Data analytics
Businesses need more prompt and sophisticated information and data analysis in today’s increased business competition environment. In order to manage performance effectively, they need to have knowledge of information processing tendencies and practices. Data analysis plays a significant role in improving companies’ performance, supporting decision-making, and minimizing the risks. Data analytics is a new area that can potentially extend the domain of Performance management in order to provide understanding of business dynamics. Performance management provides framework for leveraging business intelligence and analytics and determining what to analyze.
Performance measurement systems and data analytics have the same objective – to support the decision making process with relevant and adequate data through a series of activities such as analyzing and interpreting data from past actions and their influence to future performance. The philosophy behind data analytics and performance management is businesses to develop and apply more sophisticated mathematic and statistical tools of cause-and-effect performance relationships in order to answer business problems.
Technology is essential to enabling the insight required for effective performance management. Therefore, to get full value of the data and to perform the data analysis necessary for performance management, businesses must install adequate business intelligence software, ERP systems and planning and analytics software. ERP systems are a vital data source for successful performance management. Performance management, combined with data from ERP solutions provide businesses with clear insights of their achievements and can help them to make informed decisions. The capacity to quickly and reliably analyze a complete set of data is crucial for success.
Tools that could be used to analyze company data:
Statistical analyses
– Summary and descriptive statistics
– Classical test of hypotheses
– Non-parametric tests of hypotheses
– Distributional plots and tests
– Confidence intervals
– Cross tabulation
– Analysis of variance
– Correlation analysis
– Survey data analysis
– Statistical quality control
– Principal component analysis
– Factor analysis
Econometric analyses
– Regression models and diagnostics
– Non-parametric regression
– Regression models with binary outcomes
– Time series analysis
– Models with panel data
– Multivariate analysis
– Cluster analysis
– Bayesian analysis
– Forecasting
– Resampling methods
– Post-estimation techniques