BUS610 Study Guide

1a. Define business intelligence and outline the major historical eras of business intelligence

• What is business intelligence, and how does it influence businesses today?
• How do business intelligence systems differ from other kinds of systems?
• How do business intelligence systems support managerial decision-making?

Business intelligence (BI) is the set of concepts and methods that support decision-making through information analysis, delivery, and processing. BI is 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.
Business intelligence (BI) combines analytics, data warehousing, and mining, visualization, and data infrastructure to help organizations make effective data-driven decisions. This is a different focus than Information Management and Data Processing. Information management generally includes the management and reporting of transactions and the operational systems necessary to operate the business. This is different from the focus of BI on support for decision-making. Data processing, as generally defined, would involve the use of relational database systems and transaction processing. There might also be some use of SQL for queries and reports.
 
Business intelligence (BI) combines analytics, data warehousing, mining, visualization, and data infrastructure to help organizations make effective data-driven decisions. This requires a thoughtful process to identify which data is most relevant to any given decision problem.
 
Since the advent of data-driven decision-making in the 1950s, business intelligence has always striven to collect and analyze the largest amount of data possible. The focus of BI is on the data needed to make decisions; this is not necessary, only the most current data. There is often a need for historical data to make forecasts and support decision-making. The focus of a BI system would not be on the sheer amount and variety of data but rather on the data most relevant to the decision-maker's needs.
 
To review, see Business Intelligence.

1b. Explain how business intelligence is used today to support decision-making and process improvement

• How do business intelligence systems support decision-making?
• What kind of data is needed in business intelligence systems?
• How is data obtained?
• How is a business intelligence system managed?

Business Intelligence systems 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.
 
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. It can create better decisions, generate new business insights, and identify new business opportunities.
 
To begin making data-driven decisions, the organization must start with a clear objective about what they are trying to accomplish. It could be increased sales, reduced manufacturing costs, improved process efficiency, or other measurable outcomes. Once the objective(s) have been determined, the organization gathers and analyzes the available data to make decisions. After the decision is implemented, it is important to determine if the analysis validated the results.
 
Primary content is intentionally created by humans, typically users. When thousands or more of these are combined and anonymized, they can be used to analyze popular or emerging trends. Humans also create primary content in videos, academic papers, blogs, and the like that can be mined, for instance, for sentiment analysis. Primary content is also intentionally created by humans/users through their social media activity, browser history, or other direct activities.
 
Four distinct leadership roles are taking on the challenges of navigating big data and analytics for business intelligence:

1. Chief Data Officer - Acts as the data owner and architect and should set data definitions and strategies
2. Data Scientist - Classically trained as data engineers, mathematicians, computer scientists, or statisticians
3. Chief Analytics Officer - Owns a board realm of responsibilities and functions to maintain forward-thinking progress
4. Data Manager - Oversees a fluid connection between the data agenda and technology agenda

A way to develop an organizational culture that emphasizes empowerment toward analytics is to invest in employee training in analytics. This can create a data-literate company capable of infusing analytics throughout the organization. Suppose an organization creates a culture where all individuals have a working knowledge of data science. In that case, they will be able to ask the right questions and make stronger data-driven decisions. This emphasis on data literacy can also be promoted by adding analytics competencies to every employee role in some manner so that the organizational culture is one with a steady foundation of analytics
 
To review, see Business Intelligence.

1c. Assess how business intelligence is likely to evolve in the future based on changing business needs and technology

• What are some of the newer technologies being incorporated into business intelligence systems?
• How do business intelligence systems support unstructured decisions?
• How must organizational culture support business intelligence?

From the 1950s until about the 1990s, BI systems mainly focused on well-organized data in easily comparable formats. Since then, the design focus of these systems has been shifting and expanding to support other types of decisions, particularly unstructured decisions. Unstructured decisions have always been difficult to formulate and implement on computerized systems, including BI. At the current time, however, BI is expanding to include some of the newer systems and technologies that support unstructured decision-making.
 
BI systems are constantly evolving. The use of new tools like neural networks and autonomous AI is now facilitating the expansion of BI capabilities into areas of decision-making that have typically only been the realm of human decision-makers.
 
A key to priming an organization to be a leader in business intelligence and analytics in the future is creating a culture that values transparency and trust. Building an organizational culture that values information transparency supports an atmosphere of trust and openness. Scholars also refer to relational transparency as a primary component of authentic leadership. By openly displaying metrics, organizations hold themselves accountable to improve weak areas and encourage members to present new, innovative solutions.
 
To review, see 1.3: The Future of BI.
 
 

Unit 1 Vocabulary

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

• artificial intelligence (AI)
• big data
• big data analytics
• business intelligence
• business intelligence architecture
• chief analytics officer
• chief data officer
• database management systems
• data manager
• data mining
• data processing
• data scientist
• data warehousing
• decision support
• information management
• primary content
• transaction processing systems
• unstructured decisions
• visualization