Data Management

Data Management

The first step to building the right and solid business analytics solution is setting up the correct data flows. Examining the reliability and availability of this data at the right time with the right quality generates the initial plan for any analytics journey. It is also necessary to build the analytics capability to implement the right governance processes, ,and choose the suitable data integration methodology. And finally, such initiatives need to be sponsored right from the top with correct, timely governance and review processes.

Some of the critical phases in Data Management are:

  • Ingestion of Data:Data needs to be correctly ingested from all the organization’s data sources. It is vital to have a data discovery session first to understand the available data types and redo the sessions if necessary to fine-tune this understanding.
  • Data Integration:The integration process will integrate the data from various data sources into a single, unified view. The integration will begin with the ingestion process and include cleansing, ETL mapping, quality verification, and transformation.
  • Data Profiling and Governance:Automatically discover, model, define, and govern information content and structure, as well as understand and analyze the meaning, relationships, and lineage of information.
  • Data Store:Data Store contains the database and other components that provide the main structured storage area for data integrated from various application data sources. This data is transformed by ETL processes and integrated into the Data Warehouse and Data Marts repositories and other components, where data about any distinct category of information has a single point of reference, enabling current, accurate, consistent, high-quality data to be made available to analytical systems
Data Management

The first step to building the right and solid business analytics solution is setting up the correct data flows. Examining the reliability and availability of this data at the right time with the right quality generates the initial plan for any analytics journey. It is also necessary to build the analytics capability to implement the right governance processes, ,and choose the suitable data integration methodology. And finally, such initiatives need to be sponsored right from the top with correct, timely governance and review processes.

Some of the critical phases in Data Management are:

  • Ingestion of Data:Data needs to be correctly ingested from all the organization’s data sources. It is vital to have a data discovery session first to understand the available data types and redo the sessions if necessary to fine-tune this understanding.
  • Data Integration:The integration process will integrate the data from various data sources into a single, unified view. The integration will begin with the ingestion process and include cleansing, ETL mapping, quality verification, and transformation.
  • Data Profiling and Governance:Automatically discover, model, define, and govern information content and structure, as well as understand and analyze the meaning, relationships, and lineage of information.
  • Data Store:Data Store contains the database and other components that provide the main structured storage area for data integrated from various application data sources. This data is transformed by ETL processes and integrated into the Data Warehouse and Data Marts repositories and other components, where data about any distinct category of information has a single point of reference, enabling current, accurate, consistent, high-quality data to be made available to analytical systems

How can we help?

Our team has expertise across the full range of digital solutions. We are here to help you progress on your journey towards digital transformation.