Master Data Management

What is Data Validation?

Deliver Accurate Data to Stakeholders

Data validation refers to a number of automated, rules-based processes that identify, remove, or flag inaccurate or anomalous information, leaving behind a clean data set for consideration by the end user. Data validation has grown increasingly important as businesses, governments, and other organizations collect information from various sources. Confirming the accuracy of these details is a pillar of master data management, and ensures that analytics are informed by meaningful information.

Why Data Validation Matters to Businesses

Data validation is a necessity for both small businesses and multinational corporations. If there aren’t data checks in place, inaccuracies can cause a cascading effect that ends in missed opportunities and lost revenue.

Businesses must have confidence in the data underpinning their marketing and sales strategies. With so many sources of information available to management, data quality is essential to reaching conclusions based on sound facts and figures. After all, your business analytics reports are only as good as the data that goes into them.

Business Development & Data Validation

Business development professionals depend upon reliable data to build and maintain accurate sales leads lists. It’s impossible to keep the sales pipeline full if you’re constantly calling disconnected phone lines or messaging deleted email addresses.

Business contact information that has gone through the data validation stage will have been checked against various databases. Anomalies will either be dismissed as unreliable, or brought to the attention of a human being for review.

Data validation isn’t just instrumental in making contact with clients. Good data is needed at any stage of a business relationship. Inaccurate corporate hierarchies, outdated executive rosters, or conflicting financial details all make it more difficult to serve the customer or manage your own business risks.

It’s simply not feasible for employees to manually verify countless details on a regular basis. Data validation software works in the background to make sure all decisionmakers are presented with reliable information whenever they need it.

How Data Validation Works

There are a number of ways to examine data sets for errors and anomalies, but all depend upon preconstructed validation rules. These rules set expected parameters for data, and they vary widely in complexity based upon the needs of the user. Here’s a list of common data validation rules, and how they can apply to a business.

Cross-reference Validation
As the name implies, this validation rule compares incoming data with a trusted database. If newly ingested data doesn’t match what’s on file, it can either be rejected or set aside for review. Cross-reference validation is especially useful when assembling leads lists or trying to confirm other business details.

Data Type Validation
One of the most basic data validation rules enforces the consistency of data types. It’s easy to imagine an employee accidentally entering a dollar amount in an address field. Data type validation would recognize the inconsistency and ask the person to review what they’ve submitted.

Range Checking
We can put constraints on the numeric values that are accepted in a given field. For example, consider a company that has 3 brick-and-mortar stores. When asked to enter her hours, the manager would need to indicate she works at store 1, 2, or 3. If she enters “5,” the application would reject the submission as incorrect.

Structured/Complex Data Validation
Data validation rules are only constrained by the abilities of the person who writes them. Many businesses have a need for complex validation processes that must consider new information in the light of several parameters.

A financial professional might rely upon complex validation rules to determine whether or not a client has contributed the maximum-allowed amount to a certain fund. Simple range constraints might not be enough, as the law could have different requirements based on age, income, or other factors.

Data Quality Control

Regardless of how complex your data validation rules become, at some point they rely upon information provided or verified by humans. It’s important to recognize that introducing inaccurate data to a core database can undermine your validation efforts. It’s not just third-party data providers that present this risk; your own employees can make mistakes. For this reason, it’s important to have a data policy governing who can make updates to databases and validation rules.

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