#1 What is the Business problem?
Make sure to define success as well as the key performance indicators for measuring it.
#2 Where is the data that solves it?
Identify the source system for the data you need. Is it internal or external to the organization? Is it tacit knowledge? Who are the owners, stakeholders, and gatekeepers of the data?
#3 What is the data format?
Data formats will drive the requirements around software, security access, excrpytion, and transformations.
#4 How is the data accessed?
Choose the best tool for the job. If APIs, consider open or closer, available documention, and resources. Direct database connection may introduce complexities. Flat files may already exist, but faise security, efficiency, and functional concerns.
#5 What needs to happen to the data?
Determiine whether you need: application of business rules to exend data; transfer of data from sysyem A to system B; or all of the above. Different systems can be the source of record for specific data.
#6 How often and What trigger?
If one system is pushing the data to another, consider a custom API integration. If one system is pulling the data to another, consider services for frequent requests and scheduled scripts for infrequent.
#7 Which software is best?
Ensure language and framework work with existing systems, and in-house team can support them. Decide where the code will live, considering how disparate security policies will impact integration.
#8 Is the loop closed?
Feedback loops facilitate data quality enforcement. Error handling should be succinct and actionable.
#9 Is there consensus for the solution?
Formerly document Source Field, Source Example, Destination Field, Destination Example, and Business/Transformation Rules. Seek consensus and sign off.
#10 How will the results be measured?
Write test plans before you start development. Prepare for data quality checks throughout the development process.
The hard part is over so now it's time to do this integration.
Testing is critical in data inegrations, but the first to be sacrificed. Data exceptions are introduced when using real data. The longer the testing period, the more permutations will be found.
The compilation of project artifacts ans system documentation are key to a smooth hand off. A period of hyper-care where support staff work closely with developers is helpful
Leverage new information to make better decisions.