In our latest episode of AI Academy, #wastonx experts Luv Aggarwal and Edward Calvesbert, deconstruct the different aspects of data management needed to successfully apply generative #AI for business – touching on best practice approaches to data quality, data architecture and AI customization: https://ibm.co/3v7RK9I
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> Data quality – measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization. Data quality standards help ensure that companies meet their business goals. If data issues, such as duplicate data, missing values, outliers, aren’t properly addressed, businesses increase their risk for negative business outcomes.
> Data security – A concept encompassing the entire spectrum of information security, data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its entire lifecycle. This includes the physical security of hardware and storage devices, along with administrative and access controls. It also covers the logical security of software applications and organizational policies and procedures.
> Data Governance – Data governance promotes the availability, quality and security of an organization’s data, determining data owners, data security measures and intended uses for the data. The goal of data governance is to maintain high-quality data that’s both secure and easily accessible for deeper business insights.
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#IBM #AIAcademy #DataManagement #DataScience #Governance