AWS Partner Network (APN) Blog

The Apiphani Data Pipeline and AWS Services Industrialize Data Delivery for BI, ML, and AI

By James Kendrick, Head of Data and Analytics Principal Director – Apiphani
By Wanchen Zhao, Sr. Solution Architect – AWS

Organizations that want to realize more value from data projects with scale, speed, and quality have to look further ahead than single use cases and build a data environment and data pipelines of high reusability and leverage that increasingly accelerate value realization.

This approach has enabled businesses to develop new use cases up to 90% faster, according to studies and real-world experiences. Additionally, with this approach, these studies show that “Total cost of ownership, including technology, development, and maintenance costs, can decline by 30 percent. The risk and data-governance burden can be reduced.”

To gain these advantages, Apiphani, an AWS Partner, has industrialized the capability to deliver reliable, high-quality data for Business Intelligence (BI), Machine Learning (ML), Artificial Intelligence (AI), and Digital Products using AWS services. This industrialized approach enhances business operations through organized, augmented data, driving growth and operational efficiency.

Apiphani helps companies become data and AI driven through three components of industrializing data delivery and value realization:

  • Technologywell-architected data solution and commercial grade Data Products
  • Ways of Working – executive-driven data domains and treating data as a product
  • Culture – creating a mindset about data and AI in everything you do

In this blog post, you’ll discover how your organization can transform your data operations and unlock significant business value through Data Products and industrialized pipelines using these three pillars.

Technology – Data Architecture, Data Pipelines, and Scaling

Data architecture encompasses both technology stack and operational workflow to deliver commercial Data Products. The Sandbox provides an isolated environment for experimentation, innovation, and testing without impacting Development (Dev) and Production (Prod) systems. Teams can use Amazon SageMaker and Amazon Bedrock to build and test new Machine Learning (ML) and Artificial Intelligence (AI) applications. The Sandbox environment allows testing with Internet of Things (IoT) data from Dev and Prod environments while protecting approved IoT products. Approved tools and use cases move from Dev to Prod through managed DevOps pipelines, maintaining synchronized data between environments.

Figure 1 – 3-Tier Data Infrastructure on AWS

Harnessing the power of data pipelines within the data architecture is where the true value is unlocked. Data Pipelines create high value architecture elements reusable across use case pathways for BI, ML, and AI solutions while creating features for scaling both individual and wider integrated use cases, for example:

  • Highly Integrated Data Products – Demand chain tracking is an example of integrating data in Amazon S3 buckets and creating Amazon Athena views over IoT predictive maintenance, inventory, delivery times, and sales forecasts.
  • High Volume and Velocity Data Products – IoT monitoring, and diagnostics modeled at the Edge with tools like AWS IoT Greengrass and real-time decision visibility in Amazon Kinesis.
  • Customer-Facing Digital Products – Data Products are implemented using AWS services such as Amazon API Gateway, Amazon DynamoDB, and AWS Lambda. These solutions cross enterprise data domains such as Product Services and Product Engineering, supporting internal and external views consistently while maintaining robust security and data privacy protections.
  • In some cases, a virtual data fabric collecting data at its source can replace the Amazon S3 ingestion step.

AWS Glue and Amazon DataZone enable efficient pipeline data ingestion and processing through use case adjacency. Data Products share common data sources, allowing quick deployment of related products once initial data (like SAP tables) is ingested. Examples include preventive maintenance, invoicing, and margin analysis. This shared data approach reduces costs and accelerates implementation compared to single-use case processing.

In addition, the data pipelines organize and deliver a company’s unique knowledge graphs of content, people, permissions, interactions, etc. The knowledge graphs are fundamental building blocks for AI models with lineage back to the source data. These models will power various applications, including enhanced search capabilities, content summarization, automated responses, and workflow automation.

The Data Products provide robust security and compliance through comprehensive measures. End-to-end data encryption is implemented, both at rest and in transit, coupled with stringent, persona-based access control via Active Directory, AWS Identity and Access Management (IAM) services, and Amazon QuickSight Folder and Group definitions. The multi-layered network security utilizes Amazon Virtual Private Cloud (VPC), Network Access Control Lists (ACL), and security groups, while continuous monitoring and auditing are maintained through AWS CloudTrail. Advanced data governance is achieved using Amazon DataZone, which provides automated data classification, granular access controls, and real-time compliance monitoring. The solution adheres to industry standards such as GDPR and implements disaster recovery with backups and cross-Region replication. This integrated approach maintains data integrity, availability, and regulatory compliance across Data Products.

Ways of Working – Operating Model for Data Delivery and Sustainability

Fundamental to the Apiphani approach is the focus on Data Domains and Data Products.

Data Domain Teams

Data Domain strategy aligns directly with business strategy and execution. Business leaders manage specific domains of reasonable size and scope. While Data Products integrate multiple domains, each has a central domain for definition and implementation. Data Product Owners drive success through key lifecycle phases: Concept, Business Planning, Development, Launch, and Support. This product-based approach replaces traditional project management.

