CONTENT VALIDATION ENGINE – CASE STUDY

TNQ TECHNOLOGIES

This case study examines how Emerald Publishers used TNQ’s Content Validation Engine to ensure consistency, accuracy, and completeness of content before publishing.

What is content validation?

Content “packages” processed at various stages during the production workflow, both internally and via partners, need to be checked for the accuracy and sanctity of multiple journal-specific elements – XML, metadata, images, PDF, multimedia files, et al. before being deemed of acceptable quality. This is crucial for efficient and fast publishing across digital platforms.

Emerald’s challenge

Before TNQ’s content validation engine was implemented, Emerald would manually assess processed content packages received from multiple partners, before sending them through for online publishing. Given there are a significant number of things to check, it was prone to human error. Their internal systems would then throw errors and reject content, leading Emerald to spend a sizeable amount of time:

  • Interpreting and identifying errors
  • Seeking resupplies from partners
  • Going back and forth with several teams

Collaborative solution

TNQ’s product teams have developed and delivered configurable solutions to tackle these challenges for other customers to great positive impact, and suggested a customised version for Emerald’s specific needs. 

We then worked closely with Emerald to ideate, iterate, and implement a custom validation engine that would check the integrity of every artefact against guidelines, establish a more efficient workflow, endeavour to eliminate errors and back-and-forth, and speed up time to publication.

The tool produces a validation report that presents a “success” or “failure” message, with the latter offering details and specifics. Errors and problems are curbed at this stage, therefore nearly entirely eliminating rejections from the online platforms. Since January 2023 and across 350,000 pages, Emerald has been able to collaborate with all its partners to improve the overall quality of the delivered content packages, and seek resupplies and corrections quicker.

A bird’s eye view of the workflow

RESULTS

Bringing “failures” down to nearly zero across all content

Pinpointing errors eliminating the need to search through multiple files

Improving metadata deposit rates for Crossref by 90%

Establishing feedback loops to constantly improve and add to the tool

Quicker time to publication by diagnosing and addressing errors much earlier in the workflow and eliminating rework loops

Saving precious time by removing re-working and multiple manual checks

Setting clear requirements helping partners to develop and adapt their internal systems

Enhancing author satisfaction by ensuring accurate XML and content display

Reduction in support tickets by ensuring completeness of content before publishing

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