Every project at MI Data Works follows time-tested, transparent methodologies. All processes align with industry standards and incorporate best practices in statistics, machine learning, and software development.

When the build is done, we stay with you—offering monthly support packages or ad-hoc support to keep everything running smoothly.

Data Analytics Process

We examine your existing data to understand what happened and why. Think of it as a health check for your business before you decide what to do next.

This package delivers business insights through statistical exploration and interactive dashboards—it does not include predictive machine learning. Our analysts clean, explore, and visualize your data so leaders can act on clear, defensible evidence, free from opaque "black-box" automation.

  1. Business Understanding
    • Meet with stakeholders to clarify objectives, key questions, and success criteria.
    • Define measurable goals and document assumptions.
  2. Data Understanding
    • Inventory all relevant data sources (internal systems, third-party, public).
    • Assess data quality—completeness, consistency, and limitations.
  3. Data Preparation
    • Remove duplicates, correct errors, handle missing values.
    • Standardize formats, encode categories, normalize metrics, and flag outliers.
  4. Exploratory Analysis & Descriptive Statistics
    • Visualize distributions, correlations, and trends.
    • Compute summary statistics (mean, median, variance, etc.).
    • Form initial hypotheses about drivers and anomalies.
  5. Statistical Testing & Evaluation
    • Run appropriate tests (t-test, ANOVA, regression) and verify assumptions.
    • Quantify uncertainty with confidence intervals and effect sizes.
  6. Interpretation & Communication
    • Translate results into plain-language insights and actionable recommendations.
    • Deliver interactive dashboards, written reports, or slide decks.

Need to forecast the future or automate decisions? See our Machine Learning services.

Supervised Machine Learning Process

We teach a model with historical, labeled examples so it can predict future outcomes—like hiring a digital analyst that never sleeps.

This service builds on the Data Analytics steps and adds predictive modeling, automated deployment, and continuous monitoring.

  1. Problem Definition
    • Frame the task as regression (predict a number) or classification (predict a label).
    • Identify target variable(s), performance KPIs, and fairness or compliance constraints.
  2. Data Preparation & Feature Engineering
    • Label data, create new predictive features, and split into training/validation/test sets.
    • Check for class imbalance, leakage, and drift.
  3. Model Selection & Training
    • Compare algorithms (e.g., linear/logistic regression, random forest, XGBoost, neural nets).
    • Tune hyper-parameters using cross-validation and document experiments for reproducibility.
  4. Evaluation & Validation
    • Assess accuracy on unseen test data with relevant metrics (RMSE, F1, AUC, etc.).
    • Benchmark against baselines and perform error analysis.
  5. Deployment & MLOps
    • Package the model behind an API or batch job with version control and CI/CD.
    • Set up automated monitoring for performance, drift, and data quality.
  6. Monitoring & Iteration
    • Retrain on fresh data as needed, governed by measurable triggers.
    • Audit models regularly for fairness, bias, and regulatory compliance.
  • Predict outcomes with quantified confidence.
  • Automate complex or repetitive decisions at scale.
  • Embed real-time predictions in dashboards or business workflows.

Unsupervised Machine Learning Process

When data has no labels, we let the algorithms discover hidden structure—think of it as automatically sorting puzzle pieces into groups.

Unsupervised projects still follow rigorous data preparation and exploration, then apply clustering, anomaly detection, or dimensionality reduction to reveal insights.

  1. Data Preparation
    • Clean and transform features (scaling, encoding, handling outliers).
    • Confirm suitability for distance-based or density-based algorithms.
  2. Algorithm Selection
    • Choose methods such as K-means, DBSCAN, hierarchical clustering, PCA, or Isolation Forest.
    • Define evaluation criteria (silhouette score, explained variance, etc.).
  3. Model Fitting & Exploration
    • Run algorithms, test different parameters (e.g., cluster count), and visualize results.
    • Iterate to improve cluster coherence or anomaly precision.
  4. Interpretation & Validation
    • Review clusters/components with domain experts for business relevance.
    • Document findings and action plans (e.g., segmented marketing, risk alerts).
  5. Deployment & Feedback Loop
    • Integrate cluster labels or anomaly scores into dashboards or downstream models.
    • Collect user feedback and refine models over time.
  • Discover natural customer or product segments.
  • Detect fraud, equipment failure, or emerging trends early.
  • Simplify high-dimensional data for clearer visualization or supervised learning.

Web Development Process

We turn insights into action by building fast, accessible, and secure web applications—your data's "front door" for users.

Our full-stack team guides projects from discovery to deployment, ensuring every app performs well, meets WCAG accessibility, and scales with your growth.

  1. Discovery & Requirements
    • Clarify vision, user personas, and success metrics.
    • Capture feature lists, integration points, and compliance needs (GDPR, HIPAA, etc.).
  2. Architecture & Planning
    • Create wireframes and user journeys; select a scalable tech stack.
    • Plan APIs that surface analytics and ML results securely.
  3. Design (UI/UX)
    • Produce responsive, brand-consistent mockups with accessibility baked in (WCAG 2.2).
    • Validate with stakeholders via interactive prototypes.
  4. Development & Integration
    • Build front-end components and back-end APIs with secure coding practices.
    • Integrate live dashboards, data visualizations, and prediction endpoints.
    • Optimize for performance and Core Web Vitals.
  5. Testing & Quality Assurance
    • Run unit, integration, accessibility, and load tests.
    • Conduct user acceptance testing (UAT) and security audits (OWASP).
  6. Deployment & Release
    • Automate CI/CD pipelines, configure hosting, SSL, and backups.
    • Launch with rollback strategies and real-time monitoring.
  7. Maintenance & Growth
    • Provide ongoing security patches, feature enhancements, and performance tuning.
    • Monitor usage analytics to inform future improvements.

Ready to turn insights into interactive dashboards or data-powered apps? Let's build a solution that brings your analytics and ML results seamlessly to your users.

Solution Support & Optimization

Our commitment doesn't stop at launch. We offer ongoing support and optimization to ensure your solution remains robust, efficient, and continuously aligned with your business goals.

Choose from structured support plans or flexible ad-hoc assistance to keep your applications performing optimally and securely.

  1. Monitoring & Alerts
    • Set up real-time monitoring for performance metrics, uptime, and resource utilization.
    • Configure automatic alerts to proactively address issues.
  2. Regular Maintenance
    • Schedule updates for software dependencies, security patches, and compliance requirements.
    • Perform regular backups and recovery drills.
  3. Performance Optimization
    • Analyze and enhance application speed, responsiveness, and efficiency.
    • Optimize infrastructure scaling based on analytics-driven insights.
  4. Feature Enhancements
    • Gather user feedback and prioritize impactful improvements.
    • Plan and implement incremental feature updates without disruption.
  5. Technical Support & Training
    • Provide timely assistance through dedicated support channels.
    • Offer training sessions and detailed documentation to empower your team.
  6. Continuous Improvement
    • Regularly evaluate solution effectiveness with analytics and user feedback.
    • Implement iterative improvements to enhance user satisfaction and ROI.

Interested in dedicated or flexible support options? Explore our structured support packages or our ad-hoc assistance.