Data Engineering
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Businesses are heeding the call to embrace big data due to an increased understanding of the significant benefits it can deliver. Selecting the right data store type is essential to building a more effective Data Platform within the entire Data Architecture of an organization. The requirements have gone up tremendously, in terms of the number of users, the performance expectations, the amount of data, the complexity of the analytics, and so on. A mature Data Architecture not only has some Cloud, but a lot of Cloud in it today.
Netwoven’s data architects are experienced in helping unlock the power of data, helping make better – and often automated – decisions that differentiate your business strategically. We provide services in the following areas of Data Engineering:
Data Modernization
Data “modernization” is the concept of holistically integrating your “data” across the processes of systems integration, data migration, data quality, data governance and data archiving all through its lifetime. While every data modernization project would be different, Netwoven adopts the following approach to minimize the potential risks.
- Assessment – We will work with you to evolve a detailed data modernization strategy and methodology that aligns with your organization’s business goals
- Roadmap – A roadmap is prepared for executing data modernization program., We recommend to begin with the least risky data and gradually moving towards more critical data
- Implementation – We move stepwise to implement the modernization project
Data Governance and Master Data Management
A “Collaborative Data Management” approach including all stakeholders is essential for ensuring data quality. It is important to keep in mind the aspects of data profiling, master data management, data governance and metrics. These help to analyze data, measure quality and make sure that data quality is maintained on an on-going basis. We focus on the following areas:
- Define organization wide metrics for data in collaboration with business team and measure them
- Assess existing data to validate metrics by carrying out profiling exercise along with IT
- Identify and validate all master data covering all aspects such as Behavior, Life Cycle (CRUD), Life Time, Cardinality, Value, Complexity, Volatility and Reuse
- Work with you to define MDM governance framework identifying all the processes across the data lifecycle for management of master data.
- Enforce a data quality fire wall (MDM) such that only correct data is allowed to enter the information ecosystem as a result of the governance process
- Institute appropriate Data Governance and Stewardship programs to ensure continuous data quality as a routine and stable practice at the organizational level
- Our goal is to ensure that the data ecosystem within the company always remains distilled as the policies and practices are evolved including all stake holders e.g. business and IT users from each department at all hierarchies.
Data Quality Management
This is a crucial activity involving preparation and analysis of data to ensure data quality. We focus on the following areas:
- Data Capture & Profiling
- Checks Management
- Exception Checks
- Exception Reporting
Data Migration and Integration
In today’s world of sleepless generation of “Big Data”, it is imperative that data integration and data migration is very well-established and a seamless processes — whether you are creating the data lake from the input data, moving from one repository to another, converting data warehouse to a data mart, navigating through the cloud. Without a robust data migration plan, you are likely to run over budget, overwhelmed by a plethora of data processes, or land up with suboptimal data operations. We focus on the following
We focus on the following areas:
- Assessment of the Source
- Understand the quantum of data being pulled over, nature of the data as well as how it can be absorbed in the target system
- Create a comprehensive data-mapping plan including entity mapping, data inconsistency check, data redundancy check, data stability check, data cleansing, and maintainability analysis.
- Beyond meeting the requirements for data fields to be transferred, we collaborate with you to run an audit on the actual data contained within and help you to validate the process of migrating that data in the first place or deciding that a fixation is needed in the source system itself
- Design the Migration
- We undertake the design phase is where we define the steps of migration to take on based on importance, adequacy and stability.. This also draws out the technical architecture of the solution and details of the migration processes.
- We create a comprehensive project plan for executing the migration by the clock considering reporting and resolving for the errors.
- Security planning is embedded in the project plan. Any data that needs to be protected should have protection threaded throughout the plan.
- Build the Migration Solution
- We take a step wise approach to break the data into subsets and build out one category at a time, followed by a test. Also, the selection of right tools for your environment is crucial at this stage.
- We also provide you a path for continuous migration-capturing the changes in the source system, propagate the changes to the target system with appropriate transformation.
- We will help you to choose industry standard connectors to build your solution
- Conduct a Live Test
- Apart from testing the code during the build phase with real data, we create a complete data assurance plan to ensure the accuracy of the implementation and completeness of the application.
- Flipping the Switch
- Once a complete acceptance testing is done, implementation can proceed, using the style defined in the plan.
- Audit
- Once the system has gone live, we help you to set up a comprehensive audit plan in order to ensure the accuracy of the migration.
Netwoven specializes in Microsoft Azure Data and AI technologies to provide Data Engineering services. Our experienced consultants work with the complete array of Microsoft products e.g. Azure Cosmos DB,Azure SQLDatabase, Azure Data Lake Storage and Azure Data Services.
