Data Mapping Automation Software text file to Excel

What is Data Mapping?

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

Data mapping is the lifeblood of any data integration process. Without a proper data mapping strategy, data transformation logic, filtration errors can occur which leads to poor quality data. This directly impacts business analysis, forecasting, and business decision-making. Therefore, it is crucial to maintain integrity throughout the data mapping process.

Nearly every enterprise will, at some point, move data between systems. And different systems store similar data in different ways. So to move and consolidate data for analysis or other tasks, a roadmap is needed to ensure the data gets to its destination accurately.

For processes like data integration, data migration, data warehouse automation, data synchronization, automated data extraction, or other data management projects, quality in data mapping will determine the quality of the data to be analysed for insights.

Enterprise data is getting more dispersed and voluminous by the day, and at the same time, it has become more important than ever for businesses to leverage data and transform it into actionable insights. However, enterprises today collect information from various data points, and they may not always speak the same language. With an efficient data mapping tool, the data mapping process is used to integrate all the disparate data sources and make sense of them.

So, what is data mapping? In summary, data mapping is defined as the process of establishing relationships between separate data models from disparate sources or systems. Let’s understand the data mapping process in detail.

What is Data Mapping? Definition of Data Mapping and Examples

Whats is ETL?

ETL is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. It’s often used to build a data warehouse.

What is ETL and how it works?ETL stands for “extract, transform, and load.” The process of ETL plays a key role in data integration strategies. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.

What is ETL example?The most common example of ETL is ETL is used in Data warehousing. User needs to fetch the historical data as well as current data for developing data warehouse. The Data warehouse data is nothing but combination of historical data as well as transactional data… Then that data will be used for reporting purpose.

Data mapping is the process of extracting data fields from one or multiple source files and matching them to their related target fields in the destination. Data integration or ETL mapping helps consolidate data by extracting, transforming, and loading it to a data warehouse. The initial step of ETL is data mapping. This mapped data can then be used for producing relevant insights that can improve business efficiency.

Basically, the data mapping process is where source data is directed to the targeted database. The target database can be a relational database, or it can be a CSV document – it depends on the user’s choice. In most cases, a data mapping template is used to match fields from one database system to the other using an appropriate database mapping software.

Data mapping is the key to data management

Data mapping is an essential part of many data management processes. If not properly mapped, data may become corrupted as it moves to its destination. Quality in data mapping is key in getting the most out of your data in data migrations, integrations, transformations, and in populating a data warehouse.

Data migration

Data Migration is the process of moving data from one system to another as a one-time event. Generally, this is data that doesn’t change over time. After the migration, the destination is the new source of migrated data, and the original source is retired. Data mapping supports the migration process by mapping source fields to destination fields.

Data integration

Data integration is an ongoing process of regularly moving data from one system to another. The integration can be scheduled, such as quarterly or monthly, or can be triggered by an event. Data is stored and maintained at both the source and destination. Like data migration, data maps for integrations match source fields with destination fields.

Data transformation

Data transformation is the process of converting data from a source format to a destination format. This can include cleansing data by changing data types, deleting nulls or duplicates, aggregating data, enriching the data, or other transformations. For example, “Illinois” can be transformed to “IL” to match the destination format. These transformation formulas are part of the data map. As data is moved, the data map uses the transformation formulas to get the data in the correct format for analysis.

Data warehousing

If the goal is to pool data into one source for analysis or other tasks, it is generally pooled in data warehouse. When you run a query, a report, or do analysis, the data comes from the warehouse. Data in the warehouse is already migrated, integrated, and transformed. Data mapping ensures that as data comes into the warehouse, it gets to its destination the way it was intended.

What are the steps of data mapping?

Step 1: Define — Define the data to be moved, including the tables, the fields within each table, and the format of the field after it’s moved. For data integrations, the frequency of data transfer is also defined.
Step 2: Map the Data — Match source fields to destination fields.
Step 3: Transformation — If a field requires transformation, the transformation formula or rule is coded.
Step 4: Test — Using a test system and sample data from the source, run the transfer to see how it works and make adjustments as necessary.
Step 5: Deploy — Once it’s determined that the data transformation is working as planned, schedule a migration or integration go-live event.
Step 6: Maintain and Update — For ongoing data integration, the data map is a living entity that will require updates and changes as new data sources are added, as data sources change, or as requirements at the destination change.

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