Build your data roadmap on evidence, not assumptions
Data strategy fails when the strategy is written before the estate is measured. BCS data strategy assessments profile the current landscape, score governance maturity against DAMA-DMBOK or DCAM, measure quality across critical domains, and quantify AI-readiness before any roadmap is recommended. The board decision rests on evidence, not opinion.
of enterprise data lakes fail to deliver business value, becoming data swamps
of organisations have low business intelligence and analytics maturity
more expensive to fix data quality issues in production than at source
Three things BCS measures before any strategy is written
Before architecture, migration, analytics, or AI investment is proposed, BCS measures the current data estate, scores the quality baseline, identifies ownership gaps, and defines the controls needed for execution. The resulting roadmap is built on measurable facts, not on workshop assumptions or vendor-led architecture choices.
Measure the current estate before designing
BCS assesses critical data domains, source systems, data flows, completeness, consistency, duplication, timeliness, and business-rule conformance. Every recommendation is grounded in the condition of the actual data estate.
Sequence execution before investment begins
BCS investigates dependencies and automation opportunities across the landscape required to move from strategy to execution. This includes data remediation, integration priorities, workflow automation, reporting dependencies, and operational handoffs.
Build governance into the architecture from day one
With defined ownership, stewardship, access controls, classification needs, and audit requirements as part of the solution architecture, as governance becomes part of execution, not a policy layer added after implementation.
3 reasons enterprise data strategies stall before delivery
Data strategy failures share a pattern. The problem is rarely the technology or the budget. It is the absence of measurement, ownership, and prioritisation before the first platform decision is made.
Platforms chosen before the data estate is understood
Architecture decisions made from vendor demonstrations and analyst reports produce platforms that cannot handle the actual data volumes, quality levels, or integration patterns the estate contains. The misalignment surfaces at go-live, not during procurement. By that point, the budget for remediation is already spent.
No data quality baseline measured before the strategy is written
Strategy documents describe the target state in business language without measuring what the current data actually looks like. Roadmaps that assume clean, structured, governed data collide with the real estate at the first migration or integration programme. The gap between assumption and reality determines how much of the strategy is salvageable.
Ownership and accountability absent from the strategy model
Data strategy documents that describe the governance framework without defining who owns which data domain, who funds quality remediation, and who approves platform change produce governance frameworks that exist in documents and nowhere else. Accountability gaps create the siloed data estates that make the strategy necessary in the first place.
What the data strategy engagement delivers
Platform decisions grounded in measured data quality
deKorvai scans the existing data estate and produces a quality baseline before architecture options are evaluated. Technology selection reflects the actual data volumes, quality levels, and integration patterns, not assumptions carried from vendor demonstrations.
AI readiness gaps identified and sequenced before investment
The strategy engagement maps the gap between the current data estate and the quality, completeness, and lineage requirements of the AI use cases the organisation intends to pursue. Remediation is sequenced in the roadmap, not discovered mid-programme.
Data ownership model defined before the governance programme begins
The engagement produces a data domain ownership model with named stewards, decision rights, and accountability for quality remediation. Governance frameworks built on undefined ownership fail the first time a quality issue requires an escalation path that does not exist.
Prioritised roadmap with sequenced platform and governance initiatives
The strategy produces a roadmap where each initiative is sequenced by business value, data dependency, and organisational readiness. Programmes that follow the roadmap avoid the sequential rework that characterises platform investments made without a strategy layer.
Data catalogue and metadata foundation in place before platform build
The assessment produces an initial data catalogue covering critical data domains, lineage mapping, and metadata definitions. Platform architects and data engineers who follow have a reference foundation, not a blank canvas that generates months of discovery rework.
Board-ready business case with measurable data investment rationale
The strategy engagement produces a business case that quantifies the cost of the current data quality gap and the expected return from the proposed platform and governance investments. Data leaders who present a measured baseline win budget approval faster than those presenting architectural diagrams.
How does BCS deliver a data strategy assessment?
BCS data strategy assessments follow a four-week structured methodology with quantified outputs at each milestone. Every finding is data-backed and validated with the client team before it becomes a recommendation, so the business case survives board scrutiny.
Kickoff and Discovery
Stakeholder interviews, business priority workshops, and landscape documentation reviews run in week one. Critical data domains, business outcomes, and the in-scope estate are defined and agreed before analysis begins.
Technical and Data Estate Analysis
BCS profiles the data estate: source systems, data flows, quality, ownership, and integration debt. deKorvai measures quality across critical domains and Anugal maps the access and compliance baseline.
Capability Maturity Assessment
Capability is scored against DAMA-DMBOK or DCAM dimensions. Governance, quality, architecture, MDM, BI/analytics, and AI readiness are rated against industry benchmarks and the client's strategic priorities.
Roadmap and Business Case
The transformation roadmap is constructed from prioritised initiatives sized against business value and risk. ROI is modelled from the client's own data, not industry benchmarks. The board decision is supported by evidence.
Readout and Decision Support
An executive readout presents the board-ready business case, programme scope, risk summary, and recommended next steps. BCS architects remain available for follow-up questions during the board decision period.
What the strategy engagement covers
The BCS data strategy engagement covers the full scope of enterprise data decision-making, from current state measurement through to the platform architecture and governance model that follow.
