Correct, standardised and up-to-date product information is the key to success in e-commerce. Only with high-quality data can users quickly find the correct replacement part at the point of sale. This makes excellent data quality the basis for higher sales and fewer returns. Since summer 2021, we awarded parts manufacturers the “Premier Data Supplier” (PDS) seal of quality if their data in the TecDoc Catalogue meet particularly strict specifications. In the last evaluation in June 2022, 25 percent of TecDoc brands achieved this outstanding level of quality.
Since last year, we have been working intensively with parts manufacturers on data quality in the TecDoc Catalogue. A new set of rules for data suppliers include additional, restrictive key performance indicators (KPIs) and validations that systematically and significantly increase quality.
The “Premier Data Supplier” (PDS) seal of quality is awarded to those data suppliers who meet all the requirements by at least 99 per cent. We support the parts manufacturers intensively on their way to certification and help with all questions concerning the new rules.
At the June 2022 evaluation, 245 of the brands available in the TecDoc Catalogue achieved this premium status – 52 more than in the March 2022 evaluation, representing 25 per cent of TecDoc brands. A further 538 brands met the requirements for certification as a “Certified Data Supplier“. This involves checking whether the catalogue data supplied meet the specifications of the internationally recognised TecDoc standard. Overall, about 80 per cent of the TecDoc data suppliers provide very good or excellent product information.
Precise product descriptions help to match the correct replacement part
Our measures to increase data quality make it easier for catalogue users to identify IAM replacement parts correctly, quickly, and reliably. In the past, data suppliers could freely select from about 4,000 attributes to differentiate products and applications. Parts manufacturers regularly used similar but different attributes to provide the same information for the same product or application.
To prevent this, the DQM rules define permissible differentiation attributes and key values for each product. This improves the filtering and selection possibilities in IAM catalogue systems.
In addition, a KPI checks whether suppliers allow the differentiation of, for example, five brake discs for the same vehicle using the defined differentiation attributes on the first catalogue page.
In addition to the correct data format, the data content is now checked professionally. For example, the DQM team checked five million values of alphanumeric attributes and evaluated them with a key figure to see whether they are meaningfully related to the respective attribute or whether something else was entered.
In addition, about one million supplier text modules were examined and evaluated to see whether they contain information that can be managed as attributes. Limiting the maximum number of decimal places allowed for numeric attributes ensures that relevant attribute values are displayed.
Meaningful reports enable continuous improvement
Every month, TecDoc data suppliers receive KPI reports on all data quality measurements for all their brands. Another report shows which product designations are used in the TecDoc database for all brands per OE number.
Each parts manufacturer also finds out which product designation is used by its own brand so that the report provides important information on incorrect allocations. The aim is to track the number of different product designations per OE part number, continuously reducing errors.
What comes next?
The requirements for certification will continue to be adapted to the demands of the market. More new KPIs are already in preparation for 2022/2023.
Another focus is on the “TecDoc Data Guide”, which describes and documents the contents of the TecDoc reference data. This guide was introduced at the beginning of 2022 and is being expanded monthly. This means that all users of the TecDoc reference data now have documentation available for their correct use.
Constructive suggestions and discussions on improving data quality are always welcome at email@example.com. Additional information on our data quality management initiative is available on the website.