Integration Tips
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7 MIN READ
Data integration is the movement of data from disparate sources to a specific target(s) (also known as a data warehouse). Data integration can be conducted within real-time using webhooks or through a batch-based process depending on your business needs. Ultimately, prior to adopting any type of data integration method, it is important that you adopt an approach and strategy.
The concept of data validation refers to the process of data cleansing to ensure data quality and correctness.. People are often running into issues with their data not because of data integration specifically but because of incorrect data inputted into systems, like a misspelled address or a wrong postal code. Once incorrect data is sent to be integrated, it will flow through your technology stack. These issues can be detrimental to your overall customer experience.
This article will clear up some of these misconceptions around the validation of data so keep on reading!
Think of it as...
Garbage In and Garbage Out!
There’s been a lot of misconception that the process of data integration will clean your data from input errors.
Reality: That’s false!
The process of data integration does not clean your data from unnecessary or wrong information. The information that is inputted into the system, will be the one that is integrated through the applications. If the wrong information is inputted then it will flow through various applications as such. Think back, data integration is the movement of data from point-to-multipoint; if data needs to be “cleaned” — meaning, thoroughly proofed of errors and/or inconsistencies — it needs to occur prior to the data being integrated.
Think about it this way, what if your customer sends an order for a beautiful pair of high heels and they type the wrong address, postal code, and/or wrong city. What do you think happens? Even if the customer luckily receives their order, you will still have incorrect data surging through your applications. So, where exactly do you think the problem originates? The process of data integration does the job it’s supposed to, it sent the inputted data to your multichannel applications. Meaning that the problem is arising prior to the integration.
That’s why it’s important that you have an excellent understanding of your data. Beyond that, an understanding of how your business data moves through your infrastructure and technology stack. By having a deep knowledge of this, it will help you understand where issues may arise but also where to improve customer experience. The overarching goal is to improve the visibility of your business performance to ensure a positive customer experience.
If data needs to be “cleaned” — meaning, thoroughly proofed of errors and/or inconsistencies — it needs to occur prior to the data being integrated.
Reality: Not true!
Similar to the previous myth, data integration does not validate your data before it is integrated or during the process. When we are talking about data integration here we are referring specifically to iPaaS data integration solution. Why does iPaaS not validate data errors? In an iPaaS system, it simply moves data into multichannel systems. It does NOT change, modify, fix or alter the data in any way.
iPaaS data integration transforms the data by applying business rules into a model that can be used by other systems. Think about the following analogy, when you are moving a broken couch from one house to another is the couch fixed? As much as I wish that was the reality, the couch will still be broken when it reaches its destination. Now, apply this analogy to data integration, as data is being integrated from a source to various targets. Just like the broken couch, the data is being moved NOT validated, changed or altered in any way.
The Unified Concept model represents this logic, when we are integrating data we ensure the data reaching the target(s) is the same as when it came from the source. For example, when we have an order the information will never be modified. But, the structure will be transformed into a consistent format to provide a unified view of the data. Meaning all of the data will look identical in structure. Our goal is that the data coming from the source reflects the data on the target.
Let’s consider the consequences, what if your data is not validated prior to being integrated? The information received to all applications and channels will also be incorrect and not only will this create multiple errors in several applications, but you will also create an atmosphere where your customers will be dissatisfied.
Creating an excellent customer experience from click to delivery is key to your business’s overall growth. Implementing a strategy that uses your business data strategically is the best method of accelerating growth. In terms of validating data before it is integrated, there are tools you can implement to ensure that your data is valid like location services or address verification tools.
If data is not validated prior to being integrated, the information going to all apps and channels will also be incorrect — creating multiple errors and a dissatisfying customer experience.
Similar to the previous myths, there’s been a misrepresented view that data integration can fix your data from incompatible data issues or data with missing fields. Well, here’s your answer:
Reality: Certainly not! In fact, data integration does not construct or change data as it is being moved.
In short, the process of data integration does not alter the data as it wouldn’t reflect the data originating from the source. Meaning that if the data were to be altered, it would not reflect the information inputted by your customer. The goal of data integration is to reflect the information that has actually come in not change it. But, although we don’t change the data we do transform it by applying your business’ particular rules to give it a unified structure.
If data errors are consistently occurring then that is our first clue to figuring out where these errors are coming from in relation to the source. But, if you don’t take a strategic approach to solving data errors then customer experience will ultimately take a toll. The solution isn’t a quick fix but rather finding the root of the error.
Data integrity must reside in the central point of truth for that data element. This means the true value of the data belongs where the data was created. Allocating a random value or changing the data will distort the integrity of the data which originates from the source. The goal of iPaaS is to make sure that your business data is integrated to the best of its ability—VL by eHouse can provide this while applying your specific business rules.
Having an iPaaS solution like VL by eHouse also provides a comprehensive Dashboard where you can have visibility on any errors within your applications and help you establish a viable solution.
Let’s bring the conversation back to the real consequences of these misconceptions—the impact they will have on your customer experience.
Your business’ goal is to promote customer loyalty through unique experiences. That begins with having a data integration strategy. Without a clear understanding of how data flows throughout your business, those data integration misconceptions will have a negative impact on your customer’s overall experience. It’s crucial to understand where errors arise and how to mitigate them accordingly.
Data integrity must reside in the central point of truth for that data element.
Data integration is the movement of data from one point across multiple channels and applications. It will not clean, validate or fix your data!
Here at VL by eHouse we can and will transform your data into a unified model to provide a consistent structure and apply your unique business rules. This allows you to leverage your data strategically, enabling you to create an unforgettable customer experience.
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