In a first blog post on GRC platforms titled "GRC and Governance Platforms - Introduction", we took a bird's eye view of the topic. Now I would like to continue the article series with the topic "Audit Management", which will consist of three consecutive articles. I will address the following topics:
Internal controls are often considered to only be the responsibility of finance and audit professionals. But if internal controls work in harmony, across an organization and the Three Lines of Defense, they can help the organization avoid legal repercussions and run more effectively and efficiently. While creating a rigorous internal control system can be challenging, it’s definitely possible.
Wie die meisten von Ihnen ja wissen, ist die dab:Daten – Analysen & Beratung GmbH eine Ausgründung aus der Technischen Hochschule Deggendorf (THD). Seit jeher haben wir daher auch immer Werkstudenten, die bei uns arbeiten und manchmal auch ihre Abschlussarbeit bei uns schreiben.
Now to specific examples of use. I will show you three examples from our portfolio to give you some ideas and have divided them into three different categories. Firstly, I will use the example of a traditional duplicate payments analysis to explain how known, transaction-based analyses can be improved. I will then describe new analytical approaches, based on the specific example of a comparison of master data quality expressed as an objectively determined indicator. The third example shows the segmenting of business partners in financial accounting – not in the traditional way but based on the posting structure exhibited by these partners.
Digitisation has been a burning issue in companies not only since Corona, but the sudden switch to decentralised locations such as the home office clearly shows the weak points of digitisation. Any omissions or lack of readiness now have a strong impact on the daily work routine in each company.
This is the second part of a short series of blog posts where we present you two machine learning procedures with the analytic Software ACL Robotics. The “ACL™ Robotics” software solution, which has been established on the market for many years, supports the manual and automated analysis of large amounts of data. In addition to a multitude of interfaces to SAP (via “SAP Connector”), Salesforce, Google Hive, Amazon Redshift, Outlook, PDF imports or any ODBC data source, a script language helps to automate analysis steps. The software developer Galvanize allocates this to the field of RPA (Robotic Process Automation). Individual analytic steps are performed by several analytic commands, such as sorting, summarizing, joining and relating, to name only a few. With the Version 14 these analytic commands have been extended by three machine learning commands named as “Train”, “Predict” and “Cluster”. In these two blogposts we will introduce these three commands to you by taking examples from out of the everyday business. For all ACL users, we also offer the opportunity to download ACL projects, allowing you to try out each command, step by step.
The analysis of processes in companies with the help of process mining, especially for the processes purchase-to-pay and order-to-cash, is still in great demand. We have already presented our position and solutions on this topic in various blog posts.
In this blog post we show you two examples of methods by which the analysis software “ACL™ Robotics” - previously known as “ACL™ Analytics” - of the software manufacturer Galvanize makes it possible to implement machine learning. For expert users: Both supervised and unsupervised learning approaches are supported. “ACL™ Robotics” is a software solution which has already been assisting the manual and automated analysis of large amounts of data for many years. Besides having a variety of interfaces to e.g. SAP (via “SAP Connector”), Salesforce, Google Hive, Amazon Redshift, Outlook, PDF imports or any ODBC data sources desired, an automated script language helps to automate the sequence of analytic steps. The software developer Galvanize allocates this to the field of RPA (Robotic Process Automation). Individual analytic steps are implemented by methods or commands, such as sorting, summarizing, joining and relating to name only a few. With Version 14 these analytic commands have been extended by three machine learning commands named as “Train”, “Predict” and “Cluster”. In this blog post we are familiarising you with the use of these three commands, taking specific examples from the world of business, such as forecasting return values and the clustering of customers combined with due dates for payment. For existing ACL users, we also offer the opportunity to download ACL projects with the examples, and thus be able to try out each method, step by step, for yourself.
Process Mining is one of the trending topics in many companies, as it is hoped that by visualizing the process, weak points can be identified and eliminated. However, very few people realize that process mining does not work without the correct preparation of data.
We have already kept you up to date with updates about Galvanize's Highbond platform. The investments Galvanize is making in the GRC platform, combined with its clear and well-defined strategy, are now paying off: Forrester Research lists Galvanize as a leader in its report "The Forrester Wave™: Governance, Risk, and Compliance Platforms, Q1 2020".
Artificial intelligence and machine learning – no matter the medium you use (print media, online portals, radio or TV), you will generally come across one of these terms sooner or later. But as the saying goes – everything has already been said, but not yet by everybody.