In today’s evolving business landscape, trusted product data is crucial for accurate decision-making, customer satisfaction, and operational optimization. With the growth of digital commerce and multiple sales channels, organizations must ensure consistent and accurate product information across touchpoints. Flexible product data solutions drive personalized experiences and revenue growth. However, choosing between Product Master Data Management (Product MDM) and Product Information Management (PIM) can be confusing and challenging due to their subtle differences. 

Product MDM and PIM:  Key Capabilities and Benefits 

Both Product MDM and PIM solutions aim to establish a trusted “golden record” of product data. However, they differ in their objectives and hence, functionalities. 

Track #1: Product Master Data Management (Product MDM)

A Master Data Management (MDM) system is an enterprise-wide solution that focuses on managing and maintaining master data that can include ‘product’ as a domain, amongst other master data domains such as customers, suppliers, locations, and more. MDM aims to provide a single source of truth for data consistency and accuracy across the organization. A key purpose of MDM is also to create relationships, whether horizontal (for example, between multiple domains such as products, customers, vendors, locations, etc.), or vertical (for example, patients and products) that help fuel analytical business applications. 

The following is an illustrative diagram to depict the functional layout of a multi-domain MDM system, that consumes data from multiple sources and distributes the mastered data to consuming applications. 

Fig. 1: Sample multi-domain MDM including product as a domain 

The key benefits of a Product MDM solution are as follows: 

• Gain a trusted and comprehensive 360° view of organization-wide product data.  
• Consolidate siloed product data from diverse organizational systems. 
• Create a single unique version of an organization-wide used Product (or a Product Family) record 
• Establish clear relationships between products and other entities.  For example, products-customers (insurance industry) or product family-substances-ingredients (life sciences) 
• Boost business efficiency and IT performance by enabling data profiling, discovery, cleansing, standardizing, enriching, matching, and merging in a single central repository. 
• Leverage reporting and analytics for informed decision-making. 

Track #2: Product Information Management (PIM)

On the other hand, a Product Information Management (PIM) solution centralizes the management of product data – not necessarily just master data but hundreds of product attributes such as color, size, style, price, packaging, reviews, images, nutritional labeling, or digital assets – enabling streamlined collaboration and data enrichment. PIM standardizes and automates product information, ensuring trusted, enriched, and high-quality data for customer touchpoints, sales, and marketing channels. It might often uncover hidden customer and sales opportunities that may have been overlooked due to disconnected product data. 

The following is an illustrative diagram to depict the functional layout of a PIM solution, and the various aspects of product information that it may encompass.

Fig. 2:  Sample PIM solution 

A PIM solution aims to: 

• Streamline collaboration on product content internally (within the organization) and externally (at all customer touchpoints). 
• Automate workflows for product information management and approval. 
• Accelerate time-to-market for new products. 
• Enhance omnichannel capabilities and publish consistent, relevant, and localized product content. 
• Supply any channel with correct and up-to-date product information. 
• Expand sales and marketing reach to new channels. 
• Securely exchange product data via data pools. 
• Increase sales through rich product information, engaging customer experiences, and improved cross-selling opportunities. 

How do you decide if you need a PIM or MDM for your business? 

Let us try to figure this out by citing some common use cases businesses face:

Therefore, while both Product MDM and PIM have overlapping capabilities, they are best suited for different needs and scenarios. Product MDM focuses on managing master data, data governance, and advanced analytics, while PIM specializes in catalog management, omnichannel engagement, and quick creation of price rules. 

At Fresh Gravity, we offer robust technological and functional expertise in implementing product data solutions, whether it is Product Master Data Management or Product Information Management. With a solid understanding of the intricacies of managing product data, we excel in designing and deploying tailored solutions to meet the unique needs of our clients. Our team’s proficiency extends across various industries, allowing us to leverage best practices and innovative strategies to optimize data quality, governance, and accessibility in this space. Through our commitment to excellence, we empower organizations to harness the full potential of their product data to drive efficiency, competitiveness, and growth.

Are you considering Product MDM or PIM?  Contact us at info@freshgravity.com and we will be happy to set up a session to answer your questions. 

The post Understanding Product Data Management: Product MDM vs. PIM Solutions appeared first on Fresh Gravity.

Data load and migration are an integral and central part of any Master Data Management (MDM) implementation.

Most MDM projects involve two types of data loads:

• Initial Data Load (IDL)
• Ongoing Data Loads (ODL)

Data loads occur in various frequencies, which can include:

• One-time – often an IDL
• Batch
• Real-time

The method of the data load must also be considered.  Possible methods include:

• Direct SQL load (available in some, but not all, MDM systems)
• Flat file upload facilitated by the MDM system’s built-in tools
• Load via API
• Load by data transport, middleware, or ETL tool

Once the source data has been identified, often with the assistance of client Subject Matter Experts (SMEs), we begin with a data modeling and mapping exercise to ensure the source data will fit the target system. In many cases, is necessary to understand data volumes, amount of duplication, invalid data in fields, missing data in required fields, and other data anomalies and characteristics; this informs the modeling and mapping results and is used in other aspects of MDM implementation, such as for developing match rules and understanding enterprise reference data.

Through the initial modeling and mapping exercise, we also take care to understand the level of confidentiality of the data. If PHI and PII are involved, then we ensure only authorized personnel are allowed to handle the data, and the data must only be handled on client systems. Our consultants are trained to prevent data breaches during data loads.

Mapping considerations often require the assistance of client subject matter experts; in many (most) cases, the data models from the source and target systems do not match, and judgment and experience are required to ensure all data from the source has an appropriate landing place in the target system. In many cases, data transformations are also required, and these must be understood and designed into the load process.

Once modeling and mapping are complete, we conduct a small initial data load using a fractional subset of the initial data load set. The initial data load is used to validate the assumptions and design decisions made in the modeling and mapping exercise. The sample data load also is used to confirm connectivity between the source and target, where applicable.

The following are examples of the many things our data teams will validate upon load:

• Record counts must match, and failed records must be accounted for, with reasons for failure understood.
• Data types must be preserved or converted as expected.
• Lists of values must load or map correctly.
• Data integrity must be preserved on a field-by-field basis.
• Field lengths must be validated to avoid truncating data.
• Fields must be mapped correctly between the source and target.
• There must be no corruption due to incompatible character sets or other issues introduced by the transport method.
• NULL vs. blank characters must be handled correctly.
• Date/Time fields must be loaded correctly – either with or without conversion, as needed.
• Examination of failures (or unintended successes) due to required fields and other validation rules.
• Time stamps and other audit information (such as “created by” fields) must be preserved or recorded correctly.

As a rule, Fresh Gravity will automate this process whenever possible and permissible.

Upon validating that the sample data load worked correctly, a full data set will be loaded, and the same items will be examined as with the sample load, plus any issues due to volume will also be evaluated.

The process is similar for Ongoing Data Loads (ODL), however, additional considerations come into play, such as:

• Desired behavior when a record is updated in full or in part
• Desired behavior when new records are inserted
• Source of the ongoing updates can often be different than the source of the initial data load, and these differences must be handled carefully
• Desired behavior when records are hard deleted or marked for deletion/inactivation in the source system

If you need help with a Data Load for your MDM project, please write to us at marc.paolo@freshgravity.com or sudarsana.roychoudhury@freshgravity.com.

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