From Chaos to Clarity: Cleaning Up Product Data

Here is everything you need to know about product data management, including the key challenges, strategies, and tools you need to understand.
에 게시됨

For any retailer or e-commerce company, your product data is critical to your business success.

Given that eighty-five percent of consumers say accurate product information is essential when deciding what to buy, good product data management is a critical skill for any e-commerce business. But with thousands, tens of thousands, or more product in many different categories, onboarding and maintaining data can take time. Alongside new products, existing data has to be regularly updated.  

This is why having the right tools, strategies, and systems to help you achieve effective product data management is more important than you may first think. Here is everything you need to know about product data management, including the key challenges, strategies, and tools you need to understand.

Understanding Product Data Management

A pretty broad term, product data management refers to the entire process of collecting, organizing, and managing all of a company’s product data. Within this there can be many different types of data to manage, organize, and process such as:

  • Product descriptions
  • Product specifications
  • Basic product information such as its name, SKU, and brand
  • Pricing and cost data
  • Associated digital assets such as images, videos, or additional documents
  • Inventory levels
  • Supplier and manufacturer information
  • Relevant compliance and regulatory data
  • Customer reviews and ratings
  • Webpage analytics such as how many views a product page has received

Even for a few products this is already a lot of data, and keeping things organized is the starting point for success.

Unorganized product data management will dramatically reduce productivity as you and other employees spend more time searching for the correct information. Meanwhile, good organization speeds everything up and helps you use your data more effectively.

Through good product data management, you can more easily analyze all the data you inevitably collect, spot trends and make more informed strategic decisions.

How Effective Product Data Management Helps Your Business

The role of product data management is simple: ensure all your product information is accurate, consistent, and easily accessible.

The initial benefit of more organized data is self-explanatory, yet implementing effective product data management has many knock-on benefits beyond a more organized business.

  1. Data Consistency: Good product data management helps ensure your product data is consistent across all channels, from webpages to in-store displays. This helps increase customer trust and your overall brand reputation.
  1. Data Accuracy: The last thing you want in your system is incorrect product data. A thorough data management system helps minimize errors and inconsistencies in product data by maintaining a single source of information.
  1. Improved Customer Experience: With more accurate, consistent, and organized data, customers can easily find their desired products and make informed buying decisions. This increases customer satisfaction and reduces returns.
  1. Enhanced Efficiency: By creating a centralized data system you can massively increase your efficiency and productivity as crucial product data becomes much more accessible. Not only does this reduce the time needed for data entry and management, it also makes data analysis much easier.
  1. Informed Decision Making: Through AI or even manual analysis, organized product data can reveal a lot about how your business is performing. Good product data management can help you gain insights into seasonal events that affect product sales, different descriptions that increase conversion rates, and the value added by product development, helping you make more informed strategic decisions.

Common Challenges in Product Data Management

Good product data management can benefit your business, but implementing it is challenging.

Centralizing Your Data Silos and Combating Fragmentation

An incredibly persistent issue for larger organizations, many companies face the issue of data silos, where different departments or teams have their own separate databases of product information. While it may seem harmless at first, it’s not.

Your marketing team having a different set of product descriptions from your inventory team’s can quickly lead to inconsistent product information across your brand. Just as bad, this fragmentation can lead to lower productivity as employee effort is duplicated with different teams working on the same, but separate thing.

As you grow, this data fragmentation only proves more of a challenge as you attempt to maintain up-to-date information across your company.

Unifying Inconsistent Data Formats and Standards

What if one set of product data is in an Excel file, the next in Google Sheets, another in SQL, and perhaps more are in various inventory management systems?

It is surprisingly easy to accumulate vast amounts of product data across multiple different software formats, an issue that is only made worse when dealing with data from numerous different suppliers. This makes it difficult to analyze data in any meaningful way, or compare your products accurately.  

Presenting your product information to customers consistently also becomes much more challenging with inconsistent data formats.

The Problems With Manual Data Entry

Time-consuming and error-prone, manual data entry is the bane of any e-commerce business. It saps energy, makes employees less productive, and can often lead to burnout with increasing errors in data entry.

Typos, incorrect information, and missing fields can become more common in your product database when you choose manual data entry over automated systems. All of these errors can directly lead to lower sales, as customers become confused by incorrect or misleading information and decide not to buy.

