Why Attend
In this day and age it is no surprise for corporate staff to be overwhelmed by the abundance of unstructured data. ERPs and databases have evolved to a point where they can house amazingly large amounts of data. The question now is, what do you do with this data to add value? This program will introduce Business Intelligence (BI), hands-on, to allow you to clean, normalize, and interpret large volumes of data. You will be able to establish historical relationships, analyze the current situation and predict future strategies. The application of BI is borderless, covering operational, tactical and strategic business decisions. It spans all departments and cascades down to all users who perform data cleansing, reporting, analysis, modeling, integration and automation.
Course Methodology
20% of the course is design and structure focused while 80% uses MS Excel as a BI tool. Groups and individuals will be required to complete exercises, case studies, and projects on a daily basis.
Course Objectives
By the end of the course, participants will be able to:
- Boost Excel Business Intelligence (BI) expertise in data slicing and dicing, data massaging, and data aggregation and modeling
- Perform data normalization, consolidation, report writing, analysis and reconciliation
- Develop dynamic BI models, dashboards, scorecards and flash management reports by linking-up Excel with Access, Web, Text, Internet, SQL, ERPs and other databases
- Advance and enhance the look and feel of reports using dynamic visualization techniques
- Acquire numerous tips and tricks that enable them to work efficiently
Target Audience
Business professionals, business analysts, data analysts, research analysts, finance professionals, marketing and sales professionals, HR professionals, IT professionals, administrative staff, supervisors, general business professionals and staff from any function who need to learn and apply state-of-the-art data analysis techniques to their daily business reporting and decision making.
Target Competencies
- Massaging and normalizing data
- Reporting, analysis and reconciliation
- Interpretation of large data sets
- Modeling and 'what-if' scenarios
- Establishing data integration
- Developing dynamic dashboards and scorecards
- Designing key performance indicators
