Integrated Data Management Solution
Enterprises are in a continuous progression to transform their products, services and processes to meet their changing customer needs and strategic goals. Transformational initiatives necessitate changes in IT landscape such as modernizing legacy applications, consolidating redundant applications, moving to cloud, information delivery to customers through varied new channels.
The changing application landscape becomes operationally effective only when the data foundation layer is in sync with the evolving applications. This could be ensured by a solution that enables digital transformation of data in an agile and cost-effective manner from one platform to another, and thus entitle businesses to provide better user experience.
The key challenges involved while enterprises undergo application transformations are:
Hexaware’s Data Modernization platform is an integrated data management solution that is purpose built to ensure flawless execution and success in digital transformation initiatives.
Data Modernization platform delivers:
Core Services enabled by Data Modernization platform accelerate your digital transformation journey by 2x.
Enterprises who have made their first move towards digital transformation of data and applications landscape are looking for a solution that could hedge the risk of these projects being overrun in terms of budgets and timelines. Data modernization tools and IPs automates digital transformation process right from data extraction to data transformation and reconciliation.
Be it developing your DW from scratch for transactional data or extending the capability of your current DW, leverage Data Modernization tools for accelerating these processes. Experience agility in the entire development cycle including data profiling, data quality validation, ETL code generation and reconciliation.
Increasing complexity of data collected poses a serious and tough challenge in front of data stewards when it comes to data operations. Multiple manual touchpoints in this cumbersome process hit the business as IT teams are not able to maintain data hygiene and data synchronization. With Data Modernization platform, experience over 50% automation in data collection, data assessment and data preparation.
DASEM allows sub-setting the data from an application database for any of the predefined data segmentation (slicing) condition. It also offers standard masking types required by businesses and supports customization of masking rules to secure the data while copying from production to non-production environments.
This feature allows automated data profiling. The tool output helps in identifying business rules and data quality rules for data transformation process in the early stages of project execution. It supports “Data Science” style data profiling based on R and provide insights through Data Profiler dashboards and various reports to accelerate the data analysis time. ML techniques are applied to identify data outliers during discovery phase.
Data Profiler also helps in data modeling exercise by providing insights on most likely facts, measures and dimension attributes through various reports.
This feature is a user-friendly data quality rule engine that allows users to consume pre-defined rules from pre-built rule repository as well as to create / modify business validation rules to check the data quality based on results from Data Profiler. Data quality dashboards & reports enable users to analyze the data quality issues either through a top-down or bottom-up approach. The autocorrect facility helps in resolving data quality issues through execution using Automaton feature.
Automaton is the ETL code generator for SSIS and Informatica. It takes various inputs from end business users such as source table / file format, target table / file format and transformation rules using web interface and generates the ETL code that can be imported in SSIS or Informatica. It integrates with HexaRule to leverage data quality features for generation of technical design document in an automated way. It offers metadata search feature to expediate the analysis process for any change in data model during development phase.
This feature offers reconciliation for selective data elements from source to target and ensures that the data transformation process is executed successfully with no data loss. It supports various mathematical and string functions during the comparison process, offers data lineage in either direction (source to target or target to source) for easy traceability of data elements that participated in transformation program.
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