Product Data Mapping In eCommerce: Purpose And Techniques
With the unprecedented increase in the volume of information in eCommerce, a process called data mapping certainly helps to handle, at times, extremely intense data flows. Data coming from different sources requires systematization, data transformation, and automation, without which it remains an impossible task to manage. The process ensures that you accurately record, correctly use, and seamlessly integrate all incoming data into systems.
Gepard specialists would like to share some firsthand knowledge and experience concerning data mapping and its ultimate benefits.
What Is Data Mapping?
What Is The Purpose Of Data Mapping?
The process involves linking the data fields of the source and target systems. Product Data Mapping (PDM) helps consolidate information, so it is the first step in any data extraction, transformation, and loading (ETL) process.
With it, you can:
1. Create, transform, integrate, and move data warehouses;
2. Link data from multiple sources;
3. Check data quality with data mapping software that automatically highlights inconsistencies, inaccuracies, and other problems in databases;
4. Spot trends and share real-time reports.
The procedure links products to their respective categories, making it easier for customers to find the product. Improved catalog structuring allows you to better categorize vendors based on their products/services and allows businesses to display and transform data to make their business more efficient.
Product Data Mapping Techniques
You get a pretty sufficient choice of the most efficient model. Information mapping techniques can be commonly subdivided into automated, semi-automated, and manual approaches.
1. Automated Data Mapping
In this case, the whole process is carried out by a software tool. As a rule, this is a ready-made paid solution, but it is optimal if there is no encoder in the team or the object is temporarily unavailable. Often, such software uses promising technologies of machine learning and automation, which gives the user a number of advantages, including:
- Easy data extraction;
- Launching complex processing flows from a convenient UX;
- Data flow visualization with attractive effects;
- Receiving notifications when there are problems and getting help in fixing them.
A well-chosen data management tool saves a lot of time in solving immediate business problems as it scales.
2. Semi-Automated Mapping
This method, or, as it is also called Schema Mapping, requires knowledge in the field of coding and a little manual work. This is a hybrid process in which the data mapping tool creates links between sources and targets, and then the IT specialist checks and manually corrects them if necessary. The main benefits here are:
- Balance of performance and accessibility;
- Only some basic coding input is necessary;
- Saves time without full-on automation;
- Useful data visualizations for data analysts.
This model is ideal for teams on a tight budget for basic data integration and small data management.
3. Manual Mapping
This model requires professional implementation. You will need a data engineer or developer capable of coding rules for passing or inserting data from one field to another and a mapper that will encode and transform your data sources. The manual approach also has its strengths, like:
- The ability to fine-tune your data tasks;
- Fuller control of the whole data mapping process;
- More individual customization of elements where needed.
This is the optimal solution for a one-time process (for example, data storage) when the data warehouses are not too large.
Product Data Mapping Process
In a simplified form, the procedure includes three steps: identifying the source, and the target, and linking the two structures using matching patterns. In practice, depending on the overall data mapping approach (manual or otherwise), you may also need to define compatible formats and transform data accordingly, specify rules for its transformation, and test out schema logic.
1. Identifying Product Content Fields To Map
You need to start by determining what data will need to be restructured or moved. There is no universal recipe, so it all depends on your priorities:
For each source, you need to determine how much data to merge and how often the integrations will be performed. If you have large and frequent integrations coming up, you should choose a solution from among automated data mapping tools. You can use manual data mapping only for small one-time projects with limited amounts of data.
Take a close look at the source information and determine the tasks that need to be solved at the target location. How large the amount of data is will determine how you approach data migration. If the mapping flow is too wide, choose automated software.
Specify which data processing format should be used for the intended purpose. In most cases, automated tools are required, but small projects can be done manually.
2. Defining A Format For The Target Data
Determine the format and structure for displaying information in each of your sources and target databases. They must match so that when they reach the goal, there is no confusion (for example, in the list of the sales department).
