You Probably Don’t Need an RFP

Many organisations still use large, prescriptive RFPs to select partners for data platform and analytics projects. These documents often define architecture, scope, timelines and deliverables in detail before any discovery work has taken place.

While this approach made sense in a world of static, waterfall-style delivery, it is not well suited to modern data projects where requirements evolve as work progresses and as teams learn from early outputs.

Traditional RFP processes rarely lead to the best partner choice. Why?

1. RFPs assume the problem is fully understood upfront

Analytics projects frequently involve legacy systems, inconsistent logic, unknown data quality issues and evolving business questions. These cannot be accurately described or costed before a partner has explored the environment.

RFPs require partners to act as though all requirements are fixed and well defined. The result is proposals based on assumptions rather than real understanding. Buyers then evaluate partners on how convincing their hypothetical plan sounds, not on how well they collaborate, adapt and solve problems during delivery.

Modern data work succeeds when teams can learn, refine and respond as they go.

2. RFP timelines and fixed scopes don’t match the uncertainty of data projects

Most RFPs specify detailed milestones, sequencing and deadlines long before the underlying complexity is known. This encourages partners to either overprice to cover risk or underprice to win the work then struggle to deliver within unrealistic constraints.

Agile delivery acknowledges uncertainty and manages it through short cycles, working increments and continuous feedback. Fixed scopes undermine this by forcing teams to commit to a plan written without insight into the real environment.

3. RFPs prioritise documentation rather than collaboration

A partner’s real value comes from how they think, how they communicate and how they work alongside your team. Those qualities emerge through interaction, not through written submissions.

RFPs tend to reward firms that excel at producing polished documents rather than those who excel at designing, building and evolving data platforms. A strong proposal does not guarantee a strong delivery partnership.

4. They create a false sense of where risk sits

When a project is framed as a fixed commitment in an RFP, it can appear as if the partner is taking on all risk. In practice, if assumptions turn out to be incorrect, the buyer still carries the consequences through delays, change requests or misaligned solutions.

Agile partnerships share risk openly through transparency, iterative delivery and ongoing prioritisation. This creates far healthier outcomes for both sides.

5. RFPs require vendors to guess architecture without real discovery

The most effective data architectures emerge from examining real data, existing processes, user needs and organisational constraints. RFPs typically ask partners to define architectural decisions before that insight exists.

This leads to solutions that are either overly generic or mismatched to the organisation’s true needs. Discovery must inform design, not the other way around.

6. The selection process becomes transactional when the work requires partnership

Successful data projects rely on trust, collaboration and ongoing joint decision-making. RFPs, by nature, create a transactional process where bidders try to minimise perceived risk, present certainty where none exists and optimise for scoring rather than for partnership.

This can result in choosing the partner who looks best on paper rather than the one who will deliver the best outcomes.

A better way to choose the right partner

Organisations achieve better results when they replace traditional RFPs with a more collaborative, evidence-based selection process.

1. Start with a small discovery engagement

Run a short engagement to explore data sources, prototype initial models, assess feasibility and uncover true risks. This gives both sides real insight into complexity, capabilities and team fit.

2. Use real findings to define a shared roadmap

Discovery allows you to build a plan based on evidence rather than assumptions.

3. Deliver through short, value-focused sprints

Two-week iterations with clear priorities let teams produce working outputs quickly, gather feedback and adjust direction as needs evolve.

4. Evaluate partners based on how they work, not how they write

Look for clarity of thinking, communication, technical judgement, ability to support your team and velocity of delivering real value.

5. Choose partners who can enable long-term ownership

The right partner builds platforms that your team can operate and evolve, whether that’s immediately or after a period of structured support.

Traditional RFPs are designed for a world of fixed requirements and predictable delivery. Data projects rarely fit that mould. A collaborative selection process grounded in real discovery, agile delivery and early value creation leads to better outcomes for organisations and stronger, more effective partnerships.

Interested? Find-Out More

Rittman Analytics is a boutique data analytics consultancy that partners with successful organisations to transform their overwhelmed data teams from a reactive cost centre into a proactive, strategic asset. We deliver more than technology; we deliver the organisational change that makes it work.

You can read more about our approach to modernising data teams and data platforms on our website, where you can also take our diagnostic assessment and receive a custom playbook with targeted advice for your specific challenges.

So if you’re looking for some help and assistance scaling your data capabilities or would just like to talk shop and share your thoughts on what’s going on in your organisation and the wider data analytics world, contact us now to organise a 100%-free, no-obligation call — we’d love to hear from you!