Data Estate Discovery
Catalogue all data sources, systems, consumers, and integration points across the enterprise. Map lineage, ownership, and data flows to produce a current-state view the organisation can act on.
deKorvai Quality Baseline
Run automated data quality scans across critical data domains using deKorvai. Produce a measurable baseline covering completeness, accuracy, consistency, and business-rule conformance.
Data Maturity Assessment
Score the organisation across strategy, architecture, quality, governance, and literacy dimensions. Benchmark against industry peers and identify the gaps that are blocking priority business use cases.
Platform Architecture Design
Define the target data architecture and evaluate platform options against the quality baseline, integration requirements, and organisational constraints. Select the platform that fits the real estate, not the marketing narrative.
Data Ownership Modelling
Define the data domain ownership model: stewards, decision rights, quality accountability, and escalation paths. Governance that names owners produces outcomes; governance that describes roles produces documents.
AI Readiness Gap Analysis
Map the gap between the current data estate and the quality, completeness, and lineage requirements of planned AI and advanced analytics use cases. Produce a remediation sequence that closes the gap before the AI programme begins.
Prioritised Implementation Roadmap
Produce a sequenced roadmap where each initiative is ordered by business value, data dependency, and organisational readiness. Priorities survive the first budget review because the rationale is measurement-based, not opinion-based.
Data Catalogue Foundation
Build the initial data catalogue covering critical data domains, lineage mapping, and metadata definitions. Platform and data engineering teams who follow have a documented foundation rather than an undiscovered estate.
Board-Ready Business Case
Quantify the cost of the current data quality gap and the projected return from the proposed investments. Data leaders who present a measured baseline and sequenced plan win board approval faster than those presenting architectural diagrams alone.
What other data strategy providers cannot offer
BCS assessments are informed by three proprietary platform perspectives that generic data strategies miss entirely. Symphony scores automation potential across data workflows. deKorvai benchmarks intelligence readiness. Anugal maps data governance and compliance exposure.
Agentic Operations Platform
Symphony
An assessment that scopes data workflows but skips automation potential modelling leaves the business case incomplete. Symphony scores automation candidates across data pipelines, models agentic ROI, and identifies quick-win opportunities within the assessment scope.
- Automation potential scoring across data pipelines and operational workflows
- Agentic ROI modelling for data workflow automation in the business case
- Quick-win automation identification within the assessment scope
- Symphony deployment readiness scoring across data process candidates
AI Decision Intelligence
deKorvai
An assessment that scopes data infrastructure without benchmarking intelligence readiness produces a roadmap where AI is always deferred. deKorvai maps the analytics maturity baseline, identifies intelligence gaps, and models the AI capability roadmap.
- Analytics maturity baseline assessment across the data landscape
- Intelligence readiness gap analysis identifying barriers to AI adoption
- Predictive capability opportunity identification for the data strategy roadmap
- Data foundation quality scoring for AI and analytics readiness
Compliance and Controls Automation
Anugal
Data landscapes that have never had a structured assessment carry unquantified access risk and compliance exposure. Anugal maps the current data governance posture, access risk profile, and regulatory gap against requirements.
- Data access risk and governance gap assessment across the landscape
- Regulatory compliance exposure quantification for data handling and residency
- Sensitive data classification audit identifying unprotected high-risk datasets
- Remediation roadmap for governance and compliance gaps identified in assessment
Frequently Asked Questions
Refer to this section for answers to frequently asked questions related to data strategy and assessment.
What is covered in a BCS data strategy assessment?
How long does an assessment take and what are the deliverables?
DCAM framework, how does BCS apply it during assessment?
Can the assessment be delivered remotely?
What is the difference between data strategy and data architecture?
What makes BCS different from every other data strategy consultancy?
BCS has delivered data strategy and platform programmes across SAP, Salesforce, OpenText, and cloud environments for more than a decade. The difference is not the framework; it is the ability to measure the current state of the data estate before writing a single recommendation.
Strategy built on deKorvai measurement, not interviews
The quality baseline BCS establishes with deKorvai before writing any recommendation is what makes the strategy defensible. Recommendations are grounded in measured evidence, not workshop consensus.
SAP, cloud, and legacy estate expertise in one team
BCS assessment teams understand SAP BW, Datasphere, S/4HANA data models alongside Azure, AWS, and GCP analytics architectures. The strategy produced covers the whole estate, not just the cloud-native portion.
Ownership model defined, not just technology
The data strategy includes a data ownership model, stewardship responsibilities, and governance council design. Technology without ownership degrades within twelve months of implementation.
Roadmap sequenced by dependency, not project preference
The implementation roadmap is sequenced by technical dependency and business value, not by what is easiest to sell next. Organisations receive a roadmap that is achievable in the stated order.
Strategy to delivery: BCS completes the roadmap it designs
BCS delivers data platform setup, migration, analytics, governance, and managed operations alongside strategy. The roadmap produced is one BCS can implement, not a handover document for a different partner.
Ready to build a data strategy on measured ground?
BCS data strategy engagements begin with deKorvai quality scanning of the current data estate, not with workshops. Book an initial conversation to understand what a measurement-based strategy engagement looks like for the current data estate.