Value Proposition

Consumer Packaged Goods (CPG) or Consumer goods (FMCG) businesses operate in an extremely competitive space. Relentless competition, disruptive trends, and digital consumers bring fresh challenges as well as new opportunities. Data is critical for businesses in the FMCG industry to grow in scale and enhance reach among consumers. From supply chain management, product, and consumer information to sourcing and procurement cycles, data fuels the current and future strategies for them. However, CPG or FMCG businesses are struggling to manage data efficiently, primarily due to the staggering volume and types of data available to them.

ChainSys’ enterprise-class Multi-Domain Master Data Management Suite, powered by dataZen™, has all data quality & data governance capabilities, to provide a 360-degree view for the various MDM along with processes and controls to reduce errors, improve data usability, enhance the quality and reliability of master data– customer, product, supplier, etc.) offering a “single version of the truth”.

Why ChainSys

• Smart templated approach to MDM for Consumer-Packaged Goods (CPG) or Consumer goods (FMCG) businesses

• No programming: Configuration based implementations with Pre-Built & Configurable data models to support any of your ERP, CRM, or PLM Systems data model as a foundation to manage complex information about multiple MDM Domains like Customers, Manufacturers, Suppliers, Partners, Contacts, Products, Item Categories, Organizations, Locations etc. at one place along with hierarchical relationships among them.

• Pre-built extractors, loaders, and integration templates for above mentioned all master data domains and many more.

• Industry Standard Data Quality, Master data Cleansing, Enrichment and Deduplication

ChainSys Approach

dataZen, as a complete Master Data Management Suite, that can coexist with any system landscape with multiple ERP and CRM systems and provides various data governance tools and processes by leveraging following out of box features.

1. Data Architecture & Data Modeling: - Chainsys’s master data Suite offers Industry standard templates to match various ERP and CRM Systems for the data modeling so you don’t need to start from scratch.

2. Data Discovery: - Connect to any end point to extract master data, reference data, and transactional data, and generate metadata through data profiling.

3. Data Cleansing & Standardization by detecting, correcting, and sometimes removing undesirable data records including standardizations and enrichments through 3rd party providers

4. Data Quality Management: - Robust Workflow and ML Algorithms (NLP) to provide a powerful data Quality Management (DQM) Engine to enable tactical management and oversight of the company’s data assets as part of Data Stewardship. Data Matching to Identify and resolve duplicate records with configurable business rules to group the similar set of data. Data stewards or custodians can approve system-generated matching groups along with suggested survivor records to automatically merge or perform manual adjustments.

5. Master Data Merging & Source Updates: - Migrate the cleansed and consolidated data into peripheral system using pre-built migration and integration template as part of one-time migration (Get Clean) and in a steady state, trigger master data merge and update actions (Keep it Clean)

6. Data Governance: - Data Governance is a conscious and orchestrated effort by the Data Architects and Data Stewards to ensure the right master/transactional data goes into your ERP System, after scrutiny and corrections by various data stakeholders. dataZen offers capability to configure multi-level approval workflows based on complicated business rules and various business scenarios. The application also provides real time activity monitoring and status tracking along with audit trail for all the changes during request and review process.

7. Data Quality Monitoring: - After all your data quality and migration processes, ChainSys can help provide a comprehensive Data Quality Monitoring dashboard and reconciliation reports to assess data quality and consistency