  Database normalization is the one of main principles for designing relational
databases. The benefits of normalization can be observed through improving data
quality and performance, among the other qualities. We explore a new context of
technical debt manifestation, which is linked to ill-normalized databases. This
debt can have long-term impact causing systematic degradation of database
qualities. Such degradation can be liken to accumulated interest on a debt. We
claim that debts are likely to materialize for tables below the fourth normal
form. Practically, achieving fourth normal form for all the tables in the
database is a costly and idealistic exercise. Therefore, we propose a pragmatic
approach to prioritize tables that should be normalized to the fourth normal
form based on the metaphoric debt and interest of the ill-normalized tables,
observed on data quality and performance. For data quality, tables are
prioritized using the risk of data inconsistency metric. Unlike data quality, a
suitable metric to estimate the impact of weakly or un-normalized tables on
performance is not available. We estimate performance degradation and its costs
using Input\Output (I\O) cost of the operations performed on the tables and we
propose a model to estimate this cost for each table. We make use of Modern
Portfolio Theory to prioritize tables that should be normalized based on the
estimated I\O cost and the likely risk of cost accumulation in the future. To
evaluate our methods, we use a case study from Microsoft, AdventureWorks. The
results show that our methods can be effective in reducing normalization debt
and improving the quality of the database.
