Enterprise Data Management (EDM) using Data Pipelines

The gold standard that transforms data into currency

Data Pipelines

Data Pipelines

Data Assessment

Data Assessment

Data Cleansing

Quality Cleansing

Data Harmonization

Consolidation & Harmonization

Data Governance

Continuous Governance & health checks

Data Catalog

Data Catalog

Enterprise Data Management

Enterprise Data Management

Why is creating an Enterprise Data Management strategy important you ask? Well get this - a staggering 44% of C-level execs say they delay decision making (or lack confidence in their decisions) due to the unavailability of reliable data. If this isn’t reason enough to consider an enterprise wide EDM to centralize data management, and the decisions that the data drive, consider the complexities that digital disruptions and digital transformations can add.

The Smart data platform has streamlined the Pipeline EDM process over the years, and has laid down the golden approach to ensuring that you control the data, and not vice versa. Here are some of the reasons to consider EDM.
  • Centralizing data across legacy and cloud systems, unifying discrete data models & object sets
  • Collecting machine data, sensor data & other device information, and providing real-time analytics
  • Make clean data from the EDM ‘data lake’ available in real-time for analytics and reporting
  • Governing, centralizing & managing high volumes of data across an organization.
  • Drive analytics and data science, based on clean, factual data from the EDM ‘data lake’.
  • Create a single source of truth, along with strict quality and governance for data
  • A Catalog to make data searchable and maintain data lineage, entity relationships, business glossary and data virtualization
  • Reconcile data that is migrated/integrated across applications to ensure integrity & audit compliance
  • Uncovering valuable data may be have previously been hidden or unknown
  • Profiling data to understand what, where, and how much data exists
  • Enriching data quality once, and periodically cleansing afterward
  • Understanding the meaning of each data attribute, where it is used, and how
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Featured Clients

ChainSys has truly enabled smarter, faster & simpler data management for us. The achievements of the ChainSys & our internal teams are scarcely believable. Our EDM solution has been built to last, and I'm confident that this implementation will serve as a pathbreaking example that will be hard to replicate.

VP at a Global Hi-tech manufacturer

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ChainSys Clients - Vertiv
ChainSys Clients - Canon
ChainSys Clients - Cummins
ChainSys Clients - Mortenson

Valid Use Cases

Enterprise Data Management Valid Use Cases
  • Digital Transformations/ Cloud transformations
  • Business Mergers, acquisitions or divestitures
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Benefits

Enterprise Data Management Benefits
  • Save 40 to 70% in time & cost of managing enterprise data
  • Reduced manual effort in cleaning, governing, reporting on and searching for data
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Differentiators

Enterprise Data Management Differentiators
  • Pay-as-you-use pricing model
  • Certified Platform for Oracle, SAP, Microsoft, Salesforce & IBM
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Case Studies

How EDM is achieved - The ChainSys approach

1 - Enterprise data management Pipelines - Context Diagram

Enterprise data management Pipelines

2 - Data Harmonization for Enterprise data management - Context Diagram

Data Harmonization for Enterprise data management

Data Assessment & Data Quality

Data Profiling

Data Profiling examines source data and gives an insight into it. It helps identify data usage, cleansing requirements, and how much and what data needs to be migrated. Profiling identifies Data Patterns, Erroneous Data, Inconsistent Data, and Incomplete Data.

Data Profiling in ETL

Data Consolidation (Match and Merge)

The consolidation engine uses ML Algorithms to identify potential matches across the master data spectrum. The ML engine also has a flexible rules engine to accommodate automatic merging of the field values from the victim data over to the survivor records.

Data Consolidation Tools

Data Cleansing

Data Cleansing AI Rules Engine helps with Automated data cleansing of the fields that are essential for Business. It helps create a ‘Single Source of Truth’ and hierarchical representation of master data, which is very important for providing powerful analytics and insights.

Oracle Data Cleansing

Data Enrichment

The Source master data, especially data from some legacy systems, may not have all the values needed for the new modern target Applications. This calls for additional Enrichments of attribute columns which can be very helpful for Improved Operations, Reporting, and Analytics.

Data Enrichment Tool

Data Audits

Data audit trail is maintained from the initial raw data to the final clean golden records. Useful reporting of Raw Data, Matches Data, No Matches Data, Survivors, Victims, Data Cleansing, Enrichments, and Gold Data is provided out of the box (OTB).

