MBA in Business Intelligence Management

Become a key player in the definition and control of business strategy thanks to this MBA in Business Intelligence Management from TECH"

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills training.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Technological University

Innovation

The university offers an online learning model that combines the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system.  

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives trained each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience in a program with a global vision. They will study in a multicultural context. through which they can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH's students represent more than 200 different nationalities.   

Learn with the best

In the classroom, TECH teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

TECH's teachers represent more than 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning methodology (the most internationally recognized postgraduate learning methodology) with Harvard Business School case studies. A complex balance of traditional and state-of-the-art methods, within the most demanding academic framework.   

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs and in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.  

At TECH you will have access to Harvard Business School case studies"  

The MBA in Business Intelligence Management is an exceptional program that challenges the professional by directing their attention towards success in the business world and providing a quality service and placing a value on human capital. The program has been structured in such a way that the student will not only acquire all the knowledge and skills that they seek, but will also benefit from a uniquely stimulating experience that will propel them to the pinnacle of their professional capacity. 

You will learn how to manage your emotions and influence your company´s results and your professional future" 

Syllabus

The MBA in Business Intelligence Management at TECH Technological University is an intensive program that prepares students to face challenges and business decisions in the field of technology and data and information generation systems. 

The content of the MBA in Business Intelligence Management is designed to promote control and strategic decision making in a successful business environment. 
Over the course of 1,500 hours, the student analyzes a plethora of practical cases through individual work and teamwork. It is, therefore, an authentic immersion in real business situations. 

As such, this Executive Master's Degree deals in depth with the concept of Business Intelligence from a thoroughly up-to-date perspective, one which is fully focused on solving the real requirements of the business world. It is designed to train professionals who understand Business Intelligence from a strategic, international and innovative approach. 

A plan which has been designed for the student and is 100% focused on their professional improvement. At TECH we prepare students to achieve excellence in the field because we understand both their needs and the company's and provide ingenious content based on the latest trends which is supported by the best educational methodology and an exceptional faculty. 

This MBA takes place over 12 months and is divided into 10 modules:

Module 1. Enterprise Business Intelligence 
Module 2. Business Perspective 
Module 3. Data-Driven Business Transformation
Module 4. Data Visualization 
Module 5. Programming for Data Analysis
Module 6. Digital Marketing Analytics 
Module 7. Data Management
Module 8. Business Intelligence and Artificial Intelligence: Strategies and Applications 
Module 9. Optimization of the Company's Human Capital 

Where, when and how is it taught?

TECH offers the possibility of taking this program completely online. Over the course of the 12 months, the student will be able to access all the contents of this program at any time, allowing them to self-manage their study time. 

Module 1. Enterprise Business Intelligence

1.1. Corporate Business Intelligence 

1.1.1. The World of Data
1.1.2. Relevant Concepts
1.1.3. Main Characteristics
1.1.4. Solutions in Today's Market
1.1.5. Overall Architecture of a BI Solution
1.1.6. Cybersecurity in BI and Data Science

1.2. New Business Concept 

1.2.1. Why BI?
1.2.2. Obtaining Information
1.2.3. BI in the Different Departments of the Company
1.2.4. Reasons to Invest in BI

1.3. Data Warehouse 

1.3.1. Definition and Objectives Data Warehouse and Data Mart
1.3.2. Architecture
1.3.3. Dimensional Modeling and Its Types of Diagrams
1.3.4. Extraction, Transformation and Loading Process (ETL)
1.3.5. Metadata

1.4. Big Data and Data Capture

1.4.1. Capture
1.4.2. Transformation
1.4.3. Storage

1.5. Reporting Business Intelligence (BI) 

1.5.1. Database Structures
1.5.2. OLTP and OLAP Databases
1.5.3. Examples

1.6. Dashboards or Balanced Scorecards 

1.6.1. Balanced Scorecards
1.6.2. Decision Support Systems
1.6.3. Executive Information Systems 

1.7. Deep Learning

1.7.1. Deep Learning
1.7.2. Deep Learning Fundamentals
1.7.3. Deep Learning Applications

1.8. Machine Learning

1.8.1. Machine Learning 
1.8.2. Machine Learning Fundamentals
1.8.3. Uses of Machine Learning 
1.8.4. Deep Learning vs. Machine Learning 

