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.
We have now been dealing with the topics of data analysis, audit, risk management, compliance and ICS (ICS Internal Controls) for over 15 years. Especially in the recent past of the last 1-2 years a lot has happened here.
Those of you who are responsible for data analysis in your company will certainly have already noticed that it is one thing to run analyses. The processing of the results, on the other hand, is a completely different field, which usually takes much more time and effort than expected.
At this year's Audit Competence in Vienna, I had the opportunity to give a presentation on new possibilities for audit work through digitalisation. The aim was to illustrate that digitization offers chances to perform the functions of internal auditing better and more efficiently.
A number of suppliers now offer process mining solutions. This is good for customers, because apart from a few exceptions, prices are now in an acceptable range. However, it is important to consider which criteria to use when deciding on a tool.
The third Advent is already over, and Christmas is approaching with big steps. We look back on a (positively meant) turbulent and successful year 2019, marked by important projects, enriching encounters and exciting events. But one after the other.
From 21st until 22nd November, the biennial congress from the German Institute for Internal Audit (DIIR) took place in Dresden. Several hundred auditors from all over Germany attended more than 70 lectures on topics related to internal auditing.
While process mining has been a topic of discussion for some while, interest has recently intensified. Google currently shows 1,100,000 hits for searches on the subject, and Gartner’s most recent “Market Guide for Process Mining” published in June 2019 lists 19 suppliers in its “Representative Vendors” section. (https://www.gartner.com/en/documents/3939836/market-guide-for-process-mining)
Process Mining has been a topic in the data analysis world for several years now and represents an extension of existing data analysis solutions for many companies. While in classical data analysis one usually moves to the level of detail of individual transactions, Process Mining provides an overview of the actual processes in the company. This overview of the processes can help you to better understand the background of analysis results of classical data analysis. I would like to show you how we can support you with our experience in data extraction and data preparation. In addition, we can make recommendations from our work with various Process Mining providers to help you choose the right provider. In this article I will use the software Minit as a showcase.