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7 Steps to Overcome Data Integrity Challenges in Business

data integrity: steel bridge representing structural integrity

data integrity: steel bridge representing structural integrity

 

Your data is valuable. It’s your key to unlock insights about your customers and your business. But if your data is inconsistent, incomplete, or inaccurate, it’s not as valuable as it could be. When you have data integrity, which means that your data is as “clean” and consistent as possible, you know that data can support accurate, informed decision making. But data integrity challenges  can crop up. Read on to learn about the biggest data integrity challenges and how you can overcome them.

 

The 7 biggest data integrity challenges in business

 

1. Data that’s not fully integrated

Data integration is the work of combining all of the different data sources from all of the systems used within an organization so that analysts, users, and applications are working from the same data. It’s about creating a single source of truth for our data. But if data isn’t fully integrated, if this single source of truth doesn’t actually exist, then employees are potentially working with inconsistent data. Making sure your data is fully, properly integrated (and transformed) is necessary to protect your data integrity.

 

2. Siloed analytics efforts

Maybe your organization has made acquisitions over the years, resulting in a mix of analytics tools. Or maybe different departments have just always done their own thing. Either way, if your business has multiple analytics tools in use, then you could be duplicating efforts or generating contradictory results.

 

3. Manual processes

If your employees are still relying on manual processes for data collection, they’re probably spending too much time doing it. And they could be unintentionally compromising your data integrity. Even as many organizations strive for automation, lingering manual processes are common. But there’s simply too much error potential in manual processes. It’s important to work with your employees to eliminate as many manual processes as possible.

 

4. Improperly maintained audit trails

A key component of data integrity is making sure you can track the changes to your data. An audit trail will show you what changes have been made, who made them and when they were made. These audit trails can help you undo changes if you discover that they were made erroneously. Many organizations set up audit trails for their data, but then just forget about them. Audit trails don’t do any good if they’re not being reviewed regularly.

 

5. Lingering legacy systems

Everyone can agree that Microsoft Excel is a great spreadsheet application. But it was never meant to be used for comprehensive data management or analytics. We all know that one person who can’t let go of their Excel. But it’s time to move on from legacy systems. Modern organizations are taking advantage of the cloud, where data can be integrated, centralized and made available for use by analytics tools. Many data products, including Matillion, have connectors to Excel and legacy databases that can bring that data into the cloud where you can prepare it for analytics and combine it with other data.

 

6. Lack of training

User error is a common problem that can impact data accuracy. If users have been trained inconsistently, or not trained at all, they can be introducing errors into your data.

 

7. Lack of aoccountability

When multiple teams or departments are relying on the same data, it’s not always clear who is ultimately responsible for the integrity of that data. This can sometimes lead to finger-pointing, and that never helps anything. A better approach is to designate people as data stewards who are responsible for data integrity, among other things. (See tip #4 below.)

 

7 Steps to overcome these data integrity challenges

1. Invest in your data integration efforts

Data integration requires time and resources. It’s worth the effort to make sure that your employees are all accessing the same, consistent sets of data. Data integration also provides an opportunity to cleanse your data as you’re moving it. An ETL application can be helpful here. The transform step in the ETL process can be used to detect and remove or repair invalid, duplicate, or inconsistent data. As data volumes increase and there are increasing types of data within our organizations, the data cleansing process is essential for making sure that employees are using clean, accurate data for their analysis and decision making.

 

2. Train your employees regularly

Hold regular training sessions with your users on how to interact with data properly to minimize errors that can degrade data accuracy and integrity. Make sure that employees are entering and maintaining data properly and consistently. Training also helps keep employees invested in overall data quality and data integrity throughout the organization.

 

3. Use validation processes

Implementing proper validation processes during data entry can improve data integrity. For example, using field types ensures that the data is a number if it’s supposed to be a number, and text if it’s supposed to be text. Using drop-down lists or multiple choice for fields also ensures that the information entered is free from errors.

 

4. Establish data stewards

A data steward is responsible for the management and oversight of an organization’s data or a specific set of data. In addition to helping with data integrity, the data steward can help make sure that the data is high quality and that users can access it when they need to. A data steward can also be responsible for monitoring audit trails and taking action if the audit trail reveals any questionable activity.

 

5. Automate where possible

Eliminating manual processes wherever possible is a great way to support data integrity. Automating your data collection efforts can save time and reduce errors.

 

6. Commit to regular testing

You don’t have to guess about the state of your data if you implement regular testing processes to confirm data integrity. Testing is particularly important after transferring data from one location to another. Using commercial ETL software can also help make sure that your data transfers are complete and that data wasn’t lost or corrupted.

 

7. Stay on top of the basics

This may seem obvious, but it’s always important to stay on top of basic IT maintenance such as keeping your antiviral software up to date and maintaining data security. Viruses can result in all kinds of damage, including data loss. And while data security and data integrity are not synonymous, you can’t have data integrity if your data isn’t secure.

 

Want to learn more about data integrity?

Given the increasing volume and complexity of data, and the speed and scale needed to handle it, the only place you can compete effectively—and cost-effectively—is in the cloud. Matillion provides a complete data integration and transformation solution built for the cloud.

Only Matillion is built to support the major cloud data platforms –Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Delta Lake on Databricks – to help enterprise companies and their data teams work faster and more effectively with data.

Request a demo to learn more about how you can unlock the potential of your data with Matillion’s cloud-based approach to data transformation.