Fintech data modernization demo

Make data-platform cost, performance, and ownership easier to manage.

Explore an AWS-anchored data architecture that connects platform cost, analytics, ML, governance, and finance decisions.

Schedule this demo See the use cases

The operating problem

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

Fintech capabilities

What the demo explores.

Lakehouse foundation

Compare Databricks, Snowflake, and AWS-native options against your workload, operating, and cost requirements.

Cost and run-rate management

Measure compute, storage, and data movement so finance and engineering can evaluate right-sizing and consolidation options.

ML and AI readiness

Prepare features, training data, inference data, lineage, access, and monitoring for a selected model.

Analytics for business users

Give business owners a way to ask questions against curated data and see the definitions and sources behind the answer.

Land and govern

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

Consolidate

Identify duplicated storage and compute, then plan retirement using observed cost and workload data.

Enable ML

Prepare features, training paths, inference paths, lineage, and monitoring for one selected model.

Test with business users

Let a pilot group review self-service analytics and generative BI against curated data and definitions.

Cost baseline

Compare current and candidate compute, storage, movement, license, and operating costs.

Time to answer

Measure request wait time, analyst effort, query performance, and user review time.

ML operating fit

Test whether features, lineage, inference, monitoring, and ownership work for a selected model.

Data ownership

Review catalog, lineage, definitions, and access with finance, risk, and data owners.

Use cases to evaluate

Three workflows to put in front of your team.

Each use case starts with an operating problem, shows a candidate workflow, and identifies the outputs and measures your team can review in a pilot.

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

The demo maps data to a candidate lakehouse, identifies duplicated storage and compute, and creates a phased consolidation plan with a finance-approved baseline.

What a pilot includes

Inventory current platforms, compare target lakehouse options, select a controlled workload, and measure cost, performance, and data quality against the baseline.

Example prompt or trigger

Assess our highest-cost data workloads for the lakehouse, identify duplicated storage and compute, and model the monthly run-rate change.

Example outputs

Migration planConsolidated lakehousePlatform retirement candidatesRun-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

The demo connects a feature definition, lineage view, training path, inference path, and monitoring plan for one selected model.

What a pilot includes

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

Example prompt or trigger

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

Example outputs

Feature storeLineage viewTraining pipelineInference pathModel 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

The demo lets business owners ask questions in plain English against curated data and review the definitions, sources, charts, and drill-downs used in the response.

What a pilot includes

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

Example prompt or trigger

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

Example outputs

Semantic layerSelf-service chartsScheduled summariesSaved questionsAnalyst-supported guardrails

Pilot path

A five-step path to a useful pilot.

Start with one workflow, agree on the baseline, connect the approved data, test with users, and let the results guide the operating plan.

Plan a pilot

Inventory

Map the current state

Document platforms, workloads, dependencies, users, and cost.

Design

Compare target options

Choose a candidate platform and consolidation sequence for the pilot.

Pilot

Test one workload

Move or copy a controlled workload and compare cost, performance, and data quality.

Evaluate

Test ML or BI

Evaluate a feature-store or self-service workflow on the pilot data.

Decide

Review the case

Present observed cost, performance, adoption, risks, and next steps.

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

What to measure

Agree on a baseline before the pilot, then compare the output, review effort, exceptions, quality, and operating cost with your current process.