Why Your In-House Data Tools Are Holding You Back
Data is the oil of the modern era. It drives internal operations, businesses sell access to it, and customers pay for sophisticated visualizations and data manipulation capabilities. This has led to businesses developing complex, customized, in-house data tools to fit specific customer and internal needs. These internally built and maintained tools gave businesses the control they needed to monetize and manage data. With the ever-changing tech landscape however, these tools can quickly become antiquated and hinder agility and innovation.
Over time, the same tools developed to solve specific needs also introduce several pitfalls and pain points for organizations. Maintenance, scalability, obsolescence, aging functionalities and features, security, and resource drain are all rising concerns regarding the total cost to manage data tools built in-house in the modern era. These concerns are causing a shift towards managed platform and solution providers, like Sigma. In this blog, we’ve outlined these risks in detail and the impact they have on businesses to bring awareness to organizations leveraging in-house, custom-built data tools.
Five Reasons Why Your In-House, Custom-Built Data Tools Are Holding You Back
1. Maintenance and Overhead
Building an in-house custom solution takes time and effort upfront. But what about after it’s done? It’s easy to forget that ongoing maintenance can sometimes be even more daunting than building the tool itself. Often, internally built tools result in tribal or specialized knowledge retained by a few core individuals or teams, which creates difficulty when key members leave the company or change responsibilities over time. Ensuring teams have standardized documentation and release notes is also difficult, creating a significant knowledge gap over time. This is a common issue with solutions which use JavaScript libraries such as Angular or React, which require routine library version updates to prevent breakages to existing applications.
The time investment to continually maintain these tools long-term is significant, especially when coupled with isolated knowledge. This diverts engineering talent from other crucial core development activities. Perhaps the most alarming point – maintaining an internally built tool can consume large portions of IT budgets, making investments in core areas of the business notably harder. Many businesses find themselves trapped by the “sunk cost fallacy,” clinging to these tools despite their limitations simply because of the initial investment. However, this mindset can lead to continued financial strain and missed opportunities for growth.
2. Tech Obsolescence
It is no surprise that, in the ever-evolving world of software and technology, some tools become antiquated, less relevant, and ultimately obsolete. But that’s exactly the problem with in-house custom tools – they can become obsolete if not given the level of effort and budget required to constantly maintain and update them. Businesses need internal experts capable of leading and/or supporting these updates, and these rewrites and rebuilds are disruptive and costly. Over time, that chips away at the perceived value the tool once had.
That “tech debt” reduces the agility of a company. You can’t compete if you can’t keep up with the pace of even the slowest tech-forward competition. The modern tech stack landscape evolves quickly, and businesses need data solutions that can adapt to those changes. For example, a custom-built tool developed before the widespread adoption of cloud-based services might lack the APIs necessary to integrate with modern CRMs, marketing automation platforms, or customer support tools. As a result, costly and recurring expenditures for custom integration solutions or manual workarounds must be implemented, directly impacting the bottom line and diverting critical resources from strategic initiatives.
3. Difficulty Scaling
In the same vein as obsolescence, internally built custom tools struggle to be performant and scalable as data needs and user bases grow. For example, as a business expands its customer base and collects more data, its custom-built analytics tool might struggle to handle the increased volume and complexity of queries. When businesses scale the infrastructure behind these tools to handle these increased needs, it's typically expensive and complex, requiring significant investment in hardware upgrades, manual performance tuning, or specialized development resources. This is in stark contrast to the simple license volume upgrade typically required by third-party tools.
Custom internal tools often require significant investment in hardware upgrades, manual performance tuning, or specialized development resources to address scalability challenges. They struggle to handle ever increasing data volumes, user concurrency, and complex queries without experiencing performance bottlenecks, resulting in slow query times and a frustrating user experience. These inefficiencies make scaling both financially and operationally unsustainable compared to modern, cloud-native solutions.
4. Lack of Better Features & Functionality
In-house tools also face challenges in providing enough features and functionalities for its users. What seems like simple visualizations, features, or capabilities could take hours of work to build; more advanced analytics features take even longer. As a result, data exploration and insights generation for end users can be lacking compared to what end users expect, impacting the overall user experience.
It can also be more difficult to integrate embedded analytics into other applications. Custom internal tools often lack standardized APIs or integration capabilities, making it challenging to connect with modern ecosystems. This limits the ability to deliver interactive dashboards or real-time insights directly within customer-facing applications, forcing businesses to rely on clunky workarounds or manual processes.
5. Security Vulnerabilities
Internally built data tools often lack the dedicated security expertise needed to address evolving threats, leaving gaps like weak authentication, poor encryption, or outdated patches that increase risk. Compliance with regulations like GDPR can also be challenging, since maintaining these standards requires constant updates and oversight. Without robust security measures, businesses risk data breaches, financial penalties, and damaged customer trust.
Overall, these factors significantly contribute to the increasing level of difficulty in managing internal, custom data analytics tools. The benefit of fully customized tools, tailored to the specific needs of your client base, will always be appealing to product teams. However, the long-term TCO far outweighs any potential benefits; maintenance, upgrades, opportunity cost, security breaches, talent drain, and more. Coupled with trying to keep pace with the changing tech landscape and customer data needs, companies are increasingly looking to offload their technical overhead for a streamlined, consistent and stable customer experience.
Summary
These problems are driving the trend we see across industries to move away from custom in house tools, to something that has high customer usability, can provide a better user experience, still be customizable and enable self -service, but overall, significantly lessens the maintenance cost and lowers the total cost of ownership. This is why we have seen modern businesses transition to Sigma for embedded analytics, a Cloud Analytics solution with a spreadsheet-like interface that enables anyone to explore data at cloud scale and speed. Learn more about this with our blog “Why Modern Businesses are Transitioning to Sigma for Embedded Analytics.”
