BUS612: Data-Driven Communications

Data-driven decision-making is collecting data, extracting patterns and facts from that data, and using those facts to guide decisions. Utilizing data in decision-making is superior to using a person's, group's, or organization's intuition and can result in making better decisions, generating new business insights, and identifying new business opportunities.

To begin making data-driven decisions, the organization must start with a clear objective as to what they are trying to accomplish. It could be increased sales, reduced manufacturing costs, improved process efficiency, or any number of measurable outcomes. Once the objective(s) have been determined, the organization would gather and analyze the available data to make decisions. After the decision is implemented, it is important to determine if the results were validated by the analysis.

The objective needs to be a well-defined business challenge or query, such as determining if there is a relationship between x and y. The organization would then determine its data needs. This data has to be very robust and provide outcomes over a variety of scenarios over a consistent period of time. The results create the knowledge the organization needs to make its data-driven decision on how to proceed in the future.

Communication of analysis results with visualizations is a key component of the Data Analysis Lifecycle. Each step in the life cycle must be performed properly for the visualizations to have meaning and lead to good decisions. Understanding the steps that lead up to creating visualizations are critical to the project's success.

The goal of the analysis is to highlight useful information, suggest conclusions, and support decision-making. How the data is analyzed is dependent on the type of data (qualitative vs. quantitative).

To review, see 1.1: Data Analysis Processes.

Big data is data that is so large or complex that traditional data processing methods are inadequate. How it is used differs based on the situation, but in all cases, it has the following characteristics: volume, which is the amount of data; velocity, which is the speed of data delivered; and variety, which is the different types of data. When properly harnessed, big data can provide insights that a typical organization's internal data may not.

Big Data provides organizations with unprecedented opportunities to tap into their data to mine valuable business intelligence to uncover hidden patterns, unexpected relationships, and market trends or reveal preferences that may have been difficult to discover previously. With this information, organizations can make better decisions about production, financing, marketing, and sales than before.

Review how organizations use big data stream analysis to analyze consumer sentiments in Big Data Stream Analytics for Sentiment Analysis.

Business intelligence (BI) is the concepts and methods that support decision-making through information analysis, delivery, and processing. In many cases, BI is considered the data analysis, reporting, and query tools that help users derive valuable information.

Start-up companies also use business intelligence systems in their decision-making processes. The objectives are the same as that of an established company, which include using business intelligence, what companies need to use it successfully, and its applications in a start-up, beginning the data collection/gathering, and ending with data presentation.

This figure provides an overview of the individual processes and the components of each process step.

Review the importance of business intelligence and the required process in Using BI and Decision-Making Process in Start-ups.

Evaluating effective business analysis objectives involves assessing the quality and effectiveness of objectives set for conducting business analysis within an organization. Key topics include the importance of business analysis objectives, criteria for evaluation, relevance and alignment, clarity and measurability, and feasibility and achievability.

Business analysis objectives serve as guiding principles, defining the purpose, scope, and desired outcomes of the analysis process. Certain criteria, such as specific, measurable, achievable, relevant, and time-bound (SMART), can be applied to evaluate their effectiveness. Objectives should be clear, focused, and aligned with the goals and strategic priorities of the organization. Assessing their relevance to the specific needs and challenges of the organization is essential, ensuring they contribute to overall success. Clarity and measurability are crucial aspects, necessitating clear and concise language to avoid ambiguity and identifying specific metrics or indicators to assess progress and success. Evaluating feasibility and achievability involves considering available resources, expertise, and capabilities within the organization, ensuring objectives are realistic and achievable within given constraints.

By understanding and applying these concepts, individuals can effectively evaluate business analysis objectives, ensuring they are well-defined, aligned with organizational goals, and contribute to successful business analysis outcomes.

Effective business analysis objectives should be specific, measurable, achievable, relevant, and time-bound (SMART). They should clearly define the desired outcomes, provide a clear direction for the analysis, and align with the overall purpose and goals of the organization. It is important to evaluate the relevance of business analysis objectives to ensure they address the specific needs and challenges of the organization. Objectives should align with the strategic priorities and business goals, contributing to the organization's overall success.

