Is your business doing everything it can to ensure data consistency? Data consistency is vital to the success of any business, especially those that use databases to store, move, and access data. But, unfortunately, many businesses don’t do everything they can to ensure data consistency.
Inconsistent data can wreak havoc on business operations and lead your organization to make poor decisions that are counterintuitive or even damaging to your business goals. Consistent data is the backbone of data quality.
Data integrity has a significant impact on business operations and performance. However, data integrity might not get a second thought from your organization until inconsistent data hurts your business.
This post will explain data consistency. We will explore the different types of data consistency and share ways your organization can ensure data consistency in its databases and operations.
What Is Data Consistency?
Data consistency refers to whether the same data stored in different locations matches. Data consistency is an essential aspect of data quality. In modern business, data is everywhere, and without database consistency, opportunities will be missed and mistakes will happen.
When your organization has the same data across all systems, databases, and applications, it has consistent data. Inconsistent data leads to incorrect analysis, poor decision-making, and adverse business outcomes.
Many organizations have data stored in multiple different locations. As a result, data consistency becomes critical to business operations and success. For example, imagine your sales team operating with different data than your operations team.
In this example, data inconsistency can lead to missed opportunities, errors in service, and even costly rework. On the other hand, data consistency ensures that everyone in your organization is operating with the same information.
There are three main types of data consistency:
- Point-in-time consistency
- Transaction consistency
- Application consistency
Point-in-time consistency ensures that all data within a system is uniform at a specific time. This type of data consistency ensures that data is not lost during system crashes, improper shutdowns, and other network problems.
Without point-in-time data consistency, there could be no guarantee that all data on a crashing system could be restored to its pre-crash state. Point-in-time consistency utilizes timestamps and other data consistency markers to restore data to a specific moment in time.
Transaction consistency ensures the consistency of data across a working transaction. Database transactions occur regularly when a computer system communicates with another application or program. They are most commonly used in banking, but any data exchange can be considered a transaction.
Throughout the entire transaction, data must remain consistent to ensure that valid data is being entered into the system. For example, if you are using a banking program for a transaction, it will first request your account balance.
If the account balance data is changed at any point throughout the transaction, there is no transaction consistency, and nothing entered into the program remains reliable. From a business perspective, inconsistent data can cause serious operational issues if you are executing multiple transactions.
Application consistency is essentially just transaction consistency but between computer programs. Poor data quality and consistency between linked applications or programs can cause the same problems as transaction inconsistencies.
If data does not remain consistent between applications, there will be no way for your business to trust the information being delivered to its systems.
How To Achieve Data Consistency
Now that you know more about data consistency, you are likely wondering how to ensure that your organization’s data is consistent. There are several ways that your organization can achieve data consistency. The most effective ways to drive data consistency include the following:
- Data integration
- Data validation
- Strict data entry policies
One of the most effective ways to ensure data consistency is to combine it all in a unified view. Data integration is helpful for big data analytics and can help your organization achieve consistency.
Using data integration tools can help your organization ensure that data from multiple sources remain consistent across systems and databases. In addition, data integration will help your leaders get a clear and comprehensive understanding of your organization’s data.
Data validation ensures that data is checked against quality standards to ensure accuracy, consistency, and completeness. If you want to be sure your data is consistent, data validation is an effective way to do so.
While manual data validation is an option, some tools make validating large amounts of data easier for organizations. The most effective data validation strategies combine manual review and automated tools to ensure nothing is missed.
Strict Data Entry Policies
Enforcing strict data entry policies is an effective way to ensure data consistency. When you develop strong policies, your employees are more likely to enter data into organizational systems correctly, ensuring greater accuracy and consistency.
You should create robust data entry policies and regularly train employees on the data entry policies and educate them on the importance of data consistency. In addition, policies should be reviewed regularly to ensure they are operating at their most efficient.
Data consistency is critical when your business relies on data. Therefore, doing everything you can to ensure data consistency across applications, databases, and systems is essential.
There are several steps your organization can take to promote data consistency and integrity. If you want to learn more about data consistency and discuss ways to drive data quality, contact a big data expert like Koombea to help your organization.