Multi-sector business intelligence success stories Tellistic has implemented:
A regional nonprofit organization operating across four different countries leveraged business intelligence to mine siloed data from accounting, human resources, and field team applications to seamlessly aggregate and provide advanced, interactive visual analytics that even non-technical stakeholders utilize to monitor organization performance in Key Result Areas in near real-time.
Historical reporting (What happened?) is the first and most common stage of analytics, and this is a use-case scenario on how entities at this reporting stage can derive value from Business Intelligence.
A first of its kind medical facility in Uganda, specializing in advanced surgery utilizes business intelligence to monitor department performance metrics along the patient’s journey touchpoints during hospital visits. This enables management to easily identify bottleneck causes and visualize their impact on overall hospital processes, ensuring patient centric services above all else.
Diagnostic reporting (Why it happened) is the second stage in the analytics journey, this looks beyond the historical account of events and deeper into why these events occurred as they did. Only when the “why” is addressed can businesses refine and optimize their processes.
A tier 1 bank in East Africa looks to implement an AI enhanced business intelligence module that predicts existing bank account holders with high churn risk by mining a series of account attributes associated with tendency to churn over time. This intervention is estimated to cost only a fraction of the effort currently used by bank staff to deter churn with much lower success rates to show.
Predictive reporting (What will happen) is the third stage in the analytics journey. More complex than its predecessors, companies leverage business intelligence to predict outcomes of given dependent variables with higher degrees of certainty. This stage of analytics allows for an entity to predefine the necessary interventions required to achieve a desired outcome, thus increasing efficiency.
National Tax Authorities
A national tax authority already invested in data collection, management and analysis worked with Tellistic to design a Value Added Tax fraud detection solution. This tool using a series of statistical and machine learning models analyses taxpayer behavior at a sector, regional, size and many other attributes to not only flag taxpayers suspected of committing potential fraud but to also prescribe possible fraud schemes likely to be at play and rings involved. This solution analyzes millions of data points at speeds unattainable by human analysts in seconds and recommends possible actions to take.
Prescriptive analysis is the most advanced and complex stage in the analytics journey. Often coupled with AI and machine learning models, solutions not only learn from the past to predict future outcomes but go a step further to make possible recommendations on the optimum courses of action.