CIO
FEBRUARY 10, 2023
Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. from 2022 to 2028. As such it can help adopters find ways to save and earn money.
CIO
APRIL 8, 2024
Nearly 10 years ago, Bill James, a pioneer in sports analytics methodology, said if there’s one thing he wished more people understood about sabermetrics, pertaining to baseball, it’s that the data is not the point. Improving player safety in the NFL The NFL is leveraging AI and predictive analytics to improve player safety.
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CIO
NOVEMBER 9, 2022
One potential solution to this challenge is to deploy self-service analytics, a type of business intelligence (BI) that enables business users to perform queries and generate reports on their own with little or no help from IT or data specialists. But there are right and wrong ways to deploy and use self-service analytics.
CIO
MARCH 27, 2023
Integration with cognitive intelligence (context-sensitive knowledge management, predictive analytics, and similar) will be key for doing so. Here’s an example of how this could work: A customer calls his car insurance company after being in a minor fender bender.
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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
CIO
APRIL 4, 2024
At our upcoming Data, Analytics & AI Summit – a virtual event taking place April 11 – attendees will hear from CIO editors and contributors, including Paula Rooney, Lucas Merian, Issac Sacolick, and Today in Tech podcast host Keith Shaw. Interested in even more data, analytics and AI coverage? We have you covered.
CIO
JUNE 7, 2022
What is data analytics? Data analytics is a discipline focused on extracting insights from data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. What are the four types of data analytics?
Speaker: Speakers from SafeGraph, Facteus, AWS Data Exchange, SimilarWeb, and AtScale
Data and analytics leaders across industries can benefit from leveraging multiple types of diverse external data for making smarter business decisions. Data and analytics specialists from AWS Data Exchange and AtScale will walk through exactly how to blend and operationalize these diverse data external and internal sources.
Speaker: Eric Feinstein, Professional Services Manager, Looker
He will use the example of a product manager of a learning management software system and how she would go through the process of defining reporting for users of the product. How to evaluate embedded analytic solutions as strategy to greatly reduce initial and on-going engineering effort. Building a team to support your deployment.
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.
Speaker: Sriram Parthasarathy
Predictive analytics is an increasingly common buzzword with many forms. What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value. It seems everyone has their own take on what it is and which best practices and business benefits apply.
Speaker: Andrew Wynn, Senior Product Manager, Looker
But proper data analytics solutions take work to deliver - it's not as simple as just building a dashboard. Learn product analytics best practices from Andrew Wynn, Product Manager at Looker. In this webinar, we'll cover: Real examples for different verticals. How to adapt solutions for different company sizes.
Speaker: Megan Brown, Director, Data Literacy at Starbucks; Mariska Veenhof-Bulten, Business Intelligence Lead at bol.com; and Jennifer Wheeler, Director, IT Data and Analytics at Cardinal Health
Join data & analytics leaders from Starbucks, Cardinal Health, and bol.com for a webinar panel discussion on scaling data literacy skills across your organization with a clear strategy, a pragmatic roadmap, and executive buy-in. You’re invited! Unlocking enhanced levels of value and insight from data using a semantic layer.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance (an example of datamesh paradigm adoption).
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.
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