Topic outline

  • COURSE INTRODUCTION

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    • Time: 46 hours
    • Free Certificate

    This course focuses on how information systems that store, collect, and manage data can be paired with analytic techniques to provide organizational decision-makers with information and insights that allow them to make more effective decisions. The ultimate aim of any decision-making process is to produce more efficient and effective organizational operations that enable a company to gain a competitive advantage.

    With the advent of automated transaction processing systems, a large amount of data is created and stored as a result of organizational operations. When coupled with external data on customers, economic indicators, customer preferences, and even text-based social media, this data can be used to feed analytic models. This big data can be stored in data warehouses, extracted and organized with systems like data mining, and incorporated into data governance and quality management systems.

    Sometimes, data can be interpreted directly based on how it is presented. Data visualization and dashboards can present data to decision-makers in useful formats like graphs, heat maps, tree maps, charts, word clouds, text tables, and sentiment analysis.

    Often, raw data does not provide the decision-maker with sufficient insight, no matter how well presented. Data are used to populate models for more complex analysis to provide the decision-maker with additional insight. Models represent reality that the decision-maker can interact with through forecasting and scenario analysis. We cover many analytic approaches, including predictive analytics, prescriptive analytics, diagnostic analytics, and cluster analysis. Continuous refinement and optimization of analytical models based on feedback and evolving business requirements is important.

    Finally, the course examines the legal structures affecting data privacy, protection, and security. We look at laws and industry-specific regulations, the ethical principles underlying them, and the importance of adhering to these laws and regulations. We also discuss how to support privacy, including ways to anonymize and pseudonymize information, and consider ethical best practices in the context of organizational policies.

  • Unit 1: Business Intelligence and Its Role in Organizations

    Unit 1 covers the foundational concepts that underlie Business Intelligence (BI) systems and the significance of BI in modern organizations. Business intelligence primarily supports strategic and managerial decision-making by providing decision-makers with insights derived from data. These insights help support more informed and effective decision-making processes.

    This unit also traces the historical evolution of BI systems. BI has evolved from a supplementary tool to an integral component of modern organizational operations. The shifts in organizational paradigms accompanying BI integration into mainstream business operations have enhanced competitive advantage. The unit delves into BI's critical elements, including data sources, analytics, and decision support systems, which form the basis for developing BI solutions. We use examples of successful implementations to show how BI techniques can have a transformative effect on organizational performance and provide a competitive advantage.

    We also examine data integration, quality, and governance, which are essential to an effective BI system. We will introduce data modeling, warehousing, and managing internal and external sources. To conclude, we summarize common BI tools and technologies designed to create solutions that address specific business challenges.

    Completing this unit should take you approximately 5 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] explain the foundations of business intelligence (BI) as a mechanism to transfer raw data into intelligence through the use of databases and decision models; [2] analyze the practical applications of BI in organizations using data storage systems and decision modeling; [3] apply the fundamentals of data management, such as data modeling and relational database design for BI; [4] apply BI concepts in practical scenarios involving the collection, storage, and analysis of data

  • Unit 2: Sources of Data in BI Systems

    In the modern business world, data can come from a wide range of internal and external sources. Internal data is generated from the transaction processing systems executing business processes, and external data can come from almost anywhere. Because of this wide diversity of data sources, BI system designers need to be concerned with the quality and reliability of the data.

    Data can take many forms. In Unit 2, we examine the classifications of data sources and develop methods for assessing their quality and completeness. We also look at the different categories of data, explore how different data types can be collected and integrated, and examine data integration strategies and specific methods and tools that implement them.

    Finally, we look at the implications of poor data quality on the outcomes and decisions fostered by BI systems. This has become an especially important topic in the age of big data, distributed data, and NoSQL data (data from systems that do not use Structured Query Language).

    Completing this unit should take you approximately 6 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] identify data sources based on the type of data and how it will be used to support decision making; [2] evaluate data quality and relevance through the use of the six dimensions model; [3] analyze the effectiveness of data integration strategies and technologies such as real-time processing and the ETL model; [4] apply big data models and NoSQL sources to BI through the use of integration of data storage and retrieval systems

  • Unit 3: Data Management and Data Warehousing

    Data is the raw material for a business intelligence system. In this unit, we explore how to manage the data we have assembled. We discuss data governance in the organizational context and how to approach the management of strategic organizational data. We then extend our examination of data management to the domain of data architectures, data models, and data extraction.

