Posted By Adeptia Inc. on 05/24/2018 in Technology

Best Practices to Modernize Data Warehousing Initiatives

Best Practices to Modernize Data Warehousing Initiatives

Data Warehousing involves storage of large amounts of data in electronic forms generated by enterprises. Analytical techniques are employed for deriving business intelligence out of this data for strategic purposes. However, many enterprises still believe that it is a process of dealing with large amounts of data. To stay ahead of the rest, they should catch up with modern data warehousing practices. Enterprises should restructure their strategy to support data virtualization and data processing.  Here are some tips to execute data warehousing in an effective way:


Define the Goals: Every organization has different goals, objectives, a line of business needs, cost structure, etc. The requirements of a fully established company will be different from the company still in the initial stage. The data warehousing needs should change as per the needs and goals of the organization. Stakeholders from every department should be looped to evaluate the process-centric needs. The DW teams should answer the following questions:


  • Can the infrastructure handle diverse data sources and subject areas?
  • Can the infrastructure handle real-time data from smart machines and sensors?
  • What are the triggers in place for automated orchestration, improved agility, consistency and speed throughout the data warehouse lifecycle? 
  • Does the data warehouse provide support to bimodal business intelligence environments and analytical environments?


Evaluate the Data Sources and Formats: Teams should identify the data formats and sources that will be used by different processes. Data warehousing should support data from third-party sources, social media channels and Internet of Things (IoT). Modern data warehousing should support a wide variety of data. Forward-looking enterprises have advanced data management environments. 


Define Dimensions: Identify entities that will work together to create Key Performance Indicators (KPI). The Data warehouse solution should allow users to bring data from third-party sources, social media channels, and the Internet of Things (IoT). The teams should ensure that the data is ready for users in all forms and dimensions. They should also ensure data virtualization and distributed processing. 


Ensure Multi-tenancy Support: Multi-tenancy support is critical for the Business Intelligence (BI). It allows users to use a single software stack to address partner-centric needs and make customizations. Closely evaluate the databases you are storing. Verify how the data is being loaded, processes, and analyzed. 


Deploy Metadata Management: Meta-data management solves more than one problem. It helps in accessing many benefits with data warehousing. Users can capture necessary information to build, use and interpret warehousing elements. 



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