Data-Management

Data mining for insights to help make better management decisions is vital across all industry sectors. However, significant effort is required to use data from legacy systems and disparate data sources in ways that conform to the consistent standard needed to produce useful analysis. Data mapping is the solution that connects these data sources.

Data mapping is the process needed to conform data to applicable standards when the data comes from different sources with different structures.

This guide will teach you the essentials of data mapping, how to use it, and its importance. You will understand the most common data mapping techniques, including manual, semi-manual, and automated processes. You will understand the best practices and get expert data-mapping tips.

Use this guide to quickly get the information you need to take your data mapping efforts to a higher level of sophistication that produces superior results.

What Is Data Mapping? 

Data mapping is the process of matching a data field from one database source to a data field in another database source.

Examples Of Data Mapping 

Suppose you have two databases stored as spreadsheet files from different departments of the same company. The first database identifies a customer by the last name in column one, then the first name in column 2. The second database identifies a customer by the first name in column one, then the last name in column 2.

The data mapping process matches column one last name in database one with column two last name in database two and so forth to be able to merge the two databases and create a useful combined standard.

Data mapping works with multiple datasets and even those that require data conversion to merge. For example, one database may have the state spelled out, such as California, and another one may use CA for the state. In a data mapping project, you may set the parameters for the merge to end up with the format you prefer.

Example of Data Mapping‍

What Is The Importance Of Data Mapping? 

Data mapping ensures interoperability between datasets and systems. Data mapping is the software code that connects databases so that they can “talk” to each other without making errors. Data mapping is the foundational method used for the best practices in data management. It is the first process needed before data transformation, integration, and migration.

How To Do Data Mapping 

To begin a data mapping project, you start with a plan. First, you identify the source data that needs transferring, reformatting, and organizing.

Second, you create a defined structure for each data field in the target database where all the data will reside after processing. You do this by using standard naming conventions for all sources.

Third, you create a logical schematic of the transformational rules and test them on a sample data set.

After being satisfied that the transformation process works properly, the fourth and final step is completing the migration, transformation, and integration.

Common Techniques 

The three common techniques are manual data mapping, semi-automatic, and automatic data mapping. Data mapping tools may help match data fields from multiple data sources. Software may automate the process partially or fully.

Data Mapping In SQL 

Since SQL is very popular, there are many instances where you might want to blend data from multiple SQL sources to create a master SQL database for your purposes. SQL has a useful feature to match the column names between SQL databases automatically.

An example of a data merge that benefits from SQL data mapping might be creating a large database from multiple SQL database sources to use to train a system with machine learning and artificial intelligence programming. Many open-source databases are in the SQL format.

What Is Data Mapping In ETL? 

ETL stands for extract, transform, and load. This data integration process gets data from multiple sources, modifies it as necessary for consistency, and loads it into a data storage system such as a data warehouse or a cloud service.

One helpful feature of ETL data mapping is knowing exactly where the data came from, how it transformed, and where it ended up. The ability to track and follow data flow through systems helps when trying to uncover the source of data errors through root-cause analysis.

etl process

Choosing The Right Data Mapping Technique 

It’s important to understand what data mapping technique works best for you. We’ve detailed below a few options to consider including:

Manual Data Mapping 

You use software code programmers to create a customized data mapping solution for manual data mapping. You may have software coders in-house as part of your IT department who can do this work, or you may hire outside consultants who specialize in this effort.

The benefits of writing custom data-mapping software code include having a proprietary data-mapping system that you completely control, which is comprehensive and flexible.

The disadvantages of creating data mapping code from scratch are the expense and the time needed to write the code. This custom code may not integrate with commonly-used data mapping software tools. Your solution is code-dependent and written in a specific code such as C++, Java, or SQL.

An example of this project type is making a data map to combine SQL databases using an ETL function that tracks how the data moves between the databases.

Manual Data Mapping 

Semi-Automated Data Mapping 

If it is possible for your project to use semi-automated data mapping, you may save a lot of time and money. This method uses data-mapping software to create a schema map and connect the data sources.

The schema map provides a graphical user interface (GUI) to show how the databases are structured. Then, data mappers make the links from the fields in one database to another using the GUI to drag-and-drop connectors.

A robust data mapping software tool automatically creates an output of the data map as software code. This code output may be useful for creating a standardized mapping for use with other databases.

The process undergoes testing on a small dataset, and then manual adjustments are made to the data mapping code to eliminate any process errors.

The advantages of using this method are the ease of using a visual GUI to create the data map, efficient software code creation, and the flexibility to adjust the code manually.

The disadvantages are the requirements to have software coding skills to understand the code, make adjustments at the code level, and the need to spend time manually checking the results.

Semi-Automated Data Mapping 

Automated Data Mapping 

Automated data mapping achieves the results you want without writing any software code. For many projects, this is an ideal solution. The mapping software tools with this capability work with a GUI and drag-and-drop functions that are easy to use.

Automated Data Mapping 

Data Mapping Tools

A needs assessment is the first step in any data mapping project. Analyze source data for usefulness and accuracy. “Clean” source data is necessary to achieve satisfactory results. The well-known adage of garbage going in means garbage comes out, applies here.

The needs assessment may be by a third party, such as Cappella. You must evaluate in-house IT staff, capabilities, and workforce availability to execute a data-mapping project successfully.

The best solutions for your data-mapping needs may fall somewhere on the scale from a completely manual process with customized software code to a fully-automated solution or somewhere in between. The total ownership cost is a major consideration for most enterprises.

What To Look For In A Data Mapping Tool 

After completing the needs assessment, the next step is to identify the software tools that are candidates that meet your project requirements with “must-have” features.

Here is a checklist of things to look for when evaluating data mapping tools:

Cross-functionality:

Robust data-mapping software solutions allow for various database connections. You may be working with diverse databases that vary in structure.

Your software tools may need to accommodate many file formats and database systems, such as DB2, EDI, Excel, JSON, Marketo, MS CRM, Oracle, SAP, SAS, SQL, SugarCRM, Sybase, XML, and others.

You may make connections using application programming interfaces (APIs), such as REST and others. You may also use cloud services such as database.com and Salesforce.

Graphical user interface (GUI):

The best software tools have a GUI that is very intuitive and easy to use. Look for drag-and-drop capabilities to create data mapping schema connections without the need to write any software code.

Preview testing:

It is vital to pick up any potential errors as early in the workflow process as possible. Preview testing shows a sample of the raw and processed data during the data-mapping design process. This preview testing helps prevent mapping problems between the schemas.

Scheduling and automation:

The work process of connecting databases often includes working with actively used databases. The software tools must be able to schedule data mapping work that launches based on a certain event, such as a time of day, during system maintenance, etc.

Scale:

It is critical to evaluate the scale of the project since data processing may be extensive and ongoing. The software tools you choose must be able to handle the workload. Run mapping tests on sample data sets to determine if the scalability meets your needs.

Other Resources 

Here are some resources to help understand data mapping and data management:

Case Studies

Assisted living management

Construction

Mobile mood tracking app

Oil and gas

Use Cases

Energy asset management

Master data management (MDM)

Automated quoting

Now You Understand Data Mapping, Perhaps You Need A Guide 

With a better understanding of how data mapping works and what to look for in a data mapping tool, you can now better organize and scale for your business. However, if you need help beyond this guide, reach out to the experts at Capella for assistance with data mapping and digital transformation.

Contact Capella for an evaluation of all your data management needs.

Rasheed Rabata

Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.

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