Our Diagnostic analytics team aims to answer the question of why certain events or outcomes occurred. It involves analysing data to identify the root causes of problems or anomalies. Our Diagnostic analytics techniques include data drilling, root cause analysis, and hypothesis testing to gain insights into factors influencing specific outcomes.
Our diagnostic analytics services involve analyzing data to understand why certain events or outcomes occurred in the past. This process are done to focus on identifying the root causes of problems, investigating anomalies, and providing deeper insights into data patterns.
Root Cause Analysis
We do root cause analysis, which aims to identify the underlying factors or variables that contribute to a particular outcome or issue. This analysis involves examining relationships and correlations within the data to determine the primary drivers behind specific events or patterns.
Anomaly Detection
We utilize anomaly detection techniques to identify unusual or unexpected data points or patterns. These anomalies technique indicate errors, outliers, fraud, or other significant deviations from normal behaviour. By detecting and investigating anomalies, we help businesses to understand the causes behind unusual occurrences and take appropriate action.
Hypothesis Testing
We do statistical technique for diagnostic analytics to validate or reject hypotheses about relationships or differences within data. This process involves formulating a hypothesis, collecting relevant data, and conducting statistical tests to determine if the observed patterns are statistically significant. With such sort of technique implementation we help businesses validate assumptions and make data-driven decisions.
Regression Analysis
Our team has expertise in using regression analysis to identify relationships and quantify the impact of independent variables on a dependent variable. By analysing historical data and performing regression analysis, businesses can understand how changes in certain factors influence outcomes. This analysis helps in identifying the key drivers and making predictions or forecasts.
Sensitivity Analysis
We conduct sensitivity analysis to assess the impact of changes in variables on a particular outcome or metric. We apply sensitivity analysis to understand how variations in factors such as pricing, demand, or costs affect business performance. This analysis helps businesses evaluate different scenarios and make informed decisions.
Cohort Analysis
We apply cohort analysis for dividing data into groups based on specific characteristics or time periods to analyse patterns and behaviour. Our cohort analysis are done to understand how different groups or segments perform over time. This analysis helps businesses identify trends, compare performance, and tailor strategies for specific cohorts.
Causal Analysis
Our aims is to determine the cause-and-effect relationships between variables or events. Our causal analysis techniques such as regression analysis, time series analysis, or experimental design to establish causal links within the data. This analysis helps businesses understand the impact of specific actions or interventions on outcomes.
Comparative Analysis
We compare and contrast different groups, time periods, or scenarios to identify differences and determine the causes behind variations in performance by applying Comparative Analysis. After comparing data sets, businesses can understand the factors that lead to different outcomes and make informed decisions based on those insights.
