What is data enrichment and how to implement it

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Your data is one of the most important assets in the organization. End users should trust their organizations’ data and confidently use it to make business decisions – enriching data allows data consumers to get more meaning than raw data might otherwise provide.

The resources below will boost your understanding of data enrichment and help your organization deliver high-quality data.

In this guide, we’ll discuss:

What is data enrichment?

Data enrichment is the process of merging and adding either first-party or third-party data to a dataset you’re already working with.

Here is what data enrichment allows you to do:

A great example of data enrichment is lead scoring. Let’s say you want to look at how a user’s lead score is changing over time. Data enrichment allows you to see how a user’s pattern of behavior changes in real time as their lead score changes.

Types of data enrichment

While the types of data enrichment vary by the volume of data sources collected, there are three types of data enrichment that are common among Snowplow Analytics customers.

Why is data enrichment important?

Data enrichment allows businesses to make data useful and reliable for end users. When organizations enrich their data, they are adding value to it by making it useful. Businesses also generate a greater understanding of their customers when they enrich data, allowing them to tailor products and services to their customers’ needs.

While there are numerous benefits to enriching your data, here are three of the most common benefits that businesses attain when they enrich the data they are working with daily.

Benefit 1: data enrichment improves data accuracy

One dataset by itself is not powerful enough to build a single view of a customer. This is where data enrichment plays a crucial role in making raw data more useful. Through data enrichment, businesses add additional and missing data to their original dataset of a customer. 

With data enrichment, businesses collect data that is valuable to them. When data is accurate, stakeholders trust it and use that information moving forward.

Benefit 2: data enrichment aids in customer targeting

When data is accurate and up to date, companies can personalize marketing campaigns to users. Data enrichment enables you to segment your data if you choose to make advertisements and recommendations more relevant to specific users. 

By enriching your data, you close in on developing a “golden customer record” that your business can capitalize on moving forward. Enriched data will likely lead to an increase in conversions for marketing campaigns and sales outreach efforts, thus increasing the potential return on investment for that customer.

Benefit 3: data enrichment improves your customers’ experience

Data enrichment allows your business to stay relevant to users with advertisements that cater to their needs. When messaging is personalized to users, it not only improves your customer relations but also shows users that you care about their needs and understand them as clients. 

For example, when a user enters a company email into a form, and it just so happens that someone else from that same company interacted with that form earlier, data enrichment allows you to personalize content for that user based on the information you already gathered on that company.

Three steps to enriching data at your organization

Data enrichment is a complicated process, and it gets tricky when dealing with a large volume of data sources. Here are three steps you can follow when implementing data enrichment at your organization.

1. Establish your data enrichment goal

Before getting started with data enrichment, it’s important to establish your overall goal in enriching your data. The overall goal is to improve your data’s quality and accuracy, but that is too broad of a starting key performance indicator in most cases. When establishing your data enrichment goal, you should consider what information your business will need to collect. The data should be relevant to your business and allow workers to draw valuable insights into your users.

When deciding what data to enrich, companies need to think about the logic applied to their enrichment. Should the data be enriched in real time, or should it occur in the warehouse? In most use cases, it is better to enrich your data in real time; however, it might make more sense to enrich data in your warehouse before it gets sent off to your CRM.

Why would you want to enrich your data in real time rather than in your warehouse? Let’s say you are enriching weather data with customer data, and you are analyzing why a user acted a certain way at the time of purchase. With the weather constantly changing, enriching your data in real-time at the time of the event will return a different result compared to enriching that data down the line after the event occurred. 

For this example, if you enrich your data in real time, you will find out what the weather was like at the time of the event. Suppose you enrich your data in the warehouse. In that case, you are enriching it post-event, or you might even have to do a complicated lookup of location and time to pinpoint what the weather was like at the time, which is a far more expensive option to carry out.

2. Use data enrichment tools to prepare your data

This step is arguably the most important step when it comes to data enrichment. Utilizing the right tools allows you to effectively enrich your data, which leads to high-quality data in your organization. When it comes to data enrichment, there is an abundance of tools out there to aid you in enriching data from third-party providers. 

Not only do data enrichment tools collect, organize and cleanse data, but they also format data in a useful form for your business. It is common for companies to use more than one tool when enriching data since each tool has its own focus in the enrichment process.

Some of the top tools for data enrichment include:

3. Keep data up to date

Data enrichment isn’t a task that is done once for your organization. It’s an ongoing process to ensure your business works with data that has value for your organization. With that said, it’s crucial to make sure data is up to date and is continuously enriched either in real time or in your data warehouse. If data is not kept up to date, over time, data will naturally decay and lose its value to the point where it becomes worthless to your organization to collect

To keep up with data that is continuously changing, companies should invest in tools that automatically enrich data in their CRM system to ensure its accuracy. Companies should also implement data cleansing schedules, where they validate data every X number of months to ensure it’s up to date.

Enrich your data in real time

Delivering high-quality and complete data can be a challenge for businesses. Dealing with data that is incomplete, outdated, or even missing is common nowadays. With companies relying on data to drive decision-making, it’s more important than ever to make sure your organization is working with data that is not only complete but also up to date.

Snowplow makes it easy to enrich your first- and third-party data sources in real time.

In a Snowplow pipeline, data is:

Deliver more meaningful data with Snowplow

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