Harnessing AI for data enrichment

In this day and age there is a data overload, and this makes it difficult to analyze your business’ performance. Fortunately data enrichment can turn an overwhelming dataset into actionable insights.

We’ll go over the meaning of data enrichment, some use cases and how you can use AI to enhance your datasets. We’ll even uncover how you can implement Sheetgo to aggregate all your data.

What is data enrichment?

Data enrichment is a series of mechanisms to improve raw data. These processes add value to the original dataset, making it easier to analyze and understand. Enriched data can help organizations gain deeper insights by offering a more comprehensive view of important information.

Data enrichment can involve: appending data, cleaning inconsistencies, updating information, and adding context. This process is crucial whether your database has very little information or is completely overwhelming.

Data enrichment techniques

Data enrichment is a very broad concept that encompasses a wide range of techniques. Below are some of the most important amongst these procedures.

  • Data Cleansing: Involves identifying and correcting inconsistencies in data to make it more reliable. This includes removing duplicates, standardizing format, and updating information.
  • Predictive Analysis: Utilizes statistical models and machine learning to make projections based on existing data.
  • Appending Data: Adds additional information from external sources to enhance existing datasets . This process can enrich any type of data by bringing new insights or filling in missing details.
  • Entity Extraction: Identifies and extracts specific entities from unstructured data sources. Information that can be gathered in this way includes names, places, and dates.
  • Data Categorization: Organizes data into categories or groups based on predefined criteria or patterns. This makes data more navigable, simplifies analysis, and enhances decision-making.

By refining data through these methods, organizations can unlock deeper insights, make more informed decisions, and ultimately drive better outcomes. Nowadays information abounds, and this makes data enrichment increasingly important.

Data enrichment use cases

There are a lot of tried and tested data enrichment applications. Matter of fact, it’s likely that you already relied on data enrichment without knowing of its existence. Let’s go over examples of data enrichment techniques and their applications.

Customer Data Enrichment in Marketing

A retail company collects their customers’ names and email addresses through online sign-ups.

To enhance their marketing strategies, the company uses data enrichment to integrate additional data from external sources to include demographic information (age, gender, and income levels), and purchasing behaviors.

This enriched data allows the company to create more personalized marketing campaigns, segment their audience more effectively, and improve customer engagement by targeting their communications based on specific customer interests and demographics.

Real Estate Market Analysis

A real estate agency has lots of listings of different properties up for sale. Each one has basic property information like location, size and number of rooms.

The company enriches property data to provide better estimates and to improve their sale strategy for each listing. They add layers of information regarding neighborhood crime rates, school district ratings, and historical property values.

This enriched data helps buyers make more informed decisions and enables real estate professionals to adjust their sales strategies.

Financial Risk Assessment

A bank needs to know how much credit they can offer each customer.

The financial institution enriches customer data with external credit scores, employment history, and transaction patterns to assess the risk of lending. This process includes integrating data from lots of sources: credit bureaus, public records, and other financial institutions.

This enriched data empowers financial organizations to make more accurate risk assessments, tailor their loan offerings, and reduce the likelihood of defaults. In doing so, they protect both their interests and those of their customers.

How AI fits into all of this

Data enrichment and AI are a great match. Obviously, there is dedicated software for data enrichment, but AI lets you do a lot without breaking the bank.

ChatGPT for data enrichment

Let’s look at a few applications of AI you could start using today. These prompts are all centered around the dataset shown below, and they are merely a reference of potential uses.

ai for data enrichment 1

Extract information from emails

We could feed a list of customer emails into Chat GPT and have it extract useful information. For this example we asked for the three easiest data points to extract: first name, last name and company name. 

Based on the email column from this spreadsheet, extract the following entities and include them in new columns: First name, Last name, Company the person works for (if no company can be extracted leave blank, if it’s a generic domain leave blank).

The results give us a first name to send a personalized email. They also provide a company name, which we could use to see how many purchases a certain business has made.

ai for data enrichment 2

Categorize information

On the same dataset, you could have ChatGPT create and apply categories for existing products.

Give the products a broad category (Column “Department”) and a specific category (Column “Category”). For example, the broad category for Echo Speakers is “electronics” and the specific category is “audio”. Export as a downloadable tsv file. If you don’t know how to categorize a certain item ask me one item at a time, ideally give me 2-4 options to choose from.

Now that we have categorized items we can analyze our customer preferences and spot opportunities for improvement. For example, based on this information we could decide to dedicate more space to wearable electronics.

ai for data enrichment 3

Correct date format

Finally, this spreadsheet has a really messy date column, all of its values are expressed differently. We can ask ChatGPT to apply a single date format across the board.

Having corrected the date format, we can use that data to analyze buying patterns across days of the week or times of the month. This could be a great indicator of when to put items up for sale and increase purchases.

Having corrected the date format, we can use that data to analyze buying patterns across days of the week or times of the month. This could be a great indicator of when to put items up for sale and increase purchases.

ai for data enrichment 4

Humble AI

Humble AI offers powerful data enrichment tools, they are all accessible through the website.

These are just some of the tools available on the platform:

  • Gender prediction: Analyze a dataset containing first names and predict the gender associated with each name. This functionality is particularly useful to understand audience demographics without intrusive data collection.
  • Add country information: By selecting a column that lists countries, Humble AI automatically adds additional details like region, dialing codes, and country codes.
  • Predict professional seniority: Based on job titles Humble AI can estimate the seniority level of individuals. Allowing teams to target their outreach and tailor their messaging to the appropriate level within an organization.

To learn more about Humble AI check out this article on How to use Humble AI for data enrichment.

Use Sheetgo for data enrichment

While it’s not AI, Sheetgo can prove helpful in your data enrichment efforts, especially  for data aggregation. It has powerful features that can help automate processes and gather all your information in one place.

  • Merge: Combine data from multiple sources into one, ensuring that information from various sources is consolidated neatly. This is particularly useful for creating comprehensive datasets from fragmented data sources.
  • Left Join: This feature allows you to match and merge data from two different sheets based on common identifiers. It’s invaluable for enriching datasets with additional information from related data sources without losing any of the original data.
  • Append: Append data from one sheet to another, effectively allowing for the sequential aggregation of data over time. This function is ideal for accumulating data entries or records that grow as time goes by.

Sheetgo offers a suite of features that are essential for anyone looking to automate their data aggregation processes. Check out all of the tools it provides and how it can improve your business processes.

Enjoy data rich in insights

Now you know what data enrichment is and why it’s of crucial importance to your efforts. Armed with these tools you will be able to uncover great insights to drive your projects forward.

Ready to streamline your spreadsheet data?

You may also like…