BUS607: 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:

1. Have a clear objective.
2. Have a measurable outcome.
3. Make decisions based on current data.
4. 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.

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.

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.

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?

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.

To make informed decisions based on data, managers must have an analytics mindset that enables them to understand how the data is derived, interpreted, and communicated. A chart based on the results of an analysis may not be accurate or substantive. Therefore, managers need to develop an analytical skillset that helps them know what makes sense and understand where analytics adds value. This enables them to be confident enough to ask pertinent questions of the analyst. Read this article to learn the benefits of managers developing an analytical mindset.

Watch this video to learn the three continuums necessary to implement a data-driven decision-making function in an organization successfully.

Watch this video to learn the steps along the data/technology continuum necessary for implementing data-driven decision-making functions in an organization. For an organization you are familiar with, identify where they would be on this continuum.

Watch this video to learn the steps along the organization/people continuum necessary for implementing data-driven decision-making functions in an organization. For an organization you are familiar with, identify where they would be on this continuum.

Watch this video to learn the steps along the process/workflow continuum necessary for implementing data-driven decision-making functions in an organization. For an organization you are familiar with, identify where they would be on this continuum.

Watch this video to learn the critical success factors in implementing a data-driven decision-making function in an organization. Select two of the success factors and identify examples of successful implementation.

The world of business is ever-changing, which brings new challenges for leaders. To continue to be effective, they will have to learn and embrace new leadership models based on globalization, decentralization, and diversity needs, to name a few. Read this article to learn about the challenges leaders face and think about how data-driven decision-making will help them meet them.

Watch this video to learn how product managers utilize data-driven decision-making in their positions and how DDDM informed their work.

Leaders across a variety of industries realize the benefits of data analytics in their organization's decision-making support. Many realize that the greatest challenge is not the technology or even the personnel but a lack of leadership. Read this article to learn the key characteristics and attitudes a leader responsible for implementing DDDM must encompass and how these characteristics are used in a healthcare setting.

Leadership is a critical success factor in many disciplines, including project management, six sigma, and data analytics. This article explores how important leadership is in the innovation process. Leadership is one of the best predictors of innovation success and is regarded as a critical success factor for DDDM implementation and management.

People often evaluate effective leaders based on personality or leadership traits. These traits can vary from leader to leader. Read this article to explore some of the more common characteristics that are indicators of effective leadership. Also, learn how to differentiate between task-centered versus employee-centered leadership behavior and the various leadership styles. Be sure to answer each of the practice questions to evaluate your understanding of leadership traits and styles.

Another way to learn about effective leadership is by examining the successes and failures of other leaders. By examining other leaders, you can learn what you should emulate from their successes and avoid their failures. Read this article to explore examples of well-known leaders on both ends of the leadership spectrum. Keep in mind that it is very easy to go from being considered an effective leader to being recognized as a poor leader through a series of mishaps.

To ensure they have a pipeline of effective leaders, organizations usually implement leadership development programs. The challenge is maintaining the momentum once the initial classes and development take place. Read this article to learn the four steps organizations should take to monitor the program's success and make sure they are producing the leaders they desire.

Watch this video on qualitative and quantitative data. Pay attention to the approaches to each type of data and the researcher's role in recognizing quantitative and qualitative data.

Watch this video on qualitative and quantitative research. Can you identify the characteristics of qualitative and quantitative data?

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.

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

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.

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

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.

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.

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.

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.

"Frequency" is the number of times an event or a value occurs in a dataset. A frequency table lists each item and the number of times the item appears. Read this module on frequency, frequency tables, and the levels of measurement (nominal, ordinal, interval, and ratio scales). Pay attention to each frequency table exercise. After each exercise, use the definitions to identify and explain its level of measurement.

Watch this video on collecting data into frequency tables. Focus on how to collect, tally, and display data. Take notes and use what you learned to answer each question in the video.

Read this article, and pay attention to the definition of frequency graphs and charts. Imagine you are an analyst. How would you explain the difference between a histogram and a bar chart? Which graph would you use to display quantitative and qualitative data?

Watch this video to learn how to choose the appropriate graphic presentation. Take notes on the recommendations given that support using each type of presentation. Remember, your ability to apply the correct graph or chart improves business presentations and enhances the data-driven decision-making process.

Every analyst should provide quality graphs. Graphs present a visual aid to reviewers and decision-makers in a manner that is easy to understand. This reduces confusion and misinterpretation of data results. Therefore, graph presentations present statistical data in a visually attractive way in comparison to frequency tables.

Read this article on visual aids. Pay attention to the purpose, emphasis, support, and clarity section. Take notes of techniques to improve presentations using clarity and simplicity. Also, focus on how to prepare visual aids.

Think back to when you were at school, an event, or a business meeting. Remember the types of visual aids that were used. Did the presentation meet the visual aid criteria? Why or Why not? How might presentations that do not meet the criteria affect the audience's ability to learn?

Tables and charts are vital to effective business presentations. Read this article to learn how to paste, insert, and link Microsoft Excel charts and tables in Microsoft Powerpoint. Keep in mind that other presentation and spreadsheet tools may have the same integration features.

Read this article to learn how to paste, insert, and link charts in Microsoft Word and Powerpoint products and complete the Skills Refresher for each tool. Now you know to assess data using levels of measurement, frequency distribution, and frequency tables. Remember, graphic presentations (visual aids) are equally essential for proving effective business presentations. Make a practice of using the visual aid criteria in this unit to provide straightforward and easy-to-read business presentations.

