Data strategy before the platform, not after the failure
Most enterprises invest in data platforms before they understand what the data looks like, where it lives, or what quality baseline exists. BCS delivers structured data strategy and assessment engagements that produce a deKorvai-validated quality baseline, a target architecture, and a prioritised implementation roadmap.
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 does before every other consultancy starts building
Most delivery programmes begin at the solution layer. BCS begins at the evidence layer, measuring what exists before proposing what to build. That sequence is what separates recommendations with a measurable outcome from plans that look credible at presentation and fail in execution.
Measure before designing
deKorvai quality baseline established before any architecture decision is made. Every recommendation is grounded in measured evidence from the current data estate, not assumptions from stakeholder interviews.
Automate from the first sprint
Symphony automation scope identified and embedded in the delivery roadmap during the engagement itself, not proposed as a separate follow-on programme after delivery concludes.
Govern from day one
Anugal access governance and data classification policies are designed as part of the solution architecture and active from the first production dataset, not retrofitted after the platform is in use.
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 produces a quality baseline across the data estate before any architecture option is evaluated, grounding technology selection in measured volumes and patterns rather than vendor assumptions.
AI readiness gaps identified and sequenced before investment
The engagement maps quality, completeness, and lineage gaps against intended AI use cases, sequencing remediation in the roadmap before programme spend is committed.
Data ownership model defined before the governance programme begins
The engagement assigns named stewards, decision rights, and remediation accountability per data domain so governance has a working escalation path from the outset.
Prioritised roadmap with sequenced platform and governance initiatives
Each initiative is sequenced by business value, data dependency, and organisational readiness, avoiding the rework cycle that characterises platform investments made without a strategy layer.
Data catalogue and metadata foundation in place before platform build
The assessment delivers an initial catalogue of critical domains, lineage maps, and metadata definitions, giving platform architects a reference foundation rather than months of discovery rework.
Board-ready business case with measurable data investment rationale
The engagement quantifies the cost of the current quality gap and the expected return from proposed platform and governance investments, giving data leaders a measured baseline rather than architectural diagrams to present for budget approval.
How a data strategy engagement actually works
BCS data strategy engagements are structured as five measurable phases, each producing a concrete deliverable. The engagement moves from discovery through to a board-ready roadmap in a defined timeframe.
Estate Discovery
Catalogue all data sources, systems, owners, and consumers across the organisation. Map lineage, integration points, and data flows between operational systems and the analytics estate.
Quality Baseline
Run deKorvai quality scans across critical data domains. Measure completeness, accuracy, consistency, and timeliness against defined business rules to establish the current quality baseline.
Maturity Assessment
Score the organisation across five data maturity dimensions: strategy, architecture, quality, governance, and literacy. Identify the gaps blocking priority AI and analytics use cases.
Target Architecture
Define the target data architecture, platform selection rationale, and integration model. Validated against the quality baseline and organisational constraints to ensure the design is implementable.
Roadmap Delivery
Produce a sequenced implementation roadmap with ownership model, governance framework, and business case ready for board-level investment approval.
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
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.