When highly paid consultants and data management platform vendors talk about Data Science and AI, they talk about the latest technology, fasted processing, most secure and protected data, and many more exciting innovations of the last decade, delivered to your enterprise, on a silver platter (cost included!). But all the technology in this world will not ensure or guarantee long-term success in transforming your enterprise with Data Science and AI.
Central to a sustained and scalable implementation of data science and AI, is an efficient & scalable workflow.
A proven data science and AI workflow
mcSquared has honed a robust and tested Agile, Iterative Process for implementing Data and AI use cases on any platform. Many of these steps require involvement from sponsors, stakeholders, and subject matter experts from the customer’s organizations. Our engagement model comprises of our tried and tested AI Use Case implementation process and our embedded teams forming a functional job family powering any existing Data and Analytics organization or establishing a new AI Center of Excellence.
Phase 1: Use Case Requirements and Data Discovery
Key Business Questions and Goal Definition
- Define Use Case goals, objectives, and success criteria.
- Engage with key stakeholders, identify champions & establish clear communication channels.
Data Collection and Preparation
- Collaborate with internal and external data providers or data collection teams.
- Implement health check procedures to ensure data quality.
- Build and utilize AI tools to identify and address data quality gaps.
Phase 2: Data Analysis, Data Modeling & Feature Engineering
Exploratory Data Analysis (EDA)
- Perform EDA to gain insights into the data.
- Build descriptive analytical tools to have a dialogue with the data
- Identify patterns, outliers, and potential issues.
Data Modeling, Feature Engineering
- Develop a common object model which semantically describes business processes
- Perform feature engineering to understand most important factors impacting trends, patterns, outliers, identified before
- Enrich the descriptive analysis tools to reflect KPIs and features
Phase 3: Model Development, Training / LLM In-context Learning and Fine tuning
Develop ML/ AI models
- Reframe the business goal into a analytical problem statement
- Identify fit and select an analytical methodology to fit the problem statement
- Develop an analytics model / algorithm utilizing features developed, and train it
- Perform initial validation and testing, iterate to arrive at a stable working model
Fine-tune Generative AI models
- Identify a commercial or open-source Large Language Model, pre-train it
- Implement deeper customized & personalized reasoning capabilities by focusing on the context and implementing RAG
- Implement fine tuning and prompt engineering for improved results
Phase 4: Model Validation & Optimization, Visualization & Workflow dev
Model Validation, Deployment and Optimization
- Validate ML/AI models using appropriate metrics.
- Address issues and refine models as needed.
- Deploy ML/AI models in a real-world setting.
- Continuously monitor model performance, optimize performance and accuracy
Visualization and Workflow Automation
- Develop interactive self-serve tools for decision makers to visualize insights, what-if scenarios based on predictions from model, receive recommendations
- Develop AI assisted smart agent framework integrating with existing systems, for automated tasks and alerts
Phase 5: Interpretation, Automation, and Action
Data-Driven Insights and Recommendations, Automated Agents
- Extract meaningful insights from model outputs.
- Generate actionable recommendations based on insights.
- Deploy AI powered advisors and assistants for automating tasks, generating alerts
Implementation of Insights
- Collaborate with stakeholders to implement recommended actions.
- Monitor the impact of implemented changes.
This agile process is designed to be iterative and incremental, allowing for continuous improvement with each cycle. It emphasizes collaboration, data quality maintenance, and the translation of insights into actionable outcomes to drive meaningful change in healthcare projects.
Embedded Job Families
Job Families bring together individuals with specialized skills and knowledge related to a specific domain. For example, in the life sciences sector, we have operated in Job Families comprised of therapy area subject matter experts, brand team leads, clinical trial operations specialists, data scientists, data engineers, and business analysts.
In addition to team members within a Job Family sharing knowledge and best practices, Job Families also help align data projects with the broader objectives of the organization or specific departments, ensuring that data efforts are strategic, and fostering continuous learning and improvement.
Unlike many other consulting vendors, our embedded approach allows mcSquared team members to take on leading roles in their areas of expertise and ensure high quality deliverables with a sense of ownership.
Building AI Centers of Excellence
mcSquared.AI has proven expertise working on transformational initiatives, as a trusted partner in building AI Centers of Excellence or leading data and analytics initiatives.
We have implemented Advanced Analytics Use Cases, powered by internal and external (industry, third-party) datasets in multiple industry settings. We have extensive experience in the latest data and analytics management platforms, a developer partnership with Palantir Foundry, and experience in Azure, AWS, Databricks and Snowflake.
But central to all these experiences and successes, a few things are irreplaceable:
Establishing a robust, repeatable, agile process
Building Job Families, incorporates iterative and transparent ways of continuous improvement
Driving adoption and usage by key stakeholders within the organization
Delivering self-serve data driven tools, instead of one-off expensive reports and readouts