Database Solutions

Data Normalization

The better organization of data and knowledge of data analysis lead to more generalized designing of application development will be. There are lots of ways to analyze data and different techniques must be used depending on goal.

A primary goal of good database design is to make sure that data can be easily maintained over time. Databases are great at managing more records. They are terrible if fields need to be added since all its queries, forms, reports, and code are field dependent.

By normalizing your data and splitting your database into separate application and data MDB files, you’ll go a long way toward establishing a solid foundation for your database development efforts. Data normalization not only makes your data be more accurate, it makes it easier to analyze, and more importantly, maintain and expand. Separating your application and data databases enables you to support multiple users and upgrade the application without wiping out their data. Assuming the application doesn’t change that often, the separation also makes it easier to just backup the data database since only that is changing everyday.

By normalizing data and splitting database into separate application and data files, it will lead to a long way toward establishing a solid foundation for database development efforts. Data normalization not only makes data be more accurate, it makes it easier to analyze, and more importantly, maintain and expand. Separating application and data databases enables to support multiple users and upgrade the application without wiping out their data. Assuming the application doesn’t change that often, the separation also makes it easier to just backup the data database since only that is changing everyday.

Data Migration

Data migration has become one of the most challenging efforts facing large organizations. The race to build business-to-business and business-to-consumer systems via the web is introducing new technologies that assume the existence of relational data. Replacing legacy systems with package solutions requires moving and converting data. Similar demands apply to consolidating duplicate systems that spring from mergers and acquisitions, moving applications to distributed platforms, and modernizing existing applications.

But ensuring that all your valuable data is properly transferred and transformed and that the relationships that make the information useful are preserved can make data migration difficult and time consuming. The challenge: How do you successfully migrate hundreds of database tables and millions of individual pieces of data while avoiding the obstacles and failures that can result in months of delays and runaway costs? And we had successfully overcome this challenge

Database Modeling

Nearly every computer system relies on at least one database somewhere. Often, the database design is the core of the system itself. Proper modeling of data and choice of database management systems (DBMS) can be the most important part of a system design, so it's not something to cut corners on.

Data modeling - the process of designing structures, schema and metadata before tables are created.

Normalization - helps ensure proper relations and data integrity by applying a few rule checks to the design.

These steps are critical, because no tricks or tweaks will fix a bad data model once it's in place - only redesign will do.