There are 2.5 quintillion bytes of data created each day at our current pace: not only is there more data, but there are more scattered data sources. But to make data usable, we need to consider how the data are presented to end users and how quickly users can answer their questions.

 The value of understanding data is critical for businesses to make data-driven decisions and better business performance. Business performance in terms of profitability, productivity, efficiency, customer satisfaction, and more can benefit from Data Modeling and this in turn helps business leaders quickly and easily get answers to more important questions.

What is Data Modeling? 

Data modeling is nothing but the process of discovering, analyzing, representing and communicating data requirements in a precise form. Data models depict and enable an organization to understand its data assets.  Improved data modeling  leads to greater business benefits. Key success factors for this include linking to organizational needs and objectives, using tools to speed up the steps in readying data for answers to all queries, while prioritizing simplicity and common sense. Once these conditions are met, every business, whether small, medium, or big, can expect data modeling to bring significant business value. The data modeling workflow progresses from business requirement to the  physical implementation of the database.

Better Data Modeling leads to greater Business Intelligence

Today businesses are looking at data models as the foundation, to solve their business challenges, bolstering a need for BI and analytics within the organization. At the foundation of every BI & Analytics team, building business-driven data models are at their core. These models help create key database frameworks for business and technical team collaboration, which in turn help build effective reporting & meaningful analytical insights to drive better decision making.

Data Modeling creates an ‘Active Inventory’ of Data Assets 

• Know what data you have: Create a visual inventory of database systems
• Know what your data means: Communicate key business requirements between business and IT stakeholders
• Support data consistency: Build consistent database structures & support data governance initiatives

However, the entire process of data modeling is not as easy as it seems. A data model for one line of business is hardly appropriate for another line of business. Using data models to drive your key business decisions efficiently, you must have a clear understanding of your organization’s requirements and organize your data properly using individual tables for facts and dimensions to enable quick analysis and keep the models updated overtime. 

From a high level, data modeling is a process that: 

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  Gather business requirements (analyze the data, identify data relationships)

  
  
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  Create various data models (conceptual. logical. physical)

  
  
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  Supports application development (create application specification, develop or integrate applications, deploy applications)

  
  

Data Modeling in Business Intelligence 

Today businesses are looking at data models as the foundation, to solve their business challenges, bolstering a need for BI and analytics within the organization. At the foundation of every BI & Analytics team, building business-driven data models are at their core. These models help create key database frameworks for business and technical team collaboration, which in turn help build effective reporting & meaningful analytical insights to drive better decision making. 

• Excel Dashboards 
• Tableau  
• Power BI 

• A data model ensures that all data objects required by the database are accurately represented  
• It provides a clear picture of the base data and can be used by database developers to create a physical database 
• A data model helps design the database at the conceptual, physical and logical levels 
• Identifies missing and redundant data 
• In the long-run data modelling makes IT infrastructure upgrade and maintenance faster cheaper 
• Fully automated data processing system
• Complete data architecture road map
• Data visualization using advanced BI tools
• Data analytics techniques to extract valuable insights