Fuel Your AI Factory with High Quality Data

Artificial Intelligence thrives on data. For AI to effectively learn, interpret, understand, make predictions, and act on new inputs, it requires access to trusted, high-quality data. This data must be representative of the dynamic real-world and business scenarios the AI models and applications will encounter.

The Critical Role of High-Quality Data in AI Development

Data forms the raw material inputs for your AI Factory. As with a physical factory, the quality and suitability of the inputs greatly impacts the quality and usability of the finished product - which in this case are the AI systems that solve important business challenges.

Each AI use case has unique data requirements, which are influenced by the specific AI techniques employed. Whether training a model or augmenting it with contextual business information, the data must meet certain quality and availability parameters for the use case. Additionally, data's lineage, ownership, and purpose should be well-documented to avoid misuse.

Managing Data Well Keeps Business Confidence High

Proving that data is AI-ready involves a continuous process of transformation and validation. Data and AI teams must work together to quickly identify and converge on data that is fit for use throughout the development and operationalization phases of the AI use case. The iterative validation process of selected data is crucial for maintaining the relevance and accuracy of the data, ensuring that AI models and applications remain effective over time.

Teams must ensure that data for AI constantly meets the use case's requirements for timeliness, integrity, and high availability.

Stay Inside Governance and Regulatory Boundaries

By maintaining rigorous governance practices, organizations can ensure that their AI systems are not only effective but also ethical and compliant with relevant standards. Classifying and tagging data sources helps support regulatory compliance and avoid misuse or leakage of sensitive data or IP. Dependencies must be tracked, especially when one AI system provides input for another.

With the right tools and processes, organizations can ensure that their AI models are built on a foundation of high-quality, trustworthy data, capable of delivering reliable and accurate results in real-world applications.

A Robust Data Catalog is Premium Fuel for AI Data Readiness

Organizations that implement a modern enterprise data catalog enable data and business analysts to quickly find relevant data and understand its context, instead of wasting time searching for it. The data catalog consolidates metrics and contextual information from various sources, so that analysts don't have to navigate multiple systems to find the right data. Custom tags can map key business logic, terms and processes to data assets.

A data catalog provides comprehensive data context by offering detailed metadata to classify how data assets should be used. Data lineage, data quality metrics, and usage history are examples of metadata. This context helps understand the data's origin, transformations, and sensitivity, enabling more accurate and appropriate analysis.

Data catalogs facilitate collaboration and sharing, allowing catalog users to annotate data assets, share queries, and document insights. A proper enterprise data catalog creates a single source of reference of your data. The catalog also helps in governing data and ensuring policies and industry- or region-specific regulations are followed.

Data Pipelines are the Fuel Injectors for Your AI Models

Data pipelines connect multiple data sources, apply transformations, and deliver refined data to AI systems, as well as data warehouses, lakes, lakehouses or other target systems. To keep up with the exponential growth in the amount of data used by AI systems, automated data pipelines are a necessity.

The steps along a data pipeline may involve transforming, optimizing, cleaning, filtering, integrating, and aggregating the data. The pipeline automates the integration and transformation of data to standardize these processes to feed into AI use cases, promoting reliable data quality and movement at scale. Pipelines can integrate data from multiple sources, such as for fraud detection which would integrate data from customer accounts, transaction records, and risk management platforms.

Rev Up Your AI Use Cases with a Powerful Data Engine

The Dell Data Lakehouseis a great vehicle to centralize transformed data for AI applications and data analytics and can be a destination or source for data pipelines in the enterprise. You can easily orchestrate all our data pipelines from the Dell Data Lakehouse to your data sources and AI use cases, leveraging integrations with best-in-class tools like Data Build Tool (DBT) and Apache Airflow. Your employees gain a comprehensive view of data in the Lakehouse when you integrate the Lakehouse with an enterprise data catalog such as Alation.

Get Your Data Management "AI-ready"

As enterprises adopt and scale their early AI use cases, Dell Technologies recommends a systematic, factory-like approach to avoid having to "reinvent the wheel" in later rounds of AI development. A critical component of an AI Factory is being able to quickly and consistently identify, prepare and deliver the data needed by an AI system to do its work.

Dell Technologies Data Management Servicescan help you establish data management practices that promote rapid, agile development of AI systems. Optimization Services for Data Cataloginghelp you maximize data transparency and usability through a data catalog that efficiently collects and organizes information about data sources. Implementation Services for Data Pipelineshelp you implement and orchestrate automated data pipelines to integrate data from disparate sources and transform data to meet requirements of target AI systems.

Dell consultants are ready to work with youto modernize your data environment and accelerate AI contributions to your business. Find out more by contacting your Dell account representative.

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