DAIN Studios Data & AI Maturity Model
Guidance in the maze of data & AI transformation
It is easy to lose focus in the buzzing world of data & AI, how it impacts modern-day businesses and what you can do to elevate your organisation’s maturity.
To help you avoid the hype and understand where you are on your path to becoming a data-driven company, we have created the DAIN Studios data & AI maturity model.
Together with our expert consultants, you can use our model to gauge your current state across the fundamental building blocks of successful data & AI execution. You can identify key focus areas and build your roadmap with tangible next steps to guide you from discovery to data & AI leadership.
The model successfully links together our experience from consulting and leading data & AI organisations across numerous industries, leveraging the accumulated knowledge of DAIN Studios’ diverse team of engineers, scientists and strategists.
This makes our model uniquely comprehensive, practical and easily tailored to your needs. Because we know that no organisation is the same, and frameworks cannot be one-size fits all solutions in a diverse problem space.
Need an assessment of your company's Data & AI maturity?
DAIN Data & AI Maturity Model (DAMM)
Strategy & Vision
No documented data & AI strategy
Use Cases & Value Gen.
Ad-hoc generation of data & AI use cases, lacking systematic prioritisation and follow-up
Leadership
Unclear leadership support for data & AI initiatives
Organization & Culture
Data is not a dominant topic in the organisation, data capabilities are ad-hoc and unorganised
Human Skills
Very few data professionals covering wide range of data topics, expected to drive data & AI transformation
Privacy & Ethics
Focus on basic compliance with GDPR and contractual data requirements, but often with scattered responsibilities
Analytics & AI Portfolio
No advanced analytics deployed, focus primarily on reporting use cases
Data Assets
Data storage is highly siloed, access by ad-hoc request, data quality uncertain
Architecture & Tech
Data processing is primarily a manual task, technology stack is unable to support scalable AI solutions
Strategy & Vision
Data & AI strategy created within a specific technology domain, e.g., IT or analytics
Use Cases & Value Gen.
Use cases are structured, but often around technology rather than business value
Leadership
Data & AI is a stated leadership priority but limited concrete action taken to date
Organisation & Culture
Desire to improve and organise data capabilities with appropriate governance processes
Human Skills
Established data roles/functions but the organization struggles to find or develop the right capabilities internally
Privacy & Ethics
Responsibilities are defined but guidelines, processes, and awareness of wider AI ethics topics are limited
Analytics & AI Portfolio
Advanced analytics algorithms are in piloting phase
Data Assets
Need for improved data assets is recognized, initiatives launched to improve quality and (re)usability
Architecture & Tech.
Some automated data processing, understanding of tool requirements and gaps
Strategy & Vision
Data & AI strategy is complete and broadly aligned with overall business strategy
Use Cases & Value Gen.
Use cases are linked to business value generation
Leadership
Leadership supports and invests in accelerating impact from data & AI
Organisation & Culture
Data capabilities organised and supported by fit-for-purpose governance processes
Human Skills
Data roles are filled with required seniority, dedicated training programs for both expert and non-expert staff
Privacy & Ethics
Established process for managing data privacy, users well-informed. Data & AI ethics principles defined but limited implementation.
Analytics & AI Portfolio
Some advanced analytics are deployed in production, but continuous implementation and monitoring is still a challenge
Data Assets
Data is available in unified and interoperable quality via central access and clearly defined data ownership
Architecture & Tech.
End-to-end data pipelines are established, introducing enterprise data & analytics platforms
Strategy & Vision
Data & AI is an integral element of the business strategy, vision and offering
Use Cases & Value Gen.
The business value of use cases is consistently identified, assessed, and tracked throughout the solution’s lifecycle
Leadership
Leadership understands and drives continuous transformation into a data-driven organization
Organisation & Culture
Organization views data as a competitive advantage, and can handle internal and external data & AI requirements with ease
Human Skills
Able to attract best-in-class data talent at all levels, all employees have a basic level of data literacy
Privacy & Ethics
Privacy, data & AI ethics principles are understood across the organization and embedded in daily work processes
Analytics & AI Portfolio
Advanced analytics are routinely deployed with continuous monitoring and improvement
Data Assets
High-quality data are delivered through productized channels to support internal and external use cases
Architecture & Tech.
Use cases supported by automated data processing and a fit-for-purpose technology stack