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Organizations that wish to maximize the value derived from their data initiatives must think beyond isolated cases and invest in a comprehensive data ecosystem and reusable data pipelines. This strategy can accelerate the development of new use cases by as much as 90%, according to various studies and real-world experiences. Furthermore, businesses may see a 30% reduction in total ownership costs, encompassing technology, development, and maintenance, while also lowering risks and easing data governance burdens.
To tap into these benefits, Apiphani, an esteemed AWS Partner, has refined its ability to deliver reliable, high-quality data tailored for Business Intelligence (BI), Machine Learning (ML), Artificial Intelligence (AI), and digital services by leveraging AWS offerings. This industrialized method enhances operations through structured, enriched data, propelling growth and efficiency.
Apiphani empowers organizations to become data- and AI-driven through three fundamental components of streamlining data delivery and realizing value:
- Technology – Robust data solutions and high-quality Data Products.
- Ways of Working – Executive-led data domains, treating data as a product.
- Culture – Fostering a data and AI-centric mindset across all operations.
In this article, you’ll learn how your organization can revolutionize its data operations and unlock substantial business potential through Data Products and streamlined pipelines based on these three pillars.
Technology – Data Architecture, Pipelines, and Scalability
Data architecture comprises both the technology stack and operational workflows needed to deliver commercial-grade Data Products. The Sandbox offers a secure space for experimentation, innovation, and testing without disrupting Development (Dev) and Production (Prod) systems. Teams can utilize Amazon SageMaker and Amazon Bedrock to create and refine new ML and AI applications. The Sandbox environment facilitates testing with Internet of Things (IoT) data while safeguarding sanctioned IoT products. Approved tools and use cases transition from Dev to Prod through managed DevOps pipelines, ensuring data consistency between environments.
Figure 1 – 3-Tier Data Infrastructure on AWS
The true value of data architecture is realized through the effective implementation of data pipelines. These pipelines generate reusable architecture components applicable across various use cases for BI, ML, and AI solutions while enabling scalability for both individual and integrated use cases. Examples include:
- Highly Integrated Data Products – Demand chain tracking integrates data in Amazon S3 buckets, establishing 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, ensure real-time decision visibility using Amazon Kinesis.
- Customer-Facing Digital Products – Data Products leverage AWS services such as Amazon API Gateway, Amazon DynamoDB, and AWS Lambda, unifying data domains like Product Services and Product Engineering while upholding robust security and data privacy.
In some cases, a virtual data fabric can replace the Amazon S3 ingestion step, streamlining the process.
AWS Glue and Amazon DataZone facilitate efficient data ingestion and processing through use case adjacency. Data Products share common data sources, enabling swift deployment of related products once initial data, such as SAP tables, is ingested. This collective data strategy lowers costs and accelerates implementation compared to processing single-use cases.
Moreover, data pipelines structure and deliver an organization’s unique knowledge graphs encompassing content, personnel, permissions, and interactions. These graphs are vital for AI models with roots tracing back to the original data. Such models will enhance various applications, including improved search capabilities, content summarization, automated responses, and workflow automation.
The Data Products ensure strong security and compliance through comprehensive measures. End-to-end data encryption is applied, both at rest and in transit, alongside 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 employs 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 automates data classification, implements granular access controls, and monitors compliance in real-time, adhering to industry standards like GDPR. Disaster recovery measures, including backups and cross-Region replication, are also part of this integrated approach, ensuring data integrity and compliance across Data Products.
Ways of Working – Operational Model for Sustainable Data Delivery
A core principle of the Apiphani approach is focusing on Data Domains and Data Products.
Data Domain Teams
The strategy surrounding Data Domains is directly aligned with business objectives and execution. Business leaders oversee specific domains of manageable size and scope. While Data Products may integrate multiple domains, each one has a designated central domain for definition and execution. Data Product Owners drive success through essential lifecycle phases: Concept, Business Planning, Development, Launch, and Support. This product-centric approach replaces traditional project management methodologies.
Center of Excellence
The Center of Excellence (CoE) spearheads enterprise-wide data management through four key functions: discovery, governance, innovation, and community engagement. The CoE collaborates with Data Domain and Product Owners to catalog and manage data assets, partners with IT infrastructure teams on enterprise-wide permissions, and maintains a forum for sharing data tools and use case patterns to foster continuous innovation. The Data Catalog serves as the primary instrument for CoE activities.
The IT and Apiphani teams work together to maintain secure infrastructure operations, handling system requests and incidents for Data Products. This collaboration ensures ongoing stability and optimization of the infrastructure.
Constructing effective data pipelines requires specialized expertise in architecture, engineering, DevOps, and consumption design. The implementation necessitates deep knowledge of system integration, security protocols, and performance monitoring across enterprise environments.
Apiphani’s Managed Data Service alleviates the burden of hiring specialists, establishing processes, or implementing monitoring, streamlining the onboarding process for organizations. To learn more about emotional awareness in the workplace, visit this resource on the emotion wheel. Moreover, it is essential to stay informed as positive workplace drug tests have recently hit a 16-year high, which is a growing concern for many organizations. Additionally, for those interested in understanding how Amazon warehouse workers are trained and onboarded, this Business Insider article provides excellent insights.