5 Aspects of Business Intelligence Development

Core Components of Business Intelligence Development

  • Research and Planning
  • Design, Coding, and Testing
  • Database Management
  • User Interface and Accessibility
  • Automation and Machine Learning

Business intelligence (BI) development is a relatively new umbrella term that describes the process of creating processes and tools that help users leverage data. It is one of the primary practical applications of data science for commercial-scale use, as it allows companies and organizations to actually apply data to solve problems and identify opportunities. Developing these processes and tools can be a complex process that involves feedback and input from team members throughout an organization’s structure.


1. Research and Planning

The initial stage of any BI development project is to identify the problems facing an organization and the potential solutions available. Data scientists working on these projects need to have a firm understanding of business practices in addition to the technical expertise demanded by their occupation. The research and planning phase typically involves comprehensive research to assess the organization’s available data and key objectives to establish parameters for a successful solution.

2. Design, Coding, and Testing

Intelligence tools typically take the form of software and digital database infrastructure that supports essential processes. This aspect of development is often the most lengthy and difficult part of the cycle because of the detailed nature of software design. Designers often create a mock-up program before coding and testing a complete solution for implementation throughout an organization.

3. Database Management

Databases are the foundation of all virtual BI practices. This means that managing and integrating databases is pivotal in establishing effective and comprehensive tools. This aspect can be particularly challenging as it can require working with a relational and multifaceted database to provide meaningful final results. Management of individual databases may not be a part of the actual intelligence development process, but it’s essential for accomplishing intelligence objectives.

4. User Interface and Accessibility

Data scientists may be comfortable interpreting and analyzing data in an unrefined state, but this isn’t the case for most employees and leaders within a company. Accessibility is an important aspect of the process because it allows a broader range of professionals to actually understand and leverage data for the benefit of the company. All finished intelligence solutions are built with an accessible interface that allows users to interact with the digital tools without the advanced technical skills required for their creation or maintenance. Many modern BI tools are also designed to function seamlessly on a wide range of devices, ranging from on-site desktop computers to mobile technology, according to Forbes.

5. Automation and Machine Learning

Data science is useful to businesses and organizations because it allows them to apply vast amounts of information in an efficient way. Process automation and machine learning allow users to capitalize on technological developments to reduce the workload on actual employees. The sheer quantity of data used by some organizations, particularly large corporations, means that automation is the only practical way to incorporate and assess massive data sets. Trends in applied data science are closely linked to developments in process automation.

There is no doubt that a company’s ability to gather, curate and apply data impacts their potential for long-term success. Understanding the core aspects of business intelligence development is not only essential for data scientists, but also leaders and decision makers throughout an organization.