Clarity is essential for understanding the intended outcomes and actions required for successful analysis. Objectives should be written in clear and concise language, avoiding ambiguity. Measurability involves defining specific metrics or indicators that can be used to assess progress and success. Business analysis objectives should be evaluated for feasibility and achievability. This involves assessing available resources, expertise, and capabilities required to accomplish the objectives within the given constraints, such as time, budget, and organizational capacity.

To review, see 1.1: Data Analysis Processes.

Data gathering methods refer to the techniques or approaches used to collect data, while data gathering models are frameworks or structures that guide the data collection process. Data-gathering methods differ regarding the tools, procedures, and techniques employed, whereas data-gathering models differ in their underlying principles and frameworks. 

The choice of data-gathering methods significantly affects the quality and reliability of collected data. Some methods may introduce biases, while others may ensure more accurate and representative data. Each data-gathering model has its strengths and weaknesses. Some models, like surveys or interviews, allow for direct participant interaction, while others, such as observational studies or experiments, offer more control over variables.

Data gathering methods and models can vary across fields or industries based on specific requirements, data types, and ethical considerations. Refer to the course resources for visual aids, examples, and case studies that illustrate the differences and applications of various data-gathering methods and models.

To review, see Data Modeling and Data Analytics.

Data collection plays a crucial role in shaping the outcome and accuracy of the analysis. The collected data's quality, relevance, and representativeness directly impact the conclusions and insights drawn from the analysis. Reliable and well-collected data enhance the accuracy and reliability of the final analysis. In contrast, poor data collection practices can introduce errors and biases that undermine the validity of the results.

Several challenges and biases can arise during data collection. Non-response bias occurs when certain groups are less likely to participate, leading to skewed or incomplete data. Sampling bias occurs when the sample does not accurately represent the entire population, resulting in a distorted analysis. Measurement errors, such as data recording or collection inaccuracies, can introduce noise and inaccuracies into the dataset. Awareness of these challenges is essential for implementing strategies to mitigate their impact and ensure data integrity.

The choice of data collection methods significantly affects the types of analysis that can be performed. Different methods, such as surveys, interviews, or experiments, offer strengths and limitations. Surveys provide quantitative data that can be subjected to statistical analysis, while interviews allow for capturing qualitative insights and nuanced perspectives. The selection of an appropriate data collection method should align with the research objectives and the analytical approaches planned, as different methods provide different types of data and may be better suited for specific research questions.

Data quality and representativeness are critical factors influencing the reliability of the final analysis. High-quality data, free from errors and biases, ensure accurate findings and valid conclusions. Representativeness refers to the extent to which the collected data accurately reflects the characteristics of the target population or sample. Data that is representative of the intended population enhances the generalizability and applicability of the analysis results. Ensuring data quality and representativeness is crucial for conducting reliable and robust analyses.

By understanding how data collection influences the outcome and accuracy of the analysis, recognizing common challenges and biases, considering the implications of data collection methods on analysis types, and valuing data quality and representativeness, students will develop the skills necessary to conduct rigorous and trustworthy analyses.

The data collection process directly influences the outcome and accuracy of the analysis. The collected data's quality, relevance, and representativeness shape the conclusions drawn and the insights gained. Various challenges and biases can arise during data collection, such as non-response bias, sampling bias, or measurement errors. Recognizing these issues is essential to mitigate their impact and ensure the validity of the analysis.

Different data collection methods, such as surveys, interviews, or experiments, offer distinct strengths and limitations. The choice of method should align with the research goals and the type of analysis intended to be performed. Selecting an appropriate method is crucial in generating reliable and meaningful results. The collected data's quality and representativeness significantly affect the final analysis's reliability. Data accuracy, completeness, and relevance are essential for drawing valid conclusions and making informed decisions based on the analysis.