Role of PIM Data Governance in Streamlining Your Operations

Role of PIM Data Governance in Streamlining Your Operations
Role of PIM Data Governance in Streamlining Your Operations

Prasenjit Dey

Brands & Communications
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The global market for data governance is projected to surge to $7.42 billion by 2026, with an anticipated annual growth rate of 22.7 percent. Adopting data governance is paramount in the digital e-commerce market, where companies vie for relevance and a competitive edge. In this context, Product Information Management (PIM) emerges as the linchpin, facilitating informed decision-making for customers and addressing the dynamic challenges of compliance and data protection. 


We will discuss the pivotal role of PIM in data governance and illustrate its optimization potential for your operational workflows. The discussion will revolve around these core aspects:


  • What is Data Governance, & Why is it important for your e-commerce?
  • The significance of PIM in data governance
  • Implementing PIM within data governance frameworks
  • Best practices for leveraging PIM effectively.

So, let’s get started!

What is Data Governance?

Data governance involves overseeing data availability, usability, integrity, and security within enterprise systems, guided by internal standards and policies governing data usage. Its effectiveness lies in maintaining data consistency and trustworthiness and preventing misuse. As organizations confront growing data privacy regulations and increasingly rely on data analytics to enhance operations and inform strategic decisions, robust data governance becomes more crucial.

Why Data Governance?

Inadequate data governance can lead to unresolved data inconsistencies across various systems within an organization. For example, discrepancies in customer names across sales, logistics, and customer service systems may arise. Failure to address these discrepancies could impede data integration efforts, resulting in operational challenges across departments and compromising data integrity essential for accurate business intelligence (BI), enterprise reporting, and data science applications. 

Without proper oversight, data errors may go unnoticed and uncorrected, further undermining the accuracy of analytics. Deficient data governance can hinder compliance with regulatory obligations. This poses significant challenges for companies evolving in data privacy and protection laws, such as the European Union's GDPR and the California Consumer Privacy Act (CCPA). 

A robust enterprise data governance program typically entails establishing common data definitions and standardized data formats across all business systems, enhancing data consistency for operational purposes and regulatory compliance.

In simple terms, Data Governance helps businesses to:

  • Avoid inconsistent data silos in different business units.
  • Improve data quality through identifying and fixing data errors in data sets.
  • Increase accuracy in analytics and provide reliable information to other employees.
  • Implement and enforce policies and procedures that help prevent data errors and misuse. 
  • Ensure data compliance with data privacy laws and other regulations. 


Also Read: The Ultimate Guide to Product Information Management (PIM)


What’s the Difference Between Traditional vs. Modern Data Governance?
To understand better, here are some differences between Traditional vs. modern data governance:


Traditional Data GovernanceModern Data Governance
It is a centralized, top-down approach focused on controlling data access.It is a decentralized, community-centric approach focused on data enablement.
Traditional data governance requires enforcing data stewards and steering committees. Modern data governance combines traditional stewardship with self-governing behaviors to distribute the work and ensure everyone is responsible and held accountable. 
It involves manual processes in accepting approvals, tagging, classifying data, and more. Modern data governance automates as many processes as possible to avoid human error and achieve data governance at scale. 
Attempts to ensure data governance after the fact with post-hac data testing and validation. Shifts data governance left, incorporating tagging and validation early in the data lifecycle. 

Data Governance Example
Let’s take the banking sector as an example. A wealth management firm can use role-based access control to ensure only the right financial advisors can see clients' financial information. A central bank can use data encryption to keep sensitive data safe from unauthorized access. A bank's data team can do regular checks to find ways to improve, like improving data quality or updating security.

Challenges In Data Governance 
With the surge in data collection and volume, the necessity for a robust data strategy becomes increasingly evident. Enterprises grapple with managing these aspects alongside the critical framework data governance provides. Globally, we witness the collective generation of trillions of new data, a trend expected to escalate beyond 175 trillion gigabytes annually by 2025. Individual enterprises contribute significantly to this data deluge within this landscape, confronting internal and external challenges. Externally, they contend with compliance requirements, while internally, there's pressure to maximize the utility of available data resources.

Here are some data governance challenges.

1. Data Silos
A data silo refers to a stash of data controlled by a specific group, limiting its accessibility to others. Typically, data is segregated based on different departments within an organization. This setup obstructs the smooth exchange of data and information across the digital landscape, complicating tasks like sharing, organizing, and updating information internally. Dealing with such siloed and disorderly data prevents establishing effective data governance within the organization.

2. Data Quality
Data governance encompasses monitoring the quality of data entering a company and its utilization across the organization. Data stewards must adeptly recognize corrupt, inaccurate, or outdated data and instances were data analysis lacks context. They should be capable of establishing rules and processes swiftly to uphold the reliability of company data. 
Trustworthy data forms the foundation for data-driven organizations, which rely on information from diverse sources to make decisions. Therefore, inadequate data quality can pose a significant challenge when implementing data governance initiatives.

3. Data Opacity
Data governance necessitates that companies attain data transparency. They should be aware of the types of data they possess, where it's located, who can access it, and how it's utilized. Unfortunately, these answers are often concealed within legacy systems. Therefore, organizations need to introduce a data management process. This process should encompass strategies and techniques for accessing, integrating, storing, transferring, and preparing data for analytics. Such measures can enhance organizational decision-making capabilities.

