Data Analytics Services
Data Analytics
Risks are analyzed based on information. This information is generated on the basis of “data”. Much of this data is available in your operational systems, but it is difficult to determine exactly which data provides the correct (management) information. This is where data analytics fulfills comes into play. With further digitization and with the help of data analytics, your organization gains more insight into its most important risks and its level of risk management. This insight supports sound decision-making.
Data analytics and risk management
Data analytics increases the effectiveness and efficiency of risk management because it provides better insight into actual events.
For example, data analytics can play a role in:
- Identifying new – unforeseen – risks
- Carrying out risk assessments through objective assessments
- Determining control effectiveness
- Determining consequential damages
- Preventing threats from manifesting
- Determine moments of (profit) optimization
Data analytics can be used in several phases of the risk management process:
| Risk Management Phase | Function | Added Value |
|---|---|---|
| Risk Identification | Identifying existing and potential threats and opportunities | Objective and factually substantiated risks – efficient and effective |
| Risk Appetite | Determining risk tolerances | Being able to deduce the actual vs. desired risk bandwidth |
| Risk Rating | Quantifying the frequency and consequences of risks | Substantiated and factual insight into the frequency and consequential damage of risks |
| Risk Classification | -Not applicable- | |
| Control Identification | Identifying events with a positive outcome | Substantiated and factual insight into the positive consequences of measures taken |
| Risk Treatment | Supporting the recovery process and necessary resources | Substantiated and factual understanding of the effect of correcting negative events |
| Risk Monitoring | (Continuous) monitoring of existing risk and risk indicators | Being able to respond to risks and take advantage of opportunities in a timely manner. Understanding and evaluating potential events |
| Risk Reporting | Analyzing the extent to which a risk has occurred | Provide factually substantiated insight into where the organization has missed opportunities |
ONE Risk Advisory helps you to set up the appropriate analytics for each phase. Our experts can help identify the necessary information and data to support the risk management process in the best possible way. The technology can vary from simple data analysis, supported with Excel, to structured data analytics using advanced tooling.
Data analytics and internal audit
We endorse the importance of data analysis techniques in audits, such as process mining. Also as a means to deploy resources in an (even) more risk-oriented efficient way.
Data analytics is seen as a first step towards continuous monitoring and continuous auditing. By regularly analyzing (risk) data, issues and abnormalities can be quickly identified. We recommend doing this in close collaboration with the second-line risk functions, because of their responsibility in monitoring risks and control measures.
With the help of data analytics, the effectiveness and efficiency of your internal audit process can increase. For example, data analytics can play a role in:
Performing more efficient audits, by determining the objective and scope of an audit in a more risk-oriented way.
- A risk-oriented sample selection.
- Identify trends, new developments and unforeseen risks
- Performing benchmarks and comparative analyses
- Provide better insights into the effectiveness of recommendations
- Better substantiation of risk (frequency and consequence)
Data analytics can be used in various internal audit activities. Examples are:
| Internal Audit activities | Function | Added value |
|---|---|---|
| Audit planning process | Providing insight into the subareas that show more or fewer deviations (using risk models) | More targeted auditing |
| Planning an assignment | Identify important events in the risk area. | Better and sharper “scoping” of the audit |
| Executing an audit assignment | Make discrepancies visible within a data group |
|
| Reporting | Analyze all observations with impact and frequency | Reporting with better substantiation of the finding and the actual risk |
| Monitoren audit recommendations | Analyzing the factual and timely follow-up of the recommendations | Direct measurement of the effect of implementing a recommendation |
ONE Risk Advisory helps you to set up the appropriate analytics for each activity. Our experts can help identify the necessary information to support the internal audit process in the best possible way. The technology can vary from simple data analysis, supported with Excel, to structured data analytics using advanced tooling.
Process Mining
Process Mining is a special form of data analysis. Process Mining has been around for quite some time and in recent years has received more and more attention – which it deserves – from a growing number of companies. Through Process Mining, companies gain more control, manage risk more efficiently and build more sustainable and efficient processes.
What is it? Process Mining is the unlocking of already available data, which is processed into tailor-made and meaningful management information.
Through Process Mining, companies gain more control, manage risk more efficiently and build more sustainable and efficient processes.
Process Mining is a general term that has several applications, of which Process Discovery is often the best known. Nevertheless, there are several useful ways in which Process Mining can be used:
- Process discovery
Gain insight into the actual (“as is”) process flow – the sequence of activities, process loops and bottlenecks – in order to be able to implement process improvements. - Automation rate analysis
Identifying manual activities to indicate opportunities for process and control automation. - Conformance check
Validate compliance with processes and controls to gain insight into any deficiencies and deviations. - Root cause analysis
Identify root causes and quantify when the results of process or control performance are outside the set boundaries - Performance analysis
Define KPIs and measure and report actual process performance results (such as lead times and idle times) and run benchmarks. - Segregation of Duties analysis
Identify and understand the use of conflicting system authorizations.
Process Mining can provide many benefits for the entire organization. To get the most value from Process Mining, it is necessary to look beyond selecting and installing a software solution. It is about embedding Process Mining in the strategic and operational processes of the first, second and third line in order to unleash sustainable benefits.
We distinguish 4 factors for a successful implementation of Process Mining applications: System readiness, Data readiness, Organization readiness, People readiness.
