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The fundamentals of management information – D



Denglisch für kompakte Kennzahlsammlungen auf einer Seite (vgl. → Tachos).

Data Mining (Datenmustererkennung)

Unser Lieblingsthema, mit dem bei uns alles anfing. Steht für den Wunsch nach Automatisierung von Routine, für das Generieren von Verdachtsmomenten und das Erkennen neuer Zusammenhänge. Aktuell Belebung des Themas durch alles, was mit → Big Data zusammenhängt. DeltaMaster gilt mit Recht als Pionierprodukt für betriebswirtschaftliches Data Mining und hat dafür den Innovationspreis der Gesellschaft für Informatik gewonnen.

Data Scientist

Neue Berufsbezeichnung für Leute, die schon alles wissen, was hier steht. Finden wird man sie vor allem unter Wirtschaftsinformatikern, die auf dem Laufenden sind. Die Herausforderung für den Data Scientist liegt vor allem darin, zwischen Fachabteilung und IT den rechten Weg zu finden. Auf der einen Seite ist er mit der Hoffnung der Fachabteilung konfrontiert, man könne das unberechenbare Verhalten von Märkten und Kunden berechenbarer machen. Auf der anderen Seite will und kann die IT ihre datenliefernden Prozesse gar nicht so schnell anpassen, wie sich die fachlichen Fragen ändern. Auch deshalb wünscht man sich → Selfservice-BI.


A concise collection of KPIs on one page (see also → gauges).

Data collection

An underestimated means of Business Intelligence. The analysis of existing data suffers, in part, because data was collected for another purpose as the one that interests you today. DeltaMaster supports → instant BI, which means that business users can quickly build an application based on Excel or text files and then analyze it using built-in business, statistical, or data mining methods.

Data density

Criteria for reporting (see also → resolution). Good reports are filled with data yet easy to read. If they aren’t, that is probably due to the design and not the amount of information. Strangely, the market has promoted → dashboards which have only 5 to 10 % of the data density of a simple table showing the results from last week­end’s soccer matches. In DeltaMaster, sparklines, microcharts, and graphical tables ensure high levels of both → readability and data density.

Data Mining

Our favorite topic and the roots of our business. Data mining stands for the desire to automate routines, generate moments of suspicion, and recognize new corre­lations. This topic is experiencing its renaissance through everything that has to do with → Big Data. DeltaMaster is a ground-breaking product for business data mining and has won the Innovation Prize from the German Informatics Society (GI).

Data scientist

A new job title for people who already know everything that is written here. You typically find them among business information systems experts who are very up to date in their field. The main challenge for data scientists is to find the right ba­lance between business and IT. On the one side, business departments have the hopes of making the impulsive behavior of markets and customers more predic­table. On the other hand, IT cannot and does not want to change their data-delivery processes so quickly because the technical questions involved are changing as well. And that is another reason why people want → self-service BI.


A property of attributes. Some common attributes that only have two forms are: on or off, yes or no, and man or woman. In management information, dichotomy interests us as a postulate for designing warning signs and dealing with colors. → Traffic lights have three states, which makes them too vague in our opinion. You can, however, designate KPIs dichotomously with the help of → color. They either have a positive effect on profits or a negative one, in which case, we color them blue or red respectively in DeltaMaster. We feel if reporting cannot differentiate what is good or bad for the business, it is dodging around its actual purpose.


A criterion for quality charts and reports. Differentiation can be measured as a percent value difference per pixel. It differentiates how quickly you realize what has stayed the same and what is new and different. Differentiation depends on the → scale and → resolution. To obtain the necessary differentiation, you need to display the variances to the average or a target value instead of the absolute values. All templates in DeltaMaster are designed according to a simple rule: Devote every pixel to the difference.

Dot bars, dot columns chart

A new chart type based on a dotted line and a large point that designates the value it is representing. Dot bars/columns were first suggested by William Cleveland. What makes them so sophisticated is that their → perspective priority applies to the gaps between the dots and not the gap to the base line. As a result, you can use them as you would use bars or columns but can apply a → logarithmic scale. This, in turn, solves the problem of → size breaks. Sadly, this chart type is not widely used and, as far as we know, is only offered in DeltaMaster.


Kognitives Ordnungsprinzip. Die Komplexität der Welt lässt sich brauchbar reduzieren durch eine Beschreibung und Einteilung in jeweils drei Gruppen. Zum Beispiel: „Mag ich“, „ist mir egal“, „mag ich nicht“. Eine Einteilung in zwei Gruppen ist dogmatisch („Gläubige/Ungläubige“), vier sind schwammig, fünf ist genauer, als wir es sagen können. Das gilt auch für die Gruppierung von Daten-Elementen, wie sie mit DeltaMaster verarbeitet werden.