AI-Ready Data for Smarter Business Decisions
Why is AI-Ready Data essential?
Steps to develop AI-driven data initiatives:
-
Assess Data Management Readiness:
To efficiently use AI, data must fulfill important use-case requirements such as quantification, semantics, quality, trust, and diversity. Data shall also meet requirements for AI use cases like validation and verification, performance, cost, and nonfunctional requirements. The ongoing data of government requirements must be defined, and it should support AI use cases like data stewardship and AI standards and regulations. -
Gain buy-in from the board:
The board must be educated on the significance of investing in AI-ready data and mapping it to use cases which enable business goals. Be to the point about what is the value of AI-ready data to overall success and what is necessary; share an outside-in view. The goals must be clearly defined and provide methods to reach them. -
Evolve data management practices:
The first action that must be taken is to focus on a specific application that demonstrates the unique abilities of retrieval-augmented generation (RAG) and illustrate success through bringing value for the business. The second action is classifying the underlying data as structured, semi-structured, or unstructured; you can assess handling procedures and identify potential risks. Lastly, metadata will support the enabling technologies while providing your current RAG implementation crucial context. -
Extend the data management ecosystem:
Establishing a metadata practice including rich semantics is important to improve the accuracy of generative AI when used for enterprise data. To safely use emerging data management technologies and detect potential risks, building on data literacy, and GenAI is crucial. Review and verify the suppliers' GenAI-enabled data management features and only implement them if accuracy, quality, security, and confidentiality standards are adequate. -
Scale and Govern:
Create governance initiatives with relevant roles, responsibilities, methods, and procedures that are focused on results; expand existing D&A governance programs, like by establishing an AI board. Identify and analyze regulatory changes, assess their impact on the business, and create ways to speed up and organize initiatives, including responsible use and AI ethics. With the goal to support governance and value realization objectives, assess current data and AI literacy levels, develop a training program, and develop a strategy for staff development as an essential part of change management.
Key stakeholders involved:
CIO:
The CIO is the person who creates a collaborative working environment with the CDAO and establishes clear responsibilities for each. He partners on technology trends, architecture, infrastructure, platforms, and tools.
CDAO and Team:
They design the foundation of management, measurement, and modernization of D&A assets for AI-driven innovation and business transformation. Create a culture that is data-driven and data-literate and leads to data analytics and governance that minimizes risk and maintains trust, which also ensures value.
CISO and Team:
They engage with leaders of CDAO and governance to make sure risk management and information security implications are properly understood in data, governance, and analytics. The CISO additionally guides the planning of risk and compliance efforts.
CFO:
Works with the CDAO to improvise D&A budget processes, value assessment, and realization methods to ensure the best feasible resource allocation and effect on business value.
Data Management Leader:
Creates an environment by investing in a modern D&A for reusable data products that fulfill enterprise-wide D&A requirements.
Enterprise Application Leaders:
They collaborate with CDAO and enterprise architects to put into action modern data management, analytical applications, and composable solutions. To achieve D&A strategy and governance objectives, they support and manage applications.
Data Engineers:
They work closely with CDAO and deliver an AI-ready data foundation.
Sourcing, Procurement, and Vendor Management Leader:
To determine, analyze, and choose technology vendors and external service providers they work with CDAO.
Data Management Architects:
They perform a number of activities from planning the D&A roadmap to implementing D&A solutions that span application, infrastructure, and design data governance tools and procedures.
AI Team:
To deliver the AI-ready data and governance necessary for AI applications, they rely on the CDAO and their team.
Client stories:
1. Client: Insurance provider.
JRD’S Solution: Supported migration of 120+ applications to Google Cloud Platform (GCP).
Benefits:
- 99% system availability.
- 25% cost and schedule efficiency.
- 30% optimized solution approach.
2. Client: Healthcare organization specializing in pharmacy services.
JRD’s Solution: Develop a Python script to extract data, query CRM for receipts, and automate email delivery.
Benefits:
- Saved 32 person-hours per run.
- Eliminated manual error.
- Improved tracking and accuracy.
Conclusion:
AI-prepared data is pivotal in getting the best out of AI solutions. It requires a formal process encompassing data management governance and stakeholder collaboration and must be continually refined. Essential roles are performed by the essential stakeholders such as CIOs, CDAOs, CISOs, and data engineers, a key component in ensuring data quality, security, and compliance. By safeguarding leadership buy-in, developing data practices, and creating a robust governance framework, organizations can create a robust AI-ready data environment. Finally, an AI-ready data strategy fortifies innovation and decision-making and ensures proper application of AI in operations. At JRD Systems, we develop innovative digital experiences that increase consumer engagement, drive business growth, and maximize operational effectiveness. We help businesses develop effective, trustworthy, and improved digital platforms by combining scalable web development, AI-driven automation, and reliable e-commerce solutions with Adobe Commerce (Magento).