Turning Data Into Business Value in the Age of AI Flashcards

Flashcards testing concepts, empirical data, AI evolution waves, industry stats, and customer quotes from Confluent's e-book on turning data into business value in the age of AI.

According to the 2025 Data Streaming Report, what percentage of IT leaders cite accelerated product and service innovation from using Data Streaming Platforms (DSPs)?

90% of IT leaders.

What are the key characteristics and limitations of traditional data warehouses in the Business Intelligence (BI) era?

Data warehouses brought enterprise data into one place for internal dashboards and reports, but were fundamentally batch-based, with data updated on a schedule—often only once a day.

What is Wave 1 in the evolution of AI, and what was its focus?

Wave 1 is Purpose-Built AI, focusing on traditional machine learning to build statistical models and predictive capabilities for narrowly defined tasks.

Why do Generative AI (Wave 2) large language models (LLMs) hallucinate, and how does data streaming solve this?

LLMs are trained on public data and lack domain-specific context, causing hallucinations; data streaming provides fresh, relevant enterprise data for grounded outputs in retrieval-augmented generation (RAG) applications.

According to Gartner predictions in the e-book, what will be the adoption rate and decision-making capability of agentic AI by 2028?

By 2028, 33% of enterprise software applications will include agentic AI (up from less than 1% in 2024), enabling 15% of work decisions to be made autonomously.

What resolution rate and operational cost reduction does Gartner predict agentic AI will achieve by 2029 in customer service?

Agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.

What percentage of organizations are already in the pilot or deployment stage with agentic AI according to the 2025 Data Streaming Report?

40% of organizations.

What are the top three data challenges hindering AI project acceleration according to the 2025 Data Streaming Report?

Fragmented ownership of data across disparate systems (68%), limited ability to seamlessly integrate new data sources (65%), and insufficient infrastructure for real-time data processing (61%).

What are the two traditional domains where enterprise data resides, and what systems do they contain?

The operational estate (databases, CRM, ERP applications, and billing systems) and the analytical estate (data warehouses for after-the-fact analysis and reporting).

What percentage of IT leaders surveyed see Data Streaming Platforms (DSPs) easing AI adoption?

89% of IT leaders.

According to the 2025 Data Streaming Report, what percentage of IT leaders say DSPs will be increasingly used to feed AI systems real-time, contextual, and trustworthy data?

87% of IT leaders.

What are example use cases that organizations can build using Agentic AI with Data Streaming Platforms?

Multi-agent sales development representatives, web scraping agents, mortgage underwriting agents, and onboarding agents.

What four capabilities define Confluent's platform value proposition for AI stacks?

Stream (share real-time streams), Connect (120+ connectors), Process (Apache Flink® stream processing), and Govern (Stream Governance).

Which stream processing tool does Confluent use to enrich data streams on the fly?

Apache Flink®.

How many pre-built and custom connectors does Confluent provide to integrate disparate data?

120+ pre-built and custom connectors.

How does Adam Watkins, Co-Founder & CTO of Reworkd, state that Confluent impacts their development velocity?

He states that Confluent makes it easy to iterate quickly and build new AI features in days instead of weeks.

How does Daniel Sternberg, Head of Data & AI at Notion, describe their use of Confluent?

Notion uses Confluent to share new content and updates in real time, allowing product and engineering teams to use data products without worrying about infrastructure, speeding up GenAI use cases.

Who identified Confluent as the 'backbone of our multi-agent platform' for agent orchestration, observation, and governance?

Saul Sparber, Founder & CEO of Agent Taskflow.

How does Chris Kapp, Software Architect at Henry Schein One, define Confluent's role in real-time data movement?

He describes Confluent as the foundation that gets high-quality data moving in real time and gets it where it needs to be.