3. Specifying Product Content Transformation Rules
This will depend on the chosen method of how to do data mapping. With an automated approach, the system will do all the work for you without the need for coding. For semi-automatic, create the connections using the program, and then have an experienced person manually check that they work correctly.
4. Testing Schema Logic And Completing The Mapping Process
If you use the automatic matching method, then the check is performed by built-in means. In other cases, move a small sample of prepared data and manually check for errors. By testing your logic, you can complete any process with high quality.
Product Data Mapping Challenges With Solutions
Let’s take a look at the most common challenges you may face and their solutions.
|Poor Data Quality||Product information may be incomplete, inaccurate, or outdated, making it difficult to map to relevant categories or attributes.||Establish clear data governance policies and processes to ensure data accuracy and completeness, and use information cleansing tools to standardize data across channels.|
|Varying Product Information Across Channels||Product information may be presented differently across channels, making it challenging to map consistently across all touchpoints.||Implement a standardized data model to ensure consistent product information across all channels and touchpoints, and collaborate with stakeholders from across the organization to align on product categorization and attribute mapping.|
|Managing Large Volumes of Data||eCommerce businesses may have thousands or even millions of products, making it difficult to manually map and categorize each item.||Use automation tools, such as PIM systems or PDM software, to streamline the mapping process and ensure accuracy and consistency.|
|Lack of Stakeholders Alignment||Stakeholders from different parts of the organization may have varying opinions on product categorization and attribute mapping, leading to confusion and inconsistencies.||Involve stakeholders from across the organization in the PDM process to ensure alignment and agreement on key decisions. Establish clear guidelines and processes for managing disagreements and resolving conflicts.|
|Difficulty in Maintaining Data Consistency||Product information may change frequently, requiring regular updates and maintenance to ensure accurate and consistent PDM.||Implement processes for ongoing information quality monitoring and management, including regular audits and reviews of product information. Use automation tools to facilitate updates and ensure consistency across all channels and touchpoints.|
By being aware of these common challenges and implementing strategies to address them, eCommerce businesses can ensure that their PDM efforts are accurate, efficient, and effective, ultimately leading to improved customer experience and increased sales.
Mapping Data Use Cases
Here are some of the most popular data mapping examples:
Product Content Integration
Successful content integration depends on how identical the structures of the source and target stores are. Product data mapping tools help eliminate differences to more effectively consolidate information from different data sources without the additional involvement of programmers.
Migrating Product Content
Moving data between repositories is easy to do using data mapping automation. It is harder to do it manually. Invalid or not quite correct comparisons will reduce the accuracy and will not ensure the completeness of the information.
Possible real-world use case. Timely product data migration can help businesses stay flexible and sturdy in the face of growing demands and shifting trends. For instance, when scaling from legacy systems to more up-to-date hardware in order to meet the seasonal demand or simply widen user outreach.
Transforming Product Content
When corporate data is located in different places and in different formats, the automated process of extracting and transforming data into valuable information has no alternative. The data is placed in the staging area for conversion to the desired format and then moved to the target database without your participation.
Possible real-world use case. Employing proper mapping data techniques in order to turn existing data into a form compatible with a new or alternative system is yet another major use case. It can help you stay open to new mediums and data management solutions.
Electronic Data Interchange Exchange
The procedure for converting files to various compatible formats uses built-in functions without writing code. This helps to implement the most simple B2B data exchange.
Possible real-world use case. Automation of file conversion through EDI accelerates the way you do business by granting seamless opportunities for B2B data exchange. This can help you stay operational no matter what type of system your B2B client uses.
What To Look At When Choosing A PIM Software With Data Mapping Capabilities?
The choice of data mapping software is critical to the success of any data-related project. The key to making the right choice is research. Online reviews can be a great help. The following are some of the must-have functions you will need when working with data:
Connectivity To Varied Data Sources
Capability is key for data mapping and modeling tools. To properly link data to the right product, choose a centralized Product Information Management (PIM) system with asset management functionality. This is especially important if product descriptions are oversaturated with media.
