Data and Analytics Maturity Map

maturity map

data strategic

Schema

		Data informed	Data Driven	Data Led	Business value of change	Cost of Change	Priority
Getting value from data	Integration into business	Basic Data Utilization with mostly retrospective analysis and limited business proccess covarage	Analytical usecases register with ranking of the data assets based on their value	Continuously refining tactical and strategic plans based on real-time data insights	XL	XL	High
			Advanced Data Utilization across all key business proccess, BI reports embedded in regular meetings	Data generates core revenue of the company
			Alignment between data initiatives and business goals
			Data monetisation strategy
	D&A Services efficiency	BI Reports Usage Statistics Monitoring	Data products active promotion practice	Analytics as a product - Adoption-centric data products development framework
			User centric navigation portals
			Proccess of measurement of ROI from Data & Analytics projects, self assessments
Analytics services	Reporting factory	Reports on requests	Central BI team with focus on cross-functional reporting	Insight generation solutions
		Company Level KPI Dashboards	Partnership: Data Consultancy Service for Business Domains
			Reporting role-based workplaces - collections of recommended reports
			Targeted insight delivery/ alerts
	Self-Serve	Excel-driven self service analytics capabilities	SSBI framework: Portal, Style guides, tempaltes, Release check lists, support	Company-wide Data Literacy project
			Community, Learning Paths, Dedicated SSBI support services, Trainings
			Center of excellance team in Federated BI model
	Experiments	Single Ad Hoc A/B Testing	Structured Experimentation Framework, Formalized Test Design	Integrated Experimentation Platforms
			Experimentation and A/B testing ingrained in the organizational culture
	Advanced analytics	Single projects: forecasting, regression, simple classification models	Several ML Models in Prod/ More sophisticated ML models	ML/AI models integrated into all business critical processes
			ML Operations (MLOps), Feature engineering practice	ML/AI works as a basis for developing new business models, products, services
			ML/AI Models are integrated in key processes / Deep and reinforcement learning, recommendation models
Analytics Governance	Content management	Reactionary Content management	Content Health SLAS (Load Time, Actuality, etc)	BI Ops Automation/ BI Governace Bots
		Content archiving procedure	Content Health Certification framework
			Business Certification framework
	Customer development	Service desk support	Requirements gethering framework	Role-based information supply demand model, coverage analysis
		direct communication	Feedback gethering framework
			Customer satisfaction monitoring
	Knowing the data	Knowledge is distributed across SMEs and analysts	Data is classified by data domains, owners and sensitivity levels identified, Critial data is documented	Enterprise Knowledge Graph and Business ontology
			Business glossary is built and aligned across all domains, No contradictions in metrics logic
			Business metrics tree structured in a glossary system and semantic leyer
Data Platform Architecture	Data Storage	Siloed On-premises Data Storage	Data Warehouse with data backup, disaster recovery	Data Lakehouse - Flexible, platformized, scalable, cloud-agnostic
			DataLake
			Governed Data Layers / Data Certification framework
			Data fabric / Data mesh
	Data ingestion and transport	Simple Data Pipelines automate the movement of data from key systems into centralized repositories	Automated Data Ingestion Pipelines, Scheduled Batch Processing	Data Ingestion as a Service
			Data Contracts	Al and ML for intelligent data ingestion, data classification, anomaly detection
			Real-time data streaming, Reverse ETL
			Scalable Infrastructure for Data Ingestion
	Data Transformation	Manual and Ad Hoc Processes, Basic Data Preparation without formal processes	Structured ETL Processes	Scalable and Flexible Data Platform transforming large datasets in real-time with minimal latency
			Self-serve data preparation environment
			Semantic Layer, Data models and Pipelines as Data Products
	Business intelligence	Pre BI / Code-based BI tools	Low-code Self-serve BI tools	Gen Al driven BI toolset
			Embedded BI
			Real time BI
			NLP capabilities
	ML/DS/AI	Manual Data Handling, no specific toolset	Specialized Software including open- source tools for building and testing models	Enterprise-grade Al platforms for end-to-end management of ML/DS/AI models lifecycle
			Advanced ML / Al platforms with AutoML capabilities	ML/Al operations Integrated into all critical business processes
			Al Strategy
Data Governance	Data Discovery & Cataloging	Pre-catalog wiki-based documentation, chats	Data Catalog, Data and reporting layers lineage	Data marketplace, Active metadata lake
			Glossary layer: linkage and business ownership
			Data Classification, Assets, Certification flow
			Catalog as a service, Open ΑΡΙ
	Data Quality system	Custom data checks for specific data objects	Critical data assets covered by sufficient data checks	Data observability tools
			DQ monitoring tool available, DQ standards defined, Data stewards involved
			DQ checks as a part of data contracts, DQ embedded in deloyment flow
	Data Security	“Common sense” data protection without centralized coordination	Data access rules are defined by key roles and domains and automated within access management software	Proactive Data Security: ML identifies potential security threats, automated compliance checks
			Structured Security Framework: Policies, Data risk assessment processes, Compliance, Awareness trainings
			End-to-end data access management through data platform, integration with data catalog, Pll encryption
	Roles engagement	“Natural” chaotic DG with no driver	Ownership, stewardship roles are allocated among Data people / teams	DG Center, Federated DG model, DG dissolved in processes
			CDO / Business Stakeholder sponsorship
			Business representatives are engaged in data stewardship roles
Data Team	People management	Roles skill profiles and structured onboarding proccess	Competence Matrix	Default data skills as part of corporate hiring policy
			Perceived goals-driven personal KPIs
			Seniority grades assessment system
	Data Roles	All-in-one’ Analysts	BI Developer / DWH Engineer	CDO, CAO
			Product Owner/ Business Analysts
			Data Analysts
			Data Scientists, ML Engineers
	Data team structure	IT department, No dedicated team	Central BI Team	Network of Data teams in federation
			Data & Analytics Center
			Data Platform Department
	Project management	Near-waterfall projects workflow	Standards, guidelines, Agile project management (Task tracking. Kanban, Scrum)	Continuous and mature change managament driven via strategic initiatives
			CI/CD, DevOps
			Team Annual Goals Setting, OKRS
Culture	Executive leadership	There is no specific focus on data & analytics from management	Executive leaders advocate for operational reporting projects	Executive leaders have high data / Al literacy and act as active sponsors and evangelists of innovation initiatives
			Executive Leaders actively sponsor advanced analytics, foster a self service in business teams
			Executive leaders are involved in the development and review of data and analytics strategy
	Decision-Making	Data is considered alongside other factors such as intuition and expertise	Prioritizing objectivity - decisions are grounded in empirical evidence derived from data analysis rather than relying on subjective judgment	Skill to combine data analysis with context and expertise, taking max from both, recognizing when fast intuitive decisions outweigh analytical ones.
	Data Culture	Mindset that data needs strong collaboration between IT and the business	Mindset that data should be easily accessible for everyone (not just for the analysts)	Challenge culture - All employees feel comfortable exploring data and proposing own data-supported hypotheses, critics welcomed
			Mindset that data is a product and all internal users who needs it are the customers of the data owner
			People are encouraged to share lessons from success and failure in data-driven decision making