Practical Tips: Database Cooking

In an interview, Carsten Fronia, the Marketing Research Manager at the SAZ-IT subsidiary, SAZ, in Garbsen, gives practical tips for users of database management.

In fine cuisine, it is the taste that counts. What counts in the practice of database management?

Data quality, data quality and again data quality! This always has to remain at the same level. This is important so that a forecast based on a model may also actually become reality. Meanwhile, the recipe is always being changed to suit changing tastes. In cooking jargon, this means: on the basis of my current culinary knowledge, how can I cultivate vegetable ingredients and change recipes so I can prepare a meal next week which tastes just as good or even better than today? In the information world, managers receive so-called progress signals providing absolute values as a basis for strategic decisions or presenting the current development trends.

Good results depend on good ingredients. What do users have to consider when preparing data models?

Good “taste”, to use the same metaphor, is based on a wide range of variables. To improve in marketing and better fulfill his potential, a retailer has to extend his range of operations or further penetrate his target group closer to home. The starting point is deciding how he selects the necessary information. To do this, relevant know-how has to be used at an early stage in strategic decisions. Example: Anyone loading the regional catchment areas of chains in Northern Germany into his data model has to be aware that Hamburg and Cuxhaven are theoretically only a few kilometers apart because the islands of Neuwerk and Scharhörn are exclaves of the City of Hamburg. Depending on the view taken, Hamburg extends to ten kilometers from Cuxhaven, although the two city centers are about 100 kilometers apart. Anyone who does not take these irregularities into account may possibly distort the results from his data model.

What advice would you give to users in the assessment of information?

Frequently, users of database management confuse gross and net response values from campaigns with various lists. However, if they apply these figures in a mathematically incorrect way, they are incorrectly assessing the results of lists. They may then be unaware that the highest reaction rate from lists is logically where they intersect.

This is the fourth post in our series Modern Database Management. The previous articles can be found here.