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Future of Data Warehouse, Data Mining & Data Visualisation February 11, 2010

A focus on the return to basics – drive revenue, cut costs, and acquire and retain customers.

Other trends indicate that the future will bring a level of complexity and business importance that will raise the bar for all of us. The real-time implementation of a business action, decision or change of direction that is based on the results of strategic data analysis is now the reality. The data issues surrounding this trend aren’t getting any easier or smaller. Combine the need for real-time data warehousing and increased data size and complexity, and we set the stage for a new type of warehouse – the “virtual” enterprise data warehouse. This virtual DW or private hub for both operational and informational needs will begin to drive new demands on the ability of organizations to assimilate vast data assets stored in merged/acquired companies or divisional enterprise resource planning (ERP) and legacy environments. The time and/or dollars needed to integrate all the operational systems will make the traditional method of data integration impractical. This intersection of Web channels and data warehousing has the potential to become the standard architecture for large, complex organizations.

Next, it is without a doubt that the entire customer relationship management (CRM) explosion is driving a large portion of the current data warehouse projects in the industry. But is this really new? I’ve always stated that those of us who are veterans in the DW world were building and running CRM environments before there was an acronym for CRM. Furthermore, we are definitely seeing a reverse evolution in the CRM space, which accentuates the importance of analyzing and measuring the effectiveness and efficiency of operational CRM capabilities. Continuing on the theme of measurement, remember the executive information system (EIS)? The EIS was the easy-to-use, show-me-the-numbers application directed at the senior business management of a company. The intent of an EIS was to make the key performance indicators essential to running a business available at the touch of a button. These applications were the rage in the mid-’80s. The EIS lives again as the digital dashboard. All the leading online analytical processing (OLAP) software vendors have developed or are in the process of developing key performance indicator modules; and many dashboard-specific vendors are starting up and/or growing rapidly.

Lastly, the complete scope of the organization, structure and processes needed for a successful customer deployment of a large, complex data warehouse continues to be a major problem for most organizations. Making it happen successfully takes a unique focus and team of people. Mainly, the ability of an organization to change or dispose of the conventional wisdom regarding the correct mix and role of staff is important. Regarding this topic, I have the following observations. Your customer deployment team structure must mimic your desired business goals; and the team should reside within the same space – business operations, business analysts and IT professionals need to learn to cohabitate. Training must go beyond basic tool training to include pure business solution analysis tied to the desired outcome. Tight tracking and measurement of the expected business return has to be easily understood and continually revisited against expectations. Include in your engineering of the customer deployment shop not just what the team members will do, but how they will do it. What process can be made repeatable, scalable and flexible enough to change with the business?

http://www.information-management.com/issues/20011101/4212-1.html

The complexity of data mining must be hidden from end-users before it will take the true center stage in an organization. Business use cases can be designed, with tight constrains, around data mining algorithms.

http://www.dwreview.com/Data_mining/Future_data_mining.html

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