Welcome back to the Invisible data enrichment mini-series. We’ve explored why data enrichment is critical, and discussed examples of good and bad data quality. In this blog, we explore what valuable characteristics to consider when approaching high-volume data enrichment.
Leading enterprises and scaling startups’ fortunes are made and lost on their ability to leverage their data. Unfortunately, data quality can fall down the list of priorities, and when it is assigned to an internal team, they are often overwhelmed and under-resourced. Some turn to BPO’s to offload their burden, but are underwhelmed with middling results that don’t solve their core problem.
Let’s explore why.
What Stops A Team of People or Tech Solution From Enriching Data?
Data enrichment is complicated by nuance. Technology struggles to reconcile the structuring of data across formatting issues, and can’t employ the human intuition needed to thoughtfully research new data from third-party sources.
Internal teams face another series of challenges. They’re well-suited to perform the task at low volumes, but are unable to invest time and resources to prioritize data enrichment among other tasks as volumes scale.
BPOs tackle this problem by matching volume of data with volume of people, but efficiency and quality in the process falter because people-only processes do not scale well.
The Solution: People, AI, and Platform
Invisible is uniquely positioned to provide scaled data enrichment across different use cases. We leverage strategic expertise and a prorietary process engine to break down problems into digestible steps, identify the steps that are best performed by automation and AI, and steps that require the intuition of our educated, global workforce.
All work is excuted on our workforce management platform. Its extreme flexibility allows us to configure individual steps that are tailor made for AI-enabled data enrichment.
Here’s what that looks like for one of our clients, in practice:
One of the world's largest retailers, attracting over 50 million online visitors per month, was facing a major operational hurdle: their SKU/product data was incomplete. Crucial details such as category, color, material, and size were absent, rendering products unsearchable on their website.
This led to a significant, strategically critical challenge, as the retailer found itself lacking the necessary processes, personnel, and technology to effectively address the problem.
Within 16 days, we implemented a unique solution to enrich thousands of products across seven unique fields. The generative AI model GPT-4 takes a first pass at enriching data fields from scraped data, with an additional layer of AI flagging what is likely to contain errors.
Operators review those flags, manually enrich what’s left, and deliver a fully enriched product listing. Through this seamless integration of human expertise and cutting-edge technology, we achieved exceptional time-to-value for the client.
In 16 days, we enriched 50,000 SKUs. 3,137 items went from being unsold to generating approximately $900k in revenue for the first time, and 49% of products saw an increase in conversions overall.
Ready to explore how data enrichment can augment your business? Contact us