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An Enterprise Data Strategy for Building the Trustworthy AI Practice

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Photo by Christina Morillo from Pexels Introduction The current decade is a time of unprecedented growth in data-driven technologies with unlimited opportunities.

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

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Enabling a data-driven IT modernization strategy

CIO Business Intelligence

The big picture : In the midst of a rush to technology modernization, it’s critical to ensure the organization’s data assets are not overlooked. Why it matters:  Data-driven business decisions must factor prominently in modernization efforts. The bottom line:  Don’t leave data behind.

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Product lifecycle management for data-driven organizations 

IBM Big Data Hub

In a world where every company is now a technology company, all enterprises must become well-versed in managing their digital products to remain competitive. In other words, they need a robust digital product lifecycle management (PLM) strategy. Data is an asset, but to provide value, it must be organized, standardized and governed.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. I suggest that the simplest business strategy starts with answering three basic questions: What? These changes may include requirements drift, data drift, model drift, or concept drift.

Strategy 289
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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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CIOs rethink all-in cloud strategies

CIO Business Intelligence

The most common motivator for repatriation I’ve been seeing is cost,” writes Linthicum , who conjectures that “most enterprise workloads aren’t exactly modern” and thus not best fits for the cloud. Such decisions are largely driven by the need to maximize performance and business benefits while not losing track of costs.”

Strategy 144