The Need for Data Validation and Cleansing

A reliable and efficient data source is required to gain the trust of the individuals. Inaccurate data is a curse to the organization that once implemented may yield poor decisions. This, in turn, may breach the long-standing trust of the customers from the system or the firm that could never be regained. Thus, it is imperative for the individuals to ensure that the data being provided is clean and valid.

Data Validationand Cleansing is a critical aspect that is worth putting our efforts upfront. Using data integration technologies, organizations can quickly remove data inaccuracies, standardize on common values and cleanse dirty data to create consistent and reliable information. Moreover, rules are made to incorporate the data transformation process that fastens up the development and implementation of a valid and cleansed data. This creates a unique workflow environment that facilitates easier augmentation of existing data with new information to make the data useful and up-to-date.

There are three core steps that must be followed before validating the usefulness of data. These are:

1. Run the data through a set of validation rules and principles.
2. Analyse the data and find exceptions, if any.
3. Fix these exceptions for proper validation.

Although these three steps are relatively simple and easy to follow for the data managers, in reality, there is a minor step that is often missed i.e. which of these exceptions must be prioritized or fixed first? Surely, not all exceptions are the same. Some may have a much bigger impact on the firm’s underlying objective that needs immediate attention. Hence, it is important to prioritize these exceptions first, before we start fixing them.

In addition to this, having credible information reduces the risk of costly errors and keeps the data accurate and correct. Hence, Data Validation and Cleansing are two methodical disciplines that must be used properly.