Data integration efforts with an advancement Constituent Relationship Management (CRM) implementation often accompany adding additional tasks to your project. But this approach often confuses and wastes time and effort because resources are moving in opposing directions.
This is why a data integration strategy is a must for CRM advancement implementation. Your data integration strategy requires advanced planning and discussion. Here’s what to consider.
First, decide on the platform for your data integration. This is especially important in today’s cloud-based atmosphere. The old method of point-to-point integration (extracting specific data types from one system and transporting it to another) is no longer viable.
Your institution must choose a platform to facilitate your exchange of data between one system and another. Popular data integration platforms include:
- Workato: A low-code/no-code platform enabling business and IT teams to build integrations at five times the speed of typical integration platforms.
- Informatica: Often used by larger institutions, Informatica transforms data from binary to the extraordinary with their Intelligent Data Management Cloud™ (IDMC).
- Jitterbit: Another low-code integration platform that connects systems and processes, including your SaaS, on-premises applications, and cloud applications, in days versus months.
An integration platform is a new paradigm. A data integration platform eliminates the need to support platforms individually and enables knowledge transfer more seamlessly.
After deciding on your data integration platform, establish data quality and integration standards. Your standards identify what systems hold the most accurate information for data types.
- A single system of record or master for points of data
- Hierarchy system that holds the most accurate system for data
Both possibilities should be discussed and decided upon upfront.
The second element of your standards to establish is practice and exception handling. Building a data integration solution for your advancement CRM is a necessity—things will go wrong, and errors will occur. Data quality standards for exception handling indicate:
- Who will be notified when exceptions occur; and
- Who will be responsible for handling this in a timely manner?
Finally, you’ll need to identify guidelines for data integration. Your data integration guidelines should not be created from scratch each time you want to move data from your advancement CRM platform to another system.
To achieve this, include a technical lead or architect who’s responsible for being the “guide.” This guide orchestrates repeatable components to build data integration procedures—helping you create a solution for today and beyond.
For example, you want to move revenue or gift information from the advancement CRM system into the financial system because that’s where the closing of the books is completed. So you built your entire data integration solution to work for that sole purpose. But this creates missed opportunities to instruct similar data points for future integrations—like exchanging data with your new email marketing platform.
A better approach would be to architect a data integration strategy with common standards and methodologies that can be repeated. Here’s how to do that.
- Establish a unique identifier. If a unique identifier isn’t available, combine data components to make each record unique. Use data components that are tightly secure but somewhat centralized in who can update them—otherwise, this requires frequent auditing.
- Find the most reliable data point. This data point should identify a matching record between the two systems. You should not create a matching point each time you want to implement a new data integration between two systems.
- Match records. No matter what integration you’re doing, you must match the record between one system and another to avoid inconsistencies. For example, if the advancement CRM uniquely identifies funds, the finance system must also know how to identify the fund. Otherwise, when you transfer data, the new system won’t know what to do. Additionally, suppose you move information from your advancement CRM into your email marketing system, but both systems don’t recognize this information. In that case, you risk contacting the wrong constituent, i.e., John Doe vs. Jonathon Doe.
Building a data integration solution for today and beyond requires architecting and establishing repeatable solutions.