Value Proposition

Both, B2B and B2C eCommerce received a major boost as businesses went into digital overdrive. The growth in digital and Big Data technologies is bringing a deluge of opportunities for e-commerce industry, but with it comes operational difficulties. Poor data management would lead to several issues such as pricing errors and discrepancies across channels and delivery errors, lack of visibility into inventory. For any e-commerce industry, its critical to have Solution Platform which is flexible to support B2B & B2C omnichannel strategy backed by an easy-to use Master data management system

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” for effective omnichannel strategy required for e-commerce organization.

Why ChainSys

• Smart templated approach to MDM for eCommerce customers

• No programming: Configuration-based, with pre-built & configurable data models to support any eCommerce master and reference data model.

• Pre-built extractors, loaders, and integration templates for all relevant eCommerce master and reference data domains.

• Industry Standard Data Quality, master data cleansing, enrichment, and deduplication

dataZen, as a complete Master Data Management Suite, is compatible and interoperable with all the data sources and targets within your IT landscape, and provides seamless MDM capabilities across them all, including:

1. Data Architecture & Data Modeling: ChainSys’ 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.

5. 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.

6. Master Data Merging & Source Updates: Migrate the cleansed and consolidated data into any source 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).

7. 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.