Unit 1: Business Intelligence Overview
Business Intelligence (BI) has had a long evolutionary path to being recognized as a distinct discipline. For decades it was lumped in with data processing and systems analysis, which are inputs to the BI process, but BI adds the forecasting and actionable information that supports business decisions. In addition, BI was, and still is, often discussed as synonymous with Competitive Intelligence (CI), now also a separate discipline. Both analyze data to provide forecasting and actionable intelligence to support business decision-making, but BI is focused inward on the business itself and how it can improve its structure, processes, and approaches. CI is focused externally on understanding trends in the business environment, and its market and how technology and other disruptors can change the environment in which it operates to ensure the business is primed to adapt to environmental changes before, not as they occur. BI is often located in the C-suite, supporting decisions related to improving operations and the firm's strategic direction. CI is typically colocated with Marketing. While many of their inputs, methods, and processes may be similar, their products are as different as night and day due to this internal vs. external dichotomy. Business intelligence is about obtaining, storing, accessing, analyzing, interpreting, and reporting actionable information that the management team can use to make effective decisions. Our understanding of business intelligence processes has changed substantially over the last century, as have the tools we use. In this unit, we will look at the history of BI, how it is used today, and how needs will likely change. You will be able to describe how business intelligence concepts and processes have changed over time due to business needs and technological change.
Completing this unit should take you approximately 15 hours.
Upon successful completion of this unit, you will be able to:
- define business intelligence and outline the major historical eras of business intelligence;
- explain how business intelligence is used today to support decision-making and process improvement; and
- assess how business intelligence is likely to evolve in the future based on changing business needs and technology.
1.1: What is Business Intelligence?
In these articles, you will learn and be able to compare the various interpretations of its major thinkers and review the historical eras of business intelligence as a discipline.
Who coined the term "Business Intelligence"? According to sources, it was Richard Millar Devens in the "Cyclopædia of Commercial and Business Anecdotes" from 1865. In this unit, you will learn about the key foundational voices of business intelligence history, from Richard Millar Devens to Hans Peter Luhn to Howard Dresner in 1989. You will also gain exposure to the growth and evolution of BI from decision support systems, data warehouses, executive information systems, OLAP, data mining, digital dashboards, and a wide array of products offered through many vendors.
Explore this article to understand the definitions and common functions of BI technologies, which include reporting, online analytical processing (OLAP), analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
1.1.1: What Business Intelligence is Not
As BI has evolved away from CI and market research, it has been conflated with other forms of decision analysis. This section will help clarify how BI is now differentiated as its own discipline devoted to evaluating and improving a firm's internal processes.To understand definitions regarding the taxonomy of BI, read this paper, where an example of the methodology in the research process is used. It also discusses how the taxonomy for BI and analysis was developed, how it is applied, and an analysis of the current status with predicted development for the next wave or 3.0 of BI, as well as potential gaps. A clear diagram of the taxonomy development process is shown in Figure 6. While a picture is worth a thousand words, sometimes you must explain complex processes narratively.
- This article provides a clear and concise description of business intelligence systems, specifically OLAP.
1.1.2: Business Intelligence vs. Competitive Intelligence
After reading, you should understand the difference between BI and CI. For clarity, note the key in the definitive aspects regarding data.Read this for a quick overview and definition of Competitive Intelligence. Remember, as we discussed in the introduction to this unit, the two may have similar methods and sometimes even inputs. However, BI is internally focused, while CI is externally focused.
1.1.3: From Systems Engineering to Business Engineering
In these articles, you will learn about the progression of BI from systems to engineering interactions.
This paper results from research surveying executives with robust analysis and offers insight into their needs. This case study shows that current tools were insufficient. More information architecture using data warehousing, OLAP tools, and data mining was required to equip them for their information needs and better decision-making. Consider when you have not had the tools to perform your best analysis. Were you able to obtain the tools and information you needed, or did you have to be creative or "make do"? How can an analyst influence the decisions on providing tools, appropriate architecture, and data sources within a firm?
System engineering can best be explained as coordinating multiple tasks within the two disciplines of engineering and engineering management. This paper highlights the systems method of coordinated tasks and its relevance concerning current and future business system life cycles: concept, design, planning, testing, optimization, and deployment. It defines the boundaries necessary for a robust life cycle and analysis to occur.
Read this definition of the uses of business engineering in developing and implementing business solutions for human-technology interaction. What solutions will be needed as data gets "bigger" and more complex? Will analysts be able to find ways to capture and manage all that is relevant, or will they have to live in constant fear of missing that "golden nugget" of source material that would have made all the difference in their findings?
