Business Data Analytics

Introduction

In this day and age, it is no surprise for the 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, analyse 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.
In this course, we use MS Excel exclusively as an ultimate and readily available BI tool allowing you to develop an exclusive level of expertise and add immediate value to your job and company.

Course Objectives

• 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

Course Outlines

Day 1: Data Visualization

• Defining Data, Information, and Insight.
• Comparing Data Visualisation and Infographics.
• Using Charts and Graphics for Data Visualization.
• Designing Visuals and Infographics for Non-financial Users.
• Using Excel and PowerPoint for Data Visualisation.
• Designing and Using Scorecards and Dashboards.
• Practical Tips for Building Dashboards and Dashboards in Excel.

Day 2: Understanding Business Models and Business Processes 

• Creating Value for Key Organisational Stakeholders.
• The Role of the 21st-Century Finance Professional.
• Business Models and Business Processes.
• Business Process Improvement and Re-engineering.
• Introduction to Business Intelligence and Business Analytics.
• Introduction to Data-Driven Decision Management (DDDM).
• Key Financial Measures and Shareholder Value Drivers.
• Key Success Factors and Key Performance Measures.

Day 3: Mastering data reporting

• Interpret data to inform business decisions.
• Recognize trends, detect outliers, and summarize data sets.
• Analyse relationships between variables.
• Develop and test hypotheses.
• Craft sound survey questions and draw conclusions from population samples.
• Implement regression analysis and other analytical techniques in Excel.

Day 4: Purpose and Principles of Business Intelligence and Business Analytics

• Implementing a Business Performance Management Framework.
• The Definition and Purpose of Business Intelligence (BI).
• History and Development.
• Features Tools and Terminology of BI Systems.
• The Definition and Aims of Business Analytics (BA).
• Understanding Descriptive and Predictive Analytics.
• Sources and Types of BI and BA Tools.

Day 5: Principles of Statistical and Predictive Analytics  

• Defining Statistical, Descriptive, and Predictive Analytics.
• Business Database Features and Design.
• Principles of Distribution and Probability Theory.
• Time Series Data Analysis.
• Trend Analysis and Forecasting using Moving Averages.
• Trend Analysis and Forecasting using Linear Regression.
• Analysis and Forecasting with Monte Carlo Simulation in Excel.
• Predictive Analytics using ‘what-if’ Forecasts.