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No Data, No AI. Introducing Data Streaming for Actionable Intelligence with AI


It is well understood that the promise of artificial intelligence (AI) is dependent on careful curation and management of data. The adage โ€œgarbage in, garbage outโ€ is particularly applicable to AI. Without clean, trustworthy, up-to-date data, AI is useless. Effective AI must continually learn and adapt. It does this via continuous data ingestionโ€”taking in new information and overlaying it with existing knowledge.ย 

Confluent, powered by the de facto standard in data streamingโ€”Apache Kafkaยฎ, is built and optimized to support the distribution of data in real time as it is created. With a massive ecosystem of connectors, organizations can tap into their existing data stores, modern or legacy, and curate them for consumption by AI tools to drive actionable intelligence.

Data streaming gives AI applications:ย 

  • The ability to perform continuous training on data streams

  • Efficient and constant data synchronization from source systems to machine learning platformsย 

  • Real-time application of AI models

  • Access to high volume data processing

Fueling the use of AI

Enabling AI at the organizational level starts with tapping into the sources of data required to train the model. It is pretty typical that the owner of the data is not on the same team that is tasked with building and maintaining the AI platform, and in many cases multiple data sources are needed to achieve the mission outcomes supported by AI. Multiple data sets from different domains need to be fused together to provide a rich context to make model training and results effective. With each successive AI effort more and more data is required, creating additional point-to-point integrations, and ultimately bottlenecking the process and adding complexity, time, and expense.ย 

To maximize the value of AI, organizations must broadly address data accessibility. Building a real-time data mesh democratizes data across the organization and enables AI teams to find data sets and tap into them in a frictionless manner without having to directly work with the owner and set up a point-to-point integration. Once a model is developed the same streams can be run through the model and drive action from the insights. This fundamental decoupling of data product owners from AI consumers creates fertile conditions in which ROI on AI accelerates.

Leveraging AI for actionable intelligence

Data becomes information when context is applied and it is re-oriented for human consumption. The ability to pull real-time data into AI systems opens the door for new correlations and connections to be made. These connections can help inform decisions at mission speed but only if data is made available in real time.ย 

Despite the tremendous impact data accessibility can have in conjunction with AI there is still significant responsibility to ensure data is governed and protected from misuse. Access to data also has to be secure and trusted. A data streaming architecture with Confluent is deployed with role-based access control (RBAC) and attribute-based access control (ABAC) at a granular level to pave the way toward a responsive, secure, and scalable approach for transforming how data is made actionable via AI. Confluent is proud to be a part of the AI ecosystem, providing a conduit for data to be turned into actionable intelligence.ย 

To learn more about how to securely feed AI systems with data, reach out to a Confluent expert today at publicsector@confluent.io.ย 

  • Will LaForest is Field CTO for Confluent. In his current position, LaForest works with customers across a broad spectrum of industries and government, enabling them to realize the benefits of a data in motion architecture with event streaming. He is passionate about data technology innovation and has spent 26 years helping customers wrangle data at massive scale. His technical career spans diverse areas from software engineering, NoSQL, data science, cloud computing, machine learning, and building statistical visualization software but began with code slinging at DARPA as a teenager. LaForest holds degrees in mathematics and physics from the University of Virginia.

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