Data Readiness for AI Assessment
Data & AI
Motivation
In AI software development, data serves as the foundation for transformative solutions. Whether it is structured or unstructured, historical or current, each type of data presents unique challenges that must be meticulously aligned with specific use cases. Before implementation, it is imperative to ensure that your data is capable of fulfilling both the functional and non-functional requirements of the specific AI use-case you intend to develop. This assessment acts as a quality gate, ensuring that your data meets the demands of an enterprise grade application.
What we bring
20 years of experience with software and data architectures for many of the world’s largest enterprises has generated the expertise needed for PRODYNA to help you design and develop applications in the public cloud. With this offering we will assist you with:
- Azure Expertise: Deep knowledge of the features and capabilities of the Azure AI and data services.
- Data Expertise: Using data to support AI use cases as knowledgebase or training source.
- AI Expertise: Comprehending the various use-cases in which the Azure AI services can be used across different sectors.
- Cost-Effectiveness Mentality: Prioritizing key aspects of your use case and data to make prompt and well-informed decisions.
What you need
For an accurate assessment, we require:
- Involvement of business specialists and technical staff: These people can explain where and how to obtain the relevant data and describe its structure, known quality aspects and regulatory obligations.
- Clearly defined use-case requirements: Detailed information on what you aim to achieve with AI and how it will produce business value.
- Access to relevant data pool: Example data is needed for data exploration.
What you get
The assessment evaluates various dimensions, including data relevance, quality, security, and usability comparing these against the functional and non-functional requirements of the proposed AI use-case. It identifies open issues and details the steps required to close these gaps. PRODYNA will guide you and your employees through the following phases:
Workshop
(up to 1 day)
Introduction: PRODYNA introduces the four principles of data readiness for AI
- Relevance
- Quality
- Security
- Usability
Understanding the specific use-case: Create a common understanding of the target use-case and the business value it will provide.
Data Mapping: Collaboratively create a list of data pools that are required for the use case. Examples include:
- Databases
- Files
- Employee knowledge
- Non-digital records
- Videos/Photos/Audio
Data Landscape: Elaborate current data infrastructure and regulations to access the data.
Exploration
(2 to 3 days)
Key Personnel: Identify key personnel that can provide access to data sources.
Experiments: Using test data with pre-built AI models to verify assumptions.
Quality Survey: Using analytical methods to identify data quality issues.
Gap Analysis: Identify gaps between current and ideal data state.
Budget Analysis: Create estimations on effort required to make needed data pools accessible and achieve the required data quality.
Results
(2 to 3 days)
Architecture: Develop a high-level architecture to visualize the required network and service connections.
Roadmap: Develop a roadmap detailing the steps needed to achieve the data requirements necessary for your use case.
Presentation: Create and present results of the assessment in a comprehensive manner.
Quick facts
- Effort: 5 – 7 days
- Thorough analysis of your data with respect to your AI use-case
- Reduces the risk incurred in the implementation of an AI use-case
Benefits
- Validates the technical suitability of your data for this specific AI use case.
- Provides a quality gate prior to investing in software implementation.
- Provides a roadmap and budget analysis for solving any findings that may result out of the assessment.
Contact us
for more information.
David Wainwright
