Everything you need to know about data mapping

Data mapping and data processing are an essential function of many roles, such as data analysts, data scientists, ML engineers, business analysts, and others. For example, a data scientist uses it to enrich a source dataset with data from other sources. With data mapping, business analysts can provide valuable insights for a company or solve various data management tasks.

In today's world of rapid digitalization, data mapping is a critical component to staying ahead of the game. Whatever your role, data can empower you to drive change in your organization by defining needs and recommending solutions that deliver value to stakeholders. If you are a beginner and want to gain a better understanding of this topic, this article can serve as a comprehensive guide.

We've outlined everything you need to know, so you'll come away knowing exactly what data mapping is, why it's needed, who needs it, and what approaches and tools are available. Let's get started.

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What is data mapping?

Simply put, data mapping refers to a set of instructions that combine information from one or more databases into a single schema from which you can derive insights. Database mapping plays a critical role in empowering a company's business intelligence platform so that it can absorb and comprehend all the information it is fed to deliver the optimal results. The platform takes the information from the dataset (or sets) and maps it for a target output.

Oftentimes, companies need an overarching view of their data in one convenient place. A data map can help clarify where information from multiple databases intersects. Take note that you'll need to decide which database to use as your benchmark in case of duplicates and have a game plan on how to approach new information.

Data mapping under the umbrella of data management

Mapping of data means bringing together data from various sources with different semantics or representations. It is an essential part of the data management process and involves creating instructions — or data maps between different models — to combine information from several datasets into a single schema, such as a table configuration, using matching and searching functionalities to find intersections or weed out duplications and contradictions.

Data management is carried out at the start of each of these integration tasks:

• Converting or transferring data between the source and the destination.
• Determining data relationships as part of a data lineage analysis.
• Detecting concealed, sensitive data, for example, when one identifier is contained within another.
• Consolidating many datasets into one and getting rid of redundancies.

It's rare to find two data sources that have the same schema. Generally speaking, data schemas contain contrasting information given that they come from different sources. This is where data mapping fulfills an essential function, bringing consistency and uniformity to the same data that's been recorded under different labels. For instance, if you want to merge multiple data sources into a data warehouse, mapping can help you combine them. It can also highlight points of intersection between similar data while dealing with conflicting or duplicate data points.

How do companies use data mapping?

Businesses interpret and make sense of their data to uncover opportunities, find new ways to innovate, and gain a competitive advantage. Mapping data from different sources allows companies to aggregate a large volume of information from different sources and merge it into a single schema (or data structure) to process data more efficiently.

Say a company processes its employee data in several designated HR systems. Data could include salary information, health insurance coverage, marital status, dependents, and so on. Let's take for example, dependents where "1" equals employees who have children. In one of the other systems the company uses, the same data falls under "true", and in a third system the data is designated by the word "yes". All of these systems use different data types to refer to the same kind of data.

This is where mapping can help add consistency by translating or recoding meanings to create a single standard by which to define the data — in this case, dependents — which has been labeled inconsistently (whether through semantics or representations) across systems. You can see how it would be useful to have clear instructions on how to combine information from multiple datasets into a single schema.

How complex does it get?

From startups to large multinational conglomerates, companies across industries are having to work with massive data systems that are constantly expanding and becoming increasingly complex. Yet, these systems have proven to be good for the bottom line and are showing no signs of slowing down anytime soon. However, before you can reap the rewards, you'll have to find a way to integrate all your data sources into one holistic entity which you can analyze and derive insights from. That's where mapping plays a critical role, but it can get pretty complex. How complex depends on several factors, including:

• The size of the datasets being compared.
• The number of combined sources being mapped.
• Data schema formats, primary keys, and foreign keys in the data sources.
• Variations between source and target data structures.
• And last, but not least, the data hierarchy.

Overall, the primary aim of mapping the data is to standardize conflicting datasets so that business intelligence systems can access and analyze the information flawlessly. Knowing how to do this accurately and effectively can give you a leg up on the competition.

Why do you need data mapping?

In essence, it ensures that you're looking at and considering all of your data as accurately as possible. It allows you to integrate data from different sources by using data maps. Here are the main benefits in a nutshell:

• Easily integrate, convert, and migrate data.
• Identify links across multiple sources simultaneously.
• Guarantee the quality and accuracy of your data (software can help with this).
• Determine real-time trends and share reports.
• Get the most out of your data and apply valuable insights.
• Simplify and automate by utilizing data mapping software and going code-free.

Ultimately, the main aim of mapping is to standardize diverging and incongruent datasets. From there, data mapping feeds your company's business intelligence platform so that it can understand everything and deliver the best possible insights. Here are a few key areas where data mapping can be useful.

Data integration for data warehousing

During data integration, data mapping defines the connections between data sources and the data warehouse's target tables (or schemas), starting with an analysis of the source information and applicable schemas.

Data transformation

Data transformations involve converting data from one structure or format to another so that it can be integrated with a data warehouse or different application. Converting data types, aggregating, and eliminating duplication are just some examples.

