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.
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.
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CIO
JUNE 14, 2023
Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.
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.
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The world of data analytics is changing fast as organizations look to gain competitive advantages through the application of timely data. Choosing the best solution for your dashboards and reports starts with understanding the types of analytics solutions on the market. 4 common approaches to analytics for your application.
CIO
SEPTEMBER 14, 2023
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. Depending on the organization, they may also be responsible for communicating data trends. What is a data engineer? Their primary responsibility is to make data available, accessible, and secure to stakeholders.
CIO
JANUARY 22, 2024
While the average person might be awed by how AI can create new images or re-imagine voices, healthcare is focused on how large language models can be used in their organizations. For healthcare organizations, what’s below is data—vast amounts of data that LLMs will have to be trained on. Consider the iceberg analogy.
Speaker: Dave Mariani, Co-founder & Chief Technology Officer, AtScale; Bob Kelly, Director of Education and Enablement, AtScale
Check out this new instructor-led training workshop series to help advance your organization's data & analytics maturity. Given how data changes fast, there’s a clear need for a measuring stick for data and analytics maturity. Using augmented analytics for predictive and prescriptive analyses. Sign up now!
Speaker: speakers from Verizon, Snowflake, Affinity Federal Credit Union, EverQuote, and AtScale
Each panelist will present and discuss actionable strategies for making data as consumable as possible by everyone in the organization and for increasing data velocity for faster insights using a semantic layer. In this webinar you will learn about: Making data accessible to everyone in your organization with their favorite tools.
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Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics. But today, dashboards and visualizations have become table stakes.
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!
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Learn how and why the world’s most data-driven organizations use a semantic layer for speed of thought query performance and consistent KPIs across all of their BI/AI tools, such as Excel, Power BI, Tableau, Looker, Databricks, DataRobot, and H20, using a live data connection to Snowflake, Google BigQuery, Databricks, Amazon Redshift, Microsoft Azure (..)
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.
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. How Agile Lab and Enel Group used Dremio to connect their disparate organizations across geographies and business units. Leveraging Dremio for data governance and multi-cloud with Arrow Flight.
Advertiser: Data Robot
Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. Case studies describing how organizations both large and small are leveraging automated machine learning. Simple rules for spotting high-value use cases within your own organization.
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