Enterprise data warehouse implementation is a huge, complex, long term project. In fact, it is not project at all, but program. Every success has its foundations and supporting pillars. In this article I will discuss three pillars of successful implementation of a data warehouse, based on IBM IFW BDW model.

In BDW implementations, gathering customer requirements and mapping them to IFW BDW model is one of the most critical phase of the project. Let’s find some hints how to do that on the right way…

Despite the good position in CIO’s priorities, huge number of running projects and significant investments - CRM systems does not rich full potential in giving the benefits for the organizations. Now is time for changes…

After five years as the top CIO technology priority (2004 – 2009) CIOs responding to the 2010 Gartner Executive Programs CIO survey placed BI as fifth among their top 10 technologies. Does this mean that companies have given up on data warehouses, analytic and the rest? Is Business intelligence rally dead?

My point of view: BI IS NOT DEAD! As long as business people making their decisions leveraging on information assets – business intelligence will be alive. However, the truth is that there is a substantial need for changing Business Intelligence approach.

Today’s business environment is highly demanding in terms of organization’s capacity to respond to the changes in a various areas such as business, regulatory, customer behavior, market, technology and so on. The most valuable resource in order to respond to these changes is information. Consequently, it is very important to make evaluation of your data foundation environment. Are you ready to respond to the changes?

Formalization of Information Strategy will allow to an organization to drive business intelligence on the strategic way and that would consequent with improved alliance with business objectives, efficient and effective solution implementations, faster user adoption and finally with lower TCO and increased ROI.

More than 85 percent of all business information exists as unstructured data – commonly appearing in e-mails, memos, news, user groups, chats, reports, letters, surveys, white papers, marketing material, research, presentations and Web pages. Can you imagine any important decision that has been made leveraging on 15 percent of the real information potential?

ETL (Extract Transform Loading) requires 70% of total effort for the data warehouse implementation. Considering that as well as fact that each organization have certain level of data quality inadequacy – effective driving of ETL process during data warehouse implementation should be one of the key success factors of the whole project. Here you can find important aspects of ETL process.

Analytical CRM analyzes customer data for a variety of purposes such as designing and executing targeted marketing campaigns, customer acquisition, cross-selling, up-selling, addon-selling, etc. Analytical CRM generally makes heavy use of data mining techniques to produce useful results for decision-making. Let’s see how you can leverage on Microsoft Data mining tools in order to design and develop Analytical CRM solution.