Data migration

Data migration is the transfer of data from one repository to another, and data mapping is needed to match the source fields with the destination fields.

Among its many other benefits, data mapping helps you establish direct links between data across multiple sources, ensure data accuracy and quality, identify real-time trends, and automate the migration process.

What are some typical uses?

There are a multitude of various data mapping applications, but we'll cover the main ones that will likely be most useful for you below.

One-time migration

A type of migration where data is moved from one system to another.

Once this process is complete, the new destination serves as the storage location for the data that's been migrated. From here the original source can be deleted. This is where you need data mapping to match the source fields with the target fields.

Regular data integration

Data is exchanged between systems during regular intervals.

Data mapping plays a key role in regular data integration. It usually requires some kind of data integration tool to match the source fields with the destination fields.

Data conversion

Data is converted from one format to another.

This includes cleaning — as in the removal of missing values and duplicates — changing data types, aggregation, enrichment, and other alterations.

Who uses data mapping?

Anyone and everyone involved in the data management process can benefit from data mapping in their daily work environment. Some examples include business analysts, data scientists, strategists, engineers, architects, and quality specialists.

In data science, for instance, data mapping is part of the preparation process for machine learning modeling. When a data scientist adds to an original dataset with third-party data, this is basically data mapping in a nutshell. You can do this manually, automatically, or using any number of the approaches and tools outlined below.

• Just note that in most cases, data mapping isn't 100% automated and requires you to take several steps, including:
• Defining the data that needs to be moved (such as tables, fields, formatting, etc.).
• Mapping source fields to destination fields.
• Converting values, including encoding formulas or data transformation rules.
• Comparing the results of the transfer (enrichment) with primary source data samples.
• Rolling out production solutions for regular data consolidation according to the migration or integration strategy.

How can data mapping process help business analysts?

If you're a business analyst working with enterprise data analysis, complex mapping projects are no doubt an important function of your role. When you do it properly, you can use data mapping to effect change within your company by delineating data pipelines and other data management tasks to put forward concrete solutions.

In your day-to-day work with data, you'll likely find that there are many extra fields in different systems which have different data structures not compatible with each other. Database mapping can help you eliminate these anomalies and determine which data formats can be migrated to a target database. This type of structured approach helps your company deliver successful change and operate its business on a unified system.

Data mapping and data-driven marketing

Another example of where professionals in the workforce use data mapping is in data-driven marketing. Many marketers use various data sources and process database data to make educated decisions about what their customers are looking for or how they might respond to a particular campaign or strategy.

For example, consumer-based companies like Amazon analyze customer data to target their customers by pulling out insights from their online actions. Ever wonder why your email, social channels, and web pages you visit are filled with specific ads based on things you've searched for or purchases you've made in the past? Companies can extract data and link it with data from outside sources that contain information on your location, age, gender, nationality, educational level, mobile devices type, and more to suggest products you may be interested in and provide you with a customized shopping experience.

Not only does data mapping help you ensure high-quality data and automate data integration, transfer, migration, and other processes, it can give your marketing team an edge by helping your company get the most out of its data. As with many teams and corporations across industries, determining which data mapping tool and technique to use is a key step.

Types of data mapping and data mapping tools

When considering data mapping from the point of view of automation and human involvement, it can be divided into three main data mapping techniques: manual, semi-automated, and automated.

Manual data mapping

This type of data mapping involves writing code for specific cases where different data sources need to be aligned under a common form.

Manual data mapping requires the knowledge and skills of professional coders and data mappers. The upside is that you can fully control and customize your maps with the support and expertise of developers custom-coding the connections from the data source to the target schema. With that said, when mapping data manually, it's hard to keep up as data systems grow in scale and complexity.

Semi-automated data mapping (schema mapping)

Also known as schema mapping, this second technique is defined by the name itself. Semi-automated data mapping doesn't rely as heavily on coding knowledge, meaning your team will be using both manual and automated data mapping.

The software creates a link between the data sources, and a developer then steps in to check on what occurred and make adjustments manually. Once the data map is finalized, the schema mapping software automatically creates the code to load the data.

Fully-automated data mapping

The third technique is also self-explanatory. The entire mapping process is carried out by a mapping tool, a great option if you don't have access to coders. These automated tools offer users a drag-and-drop visual interface for mapping procedures. You just need to learn how to use your tool correctly.

It is also possible to classify these approaches differently, for example, as hand-coded, graphical manual, data driven, and semantic. We've highlighted a few key details for each.

Graphical mapping

Graphical tools allow you to draw lines from data fields in one dataset to data fields in another. The tools also let you auto-connect a source and a destination.

Data-driven mapping

As the newest approach, data-driven mapping involves simultaneously analyzing data values in two data sources using heuristics and statistics to discover complex data mappings between the two. It can also be used to find data exceptions that don't follow the transformation logic.

Semantic mapping

Semantic mapping is similar to the auto-connect feature of data mapping tools except that you can use a metadata registry to look up data element synonyms, which is a helpful solution. However, semantic mapping can't discover data transformation logic or exceptions.