The ability to easily drag and drop files effectively overcomes the challenge of converting, managing, and sharing large digital data assets. This is one of the most important features of PIM.
A graphical interface in a richly integrated ecosystem enables critical product information to be quickly visualized and centrally managed. A quality solution supports many custom options and product hierarchies, including packages, attributes, categories, and collections.
Ability For Different Types Of Mapping
The ability to automate your workflow by time and events is invaluable. You will reduce the amount of manual work, increase productivity and free up the most valuable asset – time. The best choice would be PIM, which provides several data mapping techniques.
Product Mapping Functions Offered By Gepard PIM System
Now that you know what to search for in a PIM solution with a mapping module, let’s review the product data mapping process of the Gepard PIM software.
At Gepard, we understand taxonomy as a large set of strictly structured and interrelated data that describes real-life products. Such data sets mainly include product categories and related product features (or attributes, specifications) with values and units of measure. Some taxonomies may also include product brands.
A successful data mapping is key for further data enrichment. For this purpose, the Gepard PIM system offers a mapping module that allows mapping the product taxonomy data from the source to the target endpoint. Here are the main features of the data mapping module of Gepard PIM.
1. Creation and management of data mapping rules.
To map the data of the two taxonomies, we configure mapping rules that determine the matching relationship between the source data item and the target data item. Mapping rules are applied by the Mapper Engine module, which executes the enrichment of product data. This allows for further product description enrichment and delivery to the data endpoints.
2. Mapping user interface
Gepard’s mapping module UI is made to be understandable even for non-technical users with no need for coding and ensures a flawless mapping process. Seamlessly create mapping rules, save them in the database, edit, add new rules, or delete the existing ones. For more comfortable work, you’ll get edit history as well as an option to revert to an earlier version of mapping.
3. Management of mapping templates
The mapping module, offered by Gepard, has a Back Office service option, which is especially useful for users who do not have any experience with taxonomy mapping. With this option, experienced taxonomists can create and manage mapping templates for the user, and send the ready-made templates back to the Front Office for the next filling-in.
4. Support of simple and complex mappings
Gepard’s mapping module offers both simple and complex data mapping. The first one allows so-called “value-to-value” mapping. For example, a provider’s category “clothing” is mapped to the category “wear” on the client’s side, which allows for correctly enriching and syndicating the client’s product descriptions.
On the other hand, complex mappings are when more than one condition can be used to map the items between the provider’s and the client’s sides. For example, the client’s feature “Size” is mapped to the provider’s three features “waist”, “sleeve length”, and “shoulder width” which are labeled in the product description.
5. Support of different data formats
Gepard configures mapping of source and target product taxonomy data to enable the enrichment of final product descriptions and delivery of these product descriptions as output files in a particular format (CSV, JSON, XLS, etc.) to a particular endpoint (email, API, FTP, etc.).
Read more about choosing a PIM system:
What is better, buying or building a PIM solution? Why not both? Find out how you can get unique functionality faster than custom development.
Product Data Mapping FAQ
What Tools Are Used For Data Mapping?
Semi-automatic or fully automated tools can be used. The former helps create a link between the source and target fields, and then the developer manually verifies the link. The latter does all the work for you and can be used by ordinary personnel.
Can I Map Product Data Myself?
You surely can, but this is a laborious process that is difficult to perform correctly and accurately. At the least, you will need a tech-savvy employee with extensive experience, a database management system, and an appropriate file conversion method.
Can I Pass The Stage Of Data Mapping?
You cannot because then you will not be able to take advantage of it. The consistency of the process is critical to ensuring the accuracy and quality of data as it moves from source to destination.
Data Mapping – Sealing Up The Deal
Product data mapping is the unconditional foundation of an efficient data management strategy, and employing PIM is one of the best eCommerce strategies as a whole. Want to make the most of them? Feel free to contact us.
The Gepard PIM system makes a perfect tool for data mapping for any kind and size of product content. Request a custom demo to learn how Gepard can help grow your specific eCommerce business.
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