    A data warehouse or data mart is the central place for storing and organizing strategic data. A data warehouse must be designed to store data in many formats. These warehouses must accommodate everything from highly structured data from relational database systems to much less structured data from social media or other sources.

    We examine some of the basic structures of relational databases that support data warehouses. We also discuss data modeling and conversion of models for relational structures and the Extract, Transform, Load (ETL) process that moves data into the warehouse. Finally, we explore the integration concepts necessary to implement a data warehouse.

    Completing this unit should take you approximately 6 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] apply data management principles such as planning, development, and implementation to database system design; [2] implement a data warehousing structure to centralize data in support of BI; [3] apply effective data modeling techniques like the relational model for BI systems to analyze and define the different data types a business collects and produces; [4] integrate data management and warehousing in BI and analytics projects

  • Unit 4: Data Analysis and Interpretation

    Unit 4 begins with an overview of the key concepts and methodologies of data analysis by introducing several analytic techniques, including statistical methods and machine learning algorithms, to explore how data can support decision-making. We cover a spectrum of analytical approaches, including predictive analytics, prescriptive analytics, diagnostic analytics, and cluster analysis, as well as the distinct purposes and methodologies associated with each. Choosing the most appropriate approach for a given business challenge is important.

    We also examine data mining in analytic systems, showcasing how advanced techniques can uncover valuable patterns and insights within vast datasets. We also explore how to evaluate the accuracy and effectiveness of models via methods such as model validation and performance metrics such as robustness and reliability. Continuous refinement and optimization of analytical models based on feedback and evolving business requirements is critical.

    Completing this unit should take you approximately 5 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] apply data analysis techniques such as regression analysis, factor analysis, and cluster analysis to datasets; [2] apply data analysis interpretation skills such as pattern analysis and trend identification to develop actionable insights; [3] describe the four stages of the data mining process: data generation, data acquisition, data storage, and data analytics; [4] apply the principles of data mining to textual data analysis using tools like word clusters and text mining

  • Unit 5: Data Visualization and Reporting

    Unit 5 covers data visualization, reporting principles, and common practices in business intelligence systems. We explore the fundamental principles of data visualization and learn how to effectively communicate analytical results through visual representations. The unit emphasizes the strategic use of visualization tools and techniques to represent complex data, fostering the development of skills in selecting the most suitable visualizations for diverse datasets.

    The unit covers communication methods that can be adapted to different institutional stakeholders, such as reports and dashboards. We cover dashboard design and focus on how dashboards can be customized to meet the needs of various stakeholders. The most important principles are simplicity, clarity, and interactivity.

    This unit also covers graphical aids for presenting data, such as heat maps, treemaps, and charts. We examine the principles of good design associated with each method and understand when and how to use each technique to convey specific types of information. Finally, we explore graphing textual information through text tables, word clouds, and sentiment analysis.

    Completing this unit should take you approximately 8 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] apply data visualization techniques such as dashboards, reports, and charts to support effective communication; [2] analyze the effectiveness of BI insights through data visualization; [3] apply common data visualizations such as charts, heatmaps, tree maps, waterfall charts, and bubble charts; [4] apply common visualizations of textual information, such as word clouds and semantic networks

  • Unit 6: Data Analytics

    Now, let's look at business analytics in business intelligence systems. The unit introduces various analytic techniques, from statistical methods to machine learning algorithms, and explores how data can support decision-making. There are many analytic approaches, including predictive, prescriptive, diagnostic, and cluster analysis. Each has distinct purposes and methodologies, and an analyst must choose the most appropriate approach for specific business challenges. Data mining techniques can uncover valuable patterns and insights within vast datasets, contributing to organizations' strategic decision-making processes.

    Finally, we examine model accuracy and effectiveness. Model validation and performance metrics, such as robustness and reliability, are important when considering the continuous refinement and optimization of analytical models based on feedback and evolving business requirements.

