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Data Culture: A step toward building Digital Maturity

Aryng

Does “data culture” matter? What is the power of culture change? According to McKinsey, leaders believe that […] The post Data Culture: A step toward building Digital Maturity appeared first on Aryng's Blog. Of the 77%, they said it has a direct impact on their strategic goals.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. In the past, company culture has been the most significant barrier to AI adoption. While it’s still an issue, culture has dropped to fourth place. More people are working with AI.

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The journey to a mature asset management system

IBM Big Data Hub

Earlier posts in this series addressed the challenges of the energy transition with holistic grid asset management, the integrated asset management platform and data exchange, and merging traditional top-down and bottom-up planning processes. To proceed to the next level of “Competence”, APM capabilities take the lead.

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Data Analytics is Very Valuable for Companies Improving their Cultures

Smart Data Collective

Data analytics technology is rapidly becoming a more integral part of many company cultures. According to the 2021 State of Data Maturity Report, 32% of companies have formal data strategies. Data analytics serves many different purposes. Using Data Analytics Can Help Create a Better Company Culture.

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Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science

O'Reilly on Data

So you need to redesign your company’s data infrastructure. We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.

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Driving buy-in: How CIOs get hesitant workforces to adopt AI

CIO Business Intelligence

Artificial intelligence is either] perceived as a magic wand: You just apply AI and suddenly your data — although it might be not accurate, consistent, reliable — suddenly becomes the opposite, or it’s something that is perceived as scary,” he says. KEO International Consultants’ Damir Jaksic has navigated such AI reluctance firsthand.

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Accelerate release lifecycle with pathway to deploy: Part 2

IBM Big Data Hub

This post expands on the topic and provides a maturity model and building blocks that help enterprises accelerate their software supply chain lifecycle in the ever-evolving landscape of enterprise cloud-native software development. This builds on the earlier stages to emphasize meaningful participation between teams.

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