The global business intelligence (BI) and analytics software market is expected to reach $55.48 billion by 2026, with 70% of enterprises increasing their spending on real-time customer analytics solutions.
Unfortunately, only 22% of organizations believe they are achieving their goal to translate data into actionable insights at the optimal moment, despite 83% wanting to. That’s because SaaS and other cloud-based application providers are struggling to live up to their promise of real-time data-driven insights. What’s going wrong?
It may surprise you to learn that many data applications run on legacy architectures. These traditional data stacks were created long before the cloud existed and, therefore, they have scalability, concurrency, and performance limitations. They were never designed to run massive-scale SaaS applications with semi-structured data.
And yet, organizations have defaulted to using them without planning ahead. Rather than fall into this trap, here are three tips to ensure you build modern data apps that will meet or surpass your customers’ expectations.
Tip 1: Design for the future with growth and flexibility in mind
As a general rule, technology decisions should always align with long-term product needs. That means selecting components that accomplish the following:
- Always account for instant and infinite scalability.
- Support evolving product requirements without re-architecting.
- Avoid vendor lock-in and support a multi-cloud strategy.
Tip 2: Remember that “free” software is never free
It may be tempting to select open source solutions to go to market quickly without huge monetary investments. The problem with this strategy is the underlying infrastructure: Open source components often don’t scale and they make it tough to evolve with changing customer requirements. Three additional development challenges include:
- Lack of native support for semi-structured data
- Overhead requirements for system maintenance, upgrades, and security
- Required expertise that’s challenging to find and expensive to hire
Large amounts of development time and money are wasted by running and maintaining “free” solutions. As latency issues and incomplete data analysis frustrate customers, organizations almost always realize they need to re-architect the whole stack.
Tip 3: Use a modern infrastructure to build modern apps
Data applications such as analytics, BI, IoT, and machine learning have two basic requirements: large volumes of data must be ingested, and all data must be analyzed quickly and easily.
The key is to remove the restraints of traditional data stacks and the limitations of open source databases. Remarkably, a cloud-built data warehouse provides the exact platform needed. With a cloud-based modern stack, all the features needed to develop and scale modern data analytics apps are built into the architecture, including:
- Unlimited and automatic scalability
- Instant elasticity
- Support for semi-structured data
- Near-zero management
- Always-on, secure data environments
- Smarter query execution
- Per-second pricing
Don’t delay going modern with your technology stack
Total cost of ownership (TCO) is much lower when the right technology selections are made from the start. A modern technology stack addresses the shortcomings of legacy architectures by enabling real-time analytics in a cost-effective manner and delivering fast, reliable, and useful insights.
By planning ahead to include scalability and performance in your data stack, you can remove the technology burden from your development team, deliver data-driven insights, and match the evolving needs of your customers. This is definitely one of those cases where long-term thinking helps achieve short-term success.
To learn more about how to choose a stack that will power applications, download our ebook Three Tips for Building Modern Data Applications.