    Completing this unit should take you approximately 4 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] explain the difference between describing and analyzing data; [2] evaluate analytical models based on their validity, effectiveness, and accuracy; [3] apply descriptive analytics, predictive analytics, and cluster analysis to BI situations; [4] implement data mining techniques such as clustering and classification to support analytic systems

  • Unit 7: Business Intelligence Tools

    Many tools perform the modeling and analysis necessary to implement a business intelligence system. The unit focuses on the mathematical underpinnings and software to implement a business intelligence model. As we discuss these, we must consider model validity and some issues associated with model boundaries and limitations. Descriptive and inferential statistics provide the foundation for many of the techniques and are essential to understand along with other methods, such as time series analysis, forecasting, and predictive modeling.

    Unit 7 examines software tools like spreadsheet systems, R, and Python. We cover the basic operation of these systems and give examples of how they can be used to implement mathematical techniques and models in support of business intelligence operations.

    Completing this unit should take you approximately 5 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] apply fundamental data analysis techniques such as descriptive statistics, inferential statistics, and hypothesis testing; [2] apply statistical software and programming languages used in business intelligence, such as R or Python; [3] explain the strengths and limitations of various analytical approaches

    • 7.1: Data Analysis Techniques and BI

    • 7.2: Statistical Software and Programming Languages

    • 7.3: Strengths and Limitations of Analytical Approaches

    • Unit 7 Assessment

      • quiz icon
        Unit 7 Assessment Quiz
        Students must
        Receive a grade

        Take this assessment to see how well you understood this unit.

        • This assessment does not count towards your grade. It is just for practice!
        • You will see the correct answers when you submit your answers. Use this to help you study for the final exam!
        • You can take this assessment as many times as you want, whenever you want.

  • Unit 8: Legal and Ethical Considerations

    Much of the data we might desire to use for BI purposes has legal restrictions on its use. Unit 8 examines the legal structures affecting data privacy, protection, and security. These include the European Union's General Data Protection Regulation (GDPR), the United States' Health Insurance Portability and Accountability Act (HIPAA), and other laws. As we look at these, we explore some of the ethical principles underlying these laws and discuss the importance of adhering to them.

    Failure to follow data privacy laws or comply with regulations can negatively affect organizations and lead to increased costs and reputational damage. Damage to the organization's public perception can be difficult to manage and recover from. BI algorithms can also introduce ethical issues – some analysis techniques that rely on large data sets may include bias. This bias may then influence decisions that may be unfair or discriminatory against certain groups of people.

    Finally, we explore ethical best practices in the context of organizational policy. Corporate culture, governance structures, and ethical frameworks all contribute to the development of organizational policy. As we examine these, we discuss strategies organizations can apply to maximize compliance with data privacy laws and reduce legal risk.

    Completing this unit should take you approximately 7 hours.

    • Upon successful completion of this unit, you will be able to:

      [1] explain the different legal frameworks, such as GDPR and HIPPA, that impact business intelligence; [2] evaluate ethical dilemmas such as transparency, bias, and fairness in BI decision-making in terms of general ethical concepts like morality and agency; [3] assess the impact of BI systems and techniques on individual privacy; [4] analyze strategies for corporate governance and corporate cultural transformation to ensure that BI practices are legal and ethical

  • Study Guide

    This study guide will help you get ready for the final exam. It discusses the key topics in each unit, walks through the learning outcomes, and lists important vocabulary. It is not meant to replace the course materials!

  • Course Feedback Survey

    Please take a few minutes to give us feedback about this course. We appreciate your feedback, whether you completed the whole course or even just a few resources. Your feedback will help us make our courses better, and we use your feedback each time we make updates to our courses.

    If you come across any urgent problems, email contact@saylor.org.

  • Certificate Final Exam

    Take this exam if you want to earn a free Course Completion Certificate.

    To receive a free Course Completion Certificate, you will need to earn a grade of 70% or higher on this final exam. Your grade for the exam will be calculated as soon as you complete it. If you do not pass the exam on your first try, you can take it again as many times as you want, with a 7-day waiting period between each attempt.

    Once you pass this final exam, you will be awarded a free Course Completion Certificate.