What is your Data & AI Maturity level?

Test your data & AI maturity and get the results right away!

Start the test and find out your Data & AI Maturity

By pressing “Next” you will go through a series of 9 questions which follow our Data Strategy Building Blocks. This takes approximately 5 minutes.

How would you characterize your data & AI strategy?

We have one or more data strategies established within our technology domains (e.g., IT, analytics)

Our data strategy is an integral element of our company's business strategy, vision and offering

Our data strategy is mainly driven by our business objectives

We have not yet created a dedicated data strategy

How are data & AI projects initiated and prioritized?

Data & AI use cases arise ad-hoc from individual ideas, driven by personal push rather than systematic prioritization

We structure and prioritize our use cases more from the business rather than technology perspective

We consistently identify, link and monitor our data & AI use cases from a business value generation perspective

We structure and prioritize our use cases more from the technology rather than business perspective

How does leadership contribute to the realization of data & AI initiatives?

Data & AI is a stated leadership priority but limited concrete action taken to date

Leadership supports and invests in accelerating impact from data & AI

Leadership understands and drives continuous transformation into a data-driven organization

Unclear leadership support for data & AI initiatives

Which of the following statements best describe your organization?

Data is not a dominant topic in the organization; data capabilities are ad-hoc and unorganized

We have the desire to improve and organize data capabilities with appropriate governance processes

Data capabilities organized and supported by fit-for-purpose governance processes

Our organization views data as a competitive advantage, and can handle internal and external data & AI requirements with ease

What is the state of data skills & competencies within your organization?

We have very few data professionals, and they are expected to cover a wide range of data topics and drive data & AI transformation

We have established data roles/functions but the organization struggles to find or develop the right capabilities internally

We are able to attract best-in-class data talent at all levels; all employees have a basic level of data literacy

Our data roles are filled with required seniority; we have dedicated training programs for both expert and non-expert staff

How do you embed data & AI compliance and ethical principles in your organization's daily work?

We have established processes for managing data privacy; our users are well-informed; data & AI ethics principles defined but limited implementation

Privacy, data & AI ethics principles are understood across the organization and embedded in daily work processes

Responsibilities are defined but guidelines, processes, and awareness of wider AI ethics topics are limited

We currently focus on basic compliance with GDPR and contractual data requirements, often with scattered or unclear responsibilities

To what extent is your organization able to rely on advanced analytics to solve business problems?

We have one or more advanced analytics algorithms in piloting phase but have not yet reached production

Some of our advanced analytics solutions are deployed in production, but continuous implementation and monitoring is still a challenge

We routinely deploy advanced analytics solutions with continuous monitoring of their performance and improvements seamlessly implemented

We have no advanced analytics deployed; our focus is primarily on reporting use cases

How would you assess the current state of your data assets?

We recognize the need for improved data assets and have launched initiatives to improve quality and (re)usability

Our data asset is built such that it can be delivered through fully productised channels to support internal and external use cases

We have most of our data available in unified and interoperable quality via central access and clearly defined data ownership

Our data storage is highly siloed, access given by ad-hoc request, and data quality is uncertain

Is your architecture and technology stack built to support current and future use cases?

Our data processing is primarily a manual task; technology stack is unable to support scalable AI solutions

We have automated some of our data processing and have an understanding of the tool requirements and gaps to improve it further

All of our use cases are supported by automated data processing and a fit-for-purpose technology stack

We have end-to-end data pipelines established and currently/soon introducing enterprise data & analytics platforms

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What is your Data & AI Maturity level?

Start the test and find out your Data & AI Maturity

DAIN Studios

Berlin

Munich

Helsinki

Vienna

Studio HELSINKI

Studio VIENNA

Studio BERLIN

Studio MUNICH

What is your Data & AI Maturity level?

Start the test and find out your Data & AI Maturity

DAIN Studios

Berlin

Munich

Helsinki

Vienna

Studio HELSINKI

Studio VIENNA

Studio BERLIN

Studio MUNICH

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