Unit 1: Introduction to Data-Driven Decision-Making
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.
Upon successful completion of this unit, you will be able to:
- explain what data-driven information is and how it assists in business decision-making;
- examine the steps in the data-driven decision-making process and how each step is effectively executed; and
- analyze how data-driven decision-making techniques across business domains guide decision-making to create new opportunities.
1.1: What is Data-Driven Decision-Making?
Data-driven decision-making is collecting data, extracting patterns and facts from that data, and using those facts to guide decisions. Watch this video to explore how data is used to make decisions and the importance of including intuition in the process. Pay attention to the four-step method that guides data-driven decision-making, and think about how the speaker used data to drive decisions that improved his business outcomes.
The four-step method is:
- Have a clear objective.
- Have a measurable outcome.
- Make decisions based on current data.
- Validate the outcome.
How does this method help create business opportunities and reveal new business insights?
More and more, our personal and professional daily decisions are based on data we have accessed from a number of sources. Businesses have been tapping into this resource for years to determine everything from who will buy most likely buy a product to who they should hire. Watch this video to explore how a culture of data helps organizations make more informed and more accurate decisions to better utilize their resources.
DDDM must be utilized to impact an organization's decision-making. To be successful, the data must be delivered to the right person (decision-maker) at the right time (real-time or near real-time) to enable the right decision. Missing any of these components could lead to serious consequences. Read this article to discover the core tenets of being data-driven and the project lifecycle that encompasses these core tenets.
Today, every industry strives to become data-driven. Most professionals understand that using data decreases decision bias and false assumptions. Watch this video and focus on how data analytics creates new insights for decision-makers.
1.1.1: Data-Driven Information
DDDM collects, extracts, and finds patterns in data. Data-driven information is how we arrange or sequence that data to reveal facts or learn new things about a product or service. As you watch this video, consider the importance of gathering and arranging good product data. What kind of data is relevant? How should that data be used to meet business objectives?
DDDM helps management make better-informed decisions, but when the data is streaming in from a number of sources, it can complicate the process as management attempts to match and keep up with the velocity of the incoming data. Read this article to explore the challenges of making decisions with streaming data and the adaptation needed in the decision-making framework to continue making informed decisions.
1.1.2: Data-Driven Learning
DDDM is becoming more widely used in the education field to study the impact of teaching methods on student outcomes. Read this article to explore how educational institutions incorporate situational contexts to help explain causes determined by their DDDM processes.
Data-driven learning places a strong emphasis on business objectives and outcomes. These strategies improve business leaders' confidence that their company's focus reflects its goals by prioritizing effective decision-making, training, and development. Watch these videos on data-driven learning strategies in education. Pay attention to how the teacher uses data to align students' reading assignments with education outcomes. Students that collect and analyze data based on rubrics feel a sense of ownership and confidence.
1.1.3: Data-Driven Science
Big data analytics enables scientists to analyze large quantities of data unencumbered by any preconceived theories. Read this article to discover the difference between theory-based and process-based prediction, as well as the necessity of utilizing a combined approach to overcome the inherent challenges.
Watch this tutorial which defines and explains the goals of data science. Why is data a rare quality? How does data create a competitive advantage? What are the various sources of data to use in your analysis?
Data science has 3 components: coding, statistics, and domain.
1.2: Using Data-Driven Decision-Making in the Real World
DDDM not only benefits businesses but also enables governments to make better policy decisions. For instance, DDDM can be utilized to uncover hidden patterns, unexpected relationships, and market trends or reveal preferences that may have been difficult to discover previously. Armed with this information, government entities can make better decisions about healthcare, infrastructure, and finances than they could before. Read this article from the Executive Summary through Chapter 2 to explore data-driven decision models, how data is changing development, and how data can fill the holes in policymaking.
Read this section to explore how data needs to be used responsibly, the role of artificial intelligence, and the effects of data on people.
Read this section to explore the effects of data on businesses, how data can inform infrastructure decisions, and the importance of data security.
Read this article on decision-making in management. It succinctly defines decision-making in an organizational context, explores different decision-making styles, and describes the types of decisions management teams face. This is essential to understanding how managers can use data to improve decision outcomes.
DDDM changes how people make decisions. Before DDDM, many decisions contained an emotional component. With DDDM, decisions will be based on a more rational basis using data. Read this article to learn the impact of emotion-based decisions made in the past and the more sensible decisions derived using data.
Read this article on how start-up companies use business intelligence systems in their decision-making processes. It presents the objectives of using business intelligence, what companies need to use it successfully, and its applications in a start-up.
Study Guide: Unit 1
We recommend reviewing this Study Guide before taking the Unit 1 Assessment.
Unit 1 Assessment
- 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.