4 Tips for Preserving Data Integrity During Integration

Integrated systems use shared data to fuel analytics, improve big data and AI initiatives, and inform machine learning algorithms. But imagine making major decisions on marketing budgets, sales processes, and customer service programs using completely inaccurate data.
That would be crazy, right? Not that crazy...
A whopping 94% of businesses suspect their customer data is wrong. And, in a recent survey of data executives, 82% cited data quality concerns as a barrier to data integration projects. Solid data integrity is simply not there, and it’s costing businesses big time.
So, what can you do? Take steps to improve and maintain your data integrity for a smoother and more productive integration (and accurate analytics in the long term!). We’ll offer four key tips for improving data integrity in this post.
You may think data quality and data integrity are the same, but they’re actually slightly different. Data quality represents the USABILITY of information to serve an intended purpose. Is it fit for usability in operations, decision making, or planning?
Data integrity is a subset of data quality that focuses more on the RELIABILITY of that information. Is it accurate? Is it valid? Here are the characteristics that embody data integrity:
Data integrity issues during integration aren’t the shortfall of any one thing. They usually stem from an assortment of issues including migration errors that force duplications or unintended formatting changes, security misconfigurations and software bugs, and human errors that either already existed in the database or were accidentally added in during the programming of an integration field. The key to combatting poor data integrity is to stay alert and address all the potential shortfalls.
Data quality and its underlying data integrity must be preserved so your information will correctly inform your major business decisions. As you look to integrate core systems together, keep these data integrity tips in mind to help you.
Guys, I literally cannot stress this enough. Clean your data before you start ANY integration project. Get rid of the duplicates, null values, extra spaces, extra numbers, etc. If you don’t have set rules for how data should be inputted into your systems, you must establish some. Apply whatever guidelines you set consistently across databases as you clean them. So, for example, if you decide that addresses should be written as 123 N. Sesame St. versus 123 North Sesame St., then you must ensure that is reflected across all addresses wherever they exist.
Ever buy a list for marketing or sales prospecting? If you brought in data from an external source, you must validate it. This is true both before integration, and really, just in general. No one wants to inadvertently spam a potential client or have corrupted or incorrect data ruining their analytics after an integration.
Data will be mixing and moving around when you integrate. Reviewing the source data, understanding its structure, and identifying its interrelationships are all part of a complete data integrity process. Data profiling helps you identify the data’s potential for your integration and any anomalies that may affect your data quality. If there are concerns with any aspects of the data or its relationships to other data, they can be addressed prior to testing and loading the data in the target system. It’s also a great way to discover and assess your metadata as it relates to data integrity.
Many integration solutions are ETL based. For example, StarfishETL is an ETL based iPaaS (integration Platform as a Service). ETL stands for Extract – Transform – Load. This process is meant to pull the data from the source system, transform it into a consistent data type, and then load it into the destination system. By testing the ETL you’re making sure the data transfer is being applied as you intended before you do the full data load for the integration. Proper ETL testing is done in ordered stages:
The average business uses 137 unique SaaS apps. Integrating some of those systems together just makes sense!
It helps teams collaborate, it gives analytics and AI a powerful boost, and it reveals new patterns that can be used to scale and grow revenue streams. However, none of this can be achieved if the data you’re using to make those decisions lacks integrity and quality.
Keep data integrity top of mind during your integration and establish a long term plan to maintain it in your organization to truly reap the benefits that integrated solutions can bring.
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