• Unit 4: Types of Data

    Data is, naturally, at the heart of DDDM. Numerous types of data and sources must be included in a robust DDDM initiative. Every type of data must be extracted from a source, transformed into a standard format acceptable for the data warehouse, and then made available for analysis. Once data is prepared, there are four types of analytics. Each one requires specific tools and well-formed queries to create both hindsight and foresight analytics. You will need to understand these types of analytics and how they relate to successful DDDM initiatives. This unit will cover different types of data and how they can be used, both individually and together, to generate insights into business operations and customers. We will also explore the role of "big data" in building a DDDM enterprise and how certain types of data can create additional complexity in analysis.

    Completing this unit should take you approximately 4 hours.

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

      • distinguish between different types of quantitative and qualitative data;
      • analyze the role of big data; and
      • compare different types of analytics and their role in building a data-driven decision-making enterprise.

    • 4.1: Quanitative Data

    • 4.2: Qualitative Data

      • Qualitative and Quantitative Data Page

        Qualitative and quantitative data have distinct characteristics. Read this article and pay attention to how the chart differentiates quantitative and qualitative data. Then, answer the example questions to evaluate your ability to recognize these differences.

      • More on Qualitative and Quantitative Data Page

        Read this article to further examine the differences between quantitative and qualitative data.

      • Quantitative versus Qualitative Data Summary Page

        By now, you should understand the differences between quantitative and qualitative data. It is essential to identify the proper data type to ensure your collection methods meet the business' objectives and goals. How do you know when enough data has been collected? The next section will define Big Data and how it can be used in analytics.

    • 4.3: Big Data

      • What is Big Data? Page

        Watch this video to learn what Big Data is, how it is currently being used, and expected future uses.

      • Big Data in New Product Development Book

        Big Data is a powerful tool in any data-driven decision-making scenario. Read this article to learn how it can be utilized in new product development to unlock needs customers may or may not directly state.

    • 4.4: Types of Analytics

      • Big Data Analytics Page

        Big Data Analytics can be utilized to generate useful customer, operational, and environmental insights. Read this article to learn how nonprofits use it to make better decisions.

      • Learning Analytics Page

        Watch this video on learning analytics to learn how it is used in academic settings to improve teaching outcomes. It includes some questions that should be asked to make sure the analysis is actionable.

      • Examples of Analytical Development Page

        Watch this video to learn the stages of analytics development and the types of questions that can be answered at each stage. After watching, choose an industry and identify a question that can be answered in each stage.

    • Study Guide: Unit 4

      We recommend reviewing this Study Guide before taking the Unit 4 Assessment.

    • Unit 4 Assessment

      • Unit 4 Assessment Quiz
        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.