Data Analysis for AI and ML
The Challenge: Poorly Analyzed or Unstructured Data
In our experience working with finance, healthcare, retail, and
organizations, we often see roadblocks in the data analytics part of the project, which limits the overall effectiveness of AI & ML projects. These roadblocks show up in the following ways:
- Data lakes that do not have a consistent labeling and structure
- Failure to conduct exploratory data analysis which results in missing patterns
- Irrelevant features that are degrading model performance
- Based in the training dataset that creates ethical and accurate issues
- No domain context during preprocessing and model training
What We Offer
- Exploratory data analysis (EDA) and statistical profile\
- Feature Engineering based on business needs
- Outlier identification, normalization, and data transformations
- Feature optimization for models: dimensionality reduction
- Continuous data monitoring for quality and model relevance.
We can enable organizations to deploy high-performing AI models, lower their error rates, and to make adopting automated decision-making processes more confident and believable by creating a disciplined, intelligent data analysis pipeline.
Statswork’s aI and mL data analysis services aim at supporting organizations with the foundations of intelligent systems. Each of our services applies statistical techniques in combination with domain-driven feature engineering and best practices in machine learning to generate meaningful assets from raw data.
Data Consulting
We support organizations in attaining the highest value from their data through highly customized consulting services. Whether it’s developing a data strategy and architecture, conducting maturity assessments, or technology advisory, we align your data initiatives closer to your business goals to deliver results.
Data Engineering
Our data engineering services include designing, building, and maintaining resilient data pipelines/infrastructures. We ensure that structured and unstructured data flows reliably from your source to your insight, whether through real-time data or batch processing.
Advanced Analytics and AI
We provide organizations with predictive insights and intelligent automation through machine learning, statistical modelling, and AI solutions. We turn data into a competitive advantage – whether it be forecasting customer behaviour or optimizing workflows and operations.
Data Security, Governance, and Compliance
We develop robust data governance frameworks and enforce security best practices, ensuring your data remains protected, compliant, and reliable. Our services help you meet regulations (GDPR, HIPAA, etc.), improve risk, and ensure integrity across the enterprise.
Industry Specific Applications
Domain-aligned insights
3 expert reviewers on all projects decrease relevance issues and aligns the insights you receive.
Fast & scalable workflows
Rapid data analysis pipelines, aligned to ML environment.
Secure & complaints
Consistently supported by signed NDAs, privacy policies, and regulatory-ready (GDPR, HIPAA, etc.).
Trusted AI data partner
We deliver clean, contextual, and analysis-ready data for intelligent automation.
1. Define the analytical purpose
Specify the objectives of the AI/ML project that were established through a business case or research endeavor. Enumerate the insights or predictions you require—including any data to support these.
2. Profile and explore the data
Perform exploratory data analysis (EDA)this could include many facets, including an understanding of data distribution, detection of outliers, assessment of data quality, and the detection of patterns or bias that may impact model performance.
3. Engineer and select features
Engineer and extract informative variables applying statistical knowledge and domain expertise—and apply relevance filters—removing irrelevant features with little impact on accuracy and interpretability.
4. Clean and transform data
Standardize and normalize data—and not forget to consider missing values and outlier dimension reduction where necessary to ensure optimal model performance whilst retaining model integrity.
5. Human-in-the-loop review (Important)
Statistical analysts or subject-matter-experts must sign off on the logic of features, examine for bias or drift, and legitimate data transformations, and ensure alignment with industry regulations and operational goals.
6. Deliver and span downstream
The final analysis-ready datasets are delivered to use with ML pipelines, dashboards, or APIs, where they can be fed into model training and testing: - integrating modelling and natural applicability with real-time usability, deployment and envisaged scaling.
All data dictionary mappings at Statswork undergo multiple stages of validation, with a human-in-the-loop (HITL) for review.
We can do more
"Statswork transformed our messy clinical data into a streamlined dataset that significantly improved our diagnostic model’s accuracy. Their domain understanding and attention to data relevance was unmatched."
Healthcare AI Startup – USA,
CTO, AI Healthcare Solutions
"We relied on Statswork’s data analysis team to refine our features for a credit scoring engine. Not only did our model performance improve by 30%, but we also gained much clearer insights into customer behavior."
FinTech Company – UK,
Lead Data Scientist, FinEdge Tech
"From initial data exploration to human-in-the-loop validations, Statswork helped us build a transparent ML pipeline ready for publication and peer review. Their expertise in reproducibility was invaluable."
Research Institute – Germany,
Principal Investigator, AI for Sustainability Lab
"Their feature engineering and cleansing workflows allowed us to deploy predictive models that improved customer retention and basket size. We saw results in weeks, not months."
E-commerce Platform – India
Chief Data Officer, ShopSmart RetailData analysis is the process of cleaning, exploring, transforming, and applying structures to data to make it appropriate for AI or ML models. It is a means to visualize and highlight patterns, isolate and identify anomalies, and prepare meaningful and high-quality individual components for training algorithms.
AI/ML models are only as good as the data they are trained on. If the data is not of good quality or if it is otherwise irrelevant, then the model will produce poor predictions. Data analysis is conducted to ensure that models are trained on relevant, clean and meaningful data resulting in a model capable of producing better performance and reasonable reliability.
We will incorporate industry standard tools into our work such as Python (including pandas, NumPy, and Scikit-learn), or R, SQL, Apache Spark, Jupyter Notebooks, or one of the many specialized tools used in the data community for data profiling and cleaning, feature engineering, and data visualization.
Yes. We develop our own domain-specific feature extraction and feature selection to improve model accuracy, reduce overfitting, and improve interpretability.
We maintain robust data governance practices, utilizing compliant data practices such as data management under GDPR, HIPAA, and ISO. All of our projects have data governance including signed NDA and an associated data anonymization process, plus we give you an audit ready workflow.
Yes. Our frameworks for data analysis offer support for supervised and unsupervised learning and cover a variety of approaches including classification, regression, clustering, recommendation engines, anomaly detection, etc., focused on your AI/ML objectives.
Yes, we can provide end-to-end, complete project lifecycle support from data cleaning, exploratory data analysis, feature engineering, validation, richer reporting, all the way through to embedding into ML pipelines, or BI dashboards.
We use contextual filtering, semantic enrichment, and domain-driven rule logic to filter and select only the data that relates to your business problem before it is incorporated into the model to train it.