Well-crafted data visualizations not only present data in easily understood images, but when done well, they enable the viewer to quickly perceive insights they may have missed if presented in summary tables and spreadsheets. Watch this video to learn how visualizations can take complex information and present it clearly, expertly, and precisely to an audience.

Data visualization has been around for a long time. With the advent of better software, faster processors, and cheaper memory, it has become easier to create and iterate visualizations. With this power comes responsibility. This article explores why, more than ever, it is important to create good visualizations that clearly articulate the point the analyst is trying to make. While reading the article, think about the best and the worst presentations you've seen and determine what about them generated your strong feelings either way. Based on this article, what was it that the presenter did well or could have improved on?

The most effective storytelling helps the audience reach the right conclusion and take the appropriate action. This article provides an example of how data is used to tell a story about reducing global sea ice. Do you believe the author was successful in telling their story? What would you have done differently?

Watch this video to explore best practices for including data in your presentations. Some tips include choosing the correct type of chart, using the right colors to help your point stand out, and the finishing touches that will help your visual resonate with your audience.

Storytelling has been a useful tool to communicate information and knowledge over time. Using visualizations to tell a story with data helps make the information more concise and memorable. Read this article through the 'User Engagement' section to explore the overall benefits of using data to tell stories that help the author break through the clutter and persuade people to action.

Read this article. Why is it important to understand customer purchasing behaviors? How does database marketing improve the business-to-customer relationship? Database marketing does more than build relationships with customers. It reduces marketing costs, increases profit margins, and improves product development. Currently, there are various customer database technologies utilized to build business-to-customer relationships. The key strategies enabled by a database marketing system include customer acquisition, customer retention, customer growth, and customer loyalty.

Read this article and focus on how different departments within a business use customer data for CRM. Then, complete the case study. What data did the company collect for database marketing? How did this company use database marketing to improve customer relationships (CRM)? The case study was an example of a real-world CRM process used to improve customer interaction.

Read this article and evaluate how well you understand database marketing and CRM by answering the questions at the end of the article.

Read this article, which focuses on types of data typically stored in customer databases. Then, answer the practice question at the end of this lesson to summarize the types of data stored by CRM software.

View this figure on the components of CRM. Take notes and document the CRM components and flowchart.

CRM is used in almost every industry. Read the section of this article on CRM objectives. Think about businesses you regularly interact with. Have they become more customer-focused over time, or are they still very product-focused? Why do you believe that?

Read this article on CRM in the agricultural industry. What is the benefit of adding CRM as an approach to business? How did the banking and agricultural industry meet business objectives using CRM?

CRM systems form the basis for how organizations make decisions on managing current customers and identifying prospective customers. Read this article to learn how organizations utilize CRM systems in their decision-making and the companies that provide some of the tools used in these systems. While reading this article, think about how organizations build and maintain relationships with you, their customer. List some of the advantages and disadvantages of this approach.

CLV puts a value on the customer relationship; thus, it is a key component in the CRM system. Knowing the customer's value helps an organization determine which customers they want to strengthen their relationship with and which ones they don't. Read this article to explore the concept of customer lifetime value and why an organization needs to utilize it in its decision-making. Focus on the methodology for calculating CLV and think about the variability inherent in each step of the calculation.

Before there can be any data-driven decision-making, there has to be some data. This data is 'fed' into the system to help generate business insights and be collected from various sources. This article explores how data is collected in the marketing research process to influence marketing strategy.

Data collection methods vary based on the purpose of the research, the availability of data, and its suitability for a particular use. As we've previously discussed, quantitative data and qualitative data are very different and fulfill specific needs. Watch this video to receive an overview of data collection for a research project. It delineates how and when each type of data can and should be utilized.

Data can be collected from multiple internal and external organization sources. This article reviews some of the most common types of data and how it's classified, internally or externally. Be sure to answer the Practice Questions to demonstrate your understanding of the material.

Businesses collect a wide variety of data to help inform their decision-making. To be successful, they have to differentiate between the types of data, prioritize the relevance and determine how much is relevant to their decision-making process. This article discusses the broad categories of data that can be collected, where it originates from, and how the organization can utilize it.

Before any decisions can be made from the data collected, it must be analyzed and summarized to be comprehended by the organization's management. This article summarizes the data preparation methodology used for analyzing marketing research data. To avoid bias, personal opinions should not be introduced into the decision-making process. Still, it is essential to interpret the analysis results in light of their impact on the organization. That is, does it make sense in light of the current situation? Blindly following the analysis can also result in making bad decisions.

The data that supports an organization's decision-making process has to be stored and maintained in the management information system (MIS). The MIS is not just one system but a collection of systems designed to support various organizational functions. This article explores the different types of MIS systems and the level of decision-making each supports. Focus on the Transaction Process Systems diagram and note how the various information systems (pink boxes) inform decisions at different levels in the organization.

Regardless of industry, management needs a clear vision to reach its goal. To attain management's vision, the team needs useful data. In this video, an executive explains how the need for sound data is the same in philanthropic areas as it is in for-profit businesses. Listen closely to how he focuses on the importance of useful data to inform the analysis, which leads to better decision-making.

This unit covered how data-driven information supports product development. During this unit, you incorporated data collection, data mining, sampling, data visualization, CRM and CLV, KPIs, and metrics to develop a product strategy. Remember, data-driven product strategies should (1) identify customer needs, (2) improve customer experience, (3) infuse data insights into product development, and (4) get employees thinking about the needs of the customer. You now have the skills to use data to make business and product decisions effectively.