Thursday, May 1, 2014
09:30 AM - 10:15 AM
Methodology that explains the technical pieces of an analytics solution and shows how to leverage existing data warehousing architecture to facilitate adoption of Big Data Analytics.
It will help data architecture professionals build analytics solutions without a multi-million dollar investment. The methodology covers the architecture, process, roles and responsibilities.
The emphasis is on solving business problems in a sustainable and repeatable manner so analytics solutions can be rolled out across the entire organization in a manner like data marts and reporting are rolled out.
Additionally, the methodology defines and clearly distinguishes analytics from existing reporting and analytical solutions to remove confusion and simplify the understanding of analytics.
The methodology shows how to build analytics models using data mining tools, how to validate and explain results to the business and facilitate adoption of analytics for business decisions. It also covers an audit and control framework necessary for analytics driven decision making
- What is analytics and how to differentiate from existing reporting and BI solutions
- How to build analytics models using data from the warehouse and using data mining tools instead of statistics
- How to make a business case and demonstrate the value to the business
- How to deploy predictive models in decision strategies for operational decision making
- How to tune and improve models and decision strategies for constant innovation
- What are the required skills, roles and responsibilities to build an analytics organization
Nauman Sheikh is a veteran of the data architecture profession who has built dozens of large scale operational, data warehouse and analytics systems over the last 18 years. He has worked in three continents solving business challenges in consumer credit, risk, fraud and direct marketing areas dealing with a variety of cultural, technological and legal challenges surrounding data and its use. He is a hands-on practitioner with skills ranging from data warehousing to predictive modeling to analytics driven business decisions and their audit and control frameworks.
He is the author of a recently published book on analytics implementation and adoption. He has worked with companies like Experian, Fidelity Information Systems, Navistar and Exelon where he has dealt with multi-terabyte data challenges. Last few years he has been focused on predictive analytics for unconventional problem domains in government, education, energy and agriculture sectors. He lives near Washington D.C in USA.