1.9. BI Tools and Solutions

1.9.1. Choosing the Best Tool
1.9.2. Microsoft Power BI, MicroStrategy and Tableau
1.9.3. SAP BI, SAS BI and Qlikview
1.9.4. Prometheus

1.10. BI Project Planning and Management 

1.10.1. First Steps to Define a BI Project
1.10.2. Corporate BI Solutions
1.10.3. Requirements and Objectives

Module 2. Business Perspective 

2.1. The Company  

2.1.1. Capital, Investment and Risk
2.1.2. Organizational Morphology: Size, Shape, Activity and Sectors
2.1.3. Organization and Resources
2.1.4. Management and Their Needs

2.2. Company: Market and Customer

2.2.1. Market and Customer
2.2.2. Market Analysis and Segmentation
2.2.3. Direct and Indirect Competition
2.2.4. Competitive Advantage

2.3. Business Strategy

2.3.1. Business Strategy
2.3.2. SWOT Analysis
2.3.3. Objectives and Deadlines [SMART, C/M/L/P, Cascading Objectives]
2.3.4. Measuring Results: Knowing the Reality
2.3.5. Key Indicators

2.4. Information as an Asset

2.4.1. Information and Management
2.4.2. Life Cycle Information
2.4.3. Operational System and Strategic System

2.5. Balanced Scorecard

2.5.1. Operational, Tactical and Strategic Scorecards
2.5.2. Balanced Scorecard Definition
2.5.3. Financial Perspective
2.5.4. Customer Perspective
2.5.5. Internal Processes Perspective
2.5.6. Learning and Growth Perspective

2.6. Productivity Analysis 

2.6.1.  Income, Expenditures, Investment and Consumption
2.6.2. Cost Analysis and Allocation
2.6.3. ROI and Other Ratios of Interest

2.7. Distribution and Sales

2.7.1. Relevance of the Department
2.7.2. Channels and Equipment
2.7.3. Types of Sales and Consumption

2.8. Other Common Areas

2.8.1. Production and Service Delivery
2.8.2. Distribution and Logistics
2.8.3. Commercial Communication
2.8.4. Inbound Marketing 

2.9. Data Management 

2.9.1. Roles and Responsibilities
2.9.2. Stakeholder Identification
2.9.3. Information Management Systems
2.9.4. Type of Operating Systems
2.9.5. Strategic or Decision Support Systems
2.9.6. Platforms for Information: Cloud Computing vs. On Premise

2.10. Exploring the Information

2.10.1. Intro SQL: Relational Databases Basic Concepts 
2.10.2. Networks and Communications: Public/Private Networks, Network/Subnet/Router Address and DNS. VPN Tunnel and SSH 
2.10.3. Operational System: Standardized Data Templates
2.10.4. Strategic System: OLAP, Multidimensional Model and Graphical Dashboards 
2.10.5. Strategic Analysis of Databases and Report Composition

Module 3. Data-Driven Business Transformation

3.1. Big Data 

3.1.1. Big Data in Enterprises
3.1.2. Concept of Value
3.1.3. Value Project Management

3.2. Digital Marketing 

3.2.1. Digital Marketing
3.2.2. Benefits of Digital Marketing

3.3. Action Plan

3.3.1. Campaigns and Types
3.3.2. Redemption and Drive
3.3.3. Types of Strategies
3.3.4. Digital Marketing Plan

3.4. Execution of the Marketing Plan

3.4.1. Customer Journey (Baseline-Campaign-Redemption-Improvement) and Digital Marketing
3.4.2. Web Integration of Digital Marketing Tools
3.4.3. Digital Marketing Tools