Avoiding Outdated or Missing Information

For e-commerce businesses, there’s no room for stagnation—to stay competitive, you have to constantly monitor and update your product offerings. This inevitably means you also need to constantly update your product information.

As new features are added, specifications change and products get removed or added, maintaining accurate, up-to-date information across all your channels is challenging. This becomes even more significant as you scale, with thousands of products needing regular updates.

Strategies for Effective Product Data Management

We’ve explained why good product data management is vital to e-commerce businesses and online retailers and the many challenges associated with it.

Which means you’ve reached arguably the most essential section: how to achieve effective product data management. Here are some of the top strategies and techniques you can use to organize your product data and make managing it easier:

Implementing a Centralized Data Management System

The first and most critical action is centralizing all your product data. A Product Information Management (PIM) system or AI-powered catalog can help manage this process efficiently. Whatever route you choose, you must consolidate your data into a single software or system.

Make sure this system is easily accessible to all employees who need to use it, and you’ll see this step immediately reduce inconsistencies, boost internal productivity and collaboration, and eliminate the issue of data silos.  

Using Data Harmonization and Enrichment Techniques

Data harmonization is a fancy term for standardizing product information across multiple sources. It is a crucial step for ensuring consistent and structured product data.

Meanwhile, data enrichment enhances existing product information with additional details or specifications. This could include creating more detailed descriptions, adding translated versions, or updating product data for more fine-tuned SEO keywords.

By combining these approaches you can transform your product data management, first making it easier to use and more consistent across your business, then enhancing it for increased discoverability and sales.

Establishing Data Governance Policies

Often overlooked, you must set out clear guidelines and processes for how all your product data should be created, managed, and used. You can ensure high-quality product data management from the get-go by helping employees get things right the first time with clear instructions: define data ownership, create easy workflows for updating product information, and establish data quality standards.

Completing Regular Audits and Updates

After making all your changes, regularly audit your system and update your product information. This helps identify errors or problems before they become significant issues and keep your product data consistent over time.

How AI can Help In Product Data Management

While these strategies are helpful, implementing them manually can be almost as time-consuming as working with unorganized product data.

This is why tools such as digital asset management systems are essential. By bringing in AI-driven solutions such as Trustana, you can completely automate your data enrichment and centralization, all while bringing the added benefits of AI-powered insights.

A tool such as Trustana can:

  • Automate data extraction, centralization, and categorization
  • Perform intelligent product matching
  • Carry out near-instant updates
  • Optimize product descriptions for SEO and specific customer bases
  • Provide insights into customer behavior

AI-powered or not, DAM (digital asset management) systems are vital for helping you effectively manage and scale your vast product portfolio.  

They help create centralized data storage that is easily managed, integrate simply with existing ERP systems and e-commerce platforms, and provide a dashboard to help you see version history and retrieve digital assets instantly.

Better Product Data Leads To Better Business Outcomes

Maintaining effective product data management is more than just a background admin chore. If you want to fully realize the success of your e-commerce business, implementing exceptional product data management is a must.

Through a combination of the right strategies and tools, you can quickly overcome the hurdles involved with maintaining good product data management, and skip right to the rewards.

Get a demo today to see how Trustana can help turn your product data from chaos into clarity and better business outcomes.

Table of Contents
Back

From Chaos to Clarity: Cleaning Up Product Data

product data management, including the key challenges, strategies, and tools

For any retailer or e-commerce company, your product data is critical to your business success.

Given that eighty-five percent of consumers say accurate product information is essential when deciding what to buy, good product data management is a critical skill for any e-commerce business. But with thousands, tens of thousands, or more product in many different categories, onboarding and maintaining data can take time. Alongside new products, existing data has to be regularly updated.  

This is why having the right tools, strategies, and systems to help you achieve effective product data management is more important than you may first think. Here is everything you need to know about product data management, including the key challenges, strategies, and tools you need to understand.

Understanding Product Data Management

A pretty broad term, product data management refers to the entire process of collecting, organizing, and managing all of a company’s product data. Within this there can be many different types of data to manage, organize, and process such as:

  • Product descriptions
  • Product specifications
  • Basic product information such as its name, SKU, and brand
  • Pricing and cost data
  • Associated digital assets such as images, videos, or additional documents
  • Inventory levels
  • Supplier and manufacturer information
  • Relevant compliance and regulatory data
  • Customer reviews and ratings
  • Webpage analytics such as how many views a product page has received

Even for a few products this is already a lot of data, and keeping things organized is the starting point for success.

