DAIN Studios has released a data maturity assessment model, that defines the drivers and enablers, and uncovers the issues causing friction.
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DAIN Studios has released a comprehensive data maturity assessment model (DAMM), defining the drivers and enablers, which uncover the issues causing friction, or even block organizations from using data effectively.
Investment in data and AI is at an all time high, with global corporate investment reaching more than $60 Billion in 2020. Only 30% of Fortune 1000 companies have claimed to have achieved transformational impact from their investments in data and AI. Only 10% have developed a working data strategy.
Many corporations suffer from a disconnect when using data to give direction to business management. Leadership teams are well aware of the potential of data, yet the business value generated from data has been largely non-existent. A major part of the problem is the complexity of data operations, which make it unclear where to focus the development investments. DAIN Studios, with its vast experience from working with several hundred data organizations, releases today a practical and comprehensive data maturity assessment model, the DAIN Data Maturity Model (DAMM), defining the drivers and enablers that uncover issues which cause friction, or even block organizations from using data effectively.
Building business value from data requires complex technical methods and tools, multidisciplinary teams which often have not worked together in the past, and multiple stakeholders with conflicting interests. Having an accurate and objective situational picture helps greatly in understanding the focus priorities. The creation of this snapshot is impossible without a systematic and disciplined approach. The DAIN Studios DAMM is a systematic approach for assessing the data capabilities of an organization. The model consists of nine modules, two drivers and seven enablers. Each module is assessed separately at four maturity levels. The assessment is a robust and reliable way to gain an understanding of which enablers or drivers are slowing the use of data and where to focus.
We would like to share our solution with leaders that have difficulty obtaining benefits from data operations. Therefore we published the DAMM white paper that explains the model and the methodology behind it.