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Data warehousing with BI software solution


We all are aware of the BI module used by companies for data warehousing. But why do you need a data warehouse? There are many reasons that explain the requirement for data warehousing in an organization. The key reasons are:

  • Center place for data
  • Accessible history
  • Ease of learning and understanding
  • Standardized data information
  • Uniform and clear definitions
  • Rapid access

Data warehousing offers a center place to gather data in one place. Company managers can access history and past fact files related to business. It is easy to understand & access and provides uniform and clear definitions for various business terms.

Data Quality

Each company- small and large scale is facing data quality issues in the source application. Data Quality term is defined in many styles:

  • Incomplete data
  • Duplicity
  • Wrong data
  • Conflicting data
  • Confusing data
  • Missing data
  • Unclear data
  • Null value

A quality data is a comprehensive collection of all relevant information which is important for company operations. Any duplicity, wrong, conflicting, confusing, missing or incomplete detail will affect the quality and which eventually results in improper analysis reports. BI software solution ensures quality driven data for every type of company regardless the size and location.


Data Volume and performance

Analyzing data amount is the next biggest challenge for all companies. The steps for generating analyzing reports are executed to attain an acceptable query performance by staff members for end users. Techniques in RDBMS that define performance are:

  • Partitioning
  • Indexing
  • Materialized Views
  • Aggregations
  • Archiving
  • Window Functions

Professionals follow certain steps to implement data warehousing to achieve successful execution and outcomes:

  • Identifying the business issue and solve with expertise
  • Designing relational data warehouse for solving business issues (dimensional modeling)
  • Identification of data sources and verification and checking the existence of data
  • ETL – Extraction, Transformation, and Loading
  • Building one or more cubes in the dimensional modeling

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