3.5. Customer Journey

3.5.1. Customer Life Cycle
3.5.2. Association of Campaigns to the Life Cycle
3.5.3. Campaign Metrics

3.6. Data Management for Campaigns

3.6.1. Datawarehouse and Datalab
3.6.2. Campaign Creation Tools
3.6.3. Drive Methods

3.7. Digital Marketing GDPR

3.7.1. Data Anonymization and Manipulation of Personal Data
3.7.2. Robinson Concept
3.7.3. Exclusion Lists

3.8. Control Panels

3.8.1. KPIs 
3.8.2. Audience
3.8.3. Tools
3.8.4. Storytelling

3.9. Customer Analysis and Characterization

3.9.1. 360º Customer Vision
3.9.2. Relation of Analysis to Tactical Actions
3.9.3. Analysis Tools

3.10. Business Examples Applying Big Data Techniques 

3.10.1. Upselling/Cross-Selling
3.10.2. Propensity Models
3.10.3. Risk Models
3.10.4. Predictions
3.10.5. Image Processing

Module 4. Data Visualization 

4.1. Data Visualization 

4.1.1. Data Visualization
4.1.2. Importance of Data Analysis and Visualization
4.1.3. Evolution

4.2. The Design 

4.2.1. Use of Color
4.2.2. Composition and Typography
4.2.3. Recommendations

4.3. Types of Data

4.3.1. Qualitative
4.3.2. Quantitative
4.3.3. Temporary Data

4.4. Data Sets 

4.4.1. Files
4.4.2. Databases
4.4.3. Open Data
4.4.4. Streaming Data

4.5. Common Types of Representation 

4.5.1. Columns
4.5.2. Bars
4.5.3. Lines
4.5.4. Areas
4.5.5. Dispersion

4.6. Advanced Types of Representation 

4.6.1. Circular
4.6.2. Rings
4.6.3. Bubbles
4.6.4. Maps

4.7. Application by Area 

4.7.1. Political Science and Sociology
4.7.2. Science
4.7.3. Marketing
4.7.4. Health and Well-Being
4.7.5. Meteorology
4.7.6. Business and Finance

4.8. Storytelling 

4.8.1. Importance of Storytelling
4.8.2. History of Storytelling
4.8.3. Application of Storytelling

4.9. Visualization Software 

4.9.1. Paid
4.9.2. Free
4.9.3. Online
4.9.4. Free Software

4.10. The Future of Data Visualization 

4.10.1. Virtual Reality
4.10.2. Augmented Reality
4.10.3. Artificial Intelligence

Module 5. Programming for Data Analysis

5.1. Programming for Data Analysis

5.1.1. Language for Data Analysis 
5.1.2. Evolution and Characteristics of the Main Tools 
5.1.3. Installation and Configuration 

