About the Program

Many organisations are working on big data to ensure business performance and increased productivity are apparent for the streamlining of business operations.

This is vital in the business environment today as data increases and organisations are finding it cumbersome to manage it manually.

To increase the awareness of how Big Data could be analysed and effectively implemented, this training workshop looks at the several aspects of Big Data and its business impact and significance in measuring business success and performance.

Why this course? What’s so special & unique about it?

Why the need to attend this course? What is so special and unique about this course?

This course is special because it highlights:

1. Understand Big Data and its importance and how business owners and managers can use the data to monitor organizational performance.
2. Learn how Big Data can help streamline workflow processes.
3. Understand how Big Data impact business performance.
4. Leverage the use of Big Data to increase productivity and enhance business performance.

What do you get out of this?

1. Increase awareness of the usefulness of Big Data in the organisation.
2. How to analyse Big Data to monitor business performance for my organisation.
3. How to interpret Graphical Bar Charts using Microsoft Excel spreadsheet to learn the impact of Big Data for my organisation.

How to apply Big Data analysis for use in my organisation.

Learning Outcomes

1. Understand the impact of Big Data in the business environment today and why the need for it?
2. Apply the fact-based decision-making models in the business environment
3. Understand the challenges companies face in optimising their data and consolidating it.
4. Apply the five methods to use data visualisation techniques.
5. Demonstrate using Charts and Graphs to communicate Big Data analysis to both internal and external players in my organisation for a clearer understanding.

Program Outline

DAY 1: 9:00AM – 5:00PM

MODULE 1:

• What are Business Intelligence and Data Analytics?
• Definitions of Business Intelligence
• History of Business Intelligence
• How is Business Intelligence is used to help businesses to monitor growth and performance
• Definition of Data Analytics
• Relationship between Business Intelligence and Data Analytics

MODULE 2:

• Data Here, There, and Everywhere!
• Oracle study on business data preparedness
• Overview of Study Findings-overwhelmed by volume of data and inability to utilize data effectively
• Solutions to data overflow problems

MODULE 3:

• Got Data? The Unique Role of the Data Analyst
• Role of a Data Analyst
• Skillsets required to be an effective Data Analyst

Exercise: Channeling Your Inner Analyst – Participants are told to imagine receiving a memo from their supervisor explaining that the company is downsizing. They are expected to take on additional responsibilities including doing data analysis. They must rewrite their current job description to include the new data analyst duties.

Facts or Feelings: Your Choice – As data becomes more widely available, businesses are finding more success in adopting a fact-based decision model rather than relying on traditional intuition alone. In this module, we examine more closely the two types of decision modelling businesses use as well as the benefits of the fact-based model. We cover the steps of the Rational Decision Model, a fact- based method for decision making.

MODULE 4:

• Fact-Based Decision-Making Process
• The two types of Decision Models Businesses use
• The Benefits of Fact- Based Decision Making
• Rational Decision Model: Six- Step Method
• Pal’s Diner: An Example of how the Rational Model is used in practice

Exercise: Who’s the Boss? – Participants are divided into groups; Imagine that they are the CEO of their own company. They define a business-related decision that they need to make and then apply the steps of the Rational Decision Model to arrive at the conclusion.

Big Data Anatomy – In this module, we visit the Big Data trend with a more detailed focus. We begin by defining the buzz word-“BIG DATA”, examining its core attributes, and outlining the factors that contribute to data being ‘big’. We explore how businesses collect structured and unstructured data, and the challenges they face in storing and effectively using both types of data.

Day 2: 9.00am – 5.00pm

MODULE 5:

• Big Data Anatomy
• The Attributes of Big Data
• Definition of Big Data
• The 4 V’s of Big Data
• Structured versus Unstructured Data
• The Challenges of Big Data

Exercise: Camp Data – Participants are asked to describe some of the big data challenges that their companies face and to outline what steps are being taken to address the problems.

Getting to Know Your Data – To better understand how to analyze data, we must first comprehend its depth. This requires drilling deep beneath the server it is located on and understanding its composition. Assume we are given a structured data set with labelled columns and completed rows. There are plenty of ways to summarize the story behind the data, but we cannot dive in without first getting to understand its fundamental structure. We begin by classifying the collected data as quantitative or qualitative. Then we further classify our column variables according to the way data is measured: nominal, ordinal, interval, or ratio. It is only after understanding this classification that we are able to proceed to the next step of choosing the appropriate analysis techniques which correspond to nominal, ordinal, interval or ratio variables.

MODULE 6:

• Getting to Know Your Data
• Data Types: Qualitative versus Quantitative
• Taking a Closer Look: Data Measurement
• Four Types of Data Variables:
• Definition and examples of Nominal Variables: Name only
• Definition and examples of Ordinal Variables: Order Matters
• Definition and examples of Interval Variables
• Definition and examples of Ratio Variables
• Summary of Statistics/Operations that can be performed on each type

Exercise: Marketing to Low Renters – Participants are told to put on their data analyst thinking caps. They have been employed as a junior data analyst for a Marketing Company whose goal is to make a marketing campaign for a client who plans on targeting the ‘needy’ population. Participants are given a public housing data set and told to classify each variable according to its measurement.

Data Visualization – A picture is worth a thousand words, and there definitely is no exception when it comes to summarizing data. This module is dedicated to highlighting the importance of visualizing data, and how the human eye depends on visual representation to get a quick sense of data relevance. Visual representation is the audience’s first impression of the data and forms a crucial step in inviting and maintaining genuine interest in a subject matter. We demonstrate how to create colorful, easy to understand tables, charts, and graphs that aid in helping us convey the story behind the data set being analyzed.

MODULE 7:

• The Fundamental Ways we use data Visualization techniques
• The five ways we use data visualization techniques

MODULE 8:

• Displaying Tabular Data in Excel
• How to create custom tables in Excel
• How to Sort/Filter tabular data
• How to create and manipulate pivot tables

MODULE 9:

• Using Charts and Graphs to Communicate Data
• How to create Pie, Column, and Line charts using Excel
• Communicating effectively using different chart types
• How to choose the correct chart to display the correct data type

Exercise: Table Mining – Participants develop tables to summarize trends in a data set related to low rent

Exercise: Charting Poverty – Participants develop charts and graphs to summarize the poor housing epidemic in a public housing data set.

Training Methodology

• Interactive Presentation & Input
• Demonstration of Techniques to adopt and leverage by the Course Leader
• Case Study Highlights and Discussion
• Open Discussion Initiated by the Course Leader