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…

IBM Information Framework (IFW) Banking Data Warehouse model (BDW) can be used for various purposes. In this article I will tray to catch them all.

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.

There is a lot of business intelligence projects claimed as “success” upon implementation. However, a revisit after a few months with the business sponsors of the projects shows the absolute failure. There can be a lot of causes for such epilogue. However, the authentic (necessary, but no sufficient) indicator about success or failure of business intelligence project is User adoption.

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.

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.

Data Quality is one of the most common issues in business intelligence projects implementation, especially in the data warehouse implementation. There is no organization having an optimal data quality – there is always certain level of data inadequacy. Let’s see what we can do with that…

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.