Welcome back to the second part of this series, taking you along and explaining how we do fundamental research at VI Company. Last time, in the first part of this series, I already explained how we kicked off the project. We learned about the definition of low-code (vs. no-code) and how selected the technologies we put to the test.

In this second part, I will continue to explain how we were able to gather information about each of our selected technologies in a comparable way. I will also explain more about the rapid prototyping we undertook and the results of the experiment. So, without further ado, let us continue where we left off!

Quantifying the low-code platforms

After gathering of the required information was completed, we had to create an objective comparison. But how can you do this?

We started by setting up a scorecard containing 29 criteria, each belonging to one of the subjects we used to gather the information. For each criterium, we also thought of a scale to measure it. As an example, we will look at three criteria within the ‘Features’ subject:

Using these criteria, we filled in the scorecard for each of the 13 selected alternatives to be able to compare them.

In the table below, you can see the average results per subject for five of the most popular low-code platforms that also scored high on our scorecard. Although it can be hard to read the results without the full scorecard, you can easily see they perform equally when it comes to features and the biggest differences can be found when it comes to pricing and the technical match.

Putting them to the test

However, these numbers do not give insights into the usability of these platforms and the leverage that they offer in comparison with traditional development projects. To be able to say more about that, we were going to build prototypes using each of these 5 alternatives.

For these prototypes, we selected a real client reference case that focused on ETL purposes (extracting, transforming, and exporting data from a spreadsheet). In the past, we already built a full-code prototype for this reference case that took around 16 hours to complete.

We set up a list of requirements that needed to be met:

• The prototype should be able to read data from the input spreadsheet.
• The prototype should be able to parse and use the formulas from the spreadsheet to perform calculations.
• The prototype should be able to display the output using a dashboard or report functionality.
• The maximum amount of time spent per prototype is 8 hours.
  
  

Mendix tries to deliver a single platform that can combine ETL and rapid prototyping. Although Mendix might fit many purposes very well, it was not the best choice for our reference case. It relies heavily on pre-made user apps and extensions, that often suit your basic needs. But in the case of our complex input spreadsheet, it was very difficult to import it properly. Hence, we were blocked in any additional steps and our requirements were never fulfilled.

Working with Microsoft's 'Power Platform' feels like coming home, most people with Office experience will feel familiar with the interface. In conjunction with many of the other platforms, Microsoft does not try to create one tool to rule them all. Instead, they have created three different platforms for automation, ETL, and rapid prototyping (respectively Power Automate, PowerBI, and PowerApps).

Uncharacteristically from Microsoft, it again proved difficult to automatically import the spreadsheet. We managed to read, extract, and calculate the data once using a workaround. However, after changing the parameters, we were not able to recalculate the spreadsheet and update the output. This was a bit disappointing since you would expect Microsoft to have the best integration.

Although the interface felt very familiar and lowered the learning curve, it was originally designed for Office and did not always suit the purpose of a low-code platform. Hence, the platform did not feel very intuitive and sometimes functionality was limited.

Tableau tends to have a different approach than other low-code platforms and is very focused on ETL functionality, within their own environment. Importing the spreadsheet was easy, and Tableau also offers many functions to clean, aggregate, or transform the data. Their IDE (integrated development environment) is clearly designed for these purposes and works fluently and intuitively.

One of the biggest letdowns was the inability to export or redistribute data to other platforms, although the visualizations available in Tableau looked good.

Just like Mendix, OutSystems focuses on delivering rapid application development by extracting data from standardized data sources and offering easy-to-use design tools. They offer their own IDE that can be used to create visual workflows and design for multiple platforms, which works very well.

Although the documentation is hard to find and browse through, there is still sufficient information to find your way around the platform. However, their visual workflows to create business logic feels limited and makes simple tasks, such as creating loops or lists of values, much harder in comparison to normal programming.

OutSystems does not offer spreadsheet importing functionality out of the box, but their online store contains many plugins that can be used for this purpose. Within 6 hours, we succeeded to create an app that imports the data and used the parameters to change the output.

Last, but not least, Alteryx. They offer a large platform consisting of different components to facilitate a complete development pipeline, from building it using their own IDE to releasing it using Alteryx Promote.

Most of the functionality is data-related, and they are very good at it. Importing, cleaning, aggregating, or finding relations are just some of the basic tasks they make easier. But they also support more complex tasks, like automated data modeling and pattern recognition.

There is a lot of documentation available, and an academy and community to learn how to use Alteryx’s tools properly.

In the end, for our reference case, Alteryx seemed the perfect fit because it was very data-focused. We managed to comply with all the requirements within only 5 hours.

Lessons learned…

Along the way, we learned there are many low-code solutions available that might answer your needs. However, finding the right one is a challenge.

Many tools have great marketing pages, but there is no such thing as “one tool to rule them all”. So how do you make the right decision? Our approach might help you to find the right platform for your purpose. For our specific use case, this was Alteryx. But every tool has its pros and cons, and although proper research takes time you will greatly benefit from it.

Like with any other new technique, we found that low-code does some things very well (e.g. working with lots data or rapid prototyping), but less well with other things (e.g. tasks might even take longer to complete, and are not solved better, quicker or easier using low-code instead of traditional full-code development).

We will continue to approach intrinsic or extrinsic needs tech-agnostically by finding the right solution for the right problem (as we call it, the “Pipi-mentality”). In some cases, this means low-code is a very good fit. In other cases, we would recommend full-code solutions or even hybrid solutions consisting of both low-code and full-code components.

Alteryx Partnership

After rounding up this project, we at VI Company were determined to keep investigating in low-code. This recently resulted in us becoming official Alteryx Partner, after some of our colleagues got officially certified. We hope this will contribute to keep bringing the best fitting solutions to our clients in the future.

Contact us

Are you curious to learn more about low-code? Our solution engineer Kevin Wareman is happy to tell you all about it. You can contact him via kevin@vicompany.nl.

Contact Kevin Wareman

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