The modern landscape of business is a digital one. This kind of territory is reliant on technology that has consistently been evolving and changing over the last couple of decades at a breakneck speed. Even over the last several years, the world of commerce and business has seen some truly incredible shifts and changes. As soon as the field of business moved to a digital plane, information and data quickly became one of the most important and powerful tools that a business could use to further its brand and success.
To some degree, data has always been at the heart of what drives industries forward. Whether it was analog or digit. The advantage of the digital age is the sheer volume of data that can be created. This has both strong benefits and challenges however to any business that wants to take control of their data and use it to further their company. Data may be king, but with the new frontier comes new challenges that must be faced head-on.
The Challenges of the Digital Age
In regards to data, one challenge that is nothing new to the world of commerce and industry is simply finding ways to make data accessible and usable. Every interaction and event that takes place within the digital sphere creates some kind of a digital record or piece of data. The problem has not been creating the data, but instead being able to aggregate it in a way that makes analysis possible.
For a long time now the number one solution to this kind of problem has been the data warehouse. An aggregation of data that has been processed via ETL and is now available as a source of truth across a company. In fact, companies that want to make good use of their data to help them improve their customer experience become very familiar with their data warehouses.
While this has been a solution for a long time, it has not stopped being something that continues to be worked on. What that means essentially, is that once data has been aggregated into a data warehouse, it’s not just magically ready to be used. No, there is a good amount of work to be done to data found in company data warehouses to make it actually useable to the departments that need it the most.
Data on customers can be used across several departments within a business from marketing, to sales, to finances, and customer support. Once data finds its way into a warehouse, it typically needs to undergo additional steps to continue its refinement process. One of those necessary steps is data enrichment.
What is Data Enrichment?
One way of thinking about data enrichment is to visualize a paper form that has several fields pertaining to a customer’s name, age, sex, relationship status, geographical location, and much more. Say a customer fills out only their first name and their email address on the form and then hands it in. This is a valuable piece of data, but it gives a very limited profile of the customer. While you can make some assumptions about the customer from these two pieces of data, more is needed in order to better round out your customer profile and understand them better.
This is the process of data enrichment. A process that helps to amend, or append data that already exists within the data warehouse so as to make it more useful. When it comes to data enrichment, there are three kinds of enrichment to consider:
- Behavioral data enrichment
- Demographic data enrichment
- Geographic data enrichment
What is Demographic Data Enrichment?
Each kind of data enrichment exists to further flush out and build a more robust 360-degree model of a customer. This data comes from key events and interactions that customers have on a digital platform. Demographic data enrichment is a particularly powerful piece of information that can help further analytics, sales, and marketing.
This kind of information focuses on specifics about the customer whether that be their age, gender, race, ethnicity, and much more. By compiling this kind of data enrichment with behavioral and geographic, you can give your teams an in-depth look into a customer that can ultimately lead to improved customer experiences.
Reverse ETL is the process of taking data out of a warehouse and pushing it through its systems of record. By doing this, you are taking incomplete data and putting it back in departments and systems of record that need it. From here it can organically be appended or even amended to give a more accurate view of the customer.
The whole goal of data enrichment is to help businesses understand better how to make real-life, data-driven decisions that have meaning and impact. This kind of data can spread out across a company as a source of truth that can have incredible returns.
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