Center of Excellence

The Center of Excellence (CoE) leads enterprise-wide data management through four core functions: discovery, governance, innovation, and community engagement. The CoE partners with Data Domain and Product Owners to catalog and manage data assets, collaborates with IT infrastructure teams on enterprise-wide permissions, and maintains a forum for sharing data tools and use case patterns for continuous innovation.

The Data Catalog is the primary tool for the CoE activities

The IT and Apiphani teams jointly maintain secure infrastructure operations, managing system requests and incidents for Data Products. This collaboration delivers continuous stability and optimization of the infrastructure.

Building effective data pipelines requires specialized expertise in architecture, engineering, DevOps, and consumption design. The implementation demands deep knowledge of system integration, security protocols, and performance monitoring across enterprise environments.

Apiphani’s Managed Data Service eliminates the need to hire specialists, establish processes, or implement monitoring systems. This service handles technical complexities while organizations focus on their core business objectives.

Apiphani's Managed Data Service Team Structure

Figure 2 – Apiphani’s Managed Data Service Team Structure

Culture – Mindset Changes

Along with changed ways of working, Apiphani enables organizations to transition into a data driven culture with the following elements set in motion that mature into operating at a steady state.

  1. Executive teams recognize and expect data products as key drivers of business performance, consistently delivering above-benchmark results and exceptional outcomes in strategic initiatives.
  2. Market leaders leverage embedded data products throughout their products, customer interactions, and operations. These organizations consistently generate and implement new ideas to enhance existing data products and develop new ones, driving continuous innovation.
  3. Data Pipeline Acceleration begins to show how reusable solution components and reliable data transformations and data views turn into system and user consumption at increasing speed to value, i.e., the AWS Data Flywheel.
  4. Data Self Service enables comprehensive data access across the enterprise. The platform provides streamlined data discovery, enterprise-grade analytics, and automated business insights at scale powered by tools such as Amazon Q in QuickSight.

Embedded Data Products enable an “everything, everywhere, all at once” mindset, replacing searches for siloed reports, eliminating complex data wrangling, and providing easy access to data and AI-driven decision making data and tools democratizing data for all employees, not just experts.

Real-World Experience

PSM-Data-Pipeline-Case-Study-AWS

Situation

Hanwha is South Korea’s seventh-largest business group, with innovative businesses in the areas of aerospace & mechatronics, clean energy & ocean solutions, finance, and retail & services. Hanwha is ranked in the Fortune Global 500® ($61.3B).

Power Systems Mfg, LLC (PSM) subsidiary provides technologically advanced aftermarket gas turbine components, parts reconditioning services, and full-scope long-term service agreements to gas turbine-equipped power plants worldwide.

Challenges

  • Overnight data downloads combining data from system silos is not timely, hard to support, increasingly costly, and perpetuates last-generation technology.
  • Engineers and business leaders spend days accessing, organizing, and making data reliable for performance reporting, engineering analysis, and basic operations.
  • Digital products for advancing PSM clean energy innovation requires the latest tools for digital twin modeling, real time equipment monitoring, and ML / AI integration.

Solution

Apiphani architected PSM’s data sources into domain-organized pipelines that operate at business speed. Executive commitment drove formation of a data domain core team, while the Data and Analytics CoE established governance and promoted data literacy. Through Domain workshops, the team identified priority business opportunities, defined Data Products, and secured implementation funding. Simultaneously, they established technical environments, DevOps infrastructure, and managed services support.

Deliverables and Benefits

High priority Data Pipeline and Data Products are in production and increasing in value in key business areas.

  • IoT data analysis is speeding up root cause analysis, and is providing customer insights with reusable data science advanced analytics.
  • Automated demand changes that feed allocation and planning reduces key planning time by 20%.
  • Major product and customer program resource allocation is continuously monitored by execution teams for potential reallocation requirements.
  • The data pipeline 3-tier architecture is used to deploy both Data Products internally as well as digital products for their customers.
  • Data Domain leaders are identifying and monitoring impacted KPIs as a standard way of operation.

Conclusion

Apiphani provides services and solutions to help your organization industrialize data delivery and enable data-driven business through data pipeline deployment, Data Product delivery, and value scaling and realization. The approach covers technology, ways of working, and cultural changes to deliver reliable, right data for BI, ML, AI, and digital products. Companies following these methods can realize performance improvements in the near term as well as get on a path to be data and AI driven for years to come.

The following chart summarizes the stages of the approach.

Benefits Summary: Think Big, Start Small, and Scale Fast

Figure 3 – Benefits Summary: Think Big, Start Small, and Scale Fast

The best approach to get started is to hold a discovery session together with Apiphani and AWS. We can work together to determine your situation and the starting point that builds on your progress and creates your path for increasing value in the short term and establishing a strategic path for data and AI driven business.

References

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Apiphani – AWS Partner Spotlight

apiphani is a technology enabled services company dedicated to helping businesses minimize the effort and risk associated with managing tier 1 Mission-Critical applications like SAP. By integrating decades of SAP managed services experience with intelligent automation and machine learning, we are able to drive extreme efficiency and reliability in support of our client’s mission-critical workloads.

Contact Apiphani | Partner Overview | AWS Marketplace