Data Management, Security and Warehousing Training Course

Data warehouses are the foundation of modern-day data management. Data warehouses store critical data so that you can access it when you need it. They’re the electronic systems for organizing, storing, and retrieving your company’s data. They’re often the first step in data analysis since they provide a secure location for storing and processing your data. Companies of all sizes utilize them to store, manage, and analyze their data. They lead on the path to data-driven decision-making and are essential to every data-driven organization.

Why is data security important in data management?

Data provides a competitive advantage by enabling decision-making and allowing a company to adapt to changing market conditions. However, data is also an attractive target for corruption. It becomes a liability if it’s lost or stolen.

Thus, efficient data security becomes an integral part of data management. Data security comprises establishing a set of controls, procedures and policies to protect data when it is stored or when it is being transferred from one place to another. Data security is a critical component of any modern digital business. The right data security strategy can help you avoid accidental loss, costly downtime, and data breaches. It is a responsibility shared by everyone in the organization, regardless of whether they deal with data daily. It involves knowing how your data is used, who has access to it, and the plan to protect it.

Rcademy’s comprehensive data management, security and warehousing training course has been compiled to provide fundamentals of the essential data management solutions presently relevant in the industry. In this Training Course, you’ll learn the ins and outs of data management, such as how to tackle data warehouses, build a data security program, etc. You’ll also learn about the most common data challenges and how to overcome them. This course is designed for individuals looking to establish themselves in a data-driven organization and those who want to learn more about data management but don’t have time for a full degree. This course is entirely self-paced, so you can learn when it’s most convenient.

Who should attend?

Rcademy’s training course is ideal for data-oriented individuals as well as enthusiasts such as:

• Data Managers
• Business Analysts
• Business architects
• Project manager
• IT professionals
• Graduates and Scholars
• Entrepreneurs

What are the course objectives?

Rcademy is focused on achieving the following objectives through the training course:

• To develop capabilities to identify patterns in data
• To help develop insights into data for effective data management solutions
• To enable participants’ successful optimization of data
• To empower participants with the correct use of data for decision making
• To learn ways to create and access data across a range of data tier
• To learn efficient ways to improve access to different data sets by proper data warehousing
• To understand methods of improving data quality
• To overcome the challenges of data deficiency
• To learn detailed concepts of data management and their relevance in today’s data-driven corporate world
• To learn ways to devise the most effective data security strategy suitable for the organization
• To learn the latest trends and practices prevalent in the field of data management
• To get handy with the latest tools and techniques of data management

How will this course be presented?

• Interactive sessions
• Use of case studies
• Management games
• Learning preparation of reports, charts, graphs
• Real-time exercises
• Problem-solving and Group discussion sessions

What are the topics covered in this course?

Module 1: Introducing Data Management

• Relevance of data management in the present scenario
• Treating data capital as business capital
• Components of data management
• Data management platforms
• Improving data quality

Module 2: Understanding Databases

• Databases available
• SQL
• File-based databases, hierarchical databases
• Relational database
• Cloud database
• Object-oriented databases
• Database management systems

Module 3: Data Architecture

• Enterprise data modeling
• Data fabric
• Related data architecture
• Data pipelines
• Standards of data architecture

Module 4: Data Preparation

• Data collection
• Data discovery: identifying patterns and attributes
• Data cleansing
• Data structuring
• Data transformation: ETL
• Data validation

Module 5: Data Integration

• Manual Data Integration
• Application-based integration
• Approaches; tight coupling and loose coupling
• Schema integration

Module 6: Data Warehousing

• Scope of data warehousing
• Approaches: query-driven approach, update-driven approach
• Types of data warehouses

Module 7: Data Security

• Data security management
• Methods of securing data
• Developing security protocols
• Data protection regulations
• Data Security vs Data Privacy
• Data Security risks

Module 8: Data Quality Management

• Specification
• Analysis
• Measurement
• Improvement

Module 9: Challenges to Data Management

• Lack of data insight
• Maintaining performance levels of data management
• Compliance regulations
• Unstructured to structured data
• Fostering data culture in the organization

Module 10: Latest Trends in Data Management

• Multi-cloud data strategies
• Augmented data analytics
• AI-enabled data management