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What is Data Analytics in Healthcare? Definition, Importance, Examples, Benefits, and Big Data Analytics

IdeaScale

What is Data Analytics in Healthcare Data analytics in healthcare is defined as the process of collecting, analyzing, and interpreting large volumes of healthcare data to derive actionable insights and inform decision-making aimed at improving patient care, enhancing operational efficiency, and driving organizational performance.

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You can't burn data

Jeffrey Phillips

As the concept of digital transformation takes root, you may frequently hear comparisons between data and oil. This comparison was strong enough to lead Wired magazine to define data as the new oil in a magazine article some years ago. Both data and oil are commodities, and exist to some degree in large volumes.

Data 157
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Is Your Company’s Data Ready for Generative AI?

Harvard Business Review

While CDOs and data leaders are excited about generative AI, they have much work to do to get ready for it. Despite excitement, companies have yet to see clear value from generative AI and need to do significant work to prepare their data.

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What is Agile Data Governance? Definition, Framework, and Steps

IdeaScale

What is Agile Data Governance? Agile data governance is defined as a modern approach to data management that emphasizes flexibility, collaboration, and iterative improvement.

Agile 130
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How Retailers Are Transforming Customer Experiences with Data & AI

Speaker: David Azoulay, Marc Stracuzza, Román Tejada, and Guest Speaker Sucharita Kodali

We’ll unveil the transformative potential of AI and data analytics in shaping the future of omnichannel personalization and e-commerce.

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Getting Your Company’s Data Program Back on Track

Harvard Business Review

Many senior managers find themselves wondering: If data is such a game changer, why is it so hard to extract any value from it? For companies struggling to actually see results from their data program, it might be time to make a fresh start. 2) Many companies try to tackle issues that are too difficult straight out of the gate.

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Retail Omnichannel Success: All About the Data

Business and Tech

“The experience consumers expect is seamless and consistent,” says Steve Prebble, CEO of Appriss Retail , a retail software and data analysis company. “It’s But many retailers aren’t leveraging the data they collect. They’ve got siloed data, and the e-commerce and in-store teams aren’t always working together.”.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Secrets of a Successful Sale: Optimizing Your Checkout Process

Speaker: David Nisbet, Everett Zufelt, and Michaela Weber

How do you use the data sitting behind a payment to find the next loyal customer? But payments are just one part of a chain. What’s the next touch point?

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Modern Data Architecture for Embedded Analytics

Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. Expert guidelines for a high-performance, analytics-ready modern data architecture.

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How to Build Data Experiences for End Users

Organizational data literacy is regularly addressed, but it’s uncommon for product managers to consider users’ data literacy levels when building products. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.

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ABCs of Data Normalization for B2B Marketers

Data normalization. However, if lead generation, reporting, and measuring ROI is important to your marketing team, then data normalization matters - a lot. At its core, data normalization is the process of creating context within your marketing database by grouping similar values into one common value. Why is this so essential?

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4 Approaches to Data Analytics

The world of data analytics is changing fast as organizations look to gain competitive advantages through the application of timely data.

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The 2019 Technographic Data Report for B2B Sales Organizations

In this report, ZoomInfo substantiates the assertion that technographic data is a vital resource for sales teams. reporting that technographic data is either somewhat important or very important to their organization. In fact, the majority of respondents agree—with 72.3%

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Digitizing Logistics: Harness the Power of Data in 4 Steps

That’s where your data comes in. In demand generation, data is essential for knowing who you should target and how. In this eBook, you’ll learn how to identify and target your ideal prospects — when they’re most receptive to hearing your message — using different types of data. Leveraging intent data.