In this article I will explain a fact which might sometimes be a little confusing when looking at SAP® tables using the SAP® GUI transaction SE16 compared to downloading the data using a download tool. I will explain how SAP® displays data making it look like it was in one table, but in fact the data is separated in a table containing settings and technical information (a “Customizing Table”) and a table containing the descriptions and long texts in different languages (a “Text table”). I will explain where SAP® mixes those two, but also how you can find out about where the data really is located in the database tables.
On 1st and 2nd of June, in cooperation with our subsidiary AM:DataConsult, the dab:Group was present on the DIIR - IT Conference in Frankfurt as silver sponsor. The event dealt with "current developments, methods, IT security, tools, and technology" and more than 250 participants visited the event.
Following on from our blog series on topics like terms of payment in SAP® or process mining this time I want to launch a new series of blog posts all about ACL™ Analytics.
To start we look at practical ACL™ functions on text fields, showing you how simple it is to work with and even combine these fields. In addition to the technical explanation there I want to give you some background and advice based on my daily work, which hopefully will provide some added value for you. I will use the example of “Identifying master data duplicates” for explaining the following functions. If you examine customer or vendor master data for double entries for this purpose, it is advisable to search across different columns. The field with the phone number can also be a decisive indicator. But this very seldom uses a uniform convention, so first you have to prepare and make it comparable.
This time I would like to look at an article by Kerstin Daemon headed "Big data in business — when the firm knows when you want to quit before you do", which appeared in the April 15, 2015 issue of "Wirtschaftswoche" [WiWo1] and "The great data chaos of German business" by Meike Lorenzen published April 12th, 2013 [WiWo2].
This blog post marks the end of the series on terms of payment in SAP®. In two preceding instalments I showed how terms of payment exist at different levels or between different SAP® modules, and explained what to observe when comparing master data.
Exactly 10 years after the first Forum on Digital Data Analysis get-together at the Institute of Technology, the DFDDA e.V. invites again to Deggendorf and creates a great opportunity for an exchanging of ideas.
For a number of years now process mining has been a subject in the world of data analysis that analysts are increasingly focused on. We too have already devoted a blog article to this methodology and gathered initial experience in projects of a different scale and with different tools. Through this series of articles I would like to offer insight into the experience we have managed to gain.
On 12th and 13th of March the dab:Group was, in cooperation with AM:DataConsult, attending at the 6th DIIR (German IIA) – Anti-Fraud-Management-Conference in Bremen as silver sponsor. The headline of the event was "Different faces of economic crime, the chances for brightening the number of unreported fraud cases in companies and institutions."
On February 24 ACL™ hosted an event in Frankfurt to offer insight into vision and strategy plus a look at concrete product innovations. ACL™ was represented by Sean Zuberbier, vice president of Global Sales & Services, together with Dan Zitting, vice president of Product Management & Design.
In last week’s blog post I gave some insight into the fundamentals of SAP® vendor payment terms. In this article I will continue based on that and highlight some things that should be considered when analyzing payment terms in SAP® vendor master data. One of the issues which we solved in our pre-defined analytic solutions dab:FastForwards and dab:AuditObjects is to analyze cases where payment terms are missing or where differences exist between payment terms. The key message is – casually formulated - not to compare apples with oranges when interpreting the results.