In the first blogpost of this series on data enrichment, we highlighted the challenges companies face in harnessing the power of their data due to issues like raw and incomplete datasets. Now, let’s dive deeper into the topic, focusing on practical examples of bad versus good data quality.

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## Practical B2B Data Enrichment Use Cases
Firstly, let’s examine an example sales lead dataset, extracted from a CRM that is poorly structured:

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![Bad Quality 1.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_1_fdbcf9e0da.png)


This table suffers from several issues including missing datafields and variation in structuring, making it unusable. 


A data enrichment process would normalize data structuring and use third-party sources to fill in the gaps and remove errors to produce a much more complete sheet as seen below. ‍In this example, the resulting table post enrichment process is much more useful. The growth team has all of the relevant data they need to reach out to their leads.


![Good Quality 1.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_1_49ce99558c.png)
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Let's explore another example:: 

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**Product Feed Management**: A product feed (aka: product catalog) organizes crucial product data including category, description, price, and dimensions into a searchable database for customers of E-commerce marketplaces.. 

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The largest eCommerce marketplaces receive product data from tens of thousands of vendors, all in different formats, schemas, and structures. Miscategorization of products or inaccurate descriptions can result in a database of products like the example below. Unfortunately, these inaccuracies cause products to never be found by consumers, becoming dreaded deadstock, or non-performing inventory.


![Bad Quality 2.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_2_523171a453.png)


An enriched product feed looks more like the example below, with a complete set of correctly categorized attributes that boost search engine visibility improving customer experience, and sales as a result. 


![Good Quality 2.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_2_7505f8367f.png)


How do you enrich a database of hundreds of thousands of products, and do so flexibly and at scale? By creating and executing a process using AI, automation and an educated global workforce. See how we did it for one of the two largest retailers in the world. 

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**KYC & Regulatory Compliance**: FinTech & Financial Services leaders leverage data enrichment to satisfy regulatory requirements, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. Often, customer information is sourced from several databases including government forms, watchlists, and public records, each with their own unique schema and format.


In this example, an incomplete dataset could look like this:


![Bad Quality 3.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_3_37432e54b8.png)


As you can see in the table above, this dataset is sub-optimal and would cost fintech teams several hours of manual review and enrichment to become valuable. This delay causes friction for the customer onboarding process, which could lead to churn.


A complete dataset is required in order to meet regulatory requirements, and thus enriching the unstructured data is a crucial task for Fintech teams. Using automation and an intelligent workforce to QA the process, thousands of rows of unstructured data can become enriched without requiring full-time-employee’s valuable time, and as a result, satisfy government requirements. An example of this type of dataset is below.
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![Good Quality 3.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_3_3684322acc.png)

### Bottom Line
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In the above examples, we have outlined what poor data quality can look like and the operational problems businesses face if the data is left untransformed. We have also explored what a good dataset looks like and the benefits businesses experience from performing data enrichment.


In the final post of this three-part series, we will discuss how technology nor people alone are the optimal solution for data enrichment. A unique combination of the two are required and augmented through a process engine to deliver enriched databases at scale. 


Ready to see what good data quality does for your team? [Reach out](https://www.invisible.co/get-started/).

The Difference Between Bad and Good Data Quality & Why It Matters

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. 

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### 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. 

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### 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:  



![AI-enabled data enrichment.png](https://honest-deft-cloth.media.strapiapp.com/AI_enabled_data_enrichment_fbba80c4f4.png)
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](https://www.invisible.co/get-started/).

Automation & An Educated Workforce Delivers Optimal Data Enrichment at Scale

In an increasingly digital business environment, companies are generating and have access to a higher volume of data than ever before. But most aren’t equipped to fully leverage it, leading to raw and incomplete datasets being found in every function of every company.


While they’re collecting digital dust, they could be boosting operational efficiency and improving strategic decision-making. This underutilized data should be considered an important asset, one that can boost operational efficiency and empower key stakeholders to make more informed decisions.


The key to turning underutilized raw data into a strategic asset is data enrichment. 


In this mini-series, we’ll discuss three dimensions of data enrichment: 

- Why does your data need to be enriched? What are the best ways to enrich data?
- What is the difference between good and bad data quality? Why is this important to your business?
- Which types of companies are most susceptible to poor data quality and would benefit from data enrichment? 
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Let’s dive into the first dimension. We will cover dimensions 2 and 3 in later posts.

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### Why Is Data Enrichment Necessary? 
Leading enterprises procure data through a process of data extraction from several internal and external sources, as shown below: 



![Data Extraction.png](https://honest-deft-cloth.media.strapiapp.com/Data_Extraction_6791329a2c.png)


Each original data source may have a completely different schema than the others, resulting in errors and omissions in the combined database. Restructuring this data to be usable can take hundreds of FTE hours; time that should be spent on higher functioning tasks. 

#### What is Data Enrichment?
Data enrichment transforms the resulting dataset into a strategic asset. It involves taking "first-party" data collected directly from a source like audiences or customers and supplementing it with "third-party" information from external sources. 


The goal of the data enrichment process is to improve data quality and value, which yields stronger decision-making. Here are the key elements of data enrichment:
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**Cleaning**: Removing or correcting erroneous data. This can include identifying and eliminating duplicate entries, correcting misspellings, and verifying data accuracy.
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**Augmentation**: Adding third-party data or additional data sources to existing data. One example: enriching customer data by adding demographic or behavioral information from other databases.


**Integration**: Combining data from different sources to provide a more comprehensive view. This could mean merging datasets or linking related data points across datasets.


**Transformation**: Converting data from its original form into a form more suitable for analysis. Transformation can be as simple as QOL improvements like the ease of filtering, or calculations, aggregations, or other methods to derive new data points.


**Validation**: Verifying that data is accurate, relevant, and useful. Usually, this includes checking data against known values or validating it against set rules or requirements.


At Invisible, we’re uniquely positioned to provide scaled data enrichment across a multiplicity of use cases. Through a unique combination of AI and a skilled workforce, both enabled by our process platform, data operations are our bread & butter. 


We break down data enrichment tasks into their smallest steps. We then leverage strategic expertise to identify steps that can be performed by automation tech or AI, as well as where to apply our skilled operators through our process engine.


For high volume data-enrichment processes, we utilize our extremely agile recruiting engine that scales to meet demand. As we define a process, we add automation and AI where possible to increase efficiency, improving quality while simultaneously decreasing unit cost. 

In this post, we have described what data enrichment is, and why it’s a requirement for leading enterprises. Read part 2 and part 3 of this mini-series to learn more about good vs. bad data quality, as well the types of companies who are positioned to benefit the most from data enrichment.


Bogged down by incomplete and messy datasets? [Get in touch](https://www.invisible.co/get-started/).

Data Ops

All Stories

The Difference Between Bad and Good Data Quality & Why It Matters

Automation & An Educated Workforce Delivers Optimal Data Enrichment at Scale

Data Enrichment: How to Turn Data into a Strategic Asset

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