Building Life’s data marts:

In continuation to my earlier post on A data warehouse called Life, let’s try to build small data marts for life’s departments.

A data mart by its ideal definition is a subset of the data warehouse which is oriented for specific business lines or teams. What this means is that while the data warehouse would be storing data for the entire enterprise, a data mart would have data relevant only to specific businesses within the organization. A marketing team would have no use of HR data and vice versa. Likewise for our architecture for the data warehouse called Life.

Defining Life’s areas:

In order to start building a data mart we need first define the different disparate areas of our lives. An ideal design would involve: the professional life, personal life and the individual life. Depending upon the kind of the person, life’s areas can be ideally charted out. Once the business definitions and bifurcations are through, the next step is to define tables and keys.

Defining tables:

Reiterating, the purpose of the data mart is to present only that data that is relevant to a specific area of our lives. When a particular data set is required it can be easily retrieved from a data mart instead of the query being fired on the entire data warehouse.

I usually follow the star schema to store data within the data mart. For the uninitiated, a star schema is a truly unique design that is aimed at faster retrieval of results from the repository.

Defining keys:

Keys connect the dimension tables to the facts. They provide the key to the relationship between calculated measures which reside in the fact table and the static data that resides in the dimension.

A mind map can connect the measures in the fact to the incidents, colleagues and success or failures.

Analyzing trends:

The ultimate aim of the data warehouse is to come up with trends and lessons for the future. Keeping the data structured, layering the lessons learnt and the people we have met over our life time can help come up with some of the best analytics about life.

We usually tend to forget about our mistakes in life and only seldom do we ever care to learn from other people’s errors. The journey of life is a process of continuous evolution by incorporating teachings and lessons that we learn on the way. Setting up measures like success rates and failure rates can help in tweaking the way we live life.

With a sample representation of the data as above, one can come up with a unique approach as to understanding what went wrong and why. Happy journey everyone.