Unorganized product data management will dramatically reduce productivity as you and other employees spend more time searching for the correct information. Meanwhile, good organization speeds everything up and helps you use your data more effectively.

Through good product data management, you can more easily analyze all the data you inevitably collect, spot trends and make more informed strategic decisions.

How Effective Product Data Management Helps Your Business

The role of product data management is simple: ensure all your product information is accurate, consistent, and easily accessible.

The initial benefit of more organized data is self-explanatory, yet implementing effective product data management has many knock-on benefits beyond a more organized business.

  1. Data Consistency: Good product data management helps ensure your product data is consistent across all channels, from webpages to in-store displays. This helps increase customer trust and your overall brand reputation.
  1. Data Accuracy: The last thing you want in your system is incorrect product data. A thorough data management system helps minimize errors and inconsistencies in product data by maintaining a single source of information.
  1. Improved Customer Experience: With more accurate, consistent, and organized data, customers can easily find their desired products and make informed buying decisions. This increases customer satisfaction and reduces returns.
  1. Enhanced Efficiency: By creating a centralized data system you can massively increase your efficiency and productivity as crucial product data becomes much more accessible. Not only does this reduce the time needed for data entry and management, it also makes data analysis much easier.
  1. Informed Decision Making: Through AI or even manual analysis, organized product data can reveal a lot about how your business is performing. Good product data management can help you gain insights into seasonal events that affect product sales, different descriptions that increase conversion rates, and the value added by product development, helping you make more informed strategic decisions.

Common Challenges in Product Data Management

Good product data management can benefit your business, but implementing it is challenging.

Centralizing Your Data Silos and Combating Fragmentation

An incredibly persistent issue for larger organizations, many companies face the issue of data silos, where different departments or teams have their own separate databases of product information. While it may seem harmless at first, it’s not.

Your marketing team having a different set of product descriptions from your inventory team’s can quickly lead to inconsistent product information across your brand. Just as bad, this fragmentation can lead to lower productivity as employee effort is duplicated with different teams working on the same, but separate thing.

As you grow, this data fragmentation only proves more of a challenge as you attempt to maintain up-to-date information across your company.

Unifying Inconsistent Data Formats and Standards

What if one set of product data is in an Excel file, the next in Google Sheets, another in SQL, and perhaps more are in various inventory management systems?

It is surprisingly easy to accumulate vast amounts of product data across multiple different software formats, an issue that is only made worse when dealing with data from numerous different suppliers. This makes it difficult to analyze data in any meaningful way, or compare your products accurately.  

Presenting your product information to customers consistently also becomes much more challenging with inconsistent data formats.

The Problems With Manual Data Entry

Time-consuming and error-prone, manual data entry is the bane of any e-commerce business. It saps energy, makes employees less productive, and can often lead to burnout with increasing errors in data entry.

Typos, incorrect information, and missing fields can become more common in your product database when you choose manual data entry over automated systems. All of these errors can directly lead to lower sales, as customers become confused by incorrect or misleading information and decide not to buy.

Avoiding Outdated or Missing Information

For e-commerce businesses, there’s no room for stagnation—to stay competitive, you have to constantly monitor and update your product offerings. This inevitably means you also need to constantly update your product information.

As new features are added, specifications change and products get removed or added, maintaining accurate, up-to-date information across all your channels is challenging. This becomes even more significant as you scale, with thousands of products needing regular updates.

Strategies for Effective Product Data Management

We’ve explained why good product data management is vital to e-commerce businesses and online retailers and the many challenges associated with it.

Which means you’ve reached arguably the most essential section: how to achieve effective product data management. Here are some of the top strategies and techniques you can use to organize your product data and make managing it easier:

Implementing a Centralized Data Management System

The first and most critical action is centralizing all your product data. A Product Information Management (PIM) system or AI-powered catalog can help manage this process efficiently. Whatever route you choose, you must consolidate your data into a single software or system.

Make sure this system is easily accessible to all employees who need to use it, and you’ll see this step immediately reduce inconsistencies, boost internal productivity and collaboration, and eliminate the issue of data silos.  

Using Data Harmonization and Enrichment Techniques

Data harmonization is a fancy term for standardizing product information across multiple sources. It is a crucial step for ensuring consistent and structured product data.

