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8 strategies for accelerating IT modernization

CIO Business Intelligence

Traditionally, such an initiative would involve business process analysis, a fit-gap analysis, and process re-engineering — all of which eats up time. New tech moves from bleeding edge to mainstream at an ever-increasing pace. Luckily, there are ways to slash the timelines on those projects.

Strategy 140
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PCI compliance: The best defense is a great defense

CIO Business Intelligence

Success in PCI DSS compliance and enterprise-wide efforts to protect data is not determined by luck,” adds Philipsen. “It It is an outcome achieved by design, in organizations where the importance of a great defense against today’s cyberthreats is not only endorsed and articulated from senior leadership, but ingrained in the corporate culture.

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An archetype-based approach to driving your global workforce transformation

CIO Business Intelligence

Acknowledging these challenges and complexities, how can one pressure-test their spreadsheet exercise against the realities of execution? Step 3: Apply the archetypes and perform a gap analysis Having established a master set of archetypes, it’s crucial to apply them to each function’s to-be workforce composition.

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In transition: How Kyndryl’s CIO weaned the company off IBM’s systems

CIO Business Intelligence

We didn’t do fit-gap analysis workshops because 95% of the time, the solution looks exactly like what the teams have today,” he says. “We We didn’t do fit-gap analysis workshops because 95% of the time, the solution looks exactly like what the teams have today,” he says. “We We weren’t going to go down that path.”

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What’s the Difference: Quantitative vs Qualitative Data

Alation

Data users will often set boundary limits to this sort of data, and may apply arithmetic operations to it and aggregate it for analysis. What Is the Role of Statistics in Quantitative Data Analysis? Statistics is at the heart of quantitative analysis. For a refresher on hypothesis testing, this cuemath summary is very helpful.).

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The art and science of data product portfolio management

AWS Big Data

This post is the first in a series dedicated to the art and science of practical data mesh implementation (for an overview of data mesh, read the original whitepaper The data mesh shift ). Taken together, the posts in this series lay out some possible operating models for data mesh within an organization. What is the lifecycle of a data product?