Interactive demo · sample engagement
Watch Aficiently assess a bank — end to end
This is a realistic walkthrough of a full Aficiently engagement for Meridian Commercial Bank — a fictional £1-10B UK retail and business bank. Everything below is produced by the same analysis, scoring and reporting you get in the platform. No sign-up needed to explore.
Client profile & adaptive discovery
Aficiently interviews the organisation across 20 domains, tagging every answer by evidence type and confidence.
Meridian Commercial Bank
Client profile captured in onboarding
Objectives: Single customer view across retail and business banking; regulated reporting automation; AI-assisted credit decisioning within 24 months.
Regulatory: FCA/PRA supervisory reporting, GDPR, DORA operational resilience (in force Jan 2025).
- Industry
- Banking
- Geography
- United Kingdom
- Revenue band
- 1B-10B
- Employees
- 8,500
- Data volume
- 420 TB
- Data sources
- 87
- Cloud
- Azure (primary), AWS (legacy analytics)
- Existing platform
- On-prem Teradata EDW + departmental Snowflake instances; fragmented BI estate (Power BI, Tableau, Excel)
Discovery evidence
Adaptive questionnaire — every answer carries an evidence tag and confidence level
Is there a board-endorsed data & AI strategy with named ownership?
A strategy exists but is 3 years old, CDO-owned, without measurable outcomes linked to business KPIs. Board endorsement lapsed with the last reorganisation.
Evidence: Known
Is the core platform shared or fragmented?
Teradata remains the system of record for regulated reporting; 4 business units run separate Snowflake accounts with no shared governance. Estimated 30% duplicated storage.
Evidence: Known
Is technical, business and operational metadata captured automatically and centrally?
Collibra was licensed in 2023 but adoption is limited to the data management team; automated harvesting covers ~15% of assets.
Evidence: Inferred
What proportion of decisions require sub-minute freshness and how is that delivered today?
Fraud and payments monitoring require near-real-time; served by a legacy Kafka estate with no schema governance and undocumented consumers.
Evidence: Known
Evidence-based maturity assessment
Twenty domains scored 1-5, each traceable to discovery answers and uploaded artefacts.
Overall maturity
Weighted across 20 domains
Level 2 — Emerging
Domains assessed
20
Strategy through Data Culture
Strongest domain
Security 3.4
Bank-grade controls confirmed
Weakest domain
Lineage 1.4
Manual audit reconstruction
Domain heatmap
Every score is evidence-backed with an explicit confidence level — no self-reported guesswork
Strategy
Emerging
Governance
Managed
Architecture
Emerging
Data Engineering
Managed
Data Platform
Emerging
Data Quality
Emerging
Metadata
Emerging
Data Catalogue
Emerging
Lineage
Fragmented
BI & Analytics
Managed
AI/ML
Emerging
Generative AI
Emerging
Real-Time Data
Emerging
Security
Managed
Privacy
Managed
Operating Model
Managed
Skills
Emerging
FinOps
Emerging
Business Value
Emerging
Data Culture
Managed
Prioritised findings
Weak signals become quantified findings with priority, confidence and business impact.
Aficiently converts evidence into prioritised findings — each linked to a domain, a confidence level, and quantified business impact. Here are five of the twelve the assessment produced.
No column-level lineage across the Teradata-to-Snowflake estate; regulatory reporting lineage is reconstructed manually for audits, taking ~3 analyst-weeks per cycle.
Critical data elements for credit decisioning have no automated quality SLAs; 12% of customer records show stale KYC attributes across the two platform estates.
Snowflake spend grew 68% YoY with no showback; 4 uncoordinated accounts prevent right-sizing and reserved-capacity planning.
GenAI ambitions (RM assistant, credit-memo RAG) are blocked by the absence of a governed semantic layer and metadata coverage of ~15% of assets.
Legacy Kafka estate has no schema registry; 3 of 23 downstream consumers are undocumented, a single point of DORA resilience failure.
AI readiness, grounded in your data foundations
GenAI, RAG, agents and ML scored against the foundations they actually depend on.
Generative AI
1.8 / 5
Pilots only; no guardrails
RAG & Knowledge
2.2 / 5
No semantic layer grounding
AI Agents
1.2 / 5
Not yet feasible on current data
Predictive ML
2.6 / 5
Fraud scoring in pilot
What this means for Meridian
The readiness analysis links each AI capability to the data foundations it depends on. Meridian's GenAI ambitions score 1.8/5 not because the models are immature — but because the semantic layer, metadata and lineage they must be grounded in score below 2.0. Aficiently's roadmap sequences the foundations first, so the RM assistant and credit-memo RAG launch on governed data rather than around the governance.
Recommendations & phased roadmap
Vendor-neutral, dependency-sequenced recommendations — quick wins to 18-month transformations.
Prioritised recommendations
Scored on value, effort, risk and dependencies — never a generic list
Consolidate to one governed lakehouse with medallion zones
Removes 30% duplicated storage; single governed consumption layer for regulated reporting and AI.
Deploy automated column-level lineage across both estates
Cuts audit lineage effort from ~3 analyst-weeks to hours; de-risks DORA evidence.
Establish a governed semantic layer as the GenAI grounding plane
Unblocks the RM assistant and credit-memo RAG use cases with controlled hallucination risk.
Introduce FinOps showback with per-account budgets
Immediate visibility on 68% YoY spend growth; typically 15-20% first-year optimisation.
Phased roadmap
30-day to 18-month horizon, sequenced by dependency not fashion
30-day
- FinOps showback + budgets live
- Lakehouse reference architecture & operating model signed off
- Critical data elements defined with quality SLAs
60-90-day
- Automated lineage deployed across Teradata + Snowflake
- Metadata harvesting extended from 15% to critical-reporting coverage
- Semantic layer pilot with credit-memo RAG on governed gold data
12-18-month
- Regulated reporting migrated to lakehouse; Teradata decommission plan
- GenAI RM assistant in production with guardrails
- Streaming estate rebuilt on governed schema registry
The business case
Quantified benefits, investment, ROI and payback — with a board-ready executive summary.
Annual benefit (steady state)
£8.4M
Reporting automation, storage consolidation, FinOps optimisation
Implementation investment
£5.2M
Platform consolidation, lineage, semantic layer over 18 months
Projected ROI (3-year)
214%
Cumulative net benefit £15.9M over 3 years
Payback period
14 months
FinOps + audit-effort savings land in year 1
Executive summary
Auto-drafted, board-ready, fully traceable to the evidence above
Meridian has strong security and privacy discipline but cannot safely scale AI on its current foundations: lineage, quality SLAs and metadata coverage are the binding constraints, and 68% YoY platform spend growth is untracked. Aficiently's recommended path — consolidate to one governed lakehouse, automate lineage, and build a governed semantic layer before scaling GenAI — delivers a 214% three-year ROI with payback in 14 months and directly de-risks DORA operational resilience obligations.
This was Aficiently on a sample bank.
Run the same assessment on your organisation — discovery, maturity, findings, roadmap and a quantified business case, in one guided engagement.
- Full 20-domain assessment with evidence tracking
- Innovation radar & AI-native scoring
- Executive reports and board-ready slides