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Designing data governance that delivers value | McKinsey Data was identified as a critical enabler, and a DMO and a data council were set up to develop the core framing on the future ecosystem, as well as the structure of data domains, including the strategic goals on managing data in the future
Designing data governance that delivers value Data was identified as a critical enabler, and a DMO and a data council were set up to develop the core framing on the future ecosystem, as well as the structure of data domains, including the strategic goals on managing data in the future
Elevating master data management in an organization | McKinsey Data governance models for MDM should be designed with clear roles and responsibilities, be managed by a governance council with representatives from different business units and IT, and be shepherded by someone who can serve as an MDM liaison among business, data, and technology stakeholders
Designing a data transformation that delivers value right from the . . . However, the explosion in data availability over the past few years—coupled with the dramatic fall in storage and processing costs and an increasing regulatory focus on data quality, policy, governance, models, aggregation, metrics, reporting, and monitoring—has prompted a change in focus
Revisiting data architecture for next-gen data products Reference architectures, data governance models, and thoughtful technology implementation provide a road map for forward-thinking CIOs to choose the most suitable archetype and establish strong data governance practices for next-gen data products
Putting data ethics into practice - McKinsey Company Data ethics must be integrated into governance processes to truly become part of an organization’s DNA Leaders can start by developing a comprehensive data risk framework that defines the guiding principles, risk inventory, policies and standards, and controls that govern the ethical usage of data (exhibit)
Business Technology Getting your data house in order How to unlock the value of data age requires a significant data management overhaul That includes identifying and assessing the value of existing data, designing a scalable data platform, and developing a long-term data strat