensive inference process to automatically detect issues such as duplicates, markup within data, missing and mismatching values, and outliers.
- Interactivity. Easy-to-use, interactive visuals—like the profiling page and data quality bar— make validation easier. Our interface then allows users to clean the data with a few simple clicks, rather than laborious programming.
- Multi-framework Enabled. All data validation work—standardization, cleansing, transformation, enrichment and matching/merging—are supported across all data processing frameworks. Designer Cloud even leverages semantic analytics for data quality, and white spaces can be instantly trimmed based on simple interactions within the application.
- Automation. Future at-scale validation scripts for data quality issues become automated and performed by default.