Based on a world-class consulting team of Data Warehouse experts, PHD Brasil develops a wide range of processes combining the customer's best existing practices with those adopted by other successful companies.
The methodology used by PHD Brasil results from in-depth knowledge of the best practices and also, of technology - incorporated into products and also into the data warehouse, thus clarifying how it operates and the true system capabilities. Here, the benchmark is the series of success-case projects implemented by PHD Brasil.
This methodology also allows an understanding of the effort involved in the use of products, which serves to guide the customer's demands regarding vendor responsiveness and support.
In short, PHD Brasil's experience is placed at the user's service. PHD Brasil knows how to teach, mentor, and transfer knowledge to the customer's staff members, thus ensuring the following results:
clear understanding of the value of the data warehouse investment;
robust functional organization to include roles and responsibilities;
best practice score card in each key data warehouse area;
detailed assessment of the current environment regarding strengths and reusability;
clarifying functional work flow and job responsibilities;
required technology and architecture to support your data warehouse vision;
foundation for an integrated view of customers;
a method to assure business, technology, and information quality;
a data warehouse plan that supports the customer's corporate strategy.
Planning-related activities directed to data warehouse initiatives allow PHD Brasil to assess the degree of readiness of the customer to start such a process, as well as allowing redirection of ongoing projects, even with some iterations already in place and with terabyte-sized data warehouses.
In the course of this operation, the following 15 focus areas are assessed:
organization and culture;
data warehouse development methodology;
data warehouse architecture;
data warehouse data quality;
data warehouse performance;
technical, content, and event metadata;
business value of the data warehouse;
requirements, including metadata;
data and metadata;
modeling of all data warehouse components;
data warehouse database design.