Building unified analytics capabilities that deliver trusted, timely, and actionable insights.
Analytics is no longer a peripheral function — it's a strategic enabler of performance, risk management, and customer insight. Yet many organizations struggle to harness analytics effectively due to fragmented teams, inconsistent governance, and limited adoption. The key to unlocking value lies in building an Analytics Center of Excellence (CoE) — a unified structure that integrates people, process, data, and technology under a single governance model.
Financial institutions generate enormous volumes of data across products, channels, and geographies — yet many struggle to operationalize analytics effectively. Disconnected tools, siloed teams, and inconsistent governance often result in redundant efforts, unreliable insights, and missed opportunities.
An Analytics Center of Excellence (CoE) bridges this gap by establishing a unified operating model that institutionalizes analytics across the organization. It creates the structure, standards, and governance required to deliver consistent, scalable, and high-impact analytical outcomes.
At Eklogi Consulting, we help organizations design, build, and operationalize Analytics CoEs that act as strategic engines for decision intelligence, innovation, and business performance.
We follow a structured, framework-driven approach to design Analytics CoEs that integrate people, process, technology, and governance into a single, scalable model. Our approach spans five key dimensions:
Clear mandate aligned with business goals
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Embedded analytics within business fabric
Unified digital backbone for analytics
Trust, quality, and accountability
Sustained analytics talent maturity
Establishing a clear mandate and alignment with business goals.
The foundation of a successful CoE begins with a clearly articulated analytics vision that ties directly to business outcomes — not just technology adoption. Eklogi facilitates:
Strategic Alignment Workshops: Jointly define the mission, objectives, and success criteria for the Analytics CoE.
Use Case Prioritization: Identify and rank analytical use cases based on business impact and feasibility (e.g., credit risk modeling, fraud detection, customer segmentation).
Operating Model Blueprint: Define CoE structure, stakeholder roles, and governance hierarchy.
Capability Roadmap: Develop a phased rollout plan covering analytics maturity progression — from descriptive to predictive and prescriptive analytics.
Embedding analytics into the enterprise fabric.
We help clients design sustainable CoE operating models that ensure analytics is embedded within business operations rather than functioning as an isolated technical function.
Hub-and-Spoke Model: Centralized CoE for standards and governance, with embedded analytics teams in business functions (e.g., Retail, Corporate, Risk).
Role Definition: Clarify roles across data engineers, data scientists, BI developers, analysts, and business translators.
Collaboration Mechanisms: Establish structured interaction models between IT, business, and analytics teams.
Delivery Framework: Define intake processes, sprint planning, backlog prioritization, and analytics project lifecycle management.
Creating the digital backbone for enterprise analytics.
Technology is a key enabler, but platform selection and integration must align with governance, scalability, and interoperability goals.
Technology Assessment: Evaluate current tools and identify rationalization opportunities across BI, ETL, and data science platforms.
Platform Architecture Design: Define a unified, cloud-native analytics architecture that integrates data lake, warehouse, and visualization layers.
Tool Standardization: Consolidate analytics and visualization tools (Power BI, Tableau, Qlik, etc.) for consistency and cost optimization.
Advanced Analytics Integration: Enable ML model lifecycle management using frameworks like MLflow, Vertex AI, or Azure ML.
DataOps and MLOps Enablement: Automate data pipelines and model deployment workflows for speed and reliability.
Ensuring trust, quality, and accountability in analytics.
Without strong governance, analytics initiatives often fail to gain organizational trust or scalability. We build data and analytics governance frameworks that ensure transparency, accuracy, and repeatability.
Analytics Governance Council: Cross-functional body defining priorities, approving methodologies, and tracking KPIs.
Data Quality Framework: Definition of accuracy, completeness, and timeliness standards tied to data lineage.
Model Risk Management (MRM): Governance for ML models — ensuring documentation, validation, and audit readiness.
Performance Metrics: Establish KPIs across adoption, business value realization, and user satisfaction.
Compliance Integration: Alignment with BCBS 239, DPDP 2023, and internal audit controls.
Building and sustaining analytics talent maturity.
A CoE is only as strong as its people. We embed capability incubation programs within the CoE to continuously enhance analytics literacy, technical depth, and business acumen.
Skill Development Framework: Structured learning paths for data analysts, data scientists, and business users.
Analytics Literacy Programs: Enablement for non-technical business users to interpret and act on data insights.
Internal Communities of Practice (CoPs): Facilitate collaboration, knowledge exchange, and innovation sharing.
Mentorship & Innovation Labs: Incubate new analytical models and use cases in sandbox environments.
Performance-linked Learning: Tie learning outcomes to project performance and adoption metrics.