Data Mapping: What It Means and How to Do It?

Back in the day, data mapping was paper-based, and it didn’t really pose any problems for organizations as the amount of data was limited. However, in today’s modern digital era, companies have to handle and process large amounts of data. In order to keep up with the pace of data mappings and constant changes, organizations leverage efficient and robust automated data mapping solutions. They help you significantly reduce the risk of human errors and develop reports that help stakeholders make better decisions on the basis of organized data.

Now you may be wondering what data mapping is, how it works, and its process. Read on to gain a clear understanding of data mapping that will allow you to implement it effectively.

Data Mapping

What is Data Mapping?

Data mapping refers to creating a visual representation of how data flows between different systems and processes within an organization. The map traces the origin source and destination target of data. It also documents any transformations or modifications applied to the data along the way.

In simple terms, data mapping illustrates the journey of data from raw inputs to actionable business insights. It provides visibility into the interconnected data landscape across an enterprise.

The mapping commonly uses flowchart symbology to depict processes, data stores, movements, and dependencies. It serves as a reference model for how information travels through the data architecture.


How Does Data Mapping Help Other Data Management Processes?

Data mapping provides connective tissue between other key data management disciplines. It gives context to enhance the value of efforts like:

Data Governance – The data map acts as a directory to available data assets. It also makes stewardship responsibilities clear by revealing data ownership.

Master Data Management – Master data mappings illustrate the movement of master data between operational systems. This highlights optimization needs.

Metadata Management – Metadata can be embedded in the data map to define the meaning, values, and lineage of data elements.

Data Quality – Data flows reveal processes affecting data quality like ETL routines. Remediation can target points of quality degradation.

Business Intelligence – Data maps enable BI teams to identify datasets for reporting and analytics use cases. They also aid in data warehouse design.

Regulatory Compliance – Mappings help demonstrate compliant data handling as per regulations like GDPR. They also support audits.

Data Migration – Deep understanding of legacy system data structures gained via mapping smoothens migration to new platforms.

What Are the Steps Involved in Data Mapping?

Completing a data mapping initiative involves these key phases:

Plan & Analyze – Define the scope, goal, and level of detail. Take inventory of data landscape components. Identify stakeholders and subject matter experts to engage.

Map Data Stores – Catalog organizational data stores like databases, files, APIs, archives etc. Include name, technology, volume, owner etc.

Map Data Elements – Record core data elements, their attributes, and metadata like definitions. This forms a data element catalog.

Map Data Flows – Illustrate how data moves between sources and destinations. Map process flows and transformations impacting data.

Map Data Relationships – Link related data elements like primary keys and foreign keys across systems. Show data consumption patterns.

Validate & Refine – Review mappings with stakeholders. Refine as needed and validate accuracy.

Publish & Socialize – Make mappings easily accessible via dashboards, portals etc. Communicate value to stakeholders.

Maintain & Update – Treat mapping as a living asset. Update whenever data links change like new systems or integrations.

Dedicated data mapping software like Collibra, Informatica, and Hitachi make this process smoother. But general diagraming tools also work for basic mapping needs.

The Benefits of Data Mapping

Investing in data mapping delivers manifold returns spanning technical, business, and organizational spheres:

Better Data Quality – Identifying quality issues and their root causes enables targeted remediation.

Improved Data Accessibility – Mapping makes users aware of valuable data sources and how to exploit them.

Enhanced Analytics – Directing analytics to the right datasets prevents blind spots and reveals actionable insights.

Lower Development Costs – Data mappings improve project scoping accuracy by revealing data complexities upfront.

Risk Reduction – Improper data handling risks like security lapses or compliance failures can be averted.

Informed Decision Making – Strategic business decisions benefit from complete context gained via mapping.

Agility & Innovation – Speed of response to opportunities improves when data flows and effects are predictable.

Master Data Synergy – Master data harmonization becomes easier when mapping reveals relationships.

Regulatory Compliance – Mappings help demonstrate compliant data privacy and sovereignty practices.

Legacy Modernization – Knowing existing data landscapes helps strategize and prioritize modernization initiatives.

The time and resources needed for mapping pay long-term dividends across operational efficiency, analytics, governance, and innovation.


Data mapping provides a 360-degree view of complex enterprise data ecosystems. Documenting the origins, transformations, and destinations of data assets makes their consumption and governance more effective.

Mapping initiatives do require an upfront investment. But the long-term benefits spanning data quality, regulatory compliance, analytics, and more make it well worth the effort.

With the right scoping, stakeholder participation, and tools, data mapping can be accomplished smoothly. It matures data discipline and minimizes analytical blind spots. For any organization pursuing digital transformation, developing high-quality data maps should be a priority.

Frequently Asked Questions

What are the key benefits of data mapping?

Some of the main benefits of thorough data mapping are identifying data quality issues and pain points, improving data accessibility and governance, enhancing analytics, reducing project costs and risks, supporting decisions with contextual understanding, and demonstrating regulatory compliance.

When should an organization create a data map?

Enterprises should develop data maps whenever major new systems or data sources come online, existing data flows change, or new integration and analytics use cases emerge. Annual or bi-annual map reviews also help keep pace with evolving data landscapes.

What visual notation is used in data mapping?

Data maps typically utilize flowcharting symbology like circles/ovals to denote processes, rectangles for data stores, arrows for data flows, and diamonds for decision points. Standard notations like BPMN can also be leveraged.

Can data mapping apply to both structured and unstructured data?

Yes, data mapping practices work to document relationships and flows for both structured, pre-defined data as well as less governed unstructured data like documents, emails, chats, videos, etc.

How granular can a data map get?

The level of detail captured can vary. High-level maps may focus on major data hubs and flows between departments. More granular mappings may catalogue individual data fields, tables, and attributes. The needs of the business determine optimal mapping depth.

Who are the main stakeholders to engage in mapping initiatives?

Key stakeholders include enterprise architects, data analysts, data owners, systems administrators, business teams, and subject matter experts. Collaboration between IT and business is crucial for complete, validated mappings.

What resources are needed to get started with data mapping?

Core resources required are expertise in data architecture concepts, availability of data inventories and system catalogs, diagraming software, and access to technical and business data domain experts. Budget and time also need allocation.

How often should data maps be updated?

To remain useful, data maps require ongoing maintenance and updating as source systems get retired or replaced, as new data enters the enterprise, as integrations evolve, and as ownership changes. Annual or quarterly map reviews are recommended.

Can data mapping help with digital transformation initiatives?

Yes, by providing insights into legacy data landscapes, dependencies, and gaps, data mapping gives IT teams the context needed to plan and strategize high-value modernization initiatives as part of broader digital transformation.

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