Focus on data mapping: optimizing your data landscape

Data mapping enables the effective organization and use of information - which is crucial to a company's success.

Data mapping is a practical method for understanding, integrating and optimizing data sources. 

In this blog article, you will learn how data mapping helps companies to use their data volumes to create value. From identifying relevant data sources to defining mapping rules and implementing practical solutions - we take a look at how data mapping paves the way for efficient data management. 

Data mapping - definition

data mapping definition

Data mapping is a process in which data fields from different data sources are identified, linked and assigned. The aim is to obtain a clear overview of the data flow and structure. 

Data mapping enables companies to carry out data integration, migration and analysis more efficiently. 

It enables the transformation of data between different formats and schemas. The accuracy of the mappings is crucial to ensure consistent and high-quality information. 

Data mapping is a key element for the optimization of business processes and the effective use of data resources.

The importance of data protection and compliance

Data protection and compliance are crucial for data mapping, as this process involves identifying, mapping and linking data fields from different sources. Compliance with data protection laws such as the GDPR is essential to protect the privacy of individuals. 

Data mapping must ensure that companies process personal data in accordance with the principles of data minimization and purpose limitation.

The rights of the data subjects, such as the right to information and deletion, must be respected during data mapping. Data protection regulations also require appropriate security measures to ensure the confidentiality and integrity of the data. The integration of data protection and compliance into the data mapping process serves to minimize risk, as violations can lead to legal consequences and financial penalties.

Industry-specific standards, such as in the healthcare sector (HIPAA) or finance (PCI-DSS), companies must also pay attention. By taking data protection guidelines into account, they promote data quality and integrity. Data mapping enables companies to process data correctly while ensuring compliance standards are met.

To summarize: Data privacy and compliance are essential to ensure that data mapping is done both ethically and legally. Integrating these principles into the process helps to minimize data protection and compliance risks while ensuring the quality and integrity of the processed data.

data mapping benefits

Advantages of data mapping

The use of data mapping offers the following advantages:

  • Improving data quality and consistency
  • More efficient decision-making thanks to clear data structure
  • Support in complying with data protection regulations
  • Risk minimization through legally compliant data processing
  • Increased efficiency in the analysis and use of data resources
data mapping process

Data mapping process - procedure

The data mapping process involves several steps aimed at identifying, mapping and linking data fields from different sources. Here is a description of the typical process:

Definition of the goal

  • Clearly define why data mapping is being carried out (e.g. data integration, migration, compliance).
  • Determine which data sources and targets are to be included.

Example: A company is planning to introduce a customer relationship management (CRM) system. Data mapping is to be used to integrate customer data from different departments into a central database.

Identification of data sources

  • Capture all relevant data sources, regardless of their origin (databases, files, APIs). You can find more information on exactly how this works in our article on Data Wrangling.

Example: Data sources can be internal databases, Excel files with sales information and external APIs from marketing platforms.

Create data inventory

  • Create a comprehensive inventory of all existing data fields in the identified sources.

Example: The data inventory could contain a list of customer attributes such as name, address, purchasing behavior and contact history.

Definition of data targets

  • Clearly define which data is required in the target sources.
  • Determine how data must be transformed or converted.

Example: The aim could be to create a uniform customer data profile that contains all relevant information for personalized marketing campaigns.

Develop mapping rules

  • Define rules for how data fields are mapped between sources and destinations.

should be.

  • Consideration of data formats, types and structure.
  • If discrepancies or errors need to be corrected during the mapping process, it is a good idea to use tools from the Data Cleaning to be used.

Example: Rule - The name from the sales database is matched with the name from the CRM API to ensure that they are consistent.

Carrying out the mapping

  • Actual assignment of data fields according to the defined rules.
  • Use of tools or manual processes, depending on complexity.

Example: Actual allocation of customer information according to the defined rules in order to create a consistent data structure.

Validation and quality assurance

  • Checking the accuracy of the data assignments.
  • Ensure that the transformations meet the requirements.

Example: Checking that all customer data has been correctly assigned and ensuring that all necessary transformations have been carried out successfully.

Implementation

  • Implementation of the data mapping rules in the corresponding systems or processes.
  • Ensure that the data flows in accordance with the specifications.

Example: Implementation of the data mapping rules in the new CRM system to ensure that all relevant data flows accordingly.

Documentation

  • Creation of comprehensive documentation of the data mapping process.
  • Including the rules, sources, targets and transformation requirements used.

Example: Creation of comprehensive documentation that records the rules, sources, objectives and transformations used for future reference.

Monitoring and maintenance

  • Continuous monitoring of data flows and integrity.
  • Adaptation of data mapping rules in the event of changes to data sources or targets.

Example: Continuous monitoring of data flows to ensure that data mapping remains effective even in the event of future changes in sources or targets.

