Data PrivacyData Mapping

March 1, 20230


The most essential part of any data management process is Data Mapping. Data Mapping is the process of locating the data which is disbursed throughout the several databases of any organization and connecting one database to another. One of the main aspects in Data Mapping is quality because, if Data Mapping is not properly completed, the data may become corrupted as it moves from one database to another.

Data Mapping is crucial to the success of many data processes. One misstep in data mapping can ripple throughout the organization, leading to replicated errors, and ultimately, to inaccurate analysis.

The Data Mapping process has several facets and is dependent on the progressive system of the information being mapped and exactly like how the structure of the source and the objective differ. Regardless of whether a business application is on-premises or in the cloud, it uses data to define the information fields and attributes that define the information, as well as semantic standards that regulate how the information is stored within that application or vault.

Data mapping is important for an organization to understand how the data that they hold relates to other elements or functions of the organization, identifying key data sources and personnel responsible for it and identifying any gaps or risks and mitigating these gaps or risks in order to comply with the relevant data protection regulations such as the General Data Protection Regulation (hereinafter referred to as “GDPR).


Data collected from various internal and external sources must be combined and modified into a configuration suitable for the operational and expository operations in order to be used correctly and to benefit from it. Furthermore, data integration, migration, transformation, and warehousing are the important tools of the information operations that depend on the Data Mapping process.

  • Data Integration: Data mapping can automate data integration by establishing linkages between data sources, allowing diverse systems to successfully share and use data. Data mapping, for example, can connect data from a Customer Relation Management system to an Enterprise Resource Planning system, allowing businesses to receive a more comprehensive perspective of their customers and financial performance. The data can also be standardized and integrated from many sources, resulting in a uniform      perspective of the data that can be used for decision-making, hence boosting operational efficiencies and bottom-line results.
  • Data Transformation: Data transformation involves transferring information from one format to another. Data cleansing techniques include transforming data types, eliminating nulls and duplicates, aggregating data, enriching the data, and performing additional changes.   The Data Map contains the transformation formulas mentioned above. The data map utilizes the transformation formulas to transform the data as it is transported in order to put it in the proper format for analysis.
  • Information Warehousing: Data is typically stored in a data warehouse when the intention is to use it as a single source for analysis or other purposes. The data used for queries, reports, and analyses comes from the warehouse. The transferred, integrated, and transformed data is already there in the warehouse. Data mapping makes sure that once data enters the warehouse, it gets to its designated location.
  • Data Quality:  Data Mapping can assist in identifying and correcting data inaccuracies or discrepancies. When data is mapped between two systems, for example, any differences in the data in the two systems can be recognized and fixed before the data is integrated. Data mapping can standardize data, increase data accuracy and dependability, and make it easier to comprehend, manage, and use by mapping it to a common data model or standard format. Data mapping can also aid with data validation by ensuring that it adheres to a predefined format or falls inside a predefined range of values. It guarantees that the data is correct and full, therefore boosting the overall quality of the data.
  • Data Governance: Data Mapping assists companies in improving data governance by offering a means of understanding the data and its relationships. Identifying data lineage, categorizing data, securing sensitive data, maintaining data quality, and meeting regulatory requirements are all part of this process. Data Mapping assists in identifying data items, attributes, and their relationships by building a correspondence between multiple data sets, which supports data governance objectives. Data mapping can also be used to identify sensitive data and ensure that it is securely kept, transferred, and used.
  • Saves Time and Costs: Data Mapping helps organizations to combine data from several sources effortlessly, decreasing the need for manual data entry. Data mistakes can be easily identified and repaired, decreasing the need for human error checking and correction while saving time and money. Data Mapping can also assist in meeting compliance standards, lowering the risk of non-compliance penalties and so saving money.
  • Compliance: Data Mapping can help to increase compliance by ensuring that data is in the proper format and structure for the intended usage. Data Mapping can help assess the format, accuracy, and completeness of data, as well as other compliance-related attributes. It also allows firms to trace data changes, which aids in auditing and compliance reporting.


  • Complex Process:  Companies struggle to keep up with the scale of their data environments in today’s complicated world. Database systems must create Data Maps that are both strategic and systematic. Information Technology teams require rigorous planning, the appropriate technologies, and a well-defined Data Mapping roadmap. The Data Mapping process can be overwhelming if these competencies are lacking. Automatic software solutions are required to be a part of the current approach.
  • Lack of Trust: An insight to end-to-end data changes and modifications is needed. Without it, it is difficult to secure the data or develop a Data Mapping strategy. Data formats and definitions generated by applications may differ. As a result, the users won’t be able to rely on your data transformation.


Companies practice Data Mapping as the first step on their data modernization journey. Data stewards leverage data mapping techniques to equalize data. It’s an important phase before you can analyse data for business insights and decision making.

 Mapping is usually a resource-intensive endeavor that necessitates hands-on development, assessment, and awareness of all sources and targets. However, human intervention is required for mapping design and map validation. Commercial and open source mapping tools with varied degrees of automation can help with the process. Lastly, manual review is necessary to map the areas that failed automated mapping and to check the results of automated mapping.

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