How to choose the right data mapping tool

Data mapping tools simplify the process of visualizing and interpreting your data. Oftentimes, you don't even need to know how to code: many tools have user-friendly interfaces and other useful functions like data migration support.

Choosing the right data mapping software depends on your needs, goals, and anticipated project outcomes. Nevertheless, you should pretty much always look for these three features when comparing options (more on these below):

• Data mapping features that don't require coding skills.
• Automatic data merging and data transformation.
• Support for various types of structured and unstructured data.

The perks of going code-free

Manual mapping may work just fine if your dataset is small. But as it grows in complexity and scale, going code-free may be your best bet. Choosing a data mapping platform that doesn't require high-level technical skills and coding knowledge can offer you several advantages, including:

• In contrast to solutions which require coding skills, almost anyone can use a graphical interface to carry out data mapping tasks.
• A graphical data mapping tool provides GUI (graphical user interface) and drag-and-drop features make the whole process easier, especially if you need to make any changes.

Before you start data mapping…

As mentioned above under automatic data merging and transformation, you'll probably have to convert data from different formats before you can begin the data mapping process. However, many tools come with an internal collection of predefined integrations.

Ask yourself: does the tool support different types of data? Ideally, the tool you choose should support a range of structured formats. You'll also find that many companies need to integrate structured data with unstructured data sources.

If your company uses a cloud-based CRM (like Salesforce or Microsoft Dynamics CRM) to carry out its client relationship management operations, we suggest selecting a tool that can connect all of the applications your company uses.

So, which tool should you choose?

From on-premise to in-the-cloud, there are various tools with different data mapping capabilities. We've outlined three of the most common methods below and what to consider when selecting the right data mapping tool:

On-premise (or proprietary)

On-site data processes can be more secure, accessible, and controlled. However, they can be cumbersome and costly due to the hardware, software, and other equipment you have to purchase and maintain.

Examples include products commonly used by companies across industries such as Centerprise Data Integrator, CloverDX, IBM InfoSphere, Informatica PowerCenter, and Talend Data Integration solution.

Cloud-based

Cloud-based data mapping tools are fast, flexible, and scalable, making them the best solution for today's businesses and business analysts. Among their many benefits, they are backed up by expert support and can easily adapt to changing schemas without stalling or losing information. They're also relatively inexpensive and simple to use.

Examples of data mapping tools for business analysts include Informatica Cloud Data Integration, Oracle Integration Cloud Service, Talend Cloud Integration, Dell Boomi AtomSphere, DX Mapper, Alooma, and Jitterbit.

Open source

With the latest code bases at their disposal, these data mapping tools can be cost-effective, reliable, and efficient. But if a tool is both open-source and free, you can't rely on the vendor to quickly fix bugs or take responsibility for problems caused by these bugs. On the other hand, if you have developers on your team, they might be able to improve an open-source tool themselves. Despite being less sophisticated, they're great for individual data science studies.

Examples of popular open-source tools include Pimcore, Talend Open Studio, and Pentaho data integration tools.

As a side note, most of the product examples above support the three main approaches to data mapping: manual, data-driven, and semantic. However, as noted previously, semantic mapping requires metadata registries, which aren't available in every enterprise — and public metadata registries aren't always directly related either.

It's best to consider the following factors according to the task at hand:

Data complexity — including volumes, formats, and schemes.

For example, if you want to enrich a small dataset for ML modeling by comparing data from several sources, choose a simple cloud service or write your own script. On the other hand, if you're looking to consistently load information from multiple DBMSs into corporate warehouses or data lakes, choose a reliable enterprise-level ETL tool instead.

Extensibility — a visual GUI improves usability; however, you'll still need to customize automatically generated matches.

The data mapping software you choose should be able to edit the generated mappings, customize the rules, and help you write your own transformations as software scripts.

Costs — including acquisition, use, technical support, and other expenses.

Conclusion

Now that you have a basic understanding, you can see that data mapping is an important part of working with data for data scientists and many other professionals alike. Knowing the basics of data mapping is useful for a wide variety of roles, from data scientists and machine learning engineers to data quality engineers and business analysts.

Data mapping is a chief component of both data management and data integration — and is pretty much considered to be universally useful. It plays a key role in allowing you to integrate your data from various sources in order to get an objective overview of all your data. Thanks to data mapping, you can organize, analyze, and extract insights from large amounts of data from which to draw valuable conclusions.

Furthermore, data mapping is fundamental to the preparation stages of ML modeling. Knowing how to select the best data mapping tool is key to achieving your desired outcome. If you're looking to acquire a single dataset from several different sources, you can either compare data manually or use a self-written Python script. However, this approach isn't suited for more complex systems or processes such as building corporate data warehouses and lakes.

It's particularly helpful for data scientists and data engineers to be able to select the best data mapping tools for a given task. Ensuring data quality comes down to comparing data to eliminate duplicates and inconsistencies, which is the responsibility of a data strategist and data quality engineer. As you can see, understanding the mapping process is critical for almost every data specialist.

To learn more about other key concepts of working with data, check out the Best Practices section on our blog, as well as the crowdsourcing knowledge base.