The Case for a Simplified Data Stack

Unlocking the right data stack could be the missing piece in the puzzle of data-driven success. Are businesses overcomplicating the path to the optimal solution?

In 2025, executives see data as more than just a resource. It is an enabler of real-time decision-making, a safeguard against cognitive biases that can negatively influence decision-making, and a key driver of AI ambitions. 

The ability to harness data effectively has become a competitive differentiator.

As Gartner summarises succinctly, "Organisations that regularly align to data themes outperform their peers."

Yet, while data is the backbone of strategic decision-making, its allure is often as complex as the technology stack supporting it, creating challenges for businesses trying to extract real value from it.

The rise of AI and ML-powered analytics unlocks unprecedented opportunities for organisations to leverage their data more effectively. Businesses that harness these capabilities can gain a significant competitive edge.

Behind every data-driven executive and business is a well-architected data stack - comprising infrastructure, pipelines and tools – designed to support and scale with the business. 

However, adequate data stack design is no easy feat. 

Many organisations fall into the trap of building overly complex data infrastructures and pipelines, which leads to inefficiencies that hinder their ability to derive meaningful business value. 

Others excessively simplify their data stacks, and this can limit scalability and hamper future growth. 

For CIOs and CTOs, the challenge lies in striking a balance: designing a data stack that is streamlined enough to optimise costs and efficiency while maintaining the flexibility to support a business' ambitions truly. 

Importantly, this balance is not set-and-forget: to remain at the forefront of opportunities and value that being data-driven creates, regular reassessment is required to ensure the right level of sophistication stays in your data strategy, and to ensure alignment with business needs as they evolve.

The Hidden Costs of Complexity

While technology teams may be drawn to the latest innovations in data architecture, it is crucial to assess whether the additional layers of complexity provide tangible business benefits. An overly intricate data stack could have a number of potential consequences.

• Higher Total Cost of Ownership (TCO)

Every additional component in a data pipeline incurs costs—not just in terms of licensing and infrastructure, but also in implementation, maintenance and training. A complex ecosystem requires significant resources to manage, increasing the long-term financial burden on the business.

• Barriers for Support and Operations Teams

A fragmented or convoluted data stack makes it difficult for support teams to diagnose and resolve issues efficiently. When multiple systems interact in unpredictable ways, troubleshooting becomes time-consuming and costly, leading to increased downtime and reduced agility.

• Recruitment and Skills Challenges

An overly complex environment limits the pool of available talent. Specialised technologies can be difficult to hire for, and training new employees on intricate architectures adds to onboarding time and costs. A simpler stack increases the likelihood of finding skilled professionals who can hit the ground running.

• Diverting Development Efforts Away from Core Business Value

Perhaps the most critical downside of complexity is the misallocation of development resources. When engineers are consumed by maintaining infrastructure, managing legacy systems and resolving integration challenges, they have less time to explore emerging opportunities or to focus on activities that drive business value. Lost opportunity is often the most expensive consequence of an overly complex system.

The Risk of Oversimplification

While the dangers of complexity are clear, it's equally important to acknowledge that excessive simplification can also limit future growth.

An overly basic data stack may lack the scalability required to support evolving business needs, restrict the ability to integrate with new technologies or data sources, and/or force engineering teams to work around limitations, leading to inefficiencies elsewhere.

The responsibility of designing the data architecture should be a collaborative process between business stakeholders and technical teams to ensure that the data infrastructure meets current operational needs while maintaining flexibility for future expansion.

Regular Review: A Key to Long-Term Success

Business and technology don't stand still. What made sense for the data strategy two years ago may no longer be relevant today.

For this reason, I recommend that organisations conduct an annual review of their data architecture at minimum. 

It's an opportunity to step back and ask:

• Do the assumptions made during past decision-making processes still hold true?
   
• Are there newer technologies that could potentially simplify existing workflows?
   
• Is the current setup adding unnecessary complexity or does it need additional capabilities to keep up with growth?

This process of reassessing and refinement ensures that organisations don't remain locked into outdated or inefficient architectures and can continuously optimise for both cost and innovation.

The goal isn't to create the simplest data stack possible at the expense of future scalability. Nor is it to embrace complexity for complexity's sake. Instead, the ideal approach is one that balances efficiency with strategic foresight—minimising unnecessary overhead while ensuring that the architecture remains adaptable to evolving business needs.

CIOs and CTOs play a critical role here. They must champion a culture where technology decisions are guided by business impact rather than technical enthusiasm alone. 

By regularly assessing and refining their data strategy, organisations can ensure their data strategy actively supports growth and ambitions, ready to capitalise on new opportunities rather than being constrained by the weight of their own infrastructure.

A well-designed data stack is not the one with the most features—it's the one that helps your business move faster, adapt easier, and make smarter decisions with the least resistance.
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