Data Integration is the combination of data from different sources into one single source of truth. There are a couple of steps to it, beginning with the data import, then data cleansing, and ETL Mapping (breaking down where data comes from to where it ends up). Integration enables your analytic tools to provide high quality, and importantly, correct, effective, and actionable reports.
Most companies already have the data they need but from disparate sources. Data used by companies comes from a CRM, web traffic, sales and marketing, finance and more. To properly analyse that data, it needs to be pulled together into one single source. Without integration, trying to gather enough data for accurate reports would mean going to each data source finding the information and compiling it into one source.
Any data integration will need a comprehensive plan to see the project through from beginning to end. Your strategy needs to account for several different functions. Consider security, disaster recovery and performance. Set your objectives for what you want from the project and the data.
Many companies transform their data before beginning integration, however, if you choose the right platform, it will be easier to upload all the data first and transform it later. The right platform will help users to assess and categorise the data and certain platforms will help users clean the data. This means it can scrub data that isn’t completed, is formatted incorrectly or is duplicated. This has the bonus of meaning everyone in the business has access to the same data when they access a record.
When deciding what data sources to use you need to look at the business case. What are you going to use the data for? Traditional mainframes will always have incredibly important data that is essential for integration. Many companies will also hold data on spreadsheets and across other sources. Different departments will also hold their own data that needs to become part of the whole. You need to consider what if any, external data you will use; e.g., third party analytics that give you customer location.
Integration is a complicated process. Some types of data e.g., data from the mainframe can be challenging because of variable length records and coding associated with them. It’s also difficult to find people with the skills needed to make sense of the complexities and newer technologies.
One of the main reasons to integrate your data is to make the best of systems like a CRM. Having all that data in one place is only beneficial to your business; everyone now has the ability to access the data they need for their jobs. Data integration means you can share the information you’ve gathered on a CRM with ERP and vice-versa. This eliminates mistakes and allows your business to get the best value out of the systems you have. Managing data once you have integrated it is of vital importance. Find out more about how to do that here.
These four points are just the tip of the iceberg when it comes to the best practices you should follow for Data Integration. There is much more to find out. If you or your business are interested in finding out more about Data Integration and the best tools to use, please get in touch. We would love to talk to you about the best practices and how we can help you.