PULL Workshop: Turning Data into Business Value

Organizations often struggle with data silos, inefficient pipelines, and unclear value propositions for AI/ML initiatives. These challenges lead to high employee costs, expensive systems, ineffective decision-making, and missed opportunities. As an AWS Premier Consulting Partner with Data and Analytics Consulting Competency, Armakuni addresses these challenges with a comprehensive PULL Framework (Process, Utilize, Link, Leverage). Our PULL Workshop helps teams—both strategic and technical—align their data initiatives with business outcomes, modernize architectures with AWS services, and accelerate AI/ML adoption to gain meaningful insights.

The PULL Workshop offers a dual-lens approach, blending strategic business alignment with technical execution to maximize ROI on AWS:

Processing of incoming data (P): Our team implements robust data ingestion pipelines using AWS DMS, Amazon Kinesis, AWS Glue, and Amazon MSK for real-time and batch processing. The critical questions we help answer: Is your system designed to efficiently capture raw data (Tier 1) using scalable and cost-effective pipelines? Does it then utilize EMR, Glue Jobs, Redshift ELT, and other methods to enrich data into Tier 2 and Tier 3 datasets that seamlessly integrate into analytics, machine learning, and business intelligence? How easily can new systems be onboarded with minimal effort? What mechanisms are in place to detect and resolve data failures?

Utilizing the best storage solutions (U): Choosing the right storage is key to balancing cost, performance, and scalability. How do Amazon S3 data lakes, Redshift warehouses, and purpose-built databases (RDS, DynamoDB, Timestream) factor into evaluating your data pipelines and strategy? Are these storage solutions optimized for performance, cost, and accessibility using data lifecycle management, intelligent tiering, and S3 storage classes to maximize ROI?

Linking data sources seamlessly (L): How does your data strategy account for integration challenges? Are AWS Lake Formation, AWS Glue DataBrew, and Amazon AppFlow being used effectively? Does your architecture support Zero ETL to ensure a more streamlined, scalable data pipeline?

Leveraging Data for Insights (L): Does your data answer critical business questions fully? Can it be directly plugged into tools like Amazon QuickSight for dashboards, SageMaker for predictive modeling, or Bedrock for generative AI applications? What is missing? Our team identifies gaps in data readiness for AI/ML and analytics, ensuring that the initial data preparation (transforming raw data into clean, labeled, and contextualized datasets) aligns with its intended use case (analytics, predictive modeling, or AI-driven decision-making). This helps save time, reduce costs, and avoid redundancy.

60-minute interactive session tailored to technical and business stakeholders:

1. Introduction to data strategy (10 min) Understanding data as a business enabler Key challenges in data-driven decision-making Overview of AWS-native data solutions

2. The PULL framework for data transformation (15 min) Step-by-step breakdown of the PULL methodology Mapping AWS services to each stage of the framework Best practices for integrating data across cloud native architectures

3. Interactive data maturity assessment (20 min) Collaborative evaluation of your current data architecture Group discussions on specific business and technical challenges Prioritizing high-value use cases with AWS Well-Architected best practices

4. Roadmap and next steps (15 min) Actionable implementation plan for AI/ML and analytics-driven decision-making Guidance on AWS funding programs and partner-led data transformation initiatives