Meanwhile, data enrichment enhances existing product information with additional details or specifications. This could include creating more detailed descriptions, adding translated versions, or updating product data for more fine-tuned SEO keywords.

By combining these approaches you can transform your product data management, first making it easier to use and more consistent across your business, then enhancing it for increased discoverability and sales.

Establishing Data Governance Policies

Often overlooked, you must set out clear guidelines and processes for how all your product data should be created, managed, and used. You can ensure high-quality product data management from the get-go by helping employees get things right the first time with clear instructions: define data ownership, create easy workflows for updating product information, and establish data quality standards.

Completing Regular Audits and Updates

After making all your changes, regularly audit your system and update your product information. This helps identify errors or problems before they become significant issues and keep your product data consistent over time.

How AI can Help In Product Data Management

While these strategies are helpful, implementing them manually can be almost as time-consuming as working with unorganized product data.

This is why tools such as digital asset management systems are essential. By bringing in AI-driven solutions such as Trustana, you can completely automate your data enrichment and centralization, all while bringing the added benefits of AI-powered insights.

A tool such as Trustana can:

  • Automate data extraction, centralization, and categorization
  • Perform intelligent product matching
  • Carry out near-instant updates
  • Optimize product descriptions for SEO and specific customer bases
  • Provide insights into customer behavior

AI-powered or not, DAM (digital asset management) systems are vital for helping you effectively manage and scale your vast product portfolio.  

They help create centralized data storage that is easily managed, integrate simply with existing ERP systems and e-commerce platforms, and provide a dashboard to help you see version history and retrieve digital assets instantly.

Better Product Data Leads To Better Business Outcomes

Maintaining effective product data management is more than just a background admin chore. If you want to fully realize the success of your e-commerce business, implementing exceptional product data management is a must.

Through a combination of the right strategies and tools, you can quickly overcome the hurdles involved with maintaining good product data management, and skip right to the rewards.

Get a demo today to see how Trustana can help turn your product data from chaos into clarity and better business outcomes.

