Product data is the lifeblood of modern manufacturing. As products move through design, production, sales and service, they generate a vast amount of data across the product lifecycle. Collecting, managing and leveraging this data effectively can make or break a manufacturing firm‘s success.
In this comprehensive guide, we‘ll explore what exactly product data collection involves, top methods and best practices to master it in 2024. With over 10 years of experience in data extraction and web scraping, I‘ll also share my insights on how to build a robust product data foundation.
What is Product Data Collection?
Product data collection refers to the gathering, organizing and managing of data generated around a product through its lifecycle. This includes:
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Product design data: Materials specifications, tolerances, dimensions, CAD models, product requirements etc.
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Manufacturing data: Production parameters, machine sensor data, batch details, quality test results and defect data
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Supplier & procurement data: Details of parts, raw materials and component suppliers integrated into the manufacturing process
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Inventory, logistics & distribution data: Warehouse inventory levels, material movements, transportation/delivery details
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Sales & marketing data: Pricing, channel partner information, marketing campaign performance, sales analytics
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Customer feedback & service data: Support tickets, case management, ratings, reviews, social media sentiments, warranty claims
Essentially any information created around the product across design, production, sales, service and maintenance activities is part of product data. Bringing together this high-volume, high-variety data is critical for driving decisions across the product lifecycle.
Why is Product Data Collection Important?
Here are some of the key reasons organizations invest heavily into product data collection:
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Enables data-driven decision making: With a 360-degree view of product data, teams can identify opportunities to optimize production costs, quality, design, sales, service and more through data insights.
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Powers digital transformation: Emerging technologies like IoT, AI and advanced analytics rely on unified, reliable data. Product data collection lays this foundation.
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Improves cross-functional collaboration: Sales can access the latest production specifications without having to request it manually from manufacturing.
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Informs continuous improvement: Customer feedback, warranty claims, and field performance data can feed back into new product development.
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67% of firms saw ROI from product data efforts within 2 years: According to an IDC survey of 500 manufacturers.
With the scale and pace of modern manufacturing, relying on fragmented, manual data processes leads to tremendous inefficiencies. A future-ready product data strategy is crucial for competing today.
Top 4 Methods for Product Data Collection
Collecting vast amounts of product data from across the manufacturing value chain requires a systematic approach. Here we explore popular options:
1. Manual Data Collection
This old-school approach relies on manual processes to collect product data via paper forms, spreadsheets, documents etc. which is then entered into various downstream systems.
Pros
- Simple to implement
- Low upfront investment
Cons
- Highly labor intensive and inefficient
- Prone to human errors and inaccuracies
- Siloed data across different documents and systems
- Very difficult to consolidate and analyze data
While easy to kickstart, manual collection has too many downsides to scale.
2. Outsourcing / Crowdsourcing
Companies can choose to outsource product data tasks to external providers. Common examples include inventory data collection, capturing customer feedback, and more.
Crowdsourcing platforms like Amazon Mechanical Turk allow distributing simple product data tasks to thousands of remote workers.
Pros
- Leverages external expertise
- Shifts workload from internal teams
- Pay per task model rewards accuracy
Cons
- Quality control challenges
- Risk of errors with outsourced work
- No internal capability building
Outsourcing works well for targeted efforts, but not as a system-wide strategy.
3. Robotic Process Automation
RPA tools can be programmed to automate repetitive manual product data tasks like scraping data from PDFs, copying data between systems, filling forms etc.
Pros
- Improves efficiency by reducing repetitive manual work
- Low risk approach to start automating
- Robust audit trails and logs
Cons
- Bot maintenance overhead
- Limited capability beyond basic scraping/copying tasks
RPA delivers quick ROI for automating dreary repetitive tasks. But it has limits when product data complexity and variety increases.
4. Dedicated Data Collection Platforms
Sophisticated, purpose-built product data collection systems like Siemens‘ Simatic IT Interspec offer robust, enterprise-grade capabilities.
Key features include:
- Automated data ingestion from multiple sources via APIs, web scraping etc.
- Flexible data mapping and parsing
- Rules-based data validation and quality checks
- Central data repository with version control and backup
- User-friendly portal for data access and analysis
Pros
- Highly scalable and reliable enterprise-grade system
- Integrates easily with existing data infrastructure
- Future-proof to support AI/ML data needs
Cons
- Higher upfront investment
- Complex implementation can take months
For org-wide product data management at scale, dedicated enterprise systems are highly recommended despite higher initial costs.
Sample Automated Product Data Collection Architecture
Here is one way larger manufacturers architect their automated product data pipelines leveraging an enterprise-grade system like Simatic IT Interspec:
Key components include:
- Sensors and smart manufacturing equipment feeding near real-time operational data
- Interspec central data repository, with automated ingestion from all core enterprise systems
- Dashboards, analytics and data science tools empowering users to unlock insights
Key Strategies for Effective Product Data Collection
Based on experiences across diverse manufacturing clients, here are some proven strategies:
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Centralize data into a product data management system to eliminate fragmented, siloed data.
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Standardize and automate collection processes using consistent methods. Manual entry should be a last resort.
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Start small, prove value with a pilot project, and expand systematically. Big bang implementations carry greater risk.
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Validate accuracy with statistical checks, data monitoring, and quality algorithms. Bad data will lead decisions astray.
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Monitor data health KPIs like accuracy, timeliness and coverage to maintain standards.
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Simplify access through self-serve portals so all users can securely find data needed for decisions and analysis.
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Future-proof for AI/ML by collecting granular, standardized data required for training algorithms.
Drive Better Decisions with Reliable Product Data
As products, supply chains and customer expectations get more complex, product data collection has become non-negotiable. While manufacturers may start small with manual processes, the end goal should be an automated, enterprise-grade collection system.
With robust product data infrastructure in place, manufacturers can leverage data to make smarter decisions faster across the product lifecycle. The payoff includes increased quality, lower costs, higher customer satisfaction and accelerated innovation.
To learn more about building a lean, efficient and scalable product data engine, contact me today. With over 10 years of experience in manufacturing data extraction, I look forward to helping you maximize the value of your product data.
