In just weeks, a regional planning agency replaced tedious Excel routines with automated AWS data ingestion and transformation pipelines, reducing manual work by 60% and accelerating insights by 80%. This is just one example of how Armakuni's PULL framework delivers real results. This framework provides a structured, business value-driven data strategy to process, organise, and use data to solve pressing issues.
Businesses may want to analyse what went wrong with a slow sales month or a poor-performing marketing campaign or identify inefficiencies in operations that impact overall business performance. It could be anything. But what happens is, teams often find themselves caught in endless analysis loops, with tools sitting unused and leadership losing trust in data-driven promises.
That's where Armakuni's PULL framework saves the day (and time, and money, and effort!). The PULL Workshop brings this framework to life. It's a collaborative session where strategic leaders and technical teams work together to map out a path from raw data to measurable outcomes. As an AWS Premier Partner with AWS Data and Analytics Consulting Competency, we ensure businesses maximise the value of their data through best-in-class cloud native solutions. So,
#What is the PULL Framework?
The PULL framework breaks down into four essential steps:
Each component here works together to create a comprehensive approach that transforms how organisations view and use their data.
#1. Process: "What data do we actually have?"
Most organisations have more data than they realise, yet only 20% of business executives trust its accuracy. Data often remains locked in silos, outdated systems, or inconsistent formats, leading to inefficiencies. The first step is to assess and categorise structured and unstructured data sources. Next, organisations must address integration gaps and implement optimised storage solutions that enhance accessibility and performance.
Key questions to ask:
- Is your system designed to efficiently capture raw data (Tier 1) using scalable and cost-effective pipelines?
- Does it then utilise 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?
As we mentioned earlier, we worked with a regional planning organisation managing growth for multiple municipalities. Their data was fragmented across manual Excel workflows and SharePoint, creating inefficiencies and data silos. By implementing AWS Glue and Amazon Kinesis for automated data ingestion, alongside Amazon Redshift and S3 for scalable storage, we streamlined data integration and accessibility. This eliminated data silos, reduced manual work by 60%, and improved reporting speed by 80%, enabling faster, more accurate decision-making.
How we manage data at Armakuni
- Scalable, cost-effective pipelines capture raw data (Tier 1) efficiently, storing it in data lakes like Amazon S3.
- Raw data is transformed into structured datasets (Tier 2) through Redshift ELT and Glue Jobs, further optimised for workloads (Tier 3) in databases like Amazon RDS and DynamoDB.
- A tiered approach ensures seamless integration into analytics, machine learning, and business intelligence systems.
- AWS Lake Formation and AWS Glue DataBrew streamline onboarding, reducing complexity and ensuring quick data access.
- Real-time streaming solutions with Amazon MSK and proactive monitoring mechanisms detect and resolve data failures, maintaining continuous availability and business continuity.
#2. Unlock: Utilising the best storage solutions
Raw data isn't useful until you ask the right questions and store it in the most effective way.
Key questions to ask:
- 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 optimised for performance, cost, and accessibility using data lifecycle management, intelligent tiering, and S3 storage classes to maximise ROI?
One client, a global consumer tech company, faced bottlenecks translating 60,000+ files weekly due to inefficient data storage and processing. Their existing AI tools struggled with accuracy and scale, and their storage solution lacked optimisation. By restructuring their data pipelines with Amazon S3 and Redshift for storage, alongside custom machine learning models for processing, we not only reduced translation costs by 70% but also enhanced data accessibility and processing speed, clearing a weeks-long backlog in hours while maintaining 100% data integrity.
Using AI and analytics to find hidden value
- Predictive analytics with Amazon QuickSight can help forecast business trends and make smarter decisions.
- AI and ML models in Amazon SageMaker can automate repetitive tasks, freeing up employees for higher-value work.
- Real-time insights from Amazon Kinesis and AWS Lambda allow companies to act quickly rather than relying on outdated reports.
#3. Link: Seamlessly integrating data sources
Data projects fail when they don't bring clear business benefits or suffer from integration challenges.
Key questions to ask:
- 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?
One of our clients, a global psychology association, needed to extract insights from over 3 million publications. Their existing workflow required extensive subject matter expertise and was slow due to disconnected data sources. By utilising AWS Glue DataBrew for preprocessing and an AI-driven cognitive search platform for automation, we integrated their data seamlessly, reducing SME costs by 99.9% and improving processing speed by 60 times.
Making data projects count
- Business-first approach, Data should solve real problems, not just exist for the sake of it.
- Clear ownership, Define who benefits from the data and who is responsible for managing it.
- Governance and security, Good data management with AWS Lake Formation ensures accuracy, compliance, and reliability.
#4. Leverage: Turning data into actionable insights
This is where the real payoff happens. AI and ML can take insights to the next level, improving automation, predictions, and decision-making.
Key questions to ask:
- 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? Are there gaps in data readiness for AI/ML and analytics?
Armakuni helped the psychology research association we discussed earlier to automate research extraction, cutting processing time per document from 3 hours to just 2 minutes. By integrating AI-driven analytics with Amazon SageMaker and QuickSight, we streamlined their workflow, reducing operational costs while enabling faster, data-driven decision-making at scale.
Moving from raw data to decisions
- AI-driven analytics in Amazon SageMaker help companies make sense of large datasets.
- Cloud-based tools like Amazon Redshift provide the flexibility and scalability businesses need.
- Visualisation and reporting in Amazon QuickSight make insights easier to understand and act on.
The key is to start small. Pilot AI models on specific workflows, like automating invoices or personalising customer recommendations. Use what works, then scale.
#Why the PULL Workshop works
The PULL Workshop isn't another theoretical exercise. It's a hands-on session where teams:
- Gain clarity and confidence by mapping existing data assets to business challenges while ensuring seamless integration with AWS tools like Glue DataBrew and AppFlow. As we refine your data strategy, you'll have a structured, unified view of your information ecosystem.
- Unlock efficiency by automating repetitive tasks with AI/ML models, reducing manual effort and improving accuracy. This will free up valuable time for your teams to focus on higher-value strategic initiatives.
- Develop a structured roadmap that enhances performance through optimised storage solutions (Amazon S3, Redshift) and Zero ETL capabilities, ensuring cost-effective scaling and operational agility.
- Establish clear ownership and accountability, ensuring all stakeholders understand their role in managing business-driven, scalable data pipelines. With defined responsibilities, your organisation will experience smoother execution and better decision-making.
One participant said, "We went in with a vague idea of 'doing more with data.' We left with a 12-month plan to automate 40% of our manual reporting, and a clear list of who needs to do what."
#How Armakuni helps businesses get more from their data
As data volumes continue to grow - projected to reach 175 zettabytes globally by 2025 - having a structured approach to data management and analytics becomes increasingly important.
Armakuni, an AWS Premier Partner, specialises in helping businesses at every stage of their data journey, from initial strategy to implementation of scalable, cloud-native solutions with PULL framework. This is possible because of our team of 300+ AWS-certified experts with deep experience in data architecture, analytics, and AI/ML.
If you're ready to stop collecting data and start using it, the PULL Workshop is the first step.
