I recently presented with Jessica Dunn from Bank of America at the SAS Global Forum Executive Conference. Our presentation addressed the considerations necessary to build and manage an effective analytics infrastructure. Although we both worked on our presentations separately before we had a chance to discuss teh session, we built a similar story. That story is that even though technology is important, infrastructure is much more than technology. It’s a combination of people, process and technology, with data at the center.
Hung LeHong, Gartner Research VP set the stage as the keynote presenter at the SGF Executive session. Hung spoke about the future role of analytics and highlighted the fact that IT needs to play a critical role.
To build off of this introduction, my presentation focused on providing a set of design considerations for the analytic infrastructure. I provided 15 considerations, that span strategy & vision, people, process, technology and data/information. In the coming days, I plan to blog about each of these considerations.
Analytics Infrastructure Considerations
Vision & Strategy
- Drive analytic success via common vision shared by all constituents
- Support multiple forms of analytics for maximum value
- Analytics = Art + Science. IT should focus on the science to enable analytic art
People
- It’s not just “Business” and “IT”
- Get the right people: Don’t skip on training
- Leverage Center of Excellence (COE) principles
Process
- Understand the analytics lifecycle
- Ultimately it’s about improving the business process
- Strike the right balance between control and user flexibility
Technology
- Leverage Enterprise Architecture principles to ensure proper design
- Think big! Big data & big analytics: High Performance Analytics is key
- Integrate SAS into the overall IT infrastructure
Data / Information
- Design data strategy that results in information as a strategic asset
- Leverage comprehensive Information Management approach
- Step up to data preparation: Free up the scarce analytic resources
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Thank you for this post. I got this reference from MIT's Big Data Analytics class. This would be of great used to me as I venture into Project Management for Big Data projects. I really appreciate this.