5.2. Types of Data

5.2.1. Basic Types 
5.2.2. Complex Types 
5.2.3. Other Structures

5.3. Structures and Operations 

5.3.1. Data Operations
5.3.2. Control Structures 
5.3.3. File Operations 

5.4. Data Extraction and Analysis 

5.4.1. Statistical Summaries 
5.4.2. Univariate Analysis 
5.4.3. Multivariate Analysis 

5.5. Visualization

5.5.1. Univariate Graphs 
5.5.2. Multivariable Graphs
5.5.3. Other Charts of Interest

5.6. Pre-Processing 

5.6.1. The Importance of Data Quality 
5.6.2. Outlier Detection and Analysis 
5.6.3. Other Dataset Quality Factors 

5.7. Advanced Pre-Processing

5.7.1. Subsampling 
5.7.2. Resampling 
5.7.3. Dimensionality Reduction

5.8. Data Modeling  

5.8.1. Modeling Phases 
5.8.2. Division of the Data Set 
5.8.3. Metrics for Prediction 

5.9. Advanced Data Modeling 

5.9.1. Unsupervised Models
5.9.2. Supervised Models 
5.9.3. Libraries for Modeling 

5.10. Tools and Good Practices

5.10.1. Best Practices for Modeling 
5.10.2. The Tools of a Data Analyst 
5.10.3. Conclusion and Bookstores of Interest

Module 6. Digital Marketing Analytics 

6.1. Web Analytics

6.1.1. Web Analytics Use
6.1.2. History
6.1.3. Applicable Methodology

6.2. Google Analytics 

6.2.1. About Google Analytics
6.2.2. Metrics vs. Dimension
6.2.3. Measurement Objectives

6.3.  Reports 

6.3.1. Basic Metrics
6.3.2. Advanced Metrics or KPIs (Key Performance Indicators)
6.3.3. Conversions

6.4. Dimensions

6.4.1. Campaign / Keyword)
6.4.2. Source/Media
6.4.3. Contents

6.5. Universal Analytics vs. Google Analytics 4 

6.5.1. UA Differences vs. GA4
6.5.2. Advantages and Limitations
6.5.3. Use of UA and GA4 Tools

6.6. Setting up Google Analytics 

6.6.1. Installation and Integration
6.6.2. Structure of Universal Analytics: Accounts, Properties and Views
6.6.3. Conversion Goals and Funnels

6.7. Reports 

6.7.1. Real-Time Analytics
6.7.2. Audience Analytics
6.7.3. Purchase Analytics
6.7.4. Behavior Analytics
6.7.5. Conversion Analytics

6.8. Advanced Reports

6.8.1. Panels
6.8.2. Personalized Reports
6.8.3. APIs

6.9. Segments 

6.9.1. Difference between Segment and Filter
6.9.2. Types of Segments: Predefined / Customized
6.9.3. Remarketing

6.10. Digital Analytics 

6.10.1. Measurement
6.10.2. Implementation
6.10.3. Conclusions

Module 7. Data Management

7.1. Statistics

7.1.1. Statistics: Descriptive Statistics, Statistical Inferences 
7.1.2. Population, Sample, Individual 
7.1.3. Variables: Definition, Measurement Scales 

7.2. Types of Data Statistics 

7.2.1. According to Type

 7.2.1.1. Quantitative: Continuous Data and Discrete Data
 7.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data 

7.2.2. According to Its Form: Numerical, Text, Logical
7.2.3. According to Their Source: Primary, Secondary

7.3. Data Management Planning

7.3.1. Definition of Objectives
7.3.2. Determination of Available Resources
7.3.3. Establishment of Time Lapses
7.3.4. Data Structure

7.4. Data Collection

7.4.1. Methodology of Data Collection
7.4.2. Data Collection Tools
7.4.3. Data Collection Channels

7.5. Data Cleaning 

7.5.1. Phases of Data Cleaning
7.5.2. Data Quality
7.5.3. Data Manipulation (with R)

7.6. Data Analysis, Interpretation and Evaluation of Results 

7.6.1. Statistical Measures
7.6.2. Relationship Indices
7.6.3. Data Mining

7.7. Data Visualization

7.7.1. Suitable Display According to Data Type
7.7.2. End-User Considerations
7.7.3. Executive Models of Results Presentation

7.8. Data Warehouse (Datawarehouse) 

7.8.1. Elements of a Data Warehouse
7.8.2. Design
7.8.3. Aspects to Consider

7.9. Data Availability  

7.9.1. Access
7.9.2. Uses
7.9.3. Security

7.10. Practical Applications 

7.10.1. Data Exploration
7.10.2. Manipulation and Adjustment of Patterns and Structures
7.10.3. Test Application and Modeling

Module 8. Business Intelligence and Artificial Intelligence: Strategies and Applications

8.1. Financial Services

8.1.1. The Implications of Artificial Intelligence (AI) in Financial Services. Opportunities and Challenges 
8.1.2. Case Uses 
8.1.3. Potential Risks Related to the Use of AI
8.1.4. Potential Future Developments/Uses of AI

