Accurate and structured data is essential for pricing managers and business analysts to make informed decisions. However, when collecting data from multiple sources, inconsistencies in product names, pricing formats, and addresses create major challenges.
Ficstar specializes in enterprise web scraping and data normalization, ensuring that businesses receive clean, structured, and reliable data. This article explores the key challenges businesses face in maintaining data consistency and the solutions Ficstar provides to overcome them.
Understanding the Challenges of Data Consistency
Variations in Data Structures
Every website structures its data differently, making it difficult to create a uniform dataset. Common inconsistencies include:
One site listing full price, while another lists unit price
Differences in currency formats (e.g., $10.99 vs. USD 10.99)
Variations in product categorization across platforms
To address these discrepancies, Ficstar creates a shared schema, a standardized format that applies to all sources. This ensures that the collected data is aligned and comparable.
Creating a shared schema—a standardized format that applies across all sources—is the first step in normalizing data.
Inconsistent Data Labels Across Platforms
Even with a standardized schema, different platforms may label the same data differently. For example, when tracking menu prices across food delivery platforms:
One platform might list an item as Grilled Chicken Sandwich
Another calls it Crispy Chicken
A third adds extra details like Medium Grilled Chicken Sandwich
To resolve this, Ficstar uses Natural Language Processing (NLP) algorithms to detect and match similar products. Any uncertain matches are flagged for manual review to ensure accuracy.
Address Discrepancies in Multi-Location Data
Businesses that track store locations and pricing often encounter address mismatches. A single location may appear in multiple formats across different platforms due to:
Missing suite numbers or other address details
Typos in the street number
Incorrect latitude/longitude coordinates
Ficstar applies address normalization techniques to standardize store location data. When discrepancies arise, cross-referencing phone numbers, city names, and zip codes helps identify and correct mismatches.
Ficstar’s Approach to Data Consistency
Predicting & Handling Outliers
Ficstar takes a proactive approach to data validation by identifying and correcting outliers. If most prices fall within a predictable range—such as $10 to $20—but one listing appears at $120, this triggers a review process.
An investigation may reveal that the price includes a pack of 10 units, but the system originally treated it as a single item. To fix this, Ficstar creates a new column for pack quantity, allowing clients to choose whether they want to see unit price or full pack price.
By continuously refining this process, Ficstar ensures that data accuracy improves with every iteration.
Using an ETL Pipeline for Data Transformation
Ficstar employs an ETL (Extract, Transform, Load) pipeline to clean and standardize data before it is delivered to clients. This process includes:
Extracting raw data from multiple sources
Transforming the data into a uniform structure
Loading the cleaned data into an easy-to-use format
For more complex projects, Ficstar collects raw data from multiple sites and analyzes inconsistencies before deciding the best way to standardize it. Keeping raw data available allows for verification and adjustments if needed.
Tracking Changes & Setting Variance Thresholds
Maintaining data consistency requires ongoing monitoring. Ficstar:
Tracks week-to-week variances to catch sudden data shifts
Flags unexpected price increases or decreases (e.g., +20%)
Uses historical tracking to ensure pricing trends remain accurate
If a product name or price suddenly changes, the system flags it for review. This helps businesses detect pricing errors, unauthorized updates, or supplier inconsistencies before they impact decision-making.
Standardizing Data for a Restaurant Chain
A restaurant chain needed to compare in-store pricing with food delivery app prices. The data collection process involved two major challenges:
Step 1: Matching Store Locations Across Platforms
Store addresses were collected from multiple sources, including restaurant websites and food delivery platforms. However, manual data entry by franchisees led to inconsistencies.
Common issues included:
Some addresses included a suite number, while others omitted it
Typos in street numbers caused mismatches
Different latitude/longitude coordinates resulted in incorrect store identification
To resolve these discrepancies, Ficstar applied address normalization techniques, ensuring that store locations matched correctly across platforms.
Step 2: Standardizing Product Listings
Each franchisee uploaded menu data manually, leading to variations in product names and descriptions.
Examples of discrepancies:
Grilled Chicken Sandwich vs. Crispy Chicken
Missing size indicators such as Medium
Additional words being left out, such as Bacon Deluxe missing Bacon
Ficstar used NLP models to detect naming variations and match equivalent products. When confidence in a match was low, the system flagged it for manual verification. This ensured consistent product mapping across all sources.
Results
The implementation of Ficstar’s data standardization approach led to:
Accurate price comparisons between in-store and online platforms
Standardized addresses and product names across all platforms
More reliable pricing data for decision-making
Key Takeaways for Pricing Managers
For businesses that rely on multi-source data collection, maintaining data accuracy and consistency is critical. Ficstar’s approach ensures:
Standardized data schemas for uniform pricing and product information
AI-powered NLP algorithms to detect and resolve inconsistencies
ETL pipelines for automated data cleaning and transformation
Ongoing monitoring to track data shifts and prevent errors
Manual validation of flagged data to enhance accuracy
Final Thoughts
Data consistency is a foundational requirement for businesses that rely on pricing intelligence, competitor analysis, or multi-source data aggregation.
By leveraging enterprise web scraping, NLP, and ETL pipelines, Ficstar helps businesses:
Ensure data accuracy and reliability
Reduce errors and inconsistencies in pricing and product details
Improve decision-making with structured, validated data
For businesses that need multi-source data standardization, Ficstar provides tailored solutions to keep data clean, accurate, and actionable.