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Dec 4 2024

Data Is Risky Business: Structured, Unstructured (Who Cares?)

The Irish satirist Jonathan Swift wrote “Gulliver’s Travels” almost 300 years ago, but the story of Lemuel Gulliver’s journey to Lilliput and beyond has resonance for data leaders today. There are important lessons to learn from the little people of Lilliput and the challenges encountered by the eponymous Gulliver. While Lilliput is where Gulliver first […]

How to Build a Robust Data Architecture for Scalable Business Growth

In the digital age, businesses rely on high-quality, easily accessible data to guide all manner of decisions and encourage growth. However, as a business grows, the way the organization interacts with its data can change, making processes less efficient and impairing progress toward business goals. Businesses need to think critically about their data architecture to […]

Granularity Is the True Data Advantage

Commerce today runs on data – guiding product development, improving operational efficiency, and personalizing the customer experience. However, many organizations fall into the trap of thinking that more data means more sales, when these two factors aren’t directly correlated. Often, executives will become overzealous in their digital transformations and cut blank checks for data collection, […]

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The AI Playbook: Providing Important Reminders to Data Professionals

Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations. The book, which comes out on February 6th, and its insights are captured in six statements: — Determine the value— Establish a prediction goal— Establish evaluation metrics— Prepare […]

🔴 Red Button Data

🔴 Red Button Data

Category Archives: data models

Data Is Risky Business: Structured, Unstructured (Who Cares?)

How to Build a Robust Data Architecture for Scalable Business Growth

Granularity Is the True Data Advantage

The AI Playbook: Providing Important Reminders to Data Professionals