Data Quality Management

Data Assesment

During EDM, it is important to understand the data that exists across your organization, how the data is being currently used, and a high level understanding of its current quality. This ‘assessment’ provides the foundation for critical decisions that will affect how you approach EDM

Data Archival

Data Profiling

Data Profiling does an examination of the source data and gives an insight into the source data information. It helps identify how the data is being used, the cleansing requirements, and how much data needs to be migrated. Profiling enables to find the Data Patterns, Erroneous Data, Inconsistent Data, and Incomplete Data.

Data Consolidation (Match and Merge)

The consolidation engine uses ML Algorithms to identify potential matches across the master data spectrum. The ML engine also has a flexible rules engine to accommodate the automate merging of the field values from the victim data over to the survived records

Data Cleansing

Data Cleansing AI Rules Engine helps with Automated data Cleansing of the fields that are essential for the Business. It helps create a ‘Single Source of Truth’ for Hierarchical representation of master data, which is very important for providing powerful analytics and insights.

Data Enrichment

The Source master data, especially data from some legacy systems, may not have all the values needed for the New Modern target Applications. This calls for additional Enrichments of attribute columns which can be very helpful for Improved Operations, Reporting, and Analytics.

Data Audits

Data audit trail is maintained from the initial raw data to the final clean golden records. Useful reporting of Raw Data, Matches Data, No Matches Data, Survivors, Victims, Data Cleansing, Enrichments, and Gold Data is OTB.

Data Pipelines for data movement

Extraction Pipelines

ChainSys provides pre-defined adaptors for more than 50+ applications for extracting master data, meta data and transactional data. These adaptors support multiple connection methods such as web services (REST, SOAP), JDBC, JSON, XML, Excel, Flat Files, BAPI, OData, MQ, JMS Queues etc.

Data Extraction Tools

Pre-Validation

Pre-Validation helps to check the data for functional dependencies or missing Setups in target application. Pre-validation also checks for technical data issues such as: constraints, data types, length, null check, format mask for date and time, check text case, duplicate data, currency rounding etc. The benefit of this is to understand issues before loading the data into the target application. dataZap Mass Data Loading Platform provides hundreds of pre-defined pre-validations OTB for every data adapter. Additionally, it enables users to configure additional Pre-validation rules based on business needs.

Pre-Validation for Migration

Transformation

Data Migration includes a transformation program and not just lift and shift. Transformation rules engine enables users to configure lookup and expression based transformation rules. Internal or external data can be used for transformation logic. ChainSys also provides pre-defined transformation logic for a majority of the source to target migration project needs.

Best Data Transformation Tools

Data Loading

ChainSys provides 4000+ data loading adaptors for SAP, Oracle Cloud Applications, Oracle EBS, JDE, PSOFT, Siebel etc. OTB. These adaptors support setups, master and transactional data (both open and historical) loading.

We also provide high performance packages to load High data volumes within a short time. Change data capture (CDC) engine helps reduce the cut over timelines.

Oracle Data Loading Tool

Integration Pipelines

We provide OTB data flows covering logical and physical mappings between applications such as SAP, Microsoft, Oracle Products etc. and Oracle Fusion Cloud Applications, EBS, JDE, PSOFT, Siebel etc. We have all reference data migration mappings OTB as well.

Oracle Data Migration

Change Data Capture

The Change Data Capture (CDC) function allows for incremental data movement, based on a delta comparison of data between systems. The engine automates data comparison & the data move, ensuring low lead-times and manual re-work during data movement

Source to Target Mapping

Data Flow

A ‘Data flow’, in the framework of ChainSys’ smart data platform, is the building block for any data movement activity. It is a container that holds extraction, transformation, mapping, pre-validation and loading information for a data element. For example, an Item/Material ‘data flow’ will have all the relevant information needed to move it from source to target, with referential integrity, and prevalidations. ‘Data flows’ provide an 80% jumpstart to data movement during EDM

Data Flow Dashboard

Process Flow

A ‘Process flow’ is an intelligently sequenced collection of ‘data flows’ that are executed during data movement. ‘Process flows’ enable scalability, high performance, and efficient management of migration tasks, in a single process. ‘Process flows’ are created using drag & drop functions, and can be configured to add conditions, rules, constructs and more.

Integration Process Flow

Data Extraction

ChainSys provides pre-defined adaptors for more than 50+ applications for extracting master data, meta data and transactional data. These adaptors support multiple connection methods such as web services (REST, SOAP), JDBC, JSON, XML, Excel, Flat Files, BAPI, OData, MQ, JMS Queues etc.

