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The data engineering landscape is rapidly advancing, with businesses handling vast amounts of data gathered from diverse sources and formats. Due to the sensitivity of this data, it’s critical to carefully evaluate what data is collected, how long it remains accessible, who has access to it, and the measures in place to safeguard its platforms.
Innova’s data engineering services leverage advanced architectures, digital accelerators, and cloud engineering to streamline data efforts. We emphasize data security while ensuring accuracy, adherence to business performance requirements, and accessibility for end users. With in-depth engineering expertise and innovative strategies, our teams help establish a reliable data foundation to support analytics. By leveraging the latest technology and tools, we effectively analyze structured, semi-structured, and unstructured data, transforming it into actionable insights that power data-driven transformation.
Data architecture is essential to an enterprise’s data strategy, enabling significant business value. The approach we implement is intelligent, collaborative, and constantly evolving to address new business and user needs. The four key components include:
Data Warehouse Optimization
With a comprehensive approach to data warehouse optimization, we assess existing data warehouse gaps by interviewing relevant stakeholders and offloading less frequently used data to Hadoop. This approach allows organizations to combine historical data with new data and leverage visual integration tools that eliminate the need to manually code scripts for data ingestion and blending.
Data Lake & Lakehouses
We integrate the data warehouse with a data lake and lakehouses to store data in its raw form, without the need for structuring or running analytics. Our solution offers centralized access to data to enable real-time insights, machine learning, visualizations, dashboards, and big data processing. We also facilitate:
- Strategy & Roadmap Creation
- Prototyping & Tool Evaluation
- Data Integration, Access, & Services
- Construction & Go-Live Enablement
Data Mesh
Our services enable organizations to perform enhanced analytics on their data, even without centralized architecture. Before implementing a data mesh, we evaluate whether there is a significant proliferation of data sources and use cases, and whether enabling AI/ML would serve as a strategic differentiator. We then establish a cloud-native, domain-oriented data mesh using four components:
- Data as a Product (DaaP)
- Decentralized Domain Ownership
- Self-Service Data Infrastructure Platform
- Federated Computational Data Governance
Stream Analytics
As organizations generate massive datasets from millions of events per second, real-time data processing and analysis is essential for delivering near-instant insights and enabling timely actions. With our expertise in stream analytics, organizations can quickly ingest, process, and analyze event streams. This allows them to design and deploy advanced analytics, detect and analyze patterns, automate intelligent responses, and reduce the need for human intervention. Our stream analytics solutions provide the following benefits:
- Real-time Ingestion
- Scalable Data Processing & Storage
- Analytics, Dashboarding, & Alerting
At Innova, Master Data Management (MDM) is more of an approach than a technology implementation. Through our services, organizations can efficiently manage large data volumes while ensuring quality and consistency across diverse datasets. This enables fast, scalable, unified, and trusted data, which is critical for the success of AI/ML initiatives. Once data is synchronized, our reporting and analytics solutions help drive informed strategic business decisions.
Our MDM approach is essential for providing a reliable view of data. It addresses challenges posed by siloed systems, such as multiple versions of enterprise data, data errors from manual entry, and outdated timelines. Our focus is on enhancing master data by implementing policies that validate its accuracy, coherence, and accessibility.
With a trusted 360-degree view of enterprise data, organizations can gain full visibility into the master data lifecycle, manage data preferences in one place, define relationships and hierarchies for products, locations, and customers, and leverage more accurate data for improved reporting.
Data integration addresses data silos that often lead to cost inefficiencies. We help organizations move through the six-phased data integration lifecycle by adopting the following approach.
Scope Interface
The process begins with documenting data sources and reviewing past integrations, including master data management and archiving.
Profile Sources & Targets
Data is secured and encrypted to prevent unauthorized access.
Design Data Transform & Maps
Data transformation and maps are designed based on existing templates and integration strategy. This “source-to-target” design reuses existing logic while ensuring extensibility.
Develop Integration Interface
It combines the defined scope, data profile, and design elements. The data integration Center of Excellence (CoE) guides the architecture and development style. The CoE comprises data integration specialists, domain experts, and technical architects who leverage proven processes to deliver effective solutions.
Test Integration Interface
Unit testing validates the data flow from source to target, verifying that business rules and transformations are correctly applied.
Implement & Operate Integration
The developed data integration interface is implemented, and risk levels are assessed to establish comprehensive security policies.
Our consultants leverage deep expertise in standards, frameworks, and legislation for information exchange (e.g., NIEM, FHIR) and security (e.g., HIPAA, NCIC) to design, develop, and implement interfaces between vendor products, custom applications, and other disparate systems, applications, and data. As complexities increase, organizations can manage their interfaces efficiently by implementing best practice techniques for data integration.
This is why, for each client, we look at the type of data being passed between the systems, the integration use cases, and the pain points they must address. We then provide a data integration strategy that entails the following steps:
Development of a common format to which all application interfaces will be transformed
Determine approaches for batch, real-time and big data management
Consider the formation of a data integration “Center of Excellence” t to manage the requests for new interfaces and data integration needs. This group also interfaces with other Data Governance and Management groups
Development of standard scripts for the “Dato Discovery steps. This ensures standard views of data are being analysed without missing crucial data quality issues
Engage and enforce Master Data usage. Not just for refernce, but for enforcement of validations and error reporting
SUCCESS STORIES
Web Services Automation
Enhanced Existing UI to Maximize Efficiency of Day-to-day Tasks
Microservices Architecture Enablement Using DevOps
Implementation of Scrum Delivery Methodology
Developed Integration Tests in UI Applications
Automated Mobile Application Testing
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