Fintech data modernization demo

Modernize your data stack and cut total cost of ownership.

See how your fintech can move to an AWS-anchored data architecture with the right partner platforms so analytics is faster, ML is supported, and the data run rate is defensible to finance.

Schedule this demo See the use cases

What you hear from the team

"Our data platform costs more every quarter, and the answers are still slow."

Fintech capabilities

What your team can do.

Lakehouse foundation

Stand up a governed lakehouse on AWS with the right platform partner (Databricks or Snowflake) for your workload mix.

Cost and run-rate reduction

Right-size compute, storage, and movement so the data platform run rate is defensible and improves quarter on quarter.

ML and AI readiness

Make features, training data, and inference data available with the right governance and lineage.

Analytics for business users

Bring self-service analytics and generative BI to business owners so they get answers without queue.

Land and govern

Land data on the lakehouse with the catalog, lineage, and access controls in place.

Consolidate

Retire duplicated storage and compute with measured cost impact.

Enable ML

Build features, training paths, and inference paths with lineage and monitoring.

Open to business

Bring self-service analytics and generative BI to business owners with guardrails.

Lower TCO

Cut data platform cost with consolidation and right-sizing.

Faster analytics

Compress the time from question to answer for business owners.

ML in production

Move models from notebook to production with the same data foundation.

Governed data

Catalog, lineage, and access control your finance, risk, and compliance teams trust.

Use cases your team can deploy

Three workflows that move the operating numbers.

Each use case shows the customer pain, the workflow your team gets, the kind of prompt or trigger that starts it, and the artifact the team can actually use.

UC1

Lakehouse landing and consolidation

"We have three data platforms, four warehouses, and the cost line keeps growing."

Fintechs with multiple data platforms or warehouses where consolidation is the cost play.

What this demo shows

Land data on a governed lakehouse with the right partner platform, consolidate duplicated storage, and retire warehouses with measured cost impact.

Implementation scope

Inventory current platforms, design the target lakehouse, migrate the highest-cost workloads first, and report run-rate impact monthly.

Live prompt or trigger

Migrate our highest-cost data workloads to the lakehouse, retire the duplicated warehouses, and show the monthly run-rate impact.

Migration planConsolidated lakehouseRetired platformsRun-rate reportGovernance baseline

UC2

ML feature and inference foundation

"Our ML projects stall because the feature data is hard to assemble and harder to govern."

Fintechs where ML projects exist but production deployment is the constraint.

What this demo shows

Build a feature store, lineage tracking, and inference path so ML projects can move from notebook to production with the same data foundation.

Implementation scope

Pick the priority ML use case, build the feature store, wire training and inference, and pilot with one model.

Live prompt or trigger

Build the feature store for our credit risk model and deploy the inference path with lineage and monitoring.

Feature storeLineage viewTraining pipelineInference deploymentModel monitoring

UC3

Self-service analytics for business

"Business owners wait days for analyst answers."

Fintechs where the analytics team is the bottleneck and business owners want answers they can trust.

What this demo shows

Bring self-service analytics and generative BI on top of the lakehouse so business owners ask questions in plain English and get governed answers.

Implementation scope

Connect curated data sets, configure the semantic layer with business definitions, and roll out to a pilot group.

Live prompt or trigger

Let our product leaders ask questions about feature usage and customer cohorts in plain English with charts and scheduled summaries.

Semantic layerSelf-service chartsScheduled summariesSaved questionsAnalyst-supported guardrails

Fast launch path

A five-step path your team can run.

Stand up the first workflow fast: pick the entry point, connect the data, build the experience, pilot with users, and move into steady state with the observability and governance your team needs.

Start the pilot

Week 1

Inventory current state

Map platforms, workloads, and cost.

Week 2

Design target

Pick the right partner platform and consolidation sequence.

Week 3

Migrate

Move the first high-cost workload and retire what it replaces.

Week 4

Enable ML or BI

Stand up the feature store or self-service layer.

Week 5

Report

Report run-rate impact, analytics adoption, and ML deployment.

Who this is for

Fintech data, analytics, and finance leaders who own the data platform cost, performance, and the path to ML and AI.

Fintech demo

Part of the Tactical Edge fintech demo library. See related demos for the rest of the customer journey.

Browse the library

Outcome you can measure

Every demo ends with an artifact you can put in front of your team and a metric you can track from week one.