Pre-Validation

Pre-Validation helps to check the data for functional dependencies or missing Setups in target application. Pre-validation also checks for technical data issues such as: constraints, data types, length, null check, format mask for date and time, check text case, duplicate data, currency rounding etc. The benefit of this is to understand issues before loading the data into the target application. dataZap Mass Data Loading Platform provides hundreds of pre-defined pre-validations OTB for every data adapter. Additionally, it enables users to configure additional Pre-validation rules based on business needs.

Transformation

Data Migration includes a transformation program and not just lift and shift. Transformation rules engine enables users to configure lookup and expression based transformation rules. Internal or external data can be used for transformation logic. ChainSys also provides pre-defined transformation logic for a majority of the source to target migration project needs.

Data Loading

ChainSys provides 4000+ data loading adaptors for SAP, Oracle Cloud Applications, Oracle EBS, JDE, PSOFT, Siebel etc. OTB. These adaptors support setups, master and transactional data (both open and historical) loading.

We also provide high performance packages to load High data volumes within a short time. Change data capture (CDC) engine helps reduce the cut over timelines.

Data Migration

We provide OTB data flows covering logical and physical mappings between applications such as SAP, Microsoft, Oracle Products etc. and Oracle Fusion Cloud Applications, EBS, JDE, PSOFT, Siebel etc. We have all reference data migration mappings OTB as well.

Multi domain Master Data Governance Functions

Data Governance

Data Governance is the exercise of authority and control over the management of data assets. dataZen offers a flexible workflow for achieving this.

Master Data Governance

Data Stewardship

Data Stewardship encompasses the tactical management and oversight of the company’s data assets. dataZen offers Simplicity to this process.

Data Stewardship

Data Quality Management

Data Quality management (DQM) helps in employing the right processes, methods and technologies to ensure the quality of the data meets specific business standards. dataZen offers robust Workflow and ML Algorithms (NLP) to solve this problem.

Data Quality Management

Data Integration and Synchronization

Data from Upstream systems need to be brought into MDM, and from MDM the data need to be sent to several Downstream systems. For this purpose, the dataZap Integration engine is effectively used.

data integration synchronization

Metadata Management

Metadata of the data entities within a domain is an important success factor for Multi-Domain MDM. ChainSys offers Metadata management for all Master data domains and entities, registration of the data attributes, services for CRUD against this data models and also provides data lineage from meta and also values.

Metadata Management Tools

Data Model

Data Modelling is the most time taking process for the multi-domain MDM. Each domain comprise of 1 or more data model / entity and each entity has attributes within them. ChainSys offers Industry standard templates for the Data Modelling so you don’t need to start from scratch. It provides complete reference data model also built-in. This reduces the time and cost by over 50%.

Data Modelling

Reference Data Management

Considering the holistic picture of a master data, reference data are also important building block for the master data. ChainSys provides OTB solution for Reference data synchronization into the hub from major applications like SAP (Check tables), Oracle (Setups) etc.

Reference Data Management

CRUD Management

The operation of Create, Read, Update and Delete is important for Multi-Domain MDM models. dataZen offers all these operations as standard service within its applications and also from external sources.

CRUD Management

Data Governance

Data Governance is the exercise of authority and control over the management of data assets. dataZen offers a flexible workflow for achieving this.

Data Stewardship

Data Stewardship encompasses the tactical management and oversight of the company’s data assets. dataZen offers Simplicity to this process.

Data Quality Management

Data Quality management (DQM) helps in employing the right processes, methods and technologies to ensure the quality of the data meets specific business standards. dataZen offers robust Workflow and ML Algorithms (NLP) to solve this problem.

Data Integration and Synchronization

Data from Upstream systems need to be brought into MDM, and from MDM the data need to be sent to several Downstream systems. For this purpose, the dataZap Integration engine is effectively used.

Metadata Management

Metadata of the data entities within a domain is an important success factor for Multi-Domain MDM. ChainSys offers Metadata management for all Master data domains and entities, registration of the data attributes, services for CRUD against this data models and also provides data lineage from meta and also values.

Data Model

Data Modelling is the most time taking process for the multi-domain MDM. Each domain comprise of 1 or more data model / entity and each entity has attributes within them. ChainSys offers Industry standard templates for the Data Modelling so you don’t need to start from scratch. It provides complete reference data model also built-in. This reduces the time and cost by over 50%.

