Use case
Reliability of product catalog data
Enhancing the quality of a product catalog
Optimizing a repository / database
Identification of duplicates
An urgent need for product data reliability and enrichment
Our customer is a major player in the retail sector.
The quality of the information contained in its product repository is a prerequisite for the success of all other projects, such as those involving its customer and supplier databases, and its e-commerce website.
Improving the quality of the data in its Product database, and maintaining its reliability over time, is therefore a key challenge.
The Tale of Data solution has enabled us to solve the following problemsby making data more reliable:
- Deduplicate products thanks to its specific rules ("business rules"), written in natural language
- Detect aberrant data to normalize or rectify them, without writing code
- Standardize product descriptions: color, material, units, .... which sometimes vary from one supplier to another
An automated solution for standardizing, enriching and monitoring data
Tracking down duplicates is the first step in any data quality project.
Thanks to its embedded Artificial Intelligence engine, Tale of Data can automatically match texts with similar spelling. This is one of its special features, impossible with traditional tools.
For example, Excel is not capable of detecting a duplicate between the words "logiciel" and "logitiel", and this is the added value of the Tale of Data solution.
Tale of Data integrates a range of strategies and algorithms for this initial work on duplicate detection: approximate matching, consonant or vowel frequency, fragmentation or automatic word weighting.
All these methods help to find well-hidden duplicates!
Once duplicates, triplicates and quadruplicates have been detected, data qualityoperations are automated: rectification, homogenization, fuzzy name joins and deduplication.
The desired validation rules are implemented via the Tale of Data business rules engine.
No scripting specialists were called in: everything was done via the solution's interface and the rules written via the menus. The use of a no-code tool containing ready-to-use functions enabled the business teams to work quickly, without needing to mobilize skills outside their own.
Finally, alert dashboards are created to prevent any decline in data quality.
The ability to automate and schedule processing guarantees the sustainability of corrections and prevents data degradation over time.
Data quality is sustainable.
Concrete results on product performance and supplier collaboration
Harmonization of the product catalog via deduplication and the creation of business rules enabled the implementation of a quality repository, available to the whole company - a prerequisite and foundation for other Data projects.
The project went live in just a few weeks, and the monitoring system was automated.
This essential step accelerated the uploading of corrections to the e-commerce website, a project which had not seemed feasible at the time.
Product benefits
Integrate
Seamless integration of AI and No-Code technology for efficient data refinement.
Collaborate
Collaborate across teams to ensure complete data quality.
Shareable
Go beyond data transformation scripts for a readable, shareable project.
View
Move from the "black box" of scripting to an intuitive, documented visualization of all your data transformations.
Powerful
Use powerful dashboards to simplify analysis and drive continuous improvement.
Quality
Achieve superior data quality faster and at lower cost, while demonstrating the tangible value of investing in data excellence.
Intuition
Get a complete, intuitive visual representation of the data journey, from raw data to finalized data products.
Organized
Align your organization with data quality initiatives.
Aligned
Easily align your business with ever-changing regulatory requirements.
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