Big data

Traditional Data refers to commonly used structured internal data (such as transactional) and external data (such as information from credit Bureaus) that are used in the decision-making process. It may include data that Are generated from interaction with clients such as surveys, registration Forms, salary, and demographic information traditional data sources refer to Internal data sources such as core account management system transactions, Client surveys, registration forms, or demographic information.

Geospatial Data (non traditional data) refers to data that Contain locational information, such as global positioning system (GPS) Coordinates, addresses, cities, and other geographic or proximity identifiers. In recent years, very granular geospatial data have allowed DFS providers to Examine and cross-reference demand-side factors such as level of financial Inclusion, customer location, levels of poverty, and mobile voice and data Usage, with supply-related factors such as agent activity, rural or urban Characteristics, presence of infrastructure, and similar. This can offer Insights that may be helpful to customer acquisition and marketing strategies, Agents or branch expansion, and competitor or general market analysis. Geospatial data can offer more granular insights than typical socioeconomic Indicators, which are generally only available in general format.

Data Science is the term given to the analysis of data, Which is a creative and exploratory process that borrows skills from many Disciplines including business, statistics and computing. It has been defined As multidimensional field that uses mathematics, statistics, and other advanced Techniques to find meaningful patterns and knowledge in recorded data’. 

It Is an exploratory and creative discipline, driven to find innovative solutions To complex issues through an analytical approach. The science of data refers to The scientific method of analysis: data scientists engage in problem solving by Setting a testable hypothesis and assiduously testing and refining that Hypothesis to obtain reliable and validated results.

Descriptive: Alerts, querying, searches, reporting, static visualizations, Dashboards, tables, charts, narratives, correlations, simple statistical Analysis-à What happened? What is happening now? (reports) Predictive: Machine learning, SNA, geospatial pattern Recognition, interactive visualizations: What will happen in the future? (modelling)

Descriptive: Descriptive analysis offers high-level aggregate Reports of historical records and answers questions about what occurred. Key Performance Indicators (KPIs) are also within this category. • Descriptive Statistics(Correlation Statistics) Tabulation(Cross-tabulation,pivot ) Predictive: (machine learnming, Modelling)

Cloud Computing: Third-party vendors offer hosting Solutions that provide access to computational power, data storage and Frameworks. This is an excellent solution for firms that want to engage in more Sophisticated data analytics, especially big data, but do not have the ability To invest in computer servers and hire technicians to manage them.