An End-to-End Fundamental Data Analytics Framework for Enhancing Decision-Making in SMEs

Abstract
Small and medium-sized businesses generate
substantial volumes of sales data; however, this data is
often underutilized and not systematically analysed to
identify trends or support forecasting. As a result, key
decisions related to sales planning and customer
management frequently rely on conventional reporting
rather than data-driven insights.
This study applies fundamental data analytics
techniques to improve sales optimization through
customer segmentation and sales forecasting. Simple
and practical techniques such as rule-based
classification, moving averages, and the use of Python
with relevant data analysis libraries can support the
complete process of data analysis to ensure data
quality and reliable forecasting. Graphical
visualisation through Power BI provides clear insights
into sales performance, regional patterns, and
customer segments.
Overall, the study demonstrates that the effective use
of simple and cost-efficient data analytics tools can
significantly enhance sales outcomes for small and
medium-sized businesses without requiring large
investments.