The ROI Of Custom Data Applications: When To Build Vs. Buy

Twenty years ago, it made sense for companies to buy pre-built data applications, point solutions for specific workflows. They enabled users to automate processes, due to not always having development resources in-house. But, the rise of self-service analytics and software platforms combined with increased data fluency in the average data consumer have made DIY a better option than ever before. 

Let’s discuss some of the market changes that have led to the rise of low-code data apps, as well as the “build vs. buy” considerations that come into play. 

Today’s modern data apps landscape

Over the past decade, the data and analytics landscape has transformed dramatically — from methodologies to technology stacks. Today, leveraging data isn’t optional; it’s essential. Even entry-level employees now possess far greater data knowledge than their counterparts ten years ago.

As workforce data fluency grows, so does the demand for diverse data applications to support modern organizations and specialized functions. Whether it’s spreadsheet workflows, third-party software, or proprietary apps, most companies now rely on data apps in some form. Just as self-service analytics spurred the rise of low-code visualization tools, the need for dynamic data consumption has now driven a surge in low-code platforms.

This shift presents companies with a familiar dilemma: how best to meet the demand for point solutions. Should they buy data applications from vendors or build them in-house? Let’s explore.

Four benefits of building data apps

Building data apps in-house has several benefits, including faster deployment time, customization, user democratization, and cost efficiencies.

Highly Customizable

Imagine this extremely common scenario: your team is presenting data pulled from a data app you built within your analytics platform for leadership review. The leadership team is very pleased with the visualization, but during the presentation, some feedback required changes to the data visuals and filters. As the presentation is wrapping up, the presenter easily adds additional parameters to the app that then gives the data they asked for. The leadership team loves it! Not only did they appreciate the changes that met their specific requirements, but they loved the idea that their requests could be fulfilled in real time. 

One big benefit of democratizing data apps within an organization is lowering the barriers to customization and communication between developers and stakeholders. Because the data teams understood the context of the tasks, they customized the product to meet their specific needs. 

Even if the changes had not been implemented immediately, the product team would likely have been able to leverage existing workflows and processes for their development. 

Speed to Deployment

Keeping the previous scenario in mind, if the data team was using developers or using a it could have taken weeks, if not months, to get their requirement changes implemented. This is another advantage of building your own data apps: the ability to rapidly prototype and deploy final products. 

Context of the business problem, knowledge of existing data processes and policy, and the ability to reprioritize backlog items autonomously allows for data products to usually be built faster than with a third-party provider. 

In addition to the ownership aspect of the data product, low-code or no-code applications are easy to learn and use. Instead of lines of code that need to be changed, tested, and deployed, low- or no-code data application platforms allow users to drag-and-drop their desired fields to build applications. This development approach allows for more flexibility in the final product, as well as shorter development time. 

Cost Efficiency

Since in-platform data application development provides organizations with more customization flexibility and faster speed to development, this can equate to serious financial upside and cost efficiencies for companies. Data products can be developed faster without the costs that come from ongoing third-party software subscriptions.

This allows companies to consolidate and leverage existing data apps and applicable data across their organization. Not only can this exchange of tribal knowledge and leveraging of existing data products decrease time to development, but it can also optimize the usage and data consumption of your data apps. Data application development doesn’t just add value — it’s also a way to maximize the ROI from your cloud data warehouse (CDW).

User Democratization

Long wait times and complicated processes to gain access to and interact with data can hinder a data-driven culture. Not only does enabling teams to use low- or no-code data application development platforms give those teams more ownership over their data products, but it also helps them gain more ownership over their outcomes.

Low- and no-code development platforms are developed with non-technical users in mind, enabling even the least experienced users to build their own applications that address their unique requirements. When teams have greater autonomy, they are more likely to adopt and utilize data applications. Breaking down the traditional silos between data producers and consumers can help foster a collaborative environment where everyone can contribute to the organization’s goals. 

How do data apps compare to external options?

When evaluating whether to build data apps in a low or no-code platform or purchase third-party applications, it’s important to understand how these solutions differ in terms of complexity, focus, and use case. While both are capable of creating the desired solutions, they are designed for different needs and audiences. 

Third party data apps can be easy to use, come with the right data model, and include important workflows out-of-the-box.  These platforms are often targeted at a very different persona than low-code options. Low-code data application platforms are generally targeted to builders; developers, analysts, and data teams.  

Low- or no-code platforms prioritize customization, but each platform in the market varies wildly in simplicity and accessibility. All shine in scenarios where data consumers need data applications with a high level of customization for a very specific business use case. 

Solutions such as Sigma and Airtable allow quicker turnaround time, and these high levels of configuration allow businesses to iterate quickly and get what they need at a lower cost and in a shorter amount of time. If your use case is not time-sensitive, you have a larger budget, and there are very complex feature requirements, code-backed platforms fit these needs better.  

With every decision, there are pros and cons to take into account. Choosing between building a data app or buying a third-party platform app depends on your organization’s technical expertise, the complexity of your needs, and the desired balance between speed, cost, and functionality. 

What makes a data app a success for your organization?

Whether you build or buy your data applications, every organization wants to see the deployment of software to be successful. That success is determined by whether or not the app delivers actual value to its users and the organization. Let’s dive into some different ways to ensure the success of your data application.

Clear User Requirements

A successful data app begins with a deep understanding of user needs. This means gathering stakeholder input, identifying the problems the app needs to solve, and ensuring that the app aligns with your organization’s strategic goals. Without clearly defined requirements, your data app risks missing the mark, leading to wasted time and resources.

Data Accessibility

One of the most critical measures of success is how well the data app enables users to access and interact with data. Is the app easy to use, even for non-technical users? Can users access the right data at the right time? If your app makes it easy to use data to drive decision-making, it’s a strong sign of success.

Design Principles

A successful data app should make data and reporting easy to find, read, and interpret. This includes clear, visually appealing dashboards, intuitive navigation, and a focus on user experience that enables users of all skill levels to complete or add value to their existing business processes.

User Adoption

A data app is only as valuable as its adoption rate. High user adoption indicates that the app is meeting user needs, is easy to use, and delivers value. Low adoption, on the other hand, may signal that the app is too complex, doesn’t address user’s actual problems, or that users lack sufficient training and enablement to adopt the app. Tracking usage metrics and even getting anecdotal feedback from stakeholders can be helpful in determining whether the app is actually being used by its intended audience.

Training & Enablement 

Even the most easy-to-use data application platforms require that both app developers and consumers be trained on how to develop and consume data from the platform. Without proper platform enablement, companies will not see the kind of adoption they desire from their data application, which ultimately decreases the value they see from their investment in data applications, regardless of whether they buy an app or develop it themselves.

So, when is building a data app the right choice?

The decision to build or buy a data application is a strategic one, requiring organizations to weigh their technical expertise, business needs & use cases, budget, and timelines. While buying data applications may offer simplicity and the ability to include more complex features, building your own data apps can enable faster turnaround times, provide a higher level of business-specific customization, and bring cost savings. Regardless of the approach, success hinges on clear user requirements, strong adoption, accessibility, and alignment with business goals.

By understanding your organization's unique needs and investing in training and enablement, you can ensure that your data applications deliver value and empower your teams to make data-driven decisions. Whether you build or buy, the ultimate goal is to provide data applications that empower your teams, foster a data-driven culture, and add value to your business through informed decision-making.