The lack of a reliable, error-free data import solution can result in frustration for both employees and customers. It can even result in lost business.Reliable data import is a vital necessity across an organization's departments, whether in a B2B or B2C context. In our more and more data-driven world - especially in the software and technology sector- the first thing that your customers do and experience (sometimes even before they access your solution) is data uploading and importing. Unfortunately, this seemingly simple procedure is often fraught with errors due to a lack of universal standards, technical limitations, and technical complexities. Data importing has not received the attention it should, despite its importance in rapid onboarding procedures and the overall end-user experience. Why is data import critical?Importing data from legacy systems is often needed to onboard a new customer. Data import tasks are either carried out in-house by employees or they are outsourced to the customer by requiring them to upload a file to a website or app. The data import process has high relevance in the customer onboarding experience. Correctly imported data means higher-quality data and time-savings for the business. Errors in the data import procedure can lead to frustration. Considering that 86% of customers would pay more for a better onboarding experience, improving the data import step is vital for any business.
As data import is a highly complex and specific task, the process can be error-prone resulting in various obstacles that have to be faced along the way.
There is no universal standard for how data must be exported or imported. Software and tools export data in CSV files, (comma-separated values), Excel workbooks, raw text data, database structured text, and at least a dozen other formats.Standard formats have been attempted, but it is still up to the vendor to implement those standards in their software. This variety of formats means that programmers must either program data import solutions for each type of format or opt for the two or three most popular ones and exclude the rest. This leads to incompatibility for customers who can't provide their data in the required format.
Perhaps one of the most frustrating data import errors for programmers is the high variance in data formats from region to region. For example, Germany uses the comma as a decimal point which means that "Comma-Separated Value" files are actually separated by a semi-colon, and this plays havoc with data import functionality. A pretty common example of this challenge is the difference in date formats. In contrast to the European way of displaying a date, US date formats state the month before the day. What can lead to small misunderstandings in daily doing can become a relevant source for data quality issues and lead to significant errors when it comes to automatically imported data. Oftentimes, the import even fails, leading to frustration and high manual validation and cleaning efforts.
The exported data itself might contain errors. The source of the data might not have done a great job at validating user input resulting in illogical errors such as a birth date that is 200 years in the past or an annual household income of $30 for a Fortune 500 CEO. Building a robust tool that catches data errors while importing is a mammoth task!
Computers need to determine the type of data before being able to do anything logical with it. This is best understood using Excel as an example. Although Excel might display a date as "01 Jan 2023," that date is actually stored as the number "44936".By telling Excel that the type of data above is a date and not a number, it knows what to do when performing calculations with it. Data imports can become a convoluted mess when the import tool gets confused about the type of data being imported.
In general, the challenges mentioned above are only a selection of issues that usually lead to an immense amount of resources required to build a solution that might or might not work every time, for every case, and which will be specific to one particular system. This results in "lock-in" and, if a company wants to change platforms, the entire data tool would need to be programmed again. Apart from the setup, resources required for continuous advancement and maintenance of the tool are often neglected when calculating the ROI to have a make or buy decision. In fact, companies that decide to go with an in-house built solution first, often end up overturning the decision realizing that a growing customer base, a scale across use cases, and advancements to the core product continuously add requirements to the data importer and make the original solution not usable earlier than expected.Building a data import tool is not a one-off cost. The tool needs to be maintained to keep up with dependencies, making it an ongoing cost.
There are two aspects to a complete data import solution:
Despite the plethora of formats that data can be exported in, two mainstays exist—CSV and Excel files. No matter what other formats exist, virtually all software can export into CSV, and there is wide support for Excel files as well. The second part of the solution lies squarely in the area of Artificial Intelligence. Only through using sophisticated AI algorithms is it possible to sift through the complexities, determine the type of data being imported, and understand its content so that it is imported correctly. The resources required to build such a tool are enormous, even if that tool is meant to work with just a single in-house system. Building one that is 100% flexible and so prevents vendor lock-in is virtually impossible unless it is your business's primary offering. Nuvo has engaged the necessary resources and created a 100% flexible, problem-free, reliable data import tool that can be used to import any data from any system using CSV and Microsoft Excel files, with zero errors.
The choice is ultimately up to you if you want to build your own data import tool or not. If you do, you will need to consider the five things above and plan for them. Buying a data import tool makes more sense from an economic and efficiency viewpoint, but only if the tool is reasonably priced and brings value to your business.
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