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
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
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
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.