Blog 2: Data Warehouse Design

As a self-proclaimed foodie, I often find myself creating analogies transferring the data world to the culinary.  This is in no way unique to me, with Lecture 3: Data Warehouse Design Cycle relating the ODS to a restaurant's kitchen and Kimball Group itself publishing articles around The Kitchen Metaphor. In contemplating how to create a cohesive narrative and allow for deepening understanding as I learn throughout the weeks, my foodie instincts won out. 

Mise en place: Data Warehouse Design & Advanced Star Schema

Mise en place is the french phrase describing the activities performed in advance of cooking, to be well-prepared for the requirements of a menu to ensure accuracy and consistency.  Its literal translation is "putting in place" or "everything in its place".  The some recipes, this is as simple as decanting pantry ingredients into their respective bowls and containers.  For others, it can be as complex as preparing starter components of a recipe days (even months!) before the meal is cooked. As I read through the Data Warehouse Design, Advanced Star Schema, and Data Quality Analysis lectures, I drew parallels between these concepts and the rigor of mise en place.

Imagining an organization's transaction systems like the pantry, the organization of ingredients is functionally different for transaction processing and analytical processing (cooking).  Where a cook would store flour, sugar, and other dry ingredients in separate pantry containers - "denormalized" - mise en place would have them combine all measured dry ingredients into a single bowl, like a flattened fact table of a star schema. 


Food Quality: Data Quality Analysis

Despite the obviousness of the metaphor, the food quality assessments made by a cook brings a certain tangible aspect to the concept of data quality.  When transferring bulk ingredients to a mise en place format, all ingredients must be determined for safety, readiness, quantity, consistency and cleaned and staged per the protocols of the kitchen.  These quality checks follow the data profiling steps for verifying completeness, referential integrity, and format validity. 

Menu Engineering: Balanced Scorecard

The Balanced Scorecard supports decision-making by consolidating its organizational performance into manageable and related lenses: Finance, Processes, Customers, and Learning.  Technically, the parallel with the food industry isn't a parallel - its a direct application though sometimes referred to as as menu engineering.  All four perspectives are present in the determination of what goes on (and of) a menu.  Customer perspective speaks to the type of restaurant: fast casual, fine dining, family friendly.  Internal Processes speaks to the kitchen and wait staff capabilities necessary to deliver the restaurant setting - imagine a Hibachi restaurant where the cook must be capable of executing the internal processes so precisely that they are a part actual dining experience by cooking in front of the customers.  Learning and Growth relates to how the restaurant wants to skill-up its staff, which will vary and depend on the type and variety of menus it produces.  And of course, the financial perspective is critical to menu planning from balancing ingredient costs to preparation overhead, with balancing menu pricing.