Time: 40 hours
Today is a challenging time for businesses. Leaders and managers are responsible for delivering services and products to customers and stakeholders, which requires them to make strategic and operational decisions every day. It isn't easy to find a company or leader who wouldn't want to use data to make decisions since data is much more reliable than intuition. Without data, managers may base decisions on biases and false assumptions that cloud judgment and ultimately lead to poor decision-making.
For example, Google created a department of People Analytics to help make human resource (HR) decisions using data. Google used performance reviews and employee survey data to answer its most challenging questions. Its data revealed that managers with specific performance characteristics improved employee happiness, which increased longevity at Google and, in turn, improved employee productivity.
First, read the course syllabus. Then, enroll in the course by clicking "Enroll me". Click Unit 1 to read its introduction and learning outcomes. You will then see the learning materials and instructions on how to use them.
Do you order online? Do you shop at a grocery store? Every online and in-store transaction is a new business observation of a customer's purchasing habits. These observations are all data, and smart companies use that data to make decisions. Today, businesses collect more data faster than ever before, and they continue to seek better ways to improve their decision-making using that data.
The major difference between traditional and data-driven decision-making (DDDM) is that DDDM uses facts and metrics data to guide decisions rather than intuition or stereotypes. As an analyst, leaders will look for you to derive valuable insights from the data your company collects. For example, Netflix used DDDM to gather insights on the programming customers preferred, which led them to create the hit show House of Cards. Netflix exceeded its membership goals by using data to create programming that customers couldn't get anywhere else. Being data-driven ensures decisions align with organizational goals, objectives, and initiatives.
This unit will cover the DDDM process and how business analysts, managers, human resource professionals, and decision-makers at all levels in an organization can be empowered to make better decisions.
Completing this unit should take you approximately 16 hours.
Data and technology are not the only components of a successful DDDM initiative. The enterprise must also address three continuums: data/technology, people/organization, and processes/workflow. Together, these three aspects comprise the DDDM change model, and they must be managed and implemented simultaneously for a DDDM initiative to be successful. There are milestones that a company must complete before moving forward in a DDDM initiative.
This unit will cover the DDDM implementation process as a whole and the milestones within the process for each of these three continuums. It will also present the factors required for building a successful, analytics-focused organization.
Completing this unit should take you approximately 1 hour.
Leadership is another component of the DDDM change model. It is not a continuum like data/technology, people/organization, and processes/workflow, but it must be present throughout the implementation. Unfortunately, many leaders don't understand their role in implementing and utilizing DDDM in their organizations. More than ever, leaders need to understand the value of DDDM, how they are instrumental to its success, and why they need the confidence to question everything they see.
This unit will cover how leadership is a critical success factor in becoming a DDDM, compare leadership's role to other success factors, and summarize how good leadership impacts DDDM implementation and use.
Completing this unit should take you approximately 3 hours.
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.
This unit builds on the last unit by covering data distribution techniques like the mean, median, and mode, how frequency tables can be used to organize and summarize data, and how to analyze a frequency distribution, which displays the number of times an event occurs in a set of data.
Frequency tables are useful for describing the number of occurrences for a specific event. Data analysts rely on frequency tables to identify trends within data. One common method for organizing data is to construct a frequency distribution. For example, a company may ask you to conduct an employee satisfaction survey. Once the survey responses are complete, frequency tables and distributions display employees' responses by age, position, race, gender, and other categories determined by the company. Companies use this data in many ways – for example, frequency tables based on age can reveal the numbers of employees retiring soon, which lets managers prepare for shortages and job vacancies. Frequency distribution is a way to tabulate and present data on a visual scale and compile and organize data meaningfully to help decision-makers glance at data to see the big picture quickly.
Completing this unit should take you approximately 5 hours.
Effective visualizations help users present data in such a way that the audience can easily comprehend it. There are best practices for creating compelling visualizations, and when used properly, these visualizations can tell illuminating stories.
Visualization requirements can change depending on the audience they are presented to. Senior management may want the highlights or the "big picture", while middle managers may want to see the detail that supports the conclusions presented in the visualization.
This unit examines visualization best practices in light of human visual comprehension and the audience being presented to. Emphasis will be on using the data to tell a story such that the analysis conclusion is comprehendible by all.
Completing this unit should take you approximately 6 hours.
Imagine you work for a business. You have a new product line that should arrive this month. You need a marketing campaign for surrounding neighborhoods. However, it is costly to advertise to every person in the surrounding areas. Thus, it is beneficial to apply a direct advertising technique. This should reduce costs and produce a more effective marketing campaign.
Database marketing is a direct marketing technique that uses customer data to create direct communication. Therefore, database marketing allows businesses to generate customer lists based on similar product purchases. Businesses use previous purchase behaviors to predict customer interest in the new product.
Database marketing requires interaction with customers. This relationship is known as customer relationship management (CRM). CRM is a technology that manages interactions between the business and the customer. Thus, CRM technology establishes connections to customers through direct marketing, sales, customer service, and support. We will examine database/direct marketing used to model customer acquisition and retention and the concept of lifetime value (CLV).
Completing this unit should take you approximately 3 hours.
Even if an organization utilizes data-driven decision-making, the ultimate decision on how to interpret and use the analysis results still falls into management's hands. If they interpret it properly and act accordingly, they can lead their organization to cost savings, increases in revenues, profits, market share, or customer loyalty.
Conversely, if they misinterpret the analytics results or act improperly, they could lead their organization to a damaged reputation, lost customers, or reduced revenues and profits. Management must walk this fine line every day, and the data is not always clear as to what actions they should take.
In this unit, we will examine examples of organizations that have benefited from properly using DDDM to grow their business. We will also discuss the implications of misusing or misinterpreting analytics leading to incorrect and sometimes embarrassing outcomes.
Completing this unit should take you approximately 2 hours.
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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!
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