Product data is multiplying at astounding rates. Research shows the volume of data created over the next three years will be more than double the amount of data created since the advent of digital storage[1]. For ecommerce businesses, product data lies at the heart of daily operations and customer experience. As catalogs expand and supply chains globalize, effectively managing this data avalanche becomes critical.
Enter product data management (PDM), a system of collecting, organizing and distributing product information across the enterprise. PDM consolidates massive amounts of product data into a single source of truth available to everyone who needs it. Backed by artificial intelligence and machine learning, it’s become an invaluable asset for retailers striving to delight customers in the digital age.
The Product Data Deluge
Ecommerce product catalogs can contain thousands of unique items, each with countless data points like title, description, price, images, variants, accessories, specifications and more. Multiply this across global online sellers and the scale is staggering.
A recent IDC study found that by 2025, nearly 20% of the global datasphere will consist of product data[2]. For context, the entire datasphere was estimated at 59 zettabytes in 2020 – that‘s 59 trillion gigabytes!
Managing data at this scale is impossible without automation. Ecommerce leaders are turning to PDM solutions backed by artificial intelligence to make sense of the product data deluge.
PDM in the Business Technology Stack
PDM sits at the intersection of business processes and technology infrastructure. It bridges gaps between core enterprise systems:
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Product Lifecycle Management (PLM): Source of technical product data from engineering, manufacturing, etc.
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Enterprise Resource Planning (ERP): Contains supply chain, inventory, order and other operational data.
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Customer Relationship Management (CRM): Manages customer transaction histories and marketing data.
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Digital Commerce: Channels like web stores, mobile apps, online marketplaces.
By connecting these systems, PDM provides a holistic view of products that powers data-driven decisions across the organization.
PDM in Action: vRush Case Study
vRush is a multi-brand ecommerce company selling sports equipment and apparel across Europe. They faced numerous challenges:
- 10 legacy brand websites built on different platforms
- Hundreds of new products added weekly
- Complex manufacturing with 50+ suppliers
- Highly seasonal demand swings
- Rising product returns from inconsistent data
To unify their product data, vRush implemented the Salsify PDM platform with AI-powered workflows including:
- Automated aggregation of product data from internal databases, PLM and ERP systems
- Centralized enrichment via AI and humans to add missing details like images, videos and descriptions
- Continuous syndication to keep all sales channels up to date
- Data quality monitoring to identify issues before they impact customers
The results were transformative. Product data accuracy improved by over 90%, supply chain costs fell due to fewer mismatches, and product returns declined by 22% as customers received consistent information across channels.
Key Capabilities of Leading PDM Platforms
Modern PDM solutions apply artificial intelligence, machine learning and big data architecture to tame swelling product data volumes. Here are some key capabilities:
Workflow Orchestration: Models complex processes for data ingestion, cleansing, enrichment and distribution. Ex: inRiver MAP.
Master Data Management: Creates a "golden record" for each product, harmonizing data from multiple systems. Ex: PTC Windchill.
AI-powered Enrichment: Automatically generates missing information like product descriptions. Ex: Salsify‘s AI Workbench.
Omnichannel Syndication: Pushes data to all sales and commerce endpoints in the right format. Ex: Percussion CM1.
Embedded Analytics: Enables data-driven decisions via dashboards, reporting and AI recommendations. Ex: Informatica Axon.
Implementation Guide: Steps to PDM Success
Implementing PDM brings deep transformation touching many departments. Following best practices can ensure a smooth rollout:
1. Audit Existing Systems: Document existing product data sources, flows and gaps. This guides integration planning.
2. Phase Rollout: Prioritize high-impact use cases first, e.g. a brand site relaunch. Gather quick wins before expanding.
3. Cleanse Data Initially: Fix duplicates, inconsistencies, missing fields through automation and crowdsourced humans.
4. Create Data Governance Rules: Establish sustainable processes for data oversight, issue resolution and change control.
5. Train Internal Stakeholders: Get teams onboard through demos, workshops and documentation of new data workflows.
6. Monitor Ongoing Data Quality: Use PDM tools to continuously audit product data health, identify root causes for errors.
With the right planning and change management, PDM can quickly yield dramatic improvements in ecommerce operations and customer experience.
The PDM Crystal Ball: Emerging Trends and Technologies
PDM innovation continues at a rapid pace. Some innovations on the horizon that will shape PDM include:
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IoT and sensor data integration: Collect real-time telemetry from smart, connected products to enrich PDM.
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Blockchain for product provenance: Provide transparency into product origins and custody via distributed ledger.
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Digital twins: Create virtual models of products linked to PDM to optimize manufacturing.
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Augmented data management: Use AR/VR to enhance human-in-the-loop data tasks like annotation, validation and issue resolution.
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Knowledge graphs and semantics: Add meaning to PDM via contextual knowledge graphs to power intelligent applications.
Gear Up for the Product Data Future
As product variety and complexity increases, businesses need scalable ways to manage data. Investing in capable product data management platforms is a competitive necessity for forward-thinking ecommerce leaders. With PDM, retailers can turn their product data from a liability into an asset – delighting customers, accelerating innovation and driving revenue. The time to implement PDM is now.
[1] IDC, The Digitization of the World: From Edge to Core", Nov 2018 [2] IDC, "The Future of Product Information Management Systems is AI", Doc #EUR145910219, Dec 2019