Data mapping is a set of instructions that merge the information from one or multiple data sets into a single schema (table configuration) that you can query and derive insights from. It’s rare for two data sources to have the same schema. Therefore, when we want to combine multiple data sources into a data warehouse, we need to link them together through data mapping. This involves showing where similar data intersect and what to do with new, duplicate, and conflicting information.
In this day and age, data mapping is critical. Let's start with a big-picture look at the issue. Enterprise-level companies are dealing with massive, byzantine data systems – and they're growing larger, more complex, and more profitable by the month. For example, according to TechJury, Netflix saves an estimated $1 billion annually because of how its data science insights have improved customer retention.
Nevertheless, it's not easy to experience Netflix-level results from your data systems. Before you can extract profit-boosting insights, you'll have to get through the challenge of integrating all of your information sources seamlessly and accurately into a cohesive "analyzable" whole – even if they don't fit together naturally.
That's where data mapping comes into play. In this overview of the data mapping process and its technology, we answer the following questions:
- What is Data Mapping and Why is it Important?
- When Is Data Mapping Necessary?
- What Are the Most Common Data Mapping Techniques?
- What to Look for in a Data Mapping Tool
- How Xplenty can Help
What is Data Mapping and Why is it Important?
Data mapping empowers your business intelligence platform so it can understand everything and deliver the best possible insights for your company. It does this through the most basic of data functions: taking the information from a data set or data sets and "mapping" it for a target output.
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Let's go through a real-world example. To understand data mapping, imagine three databases with data on popular movies and actors. Each organizes the information into columns and fields, and each has a different organizational strategy. Take a look at the three databases here:
Each database has similar and different types of information. For example:
- The "id" column in the Movie database and the "movieid" column in the Casting database have the same information
- The Movie database is the only one with gross earnings information ("gross")
- The Actor database is the only one with name information ("name").
Merging the three databases above into a data warehouse lets you query them (or search for information in them) as if it were a single database. That could be valuable for a business intelligence system that needs a bird's eye view of all the data from a company. Bringing the databases together requires a data map to clarify where the information intersects. Also, you need to define which database's data to use in cases of duplicate data as well as how to treat new information.
Below is an illustration of a basic data map for the movie and actor databases. The connecting lines show how we mapped the data sources to the target schema:
*The database information in these examples can be found in the SQLZoo lesson on JOIN operations.
In summary, data mapping creates instructions that merge the information from one or multiple data sets into a single schema (table configuration) that you can query and derive insights from. In more technical terms, data mapping matches the relevant fields from one or more information sources to the relevant fields in a “delimited file” (i.e., a text file that defines the schema of the target destination or data warehouse).
The above example is a simple one, but data mapping becomes exceedingly more complicated depending on the following factors:
- The size of the data sets
- The number of information sources getting mapped
- The schemas, primary keys, and foreign keys found in the data sources
- The differences between the source data structure and the target structure
- The hierarchy of the data
Ultimately, the goal of data mapping is to normalize diverse and incongruent data sets, so BI systems can seamlessly access and analyze the information. When done correctly, this can yield game-changing insights.
When Is Data Mapping Necessary?
Data professionals use data mapping to assist in three main areas:
- Data integration for data warehousing
- Data transformation
- Data migration
Data Integration for Data Warehousing
When integrating data into a data warehouse, data mapping defines the connections between the data sources and the data warehouse’s target tables (or schemas). Data mapping for a data warehouse begins with an analysis of the source information and the schemas that apply to it. For example, where do the databases intersect with the same information? The process also begins with the definition of rules to govern the mapping and integration process. For example, if duplicate data gets found in two different databases, which data should the system prefer?
Most organizations use automated data mapping technology to map the source information to the target schema. For example, platforms like Xplenty allow you to map unlimited data sources into your data warehouse and even schedule how often to update the warehouse with new data from the source files.
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Data transformation involves taking data in a specific structure or format and converting it into another structure or format. It can play a mission-critical role when preparing information so it can integrate with a data warehouse, or when trying to get data to work with a different application. Data integration involves activities like:
- Data type conversion
- Elimination of nulls and duplicate information (data cleansing)
- Data enrichment
- Performing aggregations
During the initial stages of data transformation, data mapping defines how to map, modify, join, filter, or aggregate the data fields as required by the new data type.
