Training Certificate

Prolific Consultants FZE Certificate of Course Completion will be issued to all attendees.

Course Introduction

Data management plays a significant role in an organization’s ability to generate revenue, control costs and mitigate risks. Successfully being able to share, store, protect and retrieve the ever-increasing amount of data can be the competitive advantage needed to grow in today’s business environment. Data Management plays a key role in helping an organization mitigate risks.

This course provides an overview of data management. The need to deliver good data management is increasingly being seen as providing advantage across the industry, since wise business decision depends on sound data and information.

Course Objective

This course focuses on the techniques and applications data management and analysis.  Typically, focuses on understanding the data, empirical model building using observational data for characterization, estimation, inference and prediction. 

Participants will leave this course with an understanding of the core data types, their use in the business, and data management issues facing companies. You will have the knowledge and tools to participate in developing a structured data management frame work, which deal with issues in all practical and effective manners to ensure efficiency and effectiveness.

Participants will study the theory, principles and methods for data management and analysis of observational data. Participants will develop empirical model building skills and be able to employ the models for characterization, estimation and prediction purposes.

Suitable For

This course is intended for Operators, Engineers, managers and decision makers in various industrial and service sectors, private and public fields that need a tool to plan for the future of their company. Strategic planning managers, research and development managers, general managers, and can be tailored according to company’s specific needs.

Training Methodology

A highly interactive combination of lectures and discussion sessions will be managed to maximize the amount and quality of information and knowledge transfer. The sessions will start by raising the most relevant questions, and motivate everybody find the right answers. You will also be encouraged to raise your own questions and to share in the development of the right answers using your own analysis and experiences.  Tests of multiple-choice type will be made available on daily basis to examine the effectiveness of delivering the course.

Very useful Course Materials will be given.

• 30% Lectures
• 30% Workshops and work presentation
• 20% Group Work& Practical Exercises
• 20% Videos & General Discussions

Course Content

• Resources and data types: definitions
• Common data management issues: causes of data issues, data management best practices, business impact.
• Overview of data management: definition, data lifecycle, importance and value of data management, benefits of good data management, business case aspects and barriers.
• Data management framework: governance, architecture, security, reference and master data management, data quality management.
• Data loading & Data quality control
• Best Practices

DAY WISE COURSE OUTLINE:

Below topics will be covered in detail:

PART I: Data Management

• Resource and Data types: definitions
• Common data management issues: causes of data issues, data management best practices, business impact
• Overview of data management: definition, data lifecycle, importance and value of data management, benefits of good data management, business case aspects and barriers
• Data management framework:

• Data Governance
• Data Architecture Management
• Data Security Management
• Data Development
• Data Operation Management
• Data Warehousing & Business Intelligence Management
• Document & Content Management
• Reference and Master Data Management
• Data Quality Management
• Meta Data Management
•  

• Data loading & Data quality control
• To know for what Data are important
• Data Presentation
• Data Interpretation
• Best practices

PART II: DATA ANALYSIS

• Introduction to Statistics and data description
• Sampling Distribution
• Case Study

• Parameter Estimation
• Testing of Hypotheses
• Case Study

• Simple Linear regression
• Multiple Linear Regression
• ANOVA Tables
• Case Study

Case Studies, Role Plays, Videos, Discussions, Last Day Review & Assessments will be carried out.

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