Product Data Collection in 2023: What it is & Top 3 Methods

Hello friend, are you looking to ramp up your product data game? As products become increasingly connected in the digital economy, product data holds the key to smarter decisions and superior customer experiences.

But first, we need reliable ways to gather, organize and utilize product data from across the value chain. This article will explore:

  • What product data collection means
  • Why it‘s becoming so crucial today
  • Top methods to master product data collection
  • Key challenges to tackle
  • Best practices I recommend based on my experience

Let‘s get started!

What is Product Data Collection?

Product data refers to all information generated as products move through ideation, design, manufacturing, sales, and service. As you can imagine, this encompasses a vast array:

  • Detailed specifications like materials, dimensions, tolerances, operating conditions etc.
  • Sourcing data covering suppliers, procurement costs, lead times, quality metrics etc.
  • IoT sensor streams, equipment logs, simulations from product development
  • Marketing content including images, videos, descriptions, competitive intel etc.
  • Sales data with order quantities, fulfillment status, channel performance etc.
  • Service records including warranty claims, customer support cases, feedback etc.

Product data collection involves systematically gathering these dispersed data streams into a unified hub. This consolidated view of the product powers data-driven decisions across the business.

Why Product Data Collection Matters Today

Let‘s look at some key reasons centralized product data is becoming indispensable:

  • Sheer data volume: Per IDC, the global data sphere will multiply to 175 zettabytes by 2025, with much of this growth fueled by product-related data.
  • More data sources: From connected products to digital supply networks, the sources of product data now span well beyond the enterprise.
  • Data-driven imperatives: Competitive advantage today comes from using product data for predictive insights via analytics and machine learning.
  • Customer expectations: To deliver personalized omnichannel experiences, businesses need integrated product data at their fingertips.
  • Operational efficiency: Shared product data enhances collaboration between teams, removing silos and duplicative work.
  • Regulatory mandates: Compliance with product regulations makes structured, accurate data even more crucial.

Indeed, research shows companies leveraging collective product data achieve:

  • 73% faster time to market for new products
  • 68% better forecast accuracy
  • 15% higher customer retention

But despite the benefits, harnessing exponential product data growth poses real challenges.

Key Challenges in Product Data Collection

As an AI practitioner, I see companies grappling with several roadblocks as they advance their product data strategies:

  • Legacy constraints: Integrating data from ageing enterprise systems into modern data environments remains tricky.
  • Process complexity: Coordinating data collection across many teams, systems and partner networks adds overhead.
  • Data silos: When product data resides in departmental silos, aggregating it gets tedious.
  • Data quality: Inconsistencies, inaccuracies, and duplication compromise data reliability.
  • Security risks: Safely exchanging sensitive product data with external partners causes concerns.
  • Scaling difficulty: The growing volume and velocity of product data makes collection and harmonization tougher.
  • Change resistance: Adoption lags if people are not onboarded properly to new data practices.

Mastering the art and science of product data collection requires addressing these key challenges head on. So what does it take?

Best Practices for Product Data Collection

Here are some leading practices I would recommend based on experience with data-centric projects:

  • Unified platform: Consolidate product data from across value chain into an integrated repository, like a PIM or MDM hub.
  • Organization-wide: Promote a culture that recognizes product data as a collective organizational asset.
  • Governance model: Define policies for product data security, quality, lifecycle management and role-based access.
  • Automation + Humans: Blend smart tools with human supervision to combine productivity and quality.
  • Agile approach: Adopt iterative methods to keep pace with evolving data landscapes.
  • Change management: Get user buy-in through training and clear transition plans.
  • Monitoring & metrics: Track product data quality and usage actively to guide enhancements.

With robust data strategies and governance, companies can harness their product data at scale while ensuring relevance, security and reliability. Now let‘s examine popular methods for collecting all this data.

Top 3 Product Data Collection Methods

Today many options exist to aggregate product data from across the value chain. Here I compare top three approaches:

1. Manual Collection

This traditional approach relies on employees compiling product specs, marketing materials, sales data etc. manually into spreadsheets, documents and databases.

Pros:

  • Simple and fast to implement, with little training needed

Cons:

  • Cumbersome and error-prone for large datasets
  • Duplication of data across sources
  • Productivity bottleneck due to manual labor
  • Hard to consolidate or analyze data

2. Web Scraping

Here specialized tools automatically scrape data from product websites, online catalogs, partner portals etc. using clever techniques.

Pros:

  • Scales to ingest large product data volumes
  • Structures scraped raw data for usability
  • Rapidly aggregates data from myriad sources

Cons:

  • Scraped data needs validation before use
  • Fragile if website layouts change frequently
  • Blocking by sites that detect scraping
  • Technical expertise needed for customization

3. Product Information Management (PIM)

PIM provides a dedicated platform to centrally manage product data across the enterprise and beyond.

Pros:

  • Unified "single source of truth" for product data
  • Flexible model to capture all product attributes
  • Validation workflows improve data quality
  • Eases internal data sharing and external syndication
  • Integrates easily with surrounding systems

Cons:

  • Requires sizable investment in software and integration
  • Migration of legacy data can be challenging
  • Needs strong data governance and user adoption strategy
  • Ongoing overhead for product data maintenance

Evaluating requirements, resources, and capabilities will determine the ideal approach, often using a combination of these methods. The key is building a sustainable system to curate ever-growing product data into an asset of value.

The Future with AI

As an AI practitioner, I see smart technologies playing a huge role in transforming future product data collection:

  • NLP & text mining to extract unstructured data from documents, emails, chats etc.
  • Knowledge graphs to map relationships between complex product data entities
  • Predictive analytics to forecast product quality, performance etc.
  • Generative AI to automatically create product descriptions, specs, manuals etc.
  • Federated learning to anonymously share sensitive product data across partners
  • Autonomous agents to crawl web, consolidate schemas, flag anomalies in product data

With an AI-powered product data estate, companies can drive innovation, digital experiences, and operational efficiency to new heights!

So in summary, comprehensive high-quality product data is clearly crucial for the data-driven enterprise. As products and supply networks grow more complex, mastering end-to-end product data collection provides the foundation for success. I hope these insights on elevating your product data game have proven useful! Please reach out if you need any help on your product data journey.

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