Key Performance Indicator (KPI)
key-performance-indicator-kpi
Generative Engine Optimization (GEO)
generative-engine-optimization-geo
Answer Engine Optimization (AEO)
answer-engine-optimization-aeo
Direct-to-Consumer (DTC)
direct-to-consumer-dtc
Product Content Management (PCM)
product-content-management-pcm
White Label Product
white-label-product
User Experience (UX)
user-experience-ux
UPC (Universal Product Code)
upc-universal-product-code
Third-Party Marketplace
third-party-marketplace
Structured Data
structured-data
Syndication
syndication
Stale Content
stale-content
SKU-Level Analytics
sku-level-analytics
SKU Rationalization
sku-rationalization
SKU Performance
sku-performance
SKU (Stock Keeping Unit)
sku-stock-keeping-unit
SEO (Search Engine Optimization)
seo-search-engine-optimization
Sell-Through Rate
sell-through-rate
Search Relevance
search-relevance
Search Merchandising
search-merchandising
Rich Media
rich-media
Retailer Portal
retailer-portal
Retail Content Syndication
retail-content-syndication
Retail Media
retail-media
Personalization
personalization
Product Data Versioning
product-data-versioning
Replatforming
replatforming
Retail Analytics
retail-analytics
Repricing Tool
repricing-tool
Real-Time Updates
real-time-updates
Product Visibility
product-visibility
Product Variant
product-variant
Product Validation
product-validation
Product Upload
product-upload
Product Title Optimization
product-title-optimization
Product Taxonomy Tree
product-taxonomy-tree
Product Taxonomy
product-taxonomy
Product Tagging
product-tagging
Product Syndication Lag
product-syndication-lag
Product Syndication
product-syndication
Product Status Tracking
product-status-tracking
Product Schema
product-schema
Product Page Bounce Rate
product-page-bounce-rate
Product Onboarding
product-onboarding
Product Metadata
product-metadata
Product Matching
product-matching
Product Lifecycle Stage
product-lifecycle-stage
Product Information Management (PIM)
product-information-management-pim
Product Lifecycle Management (PLM)
product-lifecycle-management-plm
Product Info Templates
product-info-templates
Product Import
product-import
Product Feed Validation
product-feed-validation
Product Feed Scheduling
product-feed-scheduling
Product Feed
product-feed
Product Family
product-family
Product Export
product-export
Product Discovery
product-discovery
Product Detail Page (PDP)
product-detail-page-pdp
Product Dimension Attributes
product-dimension-attributes
Product Description
product-description
Product Data Syndication Platforms
product-data-syndication-platforms
Product Data Sheet
product-data-sheet
Product Data Quality
product-data-quality
Product Data Harmonization
product-data-harmonization
Product Comparison
product-comparison
Product Content Enrichment
product-content-enrichment
Product Compliance
product-compliance
Product Channel Fit
product-channel-fit
Product Categorization
product-categorization
Product Badging
product-badging
Product Bundling
product-bundling
Product Attributes
product-attributes
Product Attribute Completeness
product-attribute-completeness
PDP Optimization
pdp-optimization
Price Scraping
price-scraping
Out-of-Stock Alerts
out-of-stock-alerts
PDP Heatmap
pdp-heatmap
PDP Conversion Rate
pdp-conversion-rate
Omnichannel Strategy
omnichannel-strategy
Omnichannel
omnichannel
Net New SKU Creation
net-new-sku-creation
Multichannel Retailing
multichannel-retailing
Mobile Optimization
mobile-optimization
Marketplace Listing Errors
marketplace-listing-errors
Metadata
metadata
Marketplace Reconciliation
marketplace-reconciliation
Lifecycle Automation
lifecycle-automation
Marketplace Compliance
marketplace-compliance
Marketplace
marketplace
MAP Pricing (Minimum Advertised Price)
map-pricing-minimum-advertised-price
Long-Tail Keywords
long-tail-keywords
Localization Tags
localization-tags
Listing Optimization
listing-optimization
Inventory Management
inventory-management
GTM (Go-to-Market) Strategy
gtm-go-to-market-strategy
Intelligent Search
intelligent-search
Image Optimization
image-optimization
Headless Commerce
headless-commerce
GTIN (Global Trade Item Number)
gtin-global-trade-item-number
Fuzzy Search
fuzzy-search
Flat File
flat-file
First-Mile Fulfillment
first-mile-fulfillment
First-Party Data
first-party-data-a51e9
Feed Testing Environment
feed-testing-environment
Feed-Based Advertising
feed-based-advertising
Feed Optimization Tool
feed-optimization-tool
Feed Management
feed-management
Feed Diagnostics
feed-diagnostics
Faceted Search
faceted-search
ERP (Enterprise Resource Planning)
erp-enterprise-resource-planning
EPID (eBay Product ID)
epid-ebay-product-id
Enrichment Rules
enrichment-rules
E-commerce Platform
e-commerce-platform
Enhanced Brand Content (EBC)
enhanced-brand-content-ebc
EAN (European Article Number)
ean-european-article-number
Drop Shipping
drop-shipping
Dynamic Pricing
dynamic-pricing
Duplicate Content
duplicate-content
Digital Transformation
digital-transformation
Digital Shelf
digital-shelf
Digital Asset Management (DAM)
digital-asset-management-dam
Data Syncing
data-syncing
Data Normalization
data-normalization
Data Mapping
data-mapping
Data Governance
data-governance
Data Feed Transformation
data-feed-transformation
Data Feed Error Report
data-feed-error-report
Data Feed Rules
data-feed-rules
Data Enrichment Pipeline
data-enrichment-pipeline
Data Deduplication
data-deduplication
Customer Experience (CX)
customer-experience-cx
Conversion Rate
conversion-rate
Content Scalability
content-scalability
Quality Assurance (QA)
quality-assurance-qa
Content Localization
content-localization
Content Governance
content-governance
Content Gaps
content-gaps
Channel-Specific Optimization
channel-specific-optimization
Channel Readiness
channel-readiness
Category Mapping
category-mapping
Catalog Management
catalog-management
Buy Now, Pay Later (BNPL)
buy-now-pay-later-bnpl
Breadcrumb Navigation
breadcrumb-navigation
Buy Box
buy-box
Automated Workflows
automated-workflows
Automated Categorization
automated-categorization
Automated Content Generation
automated-content-generation
Attribution Tags
attribution-tags
Attribute Standardization
attribute-standardization
API (Application Programming Interface)
api-application-programming-interface
Attribute Mapping
attribute-mapping
AI Tagging
ai-tagging
First-Party Data
first-party-data
Data Clean-up
data-clean-up
Blacklisting (in feeds)
blacklisting-in-feeds
A/B Testing
a-b-testing