Future Processing

FinDataOps

Turn cloud, data and AI spend into predictable business outcomes.

Do you know that:

30 /
of GenAI projects were dropped because of poor data quality or unclear business value in 2025 alone 

Source: Gartner

78 /
of companies estimate that 21-50% of their cloud expenditure is wasted annually 

Source: techmonitor

We help organisations regain visibility over cloud and data spend, improve forecast accuracy, and embed governance directly into delivery workflows. First decision-ready insights are typically delivered within 10 working days.

Typical challenges we solve

Recurring forecast misses and budget surprises

Baseline-driven forecasting with controlled variance

Savings claimed but not proven

Verified financial impact measured against an agreed baseline

Frequent unplanned cost volatility and reactive escalations

Driver-based cost control with reduced variance

AI workloads scaling without economic control

Defined unit economics and clear cost-to-value linkage

Our outcome-based model will provide you with financially guaranteed efficiency of the solution and predictability of delivery.

Where FinDataOps creates impact

Cloud financial governance

Tagging, allocation, forecasting, budget ownership and multi-account control.

Data platform efficiency

Query optimisation, lifecycle management, storage design and workload behaviour modelling.

AI cost predictability

Training, inference and experimentation cost visibility aligned with business value.

Design predictable growth across cloud, data and AI

Whether you need quick insight into cost drivers or a full optimisation roadmap, FinDataOps follows the Triple-A model and adapts to your business priorities.

Assess
Understand where you are and what drives cost

Analyse data storage, processing and workload behaviour

Map allocation and tagging maturity

Identify inefficiencies and volatility drivers

Deliver first insights within 10 days

Full diagnostic within 3-4 weeks

Advise
Define what should change

Workshop with business and technical stakeholders

Prioritised optimisation roadmap

Governance model and guardrails definition

Unit economics framework

Typically 2-4 weeks depending on readiness

Apply
Implement and validate outcomes

Embed guardrails into workflows and pipelines

Refactor high-cost patterns

Introduce showback / chargeback models

Deliver verified savings within 4-12 weeks

Value we delivered 

A seamless migration and 72% cost reduction, within a 20-day timescale

Read the case study

Decreasing the lead time for changes from 2 months to 1 day and saving 50% of the client’s cloud costs

Read the case study

Proactively creating AWS Cloud saving plans for the client who now saves up to 50% a month

Read the case study

FAQ

Because data platforms and AI have shifted cost drivers from infrastructure to workload behaviour. Without linking cost to queries, pipelines, products and models, growth becomes financially unpredictable.

For organisations with significant cloud and data spend (typically £25-30k+ per month on cloud data services) that experience one or more of the following:

  • struggle to explain cost drivers,
  • miss forecasts,
  • lack clear ownership of data costs,
  • scale AI or data workloads without economic control.

FinOps focuses primarily on cloud infrastructure costs. FinDataOps extends that model to data platforms and AI – integrating financial governance with data lifecycle, workload behaviour and unit economics.

  • First insights: within 10 working days
  • Full diagnostic: 3-4 weeks
  • Verified savings: typically within 4-12 weeks

It is a structured, repeatable framework delivered as a service. The first two phases are fixed-price; implementation is success-based and tied to verified outcomes. It is not a software product, but a structured consulting framework delivered with measurable outcomes.

Start your transformation today

Thank you!

Book a short meeting to assess your readiness for FinDataOps and explore what savings and insights your data can reveal.

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