Performance Analytics drives the transformation of business by allowing businesses to set, monitor and evaluate progress towards goals. It connects more people in less time with better data and lets them concentrate on the things that matter the most. Performance Analytics is the engine that uncovers insights and discloses hidden value for identifying new, tailored learning approaches. The effect is learning investment that helps the company achieve key goals.
Performance Analytics improves performance and accelerates continual service improvement by:
- Tracking metrics and trends for critical processes.
- Measuring wellness and behavioral practices against corporate goals.
- Recognition of process trends and possible bottlenecks before occurrence.
- The continuous visualization of historical and real-time health stats of processes in role-based dashboards that enable individual stakeholders to make informed decisions.
In terms of workforce-related issues, performance analytics shifts analysis to a broader view of the performance issues and opportunities. In the process, it enables performance and provides learning solutions to address present and future performance gaps. Performance analytics uncovers insights and reveals hidden value, helping to define new, targeted learning interventions. Performance Analysis is the method of analyzing or assessing a specific scenario's performance compared to the goal to be accomplished. A review of the results can be performed in finance based on ROI, income to mention a few. In HR, performance analysis can help to review an employee's contribution towards a project or assignment, which he/she was allotted.
Performance Analysis is a key functional method that tells the coach about a team and/or individual results. The input that was then made available to the players could be appropriate, accurate and objective rather than inaccurate and subjective Ian Franks, 1996. Essentially, performance analysis involves creating a valid and reliable performance record through systematic observations that can be analyzed to facilitate change. In HR although performance analytics highlights the performance of employees, it is definitely not a tool for blame games. The Philosophy Behind Performance Analysis is to pick out positives, pick out negatives, find an even Balance, find re-occurring problems and work on the problem in training. Remember, this is a Tool for Educating and Coaching!
The performance analysis step consists of 3 basic steps:
- Data Collection - It is a mechanism by which data is collected relating to a program's success. They are usually collected in a file and can be presented in real-time to a real user. The basic techniques for collecting data are:
- Profiling - It tracks the time spent in various parts of the programme. Very critical for highlighting performance issues, this phase. They get automatically collected.
- Counters - It records frequencies or the number of events cumulatively. It may require intervention by programmers.
- Events It records any occurrence of different specified events. So it produces a lot of data. It can be produced automatically or with programmer intervention.
- Data transformation - It is applied often to reduce the volume of data. For example, a profile recording the minutes spent in each subroutine job on each processor might be transformed to determine minutes spent in each subroutine on each processor and the standard deviation from this mean.
- Data visualization - While data reduction techniques can accumulate data volume, raw data is often required to be explored. The use of data visualization techniques can benefit this process far more.
When selecting a particular tool for a given task, the following issues will be considered:
- Accuracy: The output data we obtain using the sampling technique is less reliable than the data which we obtain using counters or timers.
- Simplicity: The best tools are to automatically collect the data without much programmer interference.
- Flexibility: A versatile method for gathering additional information or presenting several views of the same data can be expanded.
- Intrusiveness: Overheads need to be accounted for when analyzing data.
- Abstraction: A good performance tool allows data to be evaluated at an abstraction level that is appropriate for a parallel programming model.
Performance analytics comes with many benefits. It anticipates trends by monitoring performance to identify areas for improvement and detect service bottlenecks before they occur. It prioritizes resources by bringing clarity to what matters and quickly re-directs service coverage where it is needed the most. It maximizes automation and self-service by pinpointing where automation and self-service can increase efficiency. It guides continual service improvement by taking action on KPIs using Analytics Hub, time charts, forecasts, breakdowns, and dashboards. It acts with confidence by propelling the business and results forward by aligning service and operations with company strategy.
- Brock, T.R. (2017). Performance Analytics: The Missing Big Data Link Between Learning Analytics and Business Analytics. Performance Improvement, 56(7), pp.6–16.
- Zigu (2011). Performance Analysis Definition | Statistics Dictionary | MBA Skool-Study.Learn.Share. [online] MBA Skool-Study.Learn.Share. Available at: https://www.mbaskool.com/business-concepts/statistics/15334-performance-analysis.html [Accessed 10 Dec. 2019].
Ifeoma is a Business Analytics and Research Consultant at Industrial Psychology Consultants (Pvt) Ltd, a business management and human resources consulting firm.
Phone: +263 242 481946-48/481950
Mobile: +263 775 187 283
Main Website: www.ipcconsultants.com