Data Architecture & Infrastructure Management

Reliable data architecture is essential for scalable growth, and NeuronIntel specializes in designing frameworks that support your organization’s unique needs. Our experts develop data models, storage solutions, and processing systems that ensure high availability, speed, and flexibility. We focus on creating an infrastructure that accommodates diverse data sources and evolving data volumes, utilizing the best-fit technologies (cloud, on-premises, or hybrid) to enhance performance and cost-effectiveness. We also manage the infrastructure lifecycle, including regular monitoring, optimization, and updating, to ensure seamless, secure, and uninterrupted data operations.

Case Studies

1. Digital Transformation Strategy for leading A&D Manufacturer

Problem Statement

  • OEM rating declining due to poor delivery and quality – loss of business
  • 65% is the cost of raw material and looking to optimize the overall supply chain using digital technologies
  • Issues of High & low inventories, production downtime due to unavailability of Raw materials in-time
  • Minimal use of digital technologies leading to wastage of time, error-prone outputs

Solution

  • Created Digital Transformation Strategy by analyzing 9 key functions and 450+ processes
  • A detailed digital transformation roadmap created with 16 digital initiatives

Impact

  • The Digital ecosystem post implementation of these 16 initiatives would help
  • Generate INR 58 Cr. of additional revenues
  • Cost savings of INR 26 Cr.
  • Overall 246% ROI
  • Higher CSAT due to better delivery & quality, repeat business, complete real-time traceability, increased production efficiency, reduced costs & reduced or zero defects

Tools and Technologies

  • Digital Maturity Assessment Framework

2. Data lab for large PE Firm

Problem Statement

  • The organization faced significant challenges with data fragmentation, quality inconsistencies, and reporting delays due to disparate legacy systems and a lack of centralized data management.
  • Siloed data across departments led to redundant, outdated information, hampering accurate business analysis and compliance

Solution

  • Implemented multiple systems like Enterprise Data Warehouse, Master Data Management, Hedge Fund Accounting Waterfall System and a CRM system from scratch.
  • Helped in improving the data quality and standardizing the data across the organization by implementing a fully functional MDM system.
  • Implemented (from end to end) a very reliable data warehouse which has high availability, performance and supportability
  • Migrated the reporting portal from Cognos to SSRS. Proposed scaled out deployment strategy which eventually reduce the company’s cost and reporting performance.

Impact

  • The implementation of centralized data systems greatly enhanced data reliability, quality, and accessibility, allowing for faster and more accurate reporting and analytics.
  • This data infrastructure modernization improved decision-making and operational efficiency while reducing infrastructure costs. Furthermore, the scalable and high-availability architecture laid the foundation for sustainable growth, ensuring that systems can handle future demands and regulatory compliance with minimal disruption.
  • Network Analysis for complex scenarios like circulartrades, reverse linear trades etc.

Tools and Technologies

  • Microsoft SQL Server 2005 / 2008 / 2012 , ETL: SQL Server integration Services (SSIS) & Active Batch, Analytics: SQL Server Reporting Services, IBM Cognos 8, MS Excel and R, OLAP: SQL Server Analysis Services, Essbase, MDM: IBM Initiate, SQL Server Master Data

3. Internal Audit Analytics tool

Problem Statement

  • Resistance to run any external query in Client’s own production environment – Reliability
  • Various ERPs with multiple release or version results in numerous scripting effort. – Process Management & Governance
  • Running queries in ERP at times facing extraction failure due to complex transformation – Strategy Optimization
  • Data extraction will not work if the data size is big – Size of dataset
  • The approach does not use the in-built features/Tools of ERPs to extract data – Native ERP Capability

Solution

  • Implemented simple date ingestion framework which can be used by auditors
  • Rule engine to define all the rules for each of the processes . Comprehensive set of rules library and provision to add custom rules
  • Define the applicability of rules for a particular audit
  • Scheduler to execution of rules for each audit
  • AI/ML based anomalies detection
  • Dashboards to showcase the rule execution status and anomalies
  • Containerized and Cloud agnostic solution

Impact

  • Enhanced efficiency, better results, significant cost savings
  • Saved approximately 20,000 hours in the first year in internal audit execution
  • Shift-left philosophy empowering non-technical auditors

Tools and Technologies

  • Kylo, Spark, Azure, Superset, Hive, Hadoop