Over the last two decades, the world of technology has changed in ways that are hard to comprehend. The rise in advancements across the board from personal cell phones to eCommerce and even home technologies is astounding. Where there was a time when people knew how to use physical maps to plan out their vacations and road trips, now the average consumer can’t imagine navigating a new route to work without the aid of digital navigation. This unprecedented rise in technology and communication hasn’t just affected the consumer, however, but has drastically changed the landscape of business and commerce.
The digital world of business has created an environment where every interaction with a customer, or event, creates data. This data is as much a by-product of the event as the event itself in a lot of ways and it has posed both a massive opportunity to the industry as well as a challenge.
The Opportunity and Challenge of Data
The opportunity that data, created through events and interactions with customers in the digital age, has been created is a powerful one. Where at one point in time, a company’s interactions with a customer were mostly physical, this allowed a limited amount of data to be gathered about that person. The data was still valuable, but attaining it was a bit more complicated. If a customer did not leave a physical mark such as a paper trail, then the data about who that person is, why they were seeking you out and much more would go unknown.
Not only that, but data concerning the customer experience was also something that was more difficult to document as the physicality of their experience provided a chance that could be missed. In the digital world, however, there is always some kind of record for every event. When a customer logs onto your website, what hyperlinks they accessed, how quickly they filled their digital carts, or what kind of personal information they gave.
All of this data and much, much more has been of great value to the industry. It gives companies a chance to create a 360-degree model of their customers which gives them rich insight into their patterns. This kind of data can be used across multiple departments in any given company from marketing to sales to finance. This kind of insight into a customer can help departments make real-time data-driven decisions and fuel analytics.
The challenge that faces companies has always been very upfront, however, how to acquire, integrate, and analyze the data that is being actively created. This is no new problem and one that has been addressed for quite some time now. Data silos that held valuable data that was either unreachable or unusable have been mined, processed, and through ETL transferred to lakehouses and warehouses. This data has undergone a process of taking on uniformity and being able to be accessed by a company.
The goal of aggregating data is to have a centralized local of truth that can stretch across a company. For many businesses, their data warehouse becomes this source of truth. However, even once the data has undergone ETL and is in the warehouse, there can still be work to be done.
That’s where the process of data enrichment comes into play.
What is Geographical Data Enrichment?
Data enrichment is the process by which data is taken and appended or amended to become more useful for its end goal. The whole purpose of working with data is to create an end-to-end exchange of data from acquisition, integration, and analysis that is seamless across a company. Part of that process involves reshaping the data to one uniform format so it can be recognized.
An example of this could be a program that records a user event using a dating system that utilizes underscores, while another system recording the exact same event simply records the date as numbers next to each other. Both pieces of data refer to the same event, and depending on the source of record, could hold unique insight, however, a system would see them as separate unless uniformed into the same format. This is typically part of the ETL process of getting your data into the warehouse.
Where data enrichment comes into play, is that it takes this transformed data and amends or appends it to be more useful. An example of this could be CRM which records customer basic information. Based on one interaction or event, it was able to secure an email and a first and last name. However, geographical as well as behavioral data may be missing. Geographical data specifically deals with information surrounding the customer – including data like a home or work address.
When the customer’s profile is retrieved from the data warehouse, there could be necessary fields that need to be filled. Data enrichment would take this piece of information and fill in the missing data to make a more complete, 360-degree profile of the customer.