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Enterprise Data Quality: 3 Quick Tips from Data Leaders

Monte Carlo

But even though the data landscape is evolving, many enterprise data organizations are still managing data quality the “old” way: with simple data quality monitoring. So, what are enterprise data teams doing to turn the data quality tide? Enterprise data teams need to know the table, the column, and the row where it happened.

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Modern Data Stack: The Struggle of Enterprise Adoption

Simon Späti

As a manager or developer working with data at a mid- to large-sized enterprise, you might ask why aren’t we using any of these tools.

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Modern Data Stack: The Struggle of Enterprise Adoption

Simon Späti

As a manager or developer working with data at a mid- to large-sized enterprise, you might ask why aren’t we using any of these tools.

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Launching Hevo for Enterprise Data Excellence

Hevo

Hevo is enterprise-friendly! The needs of enterprise data systems are unique. With massive workloads and complex intertwined systems, data teams need partners who can understand their requirements intimately. Hevo can be that partner for you! Hevo has spent the last 7 years building a data platform that is simple yet powerful.

Data 40
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5 Early Indicators Your Embedded Analytics Will Fail

Many application teams leave embedded analytics to languish until something—an unhappy customer, plummeting revenue, a spike in customer churn—demands change. But by then, it may be too late. In this White Paper, Logi Analytics has identified 5 tell-tale signs your project is moving from “nice to have” to “needed yesterday.".

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How to Become an Enterprise Architect? Skills, Prerequisites, Salary

Knowledge Hut

In today's rapidly evolving digital landscape, the role of the Enterprise Architect (EA) has emerged as a critical driver of organizational success. Being an enterprise architect myself, I can attest that this role goes beyond simply managing the current systems. Who is an Enterprise Architect and what do they do?

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The Comprehensive Guide to Enterprise Data Bus

Hevo

Enterprises generate vast amounts of data from multiple sources, such as Customer Relationship Management platforms, Enterprise Resource Planning (ERP) applications, marketing metrics, and more. However, you need seamless data exchange between numerous applications to perform deep analytics and derive business insights.

Data 40
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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

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. 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.