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The Smart Data Platform offers over 9000 ready-to-use Smart Data Templates that automate data assessment, quality, governance, cataloging, visualization & reporting. The templates provide a 50% baseline to all EDM projects, that help save upto 70% in time & cost. Below are some of the endpoints we support:
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.
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 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.
The source of 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 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).
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 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.
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 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.
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 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.
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 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.
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.
ChainSys provides 4000+ data loading templates for SAP, Oracle Cloud Applications, Oracle EBS, JDE, PSOFT, Siebel etc. OTB. These templates 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.
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.
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
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
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.
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 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.
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.
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.
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.
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 encompasses the tactical management and oversight of the company’s data assets. dataZen offers simplicity to this process.
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 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 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 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 Modeling 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%.
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.
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 Governance is the exercise of authority and control over the management of data assets. dataZen offers a flexible workflow for achieving this.
Data Stewardship encompasses the tactical management and oversight of the company’s data assets. dataZen offers Simplicity to this process.
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 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 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 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%.
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.
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.
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.
Generates relationship and cardinality between datasets within an application.
Enables to determine the orphan's records and records with no child records.
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.
Translate the technical metadata into a business glossary.
Democratize the data across the enterprise by making business user as citizens
Visual representation of the data movement between the sources
Understand the data transformation rules in the process
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.
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
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
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.
Generates relationship and cardinality between datasets within an application.
Enables to determine the orphan's records and records with no child records.
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
Translate the technical metadata into a business glossary.
Democratize the data across the enterprise by making business user as citizens
Visual representation of the data movement between the sources
Understand the data transformation rules in the process
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.
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
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
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).
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.
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.
dataZen constantly measures the quality of the master data and provides the trend for data duplication, standardization, and data completeness.
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.
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
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.
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.
dataZen constantly measures the quality of the master data and provides the trend for Data duplication, standardization, and Data Completeness.
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.
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 across multiple cloud vendors leading to greater data governance challenges. This is fueled by progressive cloud 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
