Six Sigma In Data Warehousing

The primary reason that corporations introduce Sixclogged straining server resources. Then there are
Sigma into data warehousing boils down to costsome other factors that have a play in affecting the
reduction. Large corporations are incurring hugeperformance of ETL.
expenditures, most of the times running into millionsMeeting the Challenge to Quantify the Data
of dollars, which eats into stakeholders' margin, inWarehouse Effect
creating and maintaining data warehouses. TheQuantifying the effects of data warehouse is to
criticality of data warehouses can be understood byproject whether challenges can be scaled. The recent
their vital role in support to prediction of businesstrend in data warehouse development is to treat
performance.them as belonging to the same family or group.
There is no denying the fact that data warehousingConsider dedicating each family to a particular
is in a way, the powerhouse of Six Sigmageographical location, and other subsets of respective
deployment. In early stages of projects, datahierarchical data. Warehousing modules for individual
warehousing allows for better planning ofdata groups (families) are developed at their initial
deployment, design and tuning of the productionstages and new ones are taken care off as and
environment.when they arise and are just plugged into the main
Data Warehousing Basicsdata warehouse. The database could contain three
Data warehousing components are complex in naturefundamental tables such as tables to store attributes
and are multifaceted. The various components areof data; storage of linking information; and finally,
either developed in house or by a third party or inaggregated data ready for use.
joint development at the party's place of business.Applying Six Sigma Elements into Software
Typically, designers focus on functional and businessDevelopment
needs and not on performance constraints faced byApplying Six Sigma elements into software
the production environment. The consequence of thisdevelopment typically helps in identifying potential
costly mistake is the possibility of missing deadlinesproblems in production if the development is done in
and reworking the project, which are manifestationsthe early stages of the project. Secondly, the
of operational inefficiencies.mammoth task of data warehousing can return
Challenges to Data Warehouse Designpositive results if deployment plans are fine tuned
It is not new that modern day data warehouses arebefore implementation.
built for auto refreshing and/or compatible for atThe self-assessing nature and the provisions for
least real time updating. ETL, as extraction,internal auditing shed light on the course of
transformation and loading of data flow is a veryimplementation. At the same time, one cannot forget
resource-consuming exercise in data warehousing.that databases developed remain tied to the system
The importance of data warehousing increasesarchitecture on which they are built and bear heavily
several times, considering the fact that dataon the accuracy of predictions in a fluctuating
structures are both strategic and functional.business environment, ironically for which they are
Even the real time refreshing of data becomes abuilt.
daunting task with the refresh window getting