Although computer systems help solve many problems in business, they use
so many different kinds of programs that they can’t always communicate
easily with each other. A tremendous number of systems make up a modern
organization — payroll, accounting, expenses, time, inventory, sales, customer
relations, software licensing, and so on. Many of these systems have
their own databases and ways of storing data. Combining data from the
tangle of systems — let alone doing something useful with the combined
data — becomes extremely difficult.
Business intelligence creates a “big picture” by storing and organizing data
from many disparate systems in one usable format. The idea is to make the
data readily accessible for reporting, analysis, and planning. A data warehouse
is a central database created for just that purpose: making the data
from all those sources useful and accessible for the organization. The idea is
to give decision-makers the information they need for making critical business
A data mart is a more specialized tool with a similar purpose; it’s a functional
database that pulls particular information out of the overall Data Warehouse
(or even directly from source systems depending on who you ask) to answer
specific queries. For example, a manufacturing location may need to compile
some specialized data unique to the process used to make a particular product.
The overall data warehouse is too big and complex do that job (or to modify
effectively to handle it), so a smaller version — in BI lingo, a data mart — can be
created for this one manufacturing location.
The Microsoft SQL Server Database Engine manages not only data warehouses,
but also data marts — and both types of data storage can become
massive. Fortunately, SQL Server addresses this problem by storing one
database across a cluster of many different servers. This approach accommodates
the enterprise as it grows in scale.