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Having a clearly defined digitaltransformation strategy is an essential best practice for successful digitaltransformation. But what makes a viable digitaltransformation strategy? Constructing A DigitalTransformation Strategy: DataEnablement.
Data architecture goals The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the enterprise. Many organizations today are looking to modernize their data architecture as a foundation to fully leverage AI and enabledigitaltransformation.
Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with datagovernance and security. . DigitalTransformation is not without Risk.
Beginning a digitaltransformation journey in a big organization can feel daunting. How do you initiate change within a system containing many thousands of people and millions of bytes of data? So you can probably imagine: The company manages a lot of data. AmFam first implemented the Alation Data Catalog in 2018.
Telecommunications companies are currently executing on ambitious digitaltransformation, network transformation, and AI-driven automation efforts. By automating data management tasks and supporting a wide variety of access protocols, it accelerates the work of integrating dissimilar systems and processes.
These innovative solutions pave the way for future trends in healthcare, shaping the industry’s digitaltransformation journey. Challenges in Data Management Data Security and Compliance The protection of sensitive patient information and adherence to regulatory standards pose significant challenges in healthcare data management.
AI platforms assist with a multitude of tasks ranging from enforcing datagovernance to better workload distribution to the accelerated construction of machine learning models. What types of features do AI platforms offer?
See The Future of Data and Analytics: Reengineering the Decision, 2025. You mentioned a few times that most enterprises are not good at datagovernance. It is much less just about defensive controls and more about enabling capabilities. Which tools would you recommend for getting governance in place? Do you agree?
ISL is also the foundation for the process of transformingdata into wisdom and successful master data management. Fear of disruption and growing digitaltransformation initiatives have created a demand for business-driven analytics. Master data management. Datagovernance. Data pipelines.
Practitioners and hands-on data users were thrilled to be there, and many connected as they shared their progress on their own data stack journeys. People were familiar with the value of a data catalog (and the growing need for datagovernance ), though many admitted to being somewhat behind on their journeys.
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