8.2. Implications of Artificial Intelligence in the Healthcare Service 

8.2.1.  Implications of AI in the Healthcare Sector. Opportunities and Challenges 
8.2.2. Case Uses

8.3. Risks Related to the Use of AI in the Health Service

8.3.1. Potential Risks Related to the Use of AI
8.3.2. Potential Future Developments/Uses of AI 

8.4. Retail 

8.4.1. Implications of AI in Retail. Opportunities and Challenges 
8.4.2. Case Uses 
8.4.3. Potential Risks Related to the Use of AI 
8.4.4. Potential Future Developments/Uses of AI

8.5. Industry 4.0 

8.5.1. Implications of AI in the 4.0 Industry. Opportunities and Challenges
8.5.2. Case Uses

8.6. Potential Risks Related to the Use of AI in the 4.0 Industry 

8.6.1. Case Uses
8.6.2. Potential Risks Related to the Use of AI
8.6.3. Potential Future Developments/Uses of AI 

8.7. Public Administration

8.7.1. Implications of AI in Public Administration: Opportunities and Challenges
8.7.2. Case Uses 
8.7.3. Potential Risks Related to the Use of AI 
8.7.4. Potential Future Developments/Uses of AI 

8.8. Education 

8.8.1. Implications of AI in Educational: Opportunities and Challenges
8.8.2. Case Uses 
8.8.3. Potential Risks Related to the Use of AI 
8.8.4. Potential Future Developments/Uses of AI

8.9. Forestry and Agriculture 

8.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges 
8.9.2. Case Uses
8.9.3. Potential Risks Related to the Use of AI
8.9.4. Potential Future Developments/Uses of AI 

8.10. Human Resources 

8.10.1. Implications of AI for Human Resources Opportunities and Challenges
8.10.2. Case Uses 
8.10.3. Potential Risks Related to the Use of AI 
8.10.4. Potential Future Developments/Uses of AI 

 Module 9. Optimization of the Company's Human Capital 

9.1. Human Capital in the Company

9.1.1. Value of Human Capital in the Technological World 
9.1.2. Executive Skills 
9.1.3. Paradigm Shift in Management Models

9.2. The Director’s Skills 

9.2.1. Management Process
9.2.2. Management Functions
9.2.3. Group Leadership Management in Companies: Group Relations

9.3. Communication in the Company 

9.3.1. The Company's Communication Process 
9.3.2. Interpersonal Relations in the Company 
9.3.3. Communication Techniques for Change 

 9.3.3.1. Storytelling 
 9.3.3.2. Assertive Communication Techniques. Feedback, Consensus

9.4. Business Coaching 

9.4.1. Business Coaching
9.4.2. The Practice of Coaching
9.4.3. Types of Coaching and Coaching in Organizations

 9.4.3.1. Coaching as a Leadership Style

9.5. Business Mentoring

9.5.1. Mentoring in the Company 
9.5.2. The 4 Processes of a Mentoring Program
9.5.3. Benefits of this Business Tool 

9.6. Mediation and Conflict Resolution in the Company 

9.6.1. The Conflicts
9.6.2. Preventing, Addressing and Resolving Conflict
9.6.3. Stress and Work Motivation

9.7. Negotiation Techniques 

9.7.1. Negotiation at the Managerial Level in Technology Companies
9.7.2. Strategies and Main Types of Negotiation 10.7.2.1. The Figure of the Negotiating Subject 

9.8. Enterprise Change Management

9.8.1. Factors of Organizational Change 
9.8.2. Strategic Planning 
9.8.3. Organizational Change Management 

 9.8.3.1. For Intangible Change: Teams, Communication, Culture, Leadership 
 9.8.3.2. For Basic or Tangible Change: Goal Setting, Performance Measurement, Learning, Recognition and Rewards 

9.9. Techniques for Improving Equipment Performance

9.9.1. Teamwork Techniques
9.9.2. Delegating in Work Equipment

9.10. Group Dynamics. Classification

9.10.1. The Role of the Dynamizer
9.10.2. Group Dynamics Techniques

 9.10.2.1. Brainstorming+
 9.10.2.2. Philps 6/6
 9.10.2.3. Hot Air Balloon D

A unique, key, and decisive educational experience to boost your professional development and make the definitive leap"