Reference Data Management

Considering the holistic picture of a master data, reference data are also important building block for the master data. ChainSys provides OTB solution for Reference data synchronization into the hub from major applications like SAP (Check tables), Oracle (Setups) etc.

CRUD Management

The operation of Create, Read, Update and Delete is important for Multi-Domain MDM models. dataZen offers all these operations as standard service within its applications and also from external sources.

Data Catalog

Data Profiling

Learn about the data and its metadata in each data attribute.

Gives you the statistics, Patterns, and Probable elements of the data using predefined AI algorithms.

It helps to understand the quality of data and possible actions to be taken.

Data Profiling Tools

Dataset Relationship

Generates relationship and cardinality between datasets within an application.

Enables to determine the orphan's records and records with no child records.

Data Relationship

Metadata Management

Organize the metadata from multiple sources into a centralized repository.

Helps to create an enterprise data model

Speed up data tagging workflows through automation and suggestions with ML.

Metadata Management Tools

Data Registration

Translate the technical metadata into a business glossary.

Democratize the data across the enterprise by making business user as citizens

Data Registration

Data Lineage

Visual representation of the data movement between the sources

Understand the data transformation rules in the process

Data Lineage Tools

Data Governance

Identify and regulate all sensitive and privileged data across the organization.

Provide tools to information security teams to tag, monitor, and control accessibility to the data

Provide business users, and others access to relevant data.

Data Governance

Data Citizen

Make business aware of the different datasets available in the organization.

Make business understand the data, quality of data and play with the data without IT help

Citizen Data Scientist

Data Virtualization

Unify data from different sources into a single view.

Empower data analysts to understand data without IT involvement

Reduce the integration needs to analyze data

Data Virtualization Tools

Data Profiling

Learn about the data and its metadata in each data attribute.

Gives you the statistics, Patterns, and Probable elements of the data using predefined AI algorithms.

It helps to understand the quality of data and possible actions to be taken.

Dataset Relationship

Generates relationship and cardinality between datasets within an application.

Enables to determine the orphan's records and records with no child records.

Metadata Management

Organize the metadata from multiple sources into a centralized repository.

Helps to create an enterprise data model

Speed up data tagging workflows through automation and suggestions with ML

Data Registration

Translate the technical metadata into a business glossary.

Democratize the data across the enterprise by making business user as citizens

Data Lineage

Visual representation of the data movement between the sources

Understand the data transformation rules in the process

Data Governance

Identify and regulate all sensitive and privileged data across the organization.

Provide tools to information security teams to tag, monitor, and control accessibility to the data

Provide business users, and others access to relevant data.

Data Citizen

Make business aware of the different datasets available in the organization.

Make business understand the data, quality of data and play with the data without IT help

Data Virtualization

Unify data from different sources into a single view.

Empower data analysts to understand data without IT involvement

Reduce the integration needs to analyze data

Visualization Dashboards, Analytics & Reporting

Customer 360

Customer 360 is a pre built feature of dataZen. It shows details about customer, and related transactions such as Prospecting, Opportunities, Quotes, Sales Orders, Shipments, Returns, AR Transactions and Service Orders against the customer. dataZen predicts/suggests actions for effective management of your top customers (using Machine Learning).

Customer 360

Supplier 360

dataZen provides Built in Analytical MDM Solution. Supplier 360 shows the details about Supplier, its hierarchy and related transactions such as Requisitions, Purchase Orders, Receipts, Returns, AP Transactions against the Supplier. Also, through ML the system predicts actions for effective management of the top suppliers.

Supplier 360

Product 360

dataZen provides Built in Analytical MDM Solution. Product 360 shows the details about Product, Product hierarchy and related transactions such as Cost, Revenue, Profitability, Quality trends, Engineering changes against the Product. Also, through ML the system predicts actions for effective management of the top Products.

Material 360

Data Quality Dashboard

dataZen constantly measures the quality of the master data and provides the trend for Data duplication, standardization, and Data Completeness.

Data Quality Management

Dashboards and Reporting

dataZen has Built in Analytical and Reporting engine. Standard reports and dashboards are provided. You can also create customized dashboards and reports to your needs.

Customer 360

dataZen provides Built in Analytical MDM Solution. Customer 360 shows the details about customer, its hierarchy and related transactions such as Prospecting, Opportunities, Quote, Sales Orders, Shipments, Returns, AR Transactions, Service Orders against the customer. Also, through ML the system predicts actions for effective management of the top customers

Supplier 360

dataZen provides Built in Analytical MDM Solution. Supplier 360 shows the details about Supplier, its hierarchy and related transactions such as Requisitions, Purchase Orders, Receipts, Returns, AP Transactions against the Supplier. Also, through ML the system predicts actions for effective management of the top suppliers.

