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As an increasing number of businesses look to transform into “digital organizations,” what they’re truly seeking to transform into is “data-driven organizations.” More often than not, the primary mover in this direction is product data. When product data management is done right, the benefits are manifold. Customers benefit from accurate, complete, and relevant information with a touch of personalization that drives frictionless buying journeys. The result is higher retention, a greater level of trust, and satisfied customers. Similarly, organizations, their departments, and teams benefit from reliable and readily available product data that drives business results like improved sales and market share.

Key Challenges in Product Data Management:

What’s stopping organizations from unleashing their product data superpowers?

There’s the data itself, and then there are the many systems, people, and processes dealing with this data. More often than not, the inability to leverage product data comes from the latter. As companies take on digital transformation, they look to leverage end-point data that has morphed into many forms at various points of the data supply chain. This data resides in silos, including emails, spreadsheets, warehouse systems, ERPs, and CRMs. Unfortunately, this data is usually unusable, inconsistent, and lacks basic interoperability to benefit different departments. And these silos are very much a universal problem for most organizations. For example, 72% of respondents in a Forrester report stated that handling data across silos was moderate to extremely challenging.

Owing to these silos, organizations lack a unified view of data and cannot track the data complexities required by various business functions. They’re forced to rely on manual interventions to clean up this data, which leads to delayed time-to-market and increased errors. In addition, the great amount of effort required for manual or redundant tasks could be better spent on improvement and innovation.

How PIM Drives Product Data Value

PIM, or Product Information Management, gives organizations complete control over the end-to-end product data journey. From product onboarding to enrichment and distribution, organizations can leverage PIM for centralized visibility and control over product data at every touchpoint. This product data is made available in the most up-to-date version, so teams don’t need to worry about digging into multiple systems, spreadsheets, or emails. With PIM, organizations can also track product data changes and define and automate validation and governance processes to ensure that product data quality is always top-notch. 

PIM as a Multidomain MDM System

The single-domain MDM solution is dead, at least for those who wish to eliminate the silos that hold organizations back. And this is where PIM emerges as an enabler of data interoperability, connecting disparate domains into centralized, integrated sources of master data. It readies organizations for a future where these data connections are real-time, and data emerges as a department- or function-agnostic asset that is ready to serve business objectives. PIM integrates disconnected information models and standardizes data definitions for different users, applications, and processes; it eliminates data fragmentation across layers of ERP, CRM, and others, reducing costs, complexity, and time-to-market. 

PIM/MDM Meets AI/ML: A Two-Way Street For Accelerated Commerce Growth

PIM/MDM gives organizations a centralized point of visibility and access to control product data. However, a high volume of data requires a lot of work. For example, when ingesting this data, one must ensure it is complete, accurate, and aligned with varying requirements. Enriching this data is a whole other challenge. Teams must spend tireless hours ensuring that products have the right information, are placed in the correct taxonomy with the required attributes and other rich media, while adhering to brand standards. When this is done manually, the impact is prolonged time-to-market and below-par aisle assortment.

AI and ML help mitigate this challenge by automating repetitive and redundant tasks while empowering teams to do more with their time. This automation technology can be deployed at any stage of the product data life cycle – be it ingestion, enrichment, distribution, validation, governance, or all of the above. In fact, AI and ML can also be used to carry out more complex tasks such as taxonomy and schema attribution, image recognition, and writing descriptions. 

It’s important to understand there’s a symbiotic relationship between the speed and scalability AI and ML adds, and the data quality a PIM/MDM can provide. While automation accelerates the product data journey, PIM/MDM provides the required data to back this acceleration with data quality and governance. Without governance in place, acceleration is meaningless. Sure, product data is processed faster, but if the data is not high quality to begin with, teams will still need to manually fix the data.. 

The same applies to analytics. Analytics give organizations insights into product performance, correlations, trends, and other valuable aspects. Without the correct data, however, none of these insights provide value. And this is what brings home the relevance of PIM/MDM and its primary purpose – to optimize data management and offer ready data to improve business performance. 

Combine PIM/MDM and AI/ML effectively, and you get the following business results:

  1. Cost reduction: Increased efficiency and reduced technical debt to drive down costs
  2. Accelerated time-to-market: Accelerated product journey powered by AI/ML-driven data management on PIM/MDM
  3. Omnichannel presence: Consistent omnichannel experience driven by personalization and automation
  4. Differentiation: Unique brand presence across channels with product-data-driven differentiation 
  5. Governance: Improved governance built on robust validation mechanisms
  6. Customer experience: Improved customer experience built on personalization
  7. Innovation: Increased capacity leading to an organic increase in innovative practices

With a robust ecosystem of partners and unmatched expertise in data management, Pivotree enables organizations to leverage data as an asset. We help you eliminate the need to add more technology to manage data and deliver what your business needs the most – data at your service. Our data solutions are driven by advanced AI- and ML-driven automation to ensure that your organization grows at the speed of customer expectations. Learn more about Pivotree data management services here:


1. How do organizations typically encounter challenges with managing product data across different systems and processes?

Organizations often face challenges with managing product data across various systems and processes due to the presence of data silos. These silos result in disparate data sources, including emails, spreadsheets, warehouse systems, ERPs, and CRMs, leading to data inconsistency, inaccuracy, and a lack of interoperability.

2. In what ways can centralized control over product data, such as through Product Information Management (PIM) systems, benefit organizations?

Centralized control over product data, facilitated by Product Information Management (PIM) systems, offers several benefits to organizations. It provides complete visibility and control over the end-to-end product data journey, from onboarding to enrichment and distribution. With PIM, organizations can ensure that product data is always up-to-date, easily accessible, and consistent across different touchpoints. Additionally, PIM enables organizations to track data changes, automate validation processes, and maintain high-quality product data.

3. How do AI and ML technologies contribute to improving efficiency and data quality in managing product data?

AI and ML technologies play a crucial role in improving efficiency and data quality in managing product data. These technologies automate repetitive and redundant tasks involved in data ingestion, enrichment, distribution, validation, and governance. For example, AI and ML can automate tasks such as taxonomy attribution, image recognition, and description writing, thereby accelerating the product data lifecycle. By leveraging AI and ML, organizations can enhance data accuracy, reduce time-to-market, and achieve greater scalability in managing their product data.