How to Maintain Data Integrity During Digital Transformation
Compromised data integrity can completely undermine your software investments and derail your digital transformation. Still, so many companies fail to implement robust data-entry and maintenance procedures.
But what exactly does this entail?
Data integrity is the quality and accuracy of the data you record. Poor data integrity means that an employee has recorded inaccurate data. If incorrect information is used to drive decision making, this can lead to devastating consequences.
Each year 25%-30% of data recorded is found to be inaccurate, which can compromise sales and marketing efforts. Failing to verify the validity of data has a broad effect on wider processes.
To avoid these costly consequences, companies need to start making data integrity a priority. With a little bit of effort, there are some measures you can take to verify data integrity.
1. Use automation to verify data integrity
Human error is inevitable, and it’s easy to make a mistake when entering data manually (particularly if you have 101 other concerns toaddress!). Automating data entry is a much more effective way to enter data at scale.
Forrester has found that business process automation can cut operating costs by up to 90%. Automating data entry can be as simple as deploying a data capture solution which enters data automatically at the start of the pipeline.
With an automated data input process, you won’t have to worry about verifying the accuracy; the data entry solution will do that for you. Automation will not only support data integrity but also free up time to work on more pressing concerns.
2. Promote awareness among managers
Making managers aware of the centrality of data integrity is one of the most challenging parts of achieving it. The better your managers understand why data integrity is important, the more they support the optimization of smaller processes and procedures.
One way to get managers on the bandwagon is to emphasize the performance benefits of correct data entry.
For example, if you are trying to convince sales managers to take data entry more seriously, highlighting that lost productivity and poorly managed leads cost companies at least $1 trillion every year and personally damages their KPIs would clarify why they need to watch out for poor data management practices.
After all, it is the day-to-day processes that will determine the long-term success of your business. A sales team that doesn’t have access to the correct data won’t be as efficient as the one that does.
3. Develop processes based on data integrity regulations
Data integrity is not only important to being productive but avoiding legal fines. PDA suggests that organizations be aware of regulatory requirements and ‘develop site procedures that conform with regulatory requirements’.
No two systems are the same, so it is critical for your organization to be aware of what processes enter data into the system. If you’re using front-end systems to add data to the pipeline, you can make it standard practice to validate inputs to ensure employees enter the correct information.
Building regulatory compliance into your data integrity strategy will make sure that you don’t run into any unexpected fines and conform to the best practices in your industry.
Make data integrity a priority
Incorrect data is a liability. Making sure the data you record is correct is just as important as the digital transformation process. While employees might be tempted to rush through data entry, inaccurate information will cost you in the long term.
It is inevitable that incorrect information will need to be reentered or rolled back at some point.
Instead of seeing digital transformation as an insurmountable challenge, see it as an opportunity to confront your data entry practices head-on. There is no better time to reexamine how to capture your data than when you are reforming your entire technological strategy.