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The future of Gen AI in analytics

CIO

Enter Gen AI, a transformative force reshaping digital experience analytics (DXA). Gen AI as a catalyst for actionable insights One of the biggest challenges in digital analytics isn’t just understanding what’s happening, but why it’s happening—and doing so at scale, and quickly. That’s where Gen AI comes in.

Analytics 193
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Part 1: A Survey of Analytics Engineering Work at Netflix

Netflix Tech

This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.

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The future of data: A 5-pillar approach to modern data management

CIO

The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. However, the analytics/reporting function needs to drive the organization of the reports and self-service analytics.

Data 167
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Are enterprises ready to adopt AI at scale?

CIO

They may implement AI, but the data architecture they currently have is not equipped, or able, to scale with the huge volumes of data that power AI and analytics. As data is moved between environments, fed into ML models, or leveraged in advanced analytics, considerations around things like security and compliance are top of mind for many.

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How to Operationalize Data From Multiple Sources to Deliver Actionable Insights

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.

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Why thinking like a tech company is essential for your business’s survival

CIO

A great example of this is the semiconductor industry. Educating and training our team With generative AI, for example, its adoption has surged from 50% to 72% in the past year, according to research by McKinsey. For example, when we evaluate third-party vendors, we now ask: Does this vendor comply with AI-related data protections?

Company 186
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Zoho adds AI and ML capabilities in Zoho Analytics 6.0

CIO

Zoho has updated Zoho Analytics to add artificial intelligence to the product and enables customers create custom machine-learning models using its new Data Science and Machine Learning (DSML) Studio. The advances in Zoho Analytics 6.0 He enthused about the new mobile app, and new chart types in Analytics 6.0,

Analytics 197
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How Product Managers Can Learn to Love Reporting

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.

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How Banks Are Winning with AI and Automated Machine Learning

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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Predictive Analytics 101: Your Roadmap to Driving Key Product Decisions

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.

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Products for Product People: Best Practices in Analytics

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.

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Get Better Network Graphs & Save Analysts Time

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.

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How to Scale a Data Literacy Program at Your Organization

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.

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Partner Webinar: A Framework for Building Data Mesh Architecture

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).

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How Banks Are Winning with AI and Automated Machine Learning

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.