I am a product coach for Data team, who has got engaged in one of the largest data work
What work can we achieve with data mapping?
It is the process of extracting data fields from one or numerous source files and matching them to their associated target fields at the destination.
This process facilitates consolidating data by extracting, transforming, and loading it to a target system.
It is one of the essential steps to figure out how the data that they have relates to other elements or functions of the organization. Finding key data sources and people responsible for it and analyzing any gaps or risks and reducing these gaps or risks in order to adhere to the General Data Protection Regulation (GDPR).
Enterprise data is getting more separated and voluminous by the day, and that is how challenge also expands
Businesses need to leverage data and transform it into actionable insights.
To start this data Mapping work source data needs to be directed to the targeted database.
Types of data Mapping are data Integration, data Migration, Data Transformation and data warehousing
- How will the disparate data be integrated with the new solution?
Companies use a data mapping template to match fields from one database system to the other using a data mapping solution.
- The level of complexity revolves around the data hierarchy and the discrepancy between the data structure of the source and the destination.
- The business application uses metadata to illustrate the data fields and attributes that constitute the data and semantic rules. How easily can we obtain this information?
- Mapping can have a varying degree of complexity, depending on the number, data types, schema, primary keys, and foreign keys of the data sources.
- Manual data mapping is a tedious and messy journey. One mistake in data mapping can generate substantial problems in the coming days. How can we automate the process?
- There are many use cases in which data mapping needs to deal with. How quickly can we discover those?
- If the volume of data is substantial, we require to use an automated tool to manage the work. There is a learning curve involve
- Challenges in understanding data fields like understanding the tables, fields, and format, etc information is time consuming
- Missing accurate map data source to the fields of the destination field
- Data transformation "need-finding" and execution is causing alignment and delay, how can we deal with?
- Testing end-to-end data validation to check if all parameters are working well
- Co-ordination among stakeholders for alignment and data gathering is one of the massive work
- Outdated data to be mapped with new data format, data quality issues
- Risk of data sprawl, that is, the unaccounted spread of special data to numerous systems,
- Defining consistent retention policies to cycle out old information?
- Resolving data model issues or changing data model needs, managing all the stakeholders. how easily can we manage?
- Averting information silos. What process to follow to optimize this?
- Poor data management software and dealing with too much data
- The data management process is outdated and has not been updated for a long time
- Whenever the team is starting this data Mapping, Transformation journey, they first workout on all these issues and address these
What else is required to work smartly in data Mapping work?
