Definition: Data Inconsistency

What is data inconsistency?

Data inconsistency is the lack of standardization and uniformity in data across different sources, systems, or formats. This results in different data sources containing conflicting, inaccurate, or incomplete information about the same entity or attribute.

How does data inconsistency happen?

Data inconsistency is typically caused by a number of issues, such as:

• Human error: This happens when the person entering the data makes a mistake, such as typing in the wrong value or misspelling a word. This is a common cause of data inconsistency, especially when there is a massive amount of data to be manually input.
• Lack of standardization: Different individuals or teams may have their own way of formatting data, but there isn't a standard format that everyone has to follow. For example, one person may use "USA" to indicate the United States, while another may use "US" or "United States".
• Multiple data sources: Data is collected from multiple sources, such as suppliers or social media analytics, that have different data formats or standards. For example, one source may use a different currency than another or have a different naming convention for products.
• No synchronization between internal data sources: Different departments within a company may store their data within their own systems. Without any integration to facilitate communication between one system and another, any updates to the data within a system will leave the others with outdated or conflicting information. For example, a customer's address is updated in the Marketing team's system but not in the Sales team's system.
• Lack of data governance: There are no data governance policies and procedures in place. As a result, there is no standardization or accountability for data management.

What are the consequences of data inconsistency?

Data inconsistency can have several negative consequences for companies, including:

• Inaccurate decision-making: With inconsistent data, it becomes difficult, if not impossible, to correctly identify trends or patterns in the data.
• Reduced productivity: Employees will need to spend time identifying and cleaning up data inconsistencies, which could be better spent on more value-adding tasks.
• Run afoul of regulations: Regulatory bodies are extremely strict when data privacy, security, and accuracy is concerned. If regulations and laws are violated due to data inconsistency, it can result in hefty fines and damage to a company's reputation.
• Damaged reputation: Incorrect or misleading information shared with customers or stakeholders can damage a company's reputation and erode their hard-earned trust.
• Increased cost: The act of cleaning up data inconsistencies may require significant effort, time, and resources, especially when inconsistencies are widespread and deep-rooted. The longer inconsistencies go unnoticed, the more difficult and expensive they are to correct, especially if external parties need to be involved.
• Missed opportunities: Without the correct data on hand, companies may not have a clear picture of their customers or the market, and thus may not have a solid foundation for any Marketing or Sales campaigns. This means they will not be able to seize any opportunities for growth and innovation or jump in to capitalize on any rising trends.