Product 360

dataZen provides Built in Analytical MDM Solution. Product 360 shows the details about Product, Product hierarchy and related transactions such as Cost, Revenue, Profitability, Quality trends, Engineering changes against the Product. Also, through ML the system predicts actions for effective management of the top Products.

Data Quality Dashboard

dataZen constantly measures the quality of the master data and provides the trend for Data duplication, standardization, and Data Completeness.

Dashboards and Reporting

dataZen has Built in Analytical and Reporting engine. Standard reports and dashboards are provided. You can also create customized dashboards and reports to your needs.

50 Reasons dataZap can work for you!

After decades of time and efforts, companies have successfully implemented enterprise applications, established business processes, trained users, and created dashboards for all functional needs. However, Data in each application invariably comes in different formats, same content have different attribute names and cater only to specific needs with quality problems of duplicates and partial information.

Data management complexity increases as organizations handle multi-structured data spread acrossmultiple cloud vendors leading to greater data governance challenges. This is fueled by progressivecloud deployments, mergers & acquisitions, and digital transformation initiatives. EDM, or enterprise data management works to make data management easier, so you are in control of data, and not vice versa

Here’s 50 reasons why you should consider the Smart Data Platform for Enterprise Data Management

Accelerators

Helps build Extensions and Custom Applications fast
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Accelerator Icon
  • 9000+ Ready to use Templates for all EDM related activities
  • Supports 200+ endpoints
  • Data Assessment
  • Data Quality Profiling
  • Governance & MDM
  • Integrations
  • Migration
  • Cataloging
  • Analytics, Reporting & Visualization
  • Standard Validation Rules, that can be customized
  • Workflows for Parallel Processing
  • Setup Data Approvals

Proven Solution

Battle tested solution
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Proven Solution Icon
  • Cloud First, Distributed Computing
  • 200+ Endpoints Supported
  • Oracle Certified (Oracle Cloud, EBS 11i, R12.1.x, R12.2.x)
  • SAP ECC, S/4HANA, C/4HANA, Hybris, BW Certified
  • Salesforce Certified
  • Microsoft Certified
  • Works with all databases
  • Hub and Spoke Architecture
  • No Babysitting needed
  • Certified Connection Methods
  • No Baby Sitting needed
  • Certified Connection Methods
  • 22+ Years of Product Innovation and Investments
  • Joint Development Partnerships

Enterprise Ready

Can scale to support Large Enterprises
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Enterprise Ready
  • Customize & Configure Templates
  • Handle Large Volumes
  • Handle Complex Sequencing
  • Distributed Computing Platform. Multiple users can simultaneously process their tasks..
  • Robust Logger

Innovation

Technology Leadership
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Innovation Icon
  • Business Focused Solution
  • Archival & Purging Simplified
  • Automated data extracts & loads
  • Simplified Workflows
  • Available on CLOUD and ONPREM models
  • No Programming / Low Code
  • Processing Monitor
  • Audit Trail Ready

Smart Features

Work in lockstep with your creative thinking
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Smart Features Icon
  • Out of the box, low-code approach to EDM
  • Data quality profiling & health checks
  • Automated extract & load of data into 'data lake'
  • Smart sequencing of data
  • Supports setups, masters & transactions
  • Scalable to high volumes
  • Approval workflows built-in

Usability

Business users ask for
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Usability Icon
  • Established Governance Process
  • Standard Template Layouts
  • Open Training
  • Notifications
  • Quarterly Product Releases
  • Business User Friendly
  • Customer centric Design
  • Built in Project Management Capabilities (Upcoming)

Security

Data, users and application security
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  • Data Security (At rest and in motion)
  • Single Sign on (AD, On-prem and Cloud)
  • Access Control Management (Users & Groups)
  • Self-Contained User Management
  • Roles Based Access Control (Privileges)
  • Data Encryption Capability
  • Data Masking Capability
  • Operational Security (Record Level)
  • Auditing and Logging

Smart Team

We are ready to help you!
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  • Flexible Pricing models
  • Setup Management as a Service
  • Data Factory
  • 200+ Functional Specialists
  • 24 X 7 Global Product Support
  • Quarterly Business Reviews
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