This process ensures that data mapping is carried out systematically in order to integrate or migrate data correctly, consistently and in a targeted manner.

Tools and techniques for data mapping

Various tools and techniques play a central role in the application of data mapping in order to efficiently design the process of identifying, assigning and linking data fields. 

The selection of the appropriate tools depends on the specific project requirements, including the complexity of the transformations, type of data sources and available resources. 

These are the key tools and techniques:

  • ETL tools (Extract, Transform, Load): Support the extraction, transformation and loading of data between different sources and destinations.
  • Data integration platforms: Offer comprehensive solutions for the integration of data from different sources.
  • Mapping tools: Enable the visual definition of data assignments without extensive programming knowledge.
  • Databases and SQL: Allow direct data manipulation and queries for mapping data fields.
  • APIs (Application Programming Interfaces): Provide direct access to data sources and targets for integration and mapping.
  • Scripting languages: Enable the program-controlled implementation of data mapping for specific adaptations and complex transformations.
  • Data profiling tools: Assist in analyzing data sources to identify patterns and quality issues.
  • Data Governance Tools: Assist in documenting, monitoring and ensuring data quality during the mapping process.

The effective use of these tools and techniques not only ensures a smooth data mapping process, but also helps to improve data quality, integration efficiency and compliance. The selection should be based on the specific requirements of the project and the resources in the company.

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Data mapping Use Cases

Below you will find 4 different use cases that show you how you can apply data mapping in practice.

Use Case 1 - Automated assignment of e-mail attachments

Konfuzio is an AI platform for Intelligent document automation. The platform reads and understands unstructured dataenables the automatic processing of documents and accelerates processes via hybrid multi-cloud infrastructures. With functions for Low-Code- and per-code workflows, Konfuzio offers adaptive AI for various application areas.

Use Case

Problem:

Every day, a company receives a large number of emails with various attachments such as invoices, orders and contracts. Manually extracting and assigning this information is time-consuming, error-prone and hinders efficient workflows.

Solution:

Konfuzio's Document AI, in conjunction with data mapping, enables the Automatic extraction of relevant data from e-mail attachments. By identifying and assigning the data fields, the information can be precisely extracted and transferred to the relevant systems.

Example:

An e-commerce company receives numerous orders by email every day with attached invoices and shipping details. 

Konfuzio automatically recognizes the relevant information, extracts order numbers, item descriptions and prices and transfers them directly to the inventory and accounting system. 

This significantly optimizes the order processing procedure and minimizes errors.

Use Case 2 - Data integration for customer service optimization

Problem:

A company has customer data in different departments and systems (sales, marketing, support), which leads to inconsistent customer profiles. This affects the efficiency of customer service and the ability to offer personalized services.

Solution:

Data mapping is used to identify, assign and link data fields from different sources. Uniform customer data profiles are created, which optimizes customer service and enables personalized offers.

Example:

A customer calls customer support. Data mapping gives the support agent immediate access to a unified customer profile containing information on previous purchases, support tickets and marketing interactions. This enables efficient and personalized customer service.

Use Case 3 - Data migration for system consolidation

Problem:

A company has several legacy systems with different data formats. When consolidating into a central system, difficulties arise during data migration due to inconsistent data structures and missing assignments.

Solution:

Data mapping enables the identification of data fields in the legacy systems and their assignment to the new central system. This facilitates smooth data migration and ensures the integrity of the data.

Example:

The company is migrating from an older ERP system to a modern central system. Data mapping ensures that all relevant information, such as customer master data and product inventories, is seamlessly transferred to the new system without data loss or inconsistencies.

Use case 4 - Data protection compliance through data classification

Problem:

A company processes personal data and there is a need to comply with data protection laws such as the GDPR. However, there is a lack of clear allocation and documentation of the processed data.

Solution:

Data mapping helps to identify and classify personal data and document its processing. This supports compliance with data protection regulations and enables transparent data management.

Example:

A customer requests the deletion of their personal data in accordance with the GDPR. Data mapping allows the company to quickly and accurately identify where the customer data is stored and ensure that it deletes the data in accordance with legal requirements.

Conclusion - Practical solutions for efficient data management and compliance

By clearly assigning and linking data fields from different sources, data mapping not only enables smooth integration and migration, but also plays a crucial role in adhering to data protection regulations and compliance. 

The systematic application of data mapping enables companies to understand and optimize their data landscape and make informed business decisions. From the identification of relevant data sources to the implementation of mapping rules and continuous monitoring, data mapping offers a comprehensive approach to data-driven excellence. 

Do you have questions about optimal data mapping for your company? Get in touch with us and discover how Konfuzio can help you organize your data efficiently and process it for maximum benefit.

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