Data Maturity Full Assessment

Reliable Quality Data

Assess data reliability, access, storage, security, architecture, and governance.

Culture & Change Mgmt

Assess level of executive engagement, usage of data analytics in decision making, and data-driven culture.

Data Analytics Innovation

Assess pipeline of big data analytics ideas and budget for innovation.

High-Value Use Casest

Assess level of business impact, and prioritization of data analytics use cases.

Self-Service

Assess extent of self-service in reporting and analytics.

Data Analytics Resources

Assess sufficiency of data analytics resources, ways of working, data literacy, and tool literacy.

Profile for benchmarking

Provide your details for benchmarking against other companies.

Is data strategy aligned to the business strategy?

No strategy in place

Strategy in place for some functions but siloed

Data analytics stem from business strategy

Data analytics strategy drives business strategy

Do you have an AI strategy that is aligned to the business strategy?

No strategy in place

Strategy in place for some functions but siloed

AI strategy stem from business strategy

AI strategy strategy drives business strategy

What is the scope of data analytics in the company?

Marketing

Commercial scope

Cross-functional scope

Enterprise perspective

Do you have an aligned multi-year use case roadmap?

No formal roadmap

A few small initiatives each year

Annual roadmap with 5+ initiatives

Multi-year cross-functional roadmap leveraging AI, Gen AI, ML, and automation

How is AI and ML utilized in your organization?

Not much utilized

Used in a few use cases or functions

Used for predictive analytics and process automation in a few functions

Used for predictive analytics, automated data management, and Gen AI models enterprise-wide

What's the current extent of scaling of use cases?

Minimal

A few pilots but not much scaled yet

A few scaled-up use cases

Scaled use cases driving profitable growth

What is the degree of executive engagement?

Minimal

Involved when issues escalated

Executives are change champions embracing AI, Gen AI, ML, and automation

CDO on management committee

To what extent is decision-making driven by data?

Largely reporting, backward-looking

Data used to inform few decisions

Data used to inform most decisions

Analytics, AI, Gen AI, ML, and automation driving key decisions

Are employees open to change?

Unsure of need to be data-driven

Understands need but do not know how to transform

A few ambassadors of change

Many ambassadors of data-driven solutions

How many big data analytics ideas are currently in your pipeline?

1-2 pilots and POCs each year

3-5 POCs and pilots each year

5+ POCs and pilots each year

Solid test and learn pipeline

Do you have sufficient budget for data analytics innovation?

No budget for innovation

Need-based spends on innovation

Limited committed budget for innovation

Sizeable funding set aside each year

Do you have a dedicated and scalable Analytics and AI sandbox to run experiments?

No dedicated sandbox

Limited spaces

Sufficient space but not scalable

Well developed and scalable as per demand

What is the level of self-service in reporting and analytics?

Limited self-service

Some self-service, mostly performed by analysts

A few functions running self-service

Enterprise-wide self-service

What is the extent of data literacy?

Limited, <5% employees trained

5-10% employees trained

11-20% employees trained

20%+ employees trained

Do you have reliable quality data with automated monitoring?

Data with some errors, limited monitoring

Reliable data, ad-hoc monitoring

Reliable data, systematic manual monitoring, and escalations

Certified quality data, automated monitoring, and escalations

What data types do you leverage for analytics and reporting?

Largely internal transactional data

Both transactional & consumer data

Internal data enriched with external

Leverage structured and unstructured data

Have you implemented a data catalog in the organization?

No data catalog or metadata management yet

Data catalog being piloted

Data catalog implemented in a few functions

Data catalog implemented enterprise-wide

Do you have enterprise data models that help drive understanding of data assets?

No enterprise data models yet

Data models for a few data sources

Data models for most data sources

Enterprise data models implemented and leveraged

How are data stored?

Data sits in the source systems

Data stored in central data warehouse

Data stored in central data lake

Data stored in central data lakehouse

Are you integrating data from different sources?

Not much integration

Less than 25% data is integrated

Less than 50% data is integrated

Mostly pre-integrated data assets

How is your data architecture?

Lack of cohesive architecture

Some architecture to manage data flow

Well-developed architecture but need to modernize

Modern architecture built for future and business needs

Do you have data governance?

No formal governance

Largely manual and siloed tactical governance

Both tactical and strategic governance

Governance embedded in business process

Ecosystem-wide application and management of APIs

Limited or no API integration across the ecosystem

Selective API integration in a few data projects or products

Widespread API integration across functions

Comprehensive API integration and dedicated API management system

Management of Data as a product

Data is not treated as a product

Initial steps taken to manage data as a product

Data treated as a valuable asset with defined product management practices

Data is actively managed and monetized as a product

Are there data privacy and security policies, and standards in place?

Inconsistent or ad hoc data privacy and security practices

Basic data privacy and security policies and guidelines

Well-defined and documented data privacy and security policies and standards

Robust and comprehensive data privacy and security framework and practices

What is the maturity level of DataOps process?

Largely manual processes

Automated data validations and deployments

Advanced analytical models manually ran

Production of analytical models, rapid iterations

Do you have readily available AI and ML tools?

Yes, very few

Yes, a few tools under testing and development

Yes, many developed and scaled tools

Do you have sufficient data analytics resources?

A few analysts

Analysts and data scientists

Analysts, scientists, and engineers

Proficient data practitioners enterprise-wide

Competence level of the data analytics team

Basic skills and limited knowledge

Developing skills and expanding knowledge

Proficient and experienced in data analytics

Highly skilled and recognized experts in data analytics

Effectiveness of the operating model of data initiatives.

Ad hoc and inconsistent approach

Developing standardized processes and guidelines

Well-defined and consistently applied operating model

Optimized and continuously improved operating model

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