Building Block: Analytics and business intelligence
Maturity: BETA
Analytics and business intelligence logo.

Short Description: Provides data-driven insights about business processes, performance and predictive modelling.=

Full Description: The analytics and business intelligence services ICT Building Blocks enables comprehensive services providing important data-driven insights about the current state of an organization’s business. It also identifies trends to help users understand information that can drive business change and support sustainable and successful business practices. These services can aggregate, transform and extract features from data, as well as analyzing them to identify specific patterns and classifications. They can either operate on data stored in various repositories and registries, or process real-time data streams passing through the platform from one application to another. For analytics processing, warehouses are an essential component. Warehouses copy and aggregate data from collection tools and backend data repositories into a database designed specifically for analytical purposes. By doing so, these warehouses transform the data from multiple sources into formats suitable for analysis, helping optimize analytical queries and creating outputs more efficiently without affecting the performance of other applications. Web interfaces and external data visualization applications allow end-users to view the analytic outputs.

Other Names:

Key Digital Functionalities:

  • Provides a user interface (UI) to access and work within the software environment
  • Provides an administrative function to define user rights and accessibility control
  • Provides artificial intelligence tools for analysis and manipulation of data
  • Sets business rules and algorithms
  • Provides data visualization and rendering
  • Aggregates datasets from various sources into a warehouse database that organizes the data into corresponding groups identified for specific analysis
  • Processes raw data to find and remove noise, artefacts, etc, and to clean the data
  • Extracts specific features from data and feature sets
  • Combines feature sets subject to numerical or logical boundary conditions based on preconfigured rules
  • Classifies threshold outcomes and triggers corresponding responses in other applications based on classification rules


Examples of use in different sectors:

  • Agriculture sector: Use for conducting root cause analysis of problems and applying predictive analytics in order to make appropriate interventions in time, as well as to make informed decisions while implementing various schemes. Examples include:
    • Analysis of trends of cropped areas and the economics of various crop districts in order to optimize crop area planning and to give advice to farmers
    • Analysis of soil health records along with the crops grown during the period, rainfall and irrigation amounts, yield, and other parameters, in order to plan how to maximize micronutrient corrections through focused interventions
    • Prediction of commodity prices, disseminating information to farmers so they can make informed decisions on storage and future sales of non-perishable agricultural produce
    • Real-time analysis of climatic conditions and early detection of sporadic pests and other pertinent parameters to predict pest attacks on agricultural crops and fish or prawn ponds. Use data to improve preparedness for these pest infestations by issuing advisories to farmers and by adequately stocking the materials required to mitigate these attacks
  • Education sector: 
    • Use in supply chain management systems for optimizing distribution of equipment, such as desks and textbooks to schools and learning institutions
    • Track analytics on learner performance, providing diagnostic information to guide future learning interventions
    • Track teacher professional development based on self-diagnostic assessment and development of professional learning pathways
  • Health sector: 
    • Disease surveillance systems can use analytics and business intelligence services to identify disease incidence, morbidity/mortality rates, density, distribution, etc
    • Supply chain management systems can use them to optimize shipment logistics for efficient delivery and inventory management
    • Point-of-service data analysis can help optimize health commodities production and demand-based pricing
    • Health insurance companies can use this for risk analysis and region- or disease- based optimization of insurance plans


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