BUS607: Data-Driven Decision-Making

1a. Explain what data-driven information is and how it assists in business decision-making

• What is the four-step method for guiding data-driven decision-making (DDDM)?
• What are the 5-steps in the data-driven decision-making process?
• What is big data analytics, and how is it used to help make better decisions?

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 from. After the decision is implemented, it is important to determine if the results were validated by the analysis.

The clear 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.

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.

To review, see Data-Driven Decisions.

1b. Examine the steps in the data-driven decision-making process and how each step is effectively executed

• What are the differences between big data produced intentionally or unintentionally and that produced by humans and by machines?
• What are the possible outcomes of the decision-making process?
• What are the categories of decision-making styles?
• What are the primary decision-making approaches?

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, organizations can make better decisions about production, financing, marketing, and sales than they could before.

Decision-making boils down to a choice between alternatives against a defined set of selection criteria. There are usually costs versus benefits, advantages versus disadvantages, and alignment with preferences. The more factors to consider, the more challenging the decision will be. Adding time limits and personal emotions further complicates the process. Utilizing data to help optimize the alternatives informs the decision maker with much more support to make their decision.

Decision-making styles can generally be defined into three categories:

1. Psychological, which is based on needs, desires, preferences, and/or values of the decision-maker.
2. Cognitive, which involves an integrated feedback system between the decision-maker and the environment's reaction to those decisions. This style includes iterative cycles and regular reassessments to measure the impacts of the decision.
3. Normative, which derives decisions based on the communication and sharing of logic within an organization's normative construct. The decision has to fit in and support the organization's mission and goals.

There are three primary decision-making approaches:

1. Avoidance is the absence of making a decision altogether. This can happen when there is insufficient information to reach a decision, the negative impact could outweigh the benefits, no immediate need to make a decision, or if the person considering the alternatives does not have the decision-making authority.
2. Problem-solving is when the activities result in a satisfactory solution being reached. This typically reaches a definite goal from a present condition.
3. Problem-seeking occurs when the problem-solving process itself questions the focus or scope of the original problem itself. The problem could be ill-defined, too large, too small, or missing a key component to reach a satisfactory solution.

To review, see Decision-Making in Management.

1c. Analyze how data-driven decision-making techniques across business domains guide decision-making to create new opportunities

• What are some of the ways digitalization has changed the way businesses operate and make decisions?
• What is business intelligence, and how does it impact businesses today?
• What are the 5 processes that comprise business intelligence architecture?

Digitalization is capturing all areas of life and the typical management task of decision-making. Businesses will begin moving toward becoming more autonomous in their decision-making using machines and cyber systems. The important step toward autonomous decisions or decision support (cyber systems will prepare a decision, but finally executed by a human) will be the next development step for decision making.

This has led to a rapid increase in data volumes in companies which has meant that momentous and comprehensive information gathering is barely possible by manual means. Business intelligence solutions help facilitate this by providing tools and appropriate technologies to assist with the collection, integration, storage, editing, and analysis of existing data.

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.

The following processes comprise BI architecture:

1. Data collection – the operational systems that provide the required BI data
2. Data integration – involves the ETL (extract-transform-load) functions needed to transfer the data from the original source into a format compatible with other data stored in the data warehouse
3. Data storage – the data warehouse or data mart in which the data is stored
4. Data processing – includes the concepts and tools utilized in the evaluation and analysis of the data
5. Data presentation – is the process of preparing and presenting the analysis results

The following figure provides an overview of the individual processes and the components which belong to each process step.

To review, see The Effects of Using Business Intelligence Systems.

Unit 1 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• avoidance
• big data analytics
• business intelligence
• business intelligence architecture
• cognitive
• data collection
• data integration
• data presentation
• data processing
• data science
• data storages
• data-driven decision-making
• data-driven decisions
• decision-making approaches
• decision-making process
• decision-making styles
• data variety
• data velocity
• data volume
• digitalization
• measurable
• metadata
• normative
• open data
• problem-seeking
• problem-solving
• psychological
• validation