Data Analysis & Analytics

Data analysis & analytics services apply data-centric system architectures as well as technologies and tools to deliver reliable, consistent, high-quality information that enables stakeholders to better manage strategic, tactical, and operational performance.

The service provides data management, analytics, and presentation capabilities, and facilitate a phased or incremental development strategy in respect of the inclusion, coordination and control of different data sources, and the analysis and development of business information and insights. By working closely with technical and business stakeholders, Magus IT Consulting will deliver to its clients below outcomes:

  • Business process coverage: defines the scope of the change with a high level overview of the business decisions within the enterprise that are to be supported by the solution. It identifies how the information output will be used and what value it will provide.
  • Decision models: identify the information requirements of each business decision to be supported and specify the business rules logic of how the individual information components contribute to the decision outcome.
  • Source logical data model and data dictionary: the source logical data model provides a standard definition of the required data. The source data dictionary provides a definition of each element and the business rules applied to it: business description, type, format and length, legal values, and any inter-dependencies.
  • Source data quality assessment: evaluates the completeness, validity, and reliability of the data from source systems. It identifies where further verification and enhancement of source data is required to ensure consistent business definitions and rules apply across the enterprise-wide data asset.
  • Target logical data model and data dictionary: the target logical data model presents an integrated, normalized view of the data structures required to support the business domain. The target data dictionary provides the standardized enterprise-wide definition of data elements and integrity rules.
  • Transformation rules: map source and target data elements to specify requirements for the decoding/encoding of values and for data correction (error values) and enrichment (missing values) in the transformation process.