4. Unsecure Data
As data sources multiply within and outside enterprises, data breaches are increasing. As effective data management relies on traceability, data security also depends on it. IT teams must track the origin, location, access, usage, and data deletion methods. 
Data governance establishes protocols to prevent leaks of sensitive business or customer data, ensuring it remains secure. However, legacy platforms often create isolated information that's challenging to access and trace.

5. Control Over Data
Businesses often embark on data governance initiatives in response to regulatory requirements such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS), and the U.S. Sarbanes-Oxley (SOX) law. These regulations mandate that organizations implement data governance frameworks demonstrating data traceability, access logs, and oversight of data usage. 
These standards enable companies to safeguard sensitive information and assert control over their data. Nonetheless, a lack of data control can present a significant hurdle to an organization's data governance efforts.

How Does PIM Play an Important Role in Data Governance?  

Product Information Management has become essential in today's data governance journey. Given the increasing significance of data, PIM allows companies to centralize, edit, and distribute their product information efficiently across various channels. Here’s how PIM plays an important role in data governance: 

1. PIM Handles Enormous Amounts of Data
Medium-sized enterprises and large corporations frequently contend with vast volumes of content. Given the extensive array of products, numerous services, diverse vendors and suppliers, and various sales channels, it's evident that these businesses must manage substantial amounts of data. The process of sorting and filtering through content to locate accurate and pertinent information can be laborious and susceptible to mistakes. An effective remedy is to employ product information management software to organize dispersed data and standardize product information systematically. PIM assumes a pivotal role by offering a centralized repository for product information, streamlining the handling and manipulation of any volume of data.

2. Avoids managing cluttered digital assets
Your business likely houses various digital assets, including product photos, manuals, marketing visuals, videos, and presentations. As you continue to generate more of these assets, you contribute to a growing collection of digital resources. Both archived and current digital files hold significant value for your company. However, your digital assets can become disorganized without an organized storage and retrieval system. Establishing a cohesive product library and providing a centralized location for storing all digital assets in a more streamlined manner is crucial. 

A PIM system can logically store and manage various types of information, such as product attributes, photos, videos, and marketing materials. This ensures that you can easily search for and retrieve the exact assets you need precisely when needed.

3. Removes Lack of transparency
Communication within large enterprises presents a well-recognized challenge. As businesses expand, departments become isolated, hindering information sharing, tools, goals, and priorities. These communication barriers result in inefficiencies and redundant work. To address this issue, businesses must devise strategies to bridge communication gaps between teams and departments. Encouraging collaboration and fostering cross-functional teams are vital for the organization's success.

Upon implementing an open-source PIM software, users from various departments can seamlessly communicate with one another. This facilitates the sharing of important task updates and deadlines, reducing confusion among team members. Consequently, this promotes more productive work and yields better results.

4. PIM Avoid error-prone spreadsheets
If your company continues to rely on spreadsheets, you're likely aware of their management challenges. Uploading data for thousands of products into spreadsheets manually takes hours and is susceptible to errors. 

Furthermore, using separate spreadsheets by different individuals creates significant communication gaps, making it difficult to track if important data is altered. This can result in presenting customers with incorrect or ambiguous information. Implementing an open-source PIM system can enhance the accuracy of your spreadsheets. With PIM, you can input product data in bulk and establish workflows, streamlining the process.

5. Avoid Lack of Communication
Clear and timely communication between you and your supplier is essential for launching new products, updating existing ones, or changing regulations or requirements. However, it's common for businesses to receive incorrect and inadequate data for product listings. The data provided may contain misspellings, be incomplete, or be in the wrong format. Verifying and rectifying such errors before uploading product information to an e-commerce site requires significant time and effort.

A PIM platform can facilitate the creation of a customized portal to align you and your vendors. A vendor portal streamlines supply chain operations and promptly resolves issues and emergencies. By ensuring smooth communication with your suppliers, you can market your products on time while maintaining the accuracy of product information.

6. PIM Helps in Organized Product Catalogs
Large enterprises that sell numerous products understand the challenges and time constraints of maintaining product catalogs. With a vast array of products, multiple vendors, and diverse sales channels, ensuring consistent and up-to-date data across the entire product catalog can be daunting. This often leads to complex and disorganized catalogs. The solution is to utilize open-source PIM software to automate vendor data collection and consolidate product information into a centralized location. This streamlines product data management, facilitating seamless product marketing efforts.

7. Avoids Inaccuracy and Inconsistency in Product Information
If your company still relies on spreadsheets, you're likely familiar with the challenges they present in management. Manually uploading data for thousands of products into a spreadsheet can consume hours and is prone to errors. Additionally, individuals working on separate spreadsheets create significant communication gaps, making it difficult to track if important data is tampered with. This can result in presenting your customers with incorrect or ambiguous information.

Implementing a PIM solution can enhance the accuracy of your spreadsheets. With PIM software, you can input product data in bulk and establish workflows to streamline processes.

End Note

It doesn’t matter if you are a retailer or manufacturer. PIM proves to be the best tool for data governance. A PIM system enables you to efficiently distribute the most accurate and up-to-date information. It simplifies disseminating the latest updates simultaneously in various languages and for different markets.    

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