Establish the Necessary Data PlatformTo Deploy AI at Scale
A modern cloud data architecture built to run large AI models at scale is the foundation for getting value from AI.
However, according to a recent HBR survey, Chief Data Officers face three critical challenges to making AI a reality:
- Data preparedness – AI relies heavily on the quality of data. Nearly half of data leaders identified data quality as their greatest challenge. Companies need to create new data strategies and manage their data effectively for Gen AI to work for them.
- Data strategy and management – 57% of data leaders have made no changes to their data management capabilities to accommodate Gen AI. Only 37% believe they have the right data foundation for Gen AI.
- Transforming data – For Gen AI to create value, organizations need to customize LLM models with their own data, which requires internal work to prepare data for integration.
Lantern’s full line of data platform services ensures your data infrastructure is ready to meet the demands of AI. From envisioning, to strategy, to implementation and support, we help you unlock the full potential of your data assets.
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Our Data Platform Services
Data Strategy Development
We work with you to create a comprehensive data strategy that aligns with your AI goals. Utilizing our proven framework, we assess your current data estate to develop recommendations and a roadmap to meet your objectives.
Data Platform Modernization
We help you architect, design and implement a modern data platform in Azure that supports scalable and secure AI applications.
AI Model Implementation
We assist in readying the data, developing, tuning, refining and deploying AI models and ensuring they are aligned with your business objectives.
Data is the Fuelthat Powers AI
A modern data platform unifies disparate data sources into a single, accessible repository, ensuring seamless integration and management.
This centralization, combined with advanced analytics and real-time processing capabilities, empowers organizations to harness AI effectively, enabling data-driven insights and automated decision-making across various applications.
- Why is data quality important for AI success?
Data quality is crucial because AI systems rely on large volumes of data to learn and make decisions. Poor quality data can lead to inaccurate models and unreliable outcomes. Data quality is a significant concern, and ensuring high-quality, accurate, and fresh data is a prerequisite for competitive advantage in AI.
- What are the main challenges organizations face in preparing their data for AI?
Organizations often struggle with integrating diverse data sources, poor data governance, and data silos. According to an HBR report, 50% of survey respondents cited difficulty in unifying data formats, and 44% mentioned poor data quality as major challenges. Addressing these issues involves implementing robust data management and governance practices.
- How can organizations improve their data governance to support AI initiatives?
Improving data governance involves establishing clear policies, standards, and frameworks for managing data throughout its lifecycle. This includes data quality assurance, defining data ownership, and ensuring data privacy and security. A MIT report emphasizes the role of data practitioners in creating governance structures that support AI deployment.
- What roles do data practitioners play in ensuring AI success?
Data practitioners, including data engineers, analytics engineers, and data scientists, are pivotal in preparing and managing data for AI applications. Their responsibilities include data integration, ensuring data quality, building data platforms, and creating data workflows that support AI models. A key requirement for success is ensuring their evolving roles are increasingly aligned with business objectives.
- What advanced tools and frameworks are essential for managing data in the era of AI?
Advanced tools and frameworks such as data lakes, data meshes, and data fabrics are essential for effective data management. These tools help organizations integrate, process, and govern data more efficiently, making it accessible and usable for AI applications. Modern data platforms enhance data visibility and usability, thereby supporting AI readiness.
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