Build Lab – Data and Analytics

The story of your organization is written in the data generated by your customers, ledgers, CRM, application logs, partner files, and countless other sources. To understand that story, the data needs to be presented in a way that can be digested by the minds of your decision makers, whether through BI reporting, GenAI, or other natural language processing tools.

Dashboards, visualizations, and chatbots are the face of your data, but your data processing pipeline extends all the way back to sources such as transactional databases or files. The data needs to be extracted, cleansed, stored, and transformed before being served up by the query engine most appropriate for the needs of your analytics users.

AWS provides a variety of tools for creating pipelines, data lakes, data warehouses, and visualizations, but understanding which services are the best fit for your organization can be challenging. Once you’ve decided on a set of tools, getting them all to work together in harmony for the first time is the next milestone.

The Analytics Build Lab is a cooperative engagement spanning 4-6 weeks which culminates in a one-week build activity guided by a Uturn Senior Architect. The build team consists of your developers, or Uturn engineers, or a combination of both, depending upon your needs. We believe collaboration is the best approach because it includes your builders from the beginning, reducing knowledge gaps.

Leading up to the engagement, you will have an assigned project manager and Senior Architect, and through a series of planning meetings we step through your use cases, assess your current state, propose an architecture, and ensure that the team is ready to build. The result of build week is a pilot implementation of your application, built using AWS services alongside tools or SaaS offerings that are already in your arsenal.

Analytics Build Lab Deliverables

When the lab is complete, you will have a pilot version of your application with the following attributes:

• Live Environment: Built and running in your own AWS environment
• Proven Use Cases: Deliverables are focused on connecting components and proving out critical functionality
• Actionable Playbook: Work is not “throw away”. Uturn will leave you with a plan to iterate the application all the way to production future use cases.

Examples of In-Scope AWS Services

Example Use Cases

• Replicate transactional RDBMS to data warehouse and create self-service ad-hoc analytics using Amazon QuickSight
• Data lake and data mesh solutions for cross-organization, large scale data and access management
• Ingest event data from your application into a data lake, aggregate, filter, then deliver to end customers via scheduled, emailed reports, or embedded read-only dashboards
• Consolidate multiple upstream source systems into a single data warehouse, then provide dashboards and gen AI to your key decision makers
• LLM chatbot training on your organization’s data, enabling natural language insights for your users 
• Intelligent Document Processing to make the contents of your organization’s contracts and invoices available to analytics tools, chatbots, and data science teams
• Modernizing legacy ETL pipelines to leverage scalable cloud technology and reduced license costs
• Data modeling and ingestion to purpose-built data stores to support graph, search, time series, and other specialized use cases
• Data engineering pipelines to support encryption, tokenization, or anonymization of sensitive data such as medical or personally identifiable information