Capabilities
Alston Analytics provides technical services across three integrated practice areas. Our capabilities reflect the work we have performed for enterprise and institutional clients, and the infrastructure we have built and maintained in production environments.
Technical capabilities
Data infrastructure and warehousing
Amazon Redshift, dimensional data modeling, ELT pipelines, and canonical measure architecture. We work with federated data environments connecting web analytics, marketing platforms, CRM systems, and application databases into unified reporting layers.
Business intelligence engineering
Microsoft Power BI, including complex DAX measure development, Power Query transformations, paginated reporting, and deployment through on-premises data gateways to enterprise environments. Our Power BI work includes multi-page reporting systems with deep DAX measure architectures and federated data model integration.
Web and marketing analytics
Google Analytics 4, Google Search Console, Sprinklr, Siteimprove, and custom event instrumentation. We have built reporting systems that correlate paid media, organic search, and on-site behavior across long time horizons and high-volume traffic environments.
AI and machine learning
Large language model evaluation and deployment, multi-model sentiment and emotion classification, document and content analysis, and custom pipelines for large-scale text data. We have developed and deployed pipelines processing high-volume social and media data streams across multiple analytical dimensions.
Cloud and integration engineering
AWS (Redshift, EC2, S3), Salesforce, Python-based ETL, Windows Server administration for enterprise jump-host environments, and on-premises data gateway configuration for regulated and air-gapped networks.
Methodology
Alston Analytics builds reporting systems and data products with three commitments.
Subtraction over addition
Executives rarely need more numbers. They need fewer numbers they can trust. Our dashboard and report designs emphasize clear hierarchy, honest data coverage indicators, and the removal of metrics that do not directly support decisions.
Systems built for handoff
We design code, data models, and documentation so that the organizations we serve can operate, extend, and audit the systems after our engagement ends. This includes measure documentation, data lineage references, and internal training materials delivered alongside technical artifacts.
Honest data, honest design
Our dashboards disclose the coverage and limitations of underlying data. Measures are defined consistently and documented. Where data is incomplete or unreliable, the reporting surface reflects that uncertainty rather than obscuring it.
Quality and rigor
All client-facing code is developed in version control with documented review practices. Data models include canonical measure definitions and source-of-truth documentation. Reporting systems include reproducibility artifacts: DAX measure inventories, schema references, and test cases for critical business logic. Client deliverables include a handoff package documenting system architecture, data flow, and maintenance procedures.