What Knowledge Management Strategy for a data driven audit approach?

What Knowledge Management Strategy for a data driven audit approach?

Many companies are turning to the exploitation of data, for process automation and in strategic level decision making.

This research focuses on the appropriation of data to drive the audit, which is not currently fully integrated into the process because it is neither systemic nor appropriated in the same way by the team, between experienced and junior profiles, and between business and technical profiles.

The goal of this research is to reduce this gap by focusing on KM as a main tool for implementing the organisational learning of the data driven solutions among the team, besides, by encouraging the use of data analytics in the process of audit. For that, we had to define a suitable KM strategy to conduct KM projects and to reinforce organisational learning. Using the Design Science Research Methodology (DSRM), we evaluated different strategies used by other companies in the same context, which was part of the state of the art.

We proposed a KM framework based on different models presented in the literature, to guide the team through the right steps in using the KM strategy and to improve the use of data in the audit. In this framework we had outlined the targeted strategy, then measure the maturity level of the existing KM strategy, by analysing the existing KM practices (technology, process, and people) using the BPMN (Business process model notation) representation that enables us to model the process and the information flow between the different actors. Subsequently, it guided us to define the targeted processes on which the auditors require to use the knowledge from the Internal and external environments and where the need of having a KM governance is important.

Finally, we had applied the organisation learning loop based on 4 different steps of the PDCA Deming cycle (Plan, Do, Check, Act) which has enabled us to analyse the process, define the tacit and explicit knowledge for each of the processes, and improve, conserve and formalise the Knowledge in the process. The solution build from this process has been chosen based on experiences and feedback of many KM projects and has been supported by the risk analysis of each process.

The implementation of the solution to overcome the knowledge problem is one of the most difficult parts of this research. Although these solutions are based on previous studies or experiences, the team must be involved and must agree on the implementation of these solutions, to ensure the continuity of the improvement and to apply the recursive cycle of the KM framework. In this case, we had established a DELPHI method to grade the solution and define the prioritization based on the consensus of answers.

This research has enabled us to apply the framework in a real context to improve the data driven audit in an organisation by focusing on KM as a main asset to ensure the organisational learning, and as a solution to the gap of knowledge in the team, and to automate the data driven process in the audit.

Overall, it was very useful for the team to evaluate its KM process and to detect their area of improvement. Future research can further improve this framework by analysing the framework with ISO-30401, by considering the obsolete knowledge in the organizational learning loop, and by studying the appropriation of these methods.

Speaker

Jihane Britel, VINCI energies FRANCE

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom's meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk)