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Data (OR information) flow chain, is the last stage, when you look at:
- How does data flows across different systems, processing points and storage location?
- How does the data change form from the system based data to manual form?
- How does the data undergo change in the structure in terms of getting new fields, getting split and undergoing changes in the fields?
- How does it get processed?
Column analysis and data model analysis, provides more on the 'what' of the data, where as the data flow chain is on 'how' and covers the non-static aspects of the data. Data flow chain analysis provides the following inputs:
- Root cause analysis for data quality: Information flow diagrams, allow you to investigate into the various data processing points.
- Provide inputs on the business rules which can be used in structuring your new applications.
- Provide inputs on the best source to pick your data.
Following are the steps you can use in doing information flow analysis.
This is pretty much similar to the 'Data Flow Diagram (DFD)' creation. DFD is term recognized by anyone who understands system development life cycle.
Clearly define the segments of the data say –
- Customer Data,
- Moneys Data,
- Product order data and so on..
For each segment of data, list out all the fields, which are included in that segment.
For example in ‘Moneys Data’ you will have the-
- Instrument type
- Instrument Date
- Instrument number
- Collection Date
- Banking date
- Date of clearance
- Data of bounce
Start mapping the data flow of the given data segment starting from external input and track each field.
You can start with the source of the data segment, and start mapping the data flow across the processes and applications, including the BI environment. You can refer Data Flow Diagram section in wikipedia. The only difference will be that you will also include the data flow which happens in non-transactional system.
PLEASE REFER Execution-MiHPractice Tool Data Mapping & Assessment Report
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