Type or paste a DOI name into the text box. This is a guest post from Marcel Baumgartner, Data Analytics Expert at Nestlé S. One of the strongest trends observed in internal auditing communities is the more and more widespread use of Marketing audit term paper Analytics.
The term refers to the use of data, statistical methods and statistical thinking as a way of working, in addition to traditional auditing methods like interviews, document and process reviews, etc. In this article, we describe different approaches to ensure that Data Analytics is used efficiently in a large company for controlling and internal audit. The Promise of Data Analytics The main promise of data analytics is coverage. While 10 or 15 years ago, it was necessary to create a sample of financial documents in order to find potential issues, this is typically not needed anymore. Line of Defense Internal audit is considered to be the 3rd line of defense in most companies. In addition, companies may have an Internal Control department, which provides top down control mechanisms and analyses. Bottom-up The analytics that are carried out to identify potential issues in business processes can be run top-down or bottom-up.
They then share the outcome with the local process owners, and ensure that proper actions are taken. Internal control organizations typically work like this. Bottom-up up data analytics refers to scripts and algorithms that are run by internal auditors, ad-hoc, within the scope of their audit mission. With such a framework, a company can develop more sophisticated scripts, using modern statistical methods like clustering and classification, or using graph networks, in order to find issues that nobody has seen before. But clearly, it will be difficult to do this on like a global or regional level.
At Nestlé, we do both: more top-down for the 2nd line of defense, and more bottom-up for the 3rd line of defense. R or SAS, the data first need to be obtained from the source system in the form of a text file, and then be imported into the statistical software. This has naturally limitations, as soon as the data sources are too large. 24 hours previously can take a few minutes. At Nestlé, this journey has started: we have proven the feasibility e. Now suddenly, complex analytical algorithms run directly on the live system, potentially impacting business operations. Additionally, it is not allowed to develop algorithm directly on the live database: the code first needs to be written and tested on development systems, and then transported carefully into the production environment.
However, the test systems don’t contain real data. IT organizations in such companies will need to develop other processes to ensure that data scientists can develop their code efficiently, running short cycles of development, testing, corrections, on systems using real data and having a similar computing performance. Bottom-Up Is Driving Innovation The internal audit organization at Nestlé has built its data analytics strategy strongly around the bottom-up approach. That is the internal auditors are empowered via training, coaching, support and software solutions to run most of their analytics on their own. The internal audit organization at Nestlé has a Data Analytics team, who has the mission to provide the framework so that internal auditors can take full advantage of the data the company has. The team works in close collaboration with the auditors to not only coach and train them, provide clear documentation, but also to innovate and develop new scripts regularly.
In the recent years, we have been able to generate valuable insight into financial and food production processes through the use of statistical and graphical methods. During an entire year, a mid-sized business can generate hundreds of thousands such documents. Internal control has developed numerous rule-based methods to identify documents that need investigation. We know how much of a specific semi-finished product H1 was consumed in the finished product F1, and we also know how much H1 consumed of the raw material R1.
We have this information for all pairs of products in a given factory, over a period of say one year. The question now is: can we develop a more optimal algorithm to determine which finished materials are using a specific raw material? Representing the data as a graph network helped us greatly. Outlook Data-driven controlling and auditing will further accelerate, there’s no doubt about this. IT organizations, to ensure that analytics can be developed and tested in a much faster way compared with traditional change management processes. Marcel Baumgartner works for Nestlé since 1994, in its headquarters in Vevey, Switzerland. Nestlé is the world’s leading Nutrition, Health and Wellness company.
Switzerland, and a masters in Statistics from Purdue University in West Lafayette, IN, US. This article was originally published in the Swiss Analytics Magazine. The academic tip: What is Deep Learning? Brand Audit Step 1: Create Brand Summary The first step in your brand audit process is to create a brand summary.
If you’ve completed a written brand strategy, this is simply a list of the key components. You’ll use this to compare to the results of your internal and external surveys. If you’re not sure how to answer some of these questions, you may want to take more time to fully define your brand strategy before conducting your brand audit. Or, you may want to have your team work together to complete the brand summary. Note: We have an entire module devoted to a brand audit in our Brand Strategy Toolkit if you want more comprehensive guidance. Brand Audit Step 2: Determine Your Survey Method The survey complexity and the number of participants will depend upon your company situation.