Related Reading: Data Transformation Explained
Data migration is the transfer of data from one data repository to another, and data mapping is one of the stages of this process. Before data mapping automation, manually creating a data map was one of the most challenging aspects of data migration. It was error-prone and required lots of time. However, automated data mapping tools like Xplenty reduce the time required while preventing errors.
Related Reading: What is Data Migration?
What Are the Most Common Data Mapping Techniques?
There are three primary data mapping techniques you should know about:
- Manual data mapping
- Schema mapping
- Fully-automated mapping
Manual Data Mapping
Manual data mapping requires developers to hand-code the connections from the data source to the target schema. Usually, they write the code in XSLT, a programming language that converts XLM documents into other formats. Eventually, as data systems grow and become more complicated, manual coders can’t keep up with data mapping needs, and data teams will need to use automated solutions.
Schema mapping is a semi-automated strategy that uses software to map similar schemas together without too much painstaking human intervention. The software compares the data sources and the target schema to generate the connections. Then, a developer checks the map and makes adjustments where needed. After finalizing the data map, the schema mapping software automatically generates the code (usually in C++, C#, or Java) to load the data. With Xplenty, the automatic code generation process looks like this:
Fully-automated data mapping tools offer users a drag-and-drop, graphical interface to carry out data mapping procedures. These tools may feature out-of-the-box integration that allows you to manage the automatic mapping of hundreds of different formats, like Google Sheets, Hubspot, Salesforce, etc. The beauty of fully-automated mapping platforms is that they’re easy for non-coders and novice users to operate. Here’s a screenshot of Xplenty’s drag-and-drop interface:
What to Look For in a Data Mapping Tool
Selecting the right data mapping tool for your needs depends on your project requirements. However, your data mapping application should include the following features at a minimum:
- Code-free data mapping features
- Automatic data merging and transformation
- Support for diverse types of structured and unstructured data
There are plenty of other features out there, but these three are the critical ones. Be sure to look for a tool that has all three.
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Code-Free Data Mapping Features
Manual coding may work when you're just starting out and your data set is small. But the larger and more complicated your data set becomes, the more impossible it becomes. Moreover, manual data mapping requires a high level of technical expertise to implement, representing additional labor costs. By choosing a data mapping platform with no-code functionality, you’ll receive the following benefits:
- Users without coding knowledge can carry out data mapping tasks
- A graphical user interface with drag-and-drop functionality makes it easier to visualize and make alterations to data mapping projects
- Automated processes eliminate (or significantly reduce) the chances of human error that could interfere with the accuracy of data
- No-coding automation allows you to efficiently carry out mapping tasks related to data objects at any level of complexity
Automatic Data Merging and Transformation
Before data mapping, you may need to prepare the data by transforming it from different application formats. This can take a lot of time, but as we mentioned above, most mapping tools come with a built-in library of predefined integrations.
As an example, this screenshot shows a handful of Xplenty’s built-in data manipulation functions:
Support for Diverse Types of Structured and Unstructured Data
Your data mapping tools should support data from a wide variety of structured formats like RDBMS formats, JSON, XML, CSV, IDOC, EDI, fixed length and delimited files, and more. Also, because most businesses need to integrate structured data with unstructured (and semi-structured) data sources, data mapping software should support formats like RTF, PDF, weblogs, and other non-relational formats. Moreover, if your business uses a cloud-based CRM application, such as Salesforce or Microsoft Dynamics CRM, look for a data mapping tool that connectivity for all the enterprise applications you use
How Xplenty Can Help
There's a lot more to know about data mapping, but this overview should give you a solid foundation on the topic to continue expanding your knowledge.
Xplenty's data mapping tool features all three of the essential services customers need, including:
- No-code functionality that helps novice users complete complex data mapping tasks through an interactive dashboard
- Hundreds of out-of-the-box data manipulation functions
- Data mapping for a wide range of structured and unstructured data sources
As a final note, we’d like to recognize that there are many excellent data mapping platforms available to help you get through your data integration bottlenecks. However, there's one other feature that sets Xplenty apart--our team of highly-responsive customer support specialists. After all, what good is the best data integration platforms if you can't get the help you need to apply it?
Wondering how Xplenty can help your team with data mapping and data integration difficulties? Contact us to schedule your demo today and experience the platform for yourself.