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Exploring the ins and outs of the data enrichment process

Rachel Wheeler Archive

Collecting and analyzing more customer data has emerged as a key strategy for businesses that are looking to gain a competitive advantage over their rivals. If you have more information about your patrons, you can use it to improve your marketing, sales and customer service strategies, which should help with both attracting new buyers and retaining old ones.

All of this sounds well and good, but there's one problem - as technology improves and companies find themselves with so many disparate ways of collecting people's information, it becomes difficult to centralize all of that data under one roof. Of course, it's vital to do so - it's next to impossible to draw meaningful conclusions from multiple sources of data that are disjointed and scattered across different silos.

The best course of action, therefore, is for companies to look into data enrichment, which can help them centralize all of their information and put it into a format they can make some sense of.

What data enrichment really boils down to is a two-step process. Let's break it down:

Normalizing data elements
A very important initial step is to normalize the many pieces of data that are all flying into organizations' coffers from disparate sources. If a company is collecting information from a variety of places, ranging from emails and phone calls, to online interactions, to in-person interactions, it can be difficult to get everything into a uniform, identifiable format.

All of this data may include different elements - for example, does people's contact information come with email addresses, phone numbers or both? - and come with different metadata attached. This can be tricky, because the ultimate goal should be to make all pieces of data equal, so that they can all be tapped into for analytical research and eventual business improvement. The challenge is to get all of one's data on the same page.

Extracting the right pieces
An important objective in data enrichment is to make it easy for organizations to pull out key elements they need, as soon as they need them. For example, if a salesman quickly needs to dig up some key data points about a potential buyer - name, contact information, purchasing history and so on - he should be able to dig that information up quickly and easily, no matter what source it's coming from.

With data enrichment, organizations should look to make all their collective knowledge organized, normalized and easy to extract. This should set them on the fast track to success.