Roadmap for Building Fit-for-Purpose Analytics Models

In a world that requires decisions based on data, developing analytics models fit-for-purpose for an organization ensures precision, adaptability, and real-time responsiveness. It offers a technical advantage by empowering precise matching of computational resources to demands from different business functions, hence driving instant insights and operational efficiencies in the long run.

How to Build Fit-for-Purpose Analytics Models?

Phase 1: Strategic Planning and Assessment

– Define Business Goals and Data Objectives

• Key Outcomes Identification: Collaborate with business stakeholders to determine key metrics and business outcomes that this model will help enable.
• Data Requirements Analysis:: Document data sources, required data attributes, and data volume and frequency for targeted model outputs.

– Build a Unified Data Infrastructure

• Data Lakehouse: Provide a unified data repository, a so-called data lakehouse, that allows the storing of data and its retrieval and analysis.
• Data Onboarding: Connect all relevant data sources (databases, cloud platforms, APIs) to create a single, accessible data environment.

Phase 2: Model Design and Development

– Develop Data Pipelines and Processing Frameworks

• Design ETL Pipelines: Set up ETL (Extract, Transform, Load) processes to clean, normalize, and get data ready for modeling.
• Enable Real-Time Data Processing: Integrate real-time data streaming tools for models that require live insights.

– Customize Model Architecture Based on Use Case

• Select Relevant Frameworks: Select appropriate ML frameworks based on model objectives, which could be for classification, regression, or predictive analytics.
• Design for Scalability: Use containerized services and cloud-based microservices to enable flexible, cost-efficient scaling.

Phase 3: Training, Testing, and Deployment

– Implement Training and MLOps Practices

• Automate Training Pipelines: Build MLOps pipelines for continuous model training and validation, helping ensure models adapt to new data.
• Establish Model Testing Protocols: Establish tests to track data drift, model accuracy, and model performance.

– Deploy Models for Real-Time Analytics

• Continuous Integration and Deployment: Set up CI/CD pipelines to streamline deployment and updates.
• Optimize for Low-Latency: Use high-performance tools like SQL engines to support rapid, real-time analytics.

Phase 4: Governance, Security, and Compliance

– Establish Data Governance

• Control Access: Set permissions to make sure only authorized users can access data.
• Track Data Use: Monitor data flow and usage to maintain transparency.

– Implement Security and Compliance

• Protect Sensitive Data: Encrypt important data.
• Follow Industry Standards: Implement data policies that meet industry-specific compliance.

Phase 5: Scaling and Continuous Improvement

– Promote Data Reusability Across Business Units

• Develop Reusable Data Products: Create data outputs in reusable, standardized formats for different departments.
• Enable Data Democratization: Allow access to data across teams for the empowerment of decision-making at all levels.

– Monitor and Optimize Model Performance

• Set Up Performance Dashboards: Track model accuracy, usage, and performance metrics for continuous visibility.
• Regularly Review and Retrain: Schedule periodic reviews so that models could be retrained or updated with changing business needs and data.

How Portera Can Guide You Through This Roadmap

At Portera, we have developed a tailored tool that simplifies data management and prioritization, allowing businesses to make informed, impactful decisions with ease. Through this step-by-step tool, clients focus on various data types and prioritize based on business needs. Layered over from targeted categories, we provide each business unit with the capability to find and address its highest priorities in a way that cuts through complex data landscapes.

Impact

• Prediction of expected impact potential for future years
• Accurate planning using a web-based and automated tool with in-built quality checks for minimal error
• Product/business owners can track performance versus plan in real/near time
• Ability to set trends for growth objectives for each audience segment
• Data-driven tool for clear effort distribution across channels and representative

Read our Case Study for more detail.