1.2: How BI is Used Today
We have seen the evolution of BI into its own field, but it is still lumped in with data science as a general category. When this happens, hiring managers may miss the opportunities provided by trained BI professionals who can tell them what can be over data scientists, who see what is. Job descriptions are usually heavy with needs for experience using various proprietary software and statistical experience, which can be easily taught on the job, missing the analytical and soft skills the BI professional develops over time to add the ability to predict to the ability to process data.1.2.1: Contemporary Applications
After reading these articles, you should understand and be able to define the seven common functions of BI applications.Read this brief explanation of where BI sits in an Enterprise Resource Planning (ERP) system, which is used by a business to manage its processes. If you want to gain more insight regarding all ERP modules, feel free to follow the link to the whole book from which this explanation is extracted.
- Watch this quick video and read this short text for an explanation of how a company can use BI to improve its outcomes and attain its goals. How have you seen BI used for these purposes? When have you seen companies miss opportunities?
These seven articles provide a nice overview of how businesses use information, however; note that data and information are not synonymous. Jot down for yourself how they differ to be sure you can keep them straight. Hint: Our old friend "structure" is involved!
1.2.2: BI Approaches for Each Lifecycle Stage
Like living creatures, business is often described as having a life-cycle: first it is born or created, then it grows or expands, sometimes businesses seem to keep growing forever, but they usually reach a peak. This is a business' mid-life crisis. This is when it keeps doing the same old things and atrophies, eventually it begins to die, or contract (shrink), unless, like a person, it discovers a second chance at love or a new workout or vitamin supplement. With process innovation, a new product, identification of a new market target, or some other internal or even an external environmental adjustment, it can find new life and hold off the contraction phase.
- Read this article for a concise definition with examples of the four phases of an economic business cycle: expansion, peak, contraction, and trough.
Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.
Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.
1.2.3: BI for Prediction
BI systems are typically used to yield historical snapshots of performance. However, current tools allow greater flexibility regarding future modeling. They can deliver insights into areas such as the risks of new product launches or identifying the most profitable markets to secure a competitive advantage. Most notably, these tools can now be used by most employees. Business intelligence can help businesses gain the insight they need to reduce costs, increase revenue and improve, for example, healthcare, patient safety, and outcomes while complying with the vast numbers of required regulations and standards. What areas of your industry do you think the predictive factors of BI could benefit?
- This deep research showcases a detailed and useful analysis of an integrated goal-oriented, business intelligence-supported decision-making methodology. Is this the kind of mental mapping that works for you? Try to apply the model to something familiar, like purchasing a vehicle or a major appliance. Sometimes the simple examples help us understand the steps and show us where we have leaped from one to another, skipping a basic but crucial one along the way.
After reading this paper, you should clearly understand the relationship between the analytic methodologies and techniques associated with big data and how to integrate it with a new correlation taxonomy. This paper adds more distinction to the 5Vs of big data you read about previously. You will recognize the characteristics and significance of the descriptive, diagnostic, predictive, and prescriptive integration methods.
As you read, pay attention to Figure 1, which outlines the research process and provides a clear 3-step map. Follow through each section of this paper to understand modeling for effectiveness.
- Read this article to learn how data mining employs techniques from statistics, pattern recognition, and machine learning to support decision making. The article further provides clarification to the other key algorithm families, such as predictive modeling, and segmentation used in data mining.
1.3: The Future of BI
The rate of technological change is exponential. The platforms used at the beginning of a year could be rendered obsolete by the end of that same year. What does that mean for you? The speed at which information is gathered is driven by the amount generated, but is it accurate? We have seen the first wave focused on reporting tools; the second wave is harnessed in data visualization tools. The third, currently being built, is anchored in Big Data, with Artificial Intelligence and Machine Learning taking the driving seat. However, think about what lies beyond. In a world where businesses need to communicate with their employees securely and remotely, they also reach their customers in new and innovative ways, all while processing information. For example, how do you envision your embedded fitness device data output will impact the cost optimization for your department at work? As more devices become connected, your personal and professional tools will interact to provide business intelligence for business and all aspects of life. Not work-life balance but life balance. The articles and papers in this section showcase these trends.
- The chapter presents an overview of new up-and-coming technologies, from autonomous vehicles to embedded wearable technology. Note the expected impacts on society of these trends. Be sure to take the time to respond to the study questions and do the exercises to cement your knowledge.
One of the preeminent voices regarding future information systems and technology trends is Mary Meeker, an acclaimed Venture Capitalist. She has presented her annual "Internet Trends" report (this is from 2019) for the last twenty-five plus years with wide acclaim for its foresight. This 30-minute video is a quick overview of her findings for 2019.
In the video, she encourages everyone to look at the full set of slides available through her company Bond Cap. Their website has an archive of past reports showcasing her forecasting prowess for trends and how these technologies apply to the business intelligence process. This is not required for the course, but knowing the major thought leaders in your field is important.
This chapter discusses trends in information technology such as; digital forensics, the shift to a distributed workforce (very relevant during the 2020 COVID-19 global pandemic), and the increasing use of grid computing while acknowledging the rapid pace of change. Consider some benefits to the high-level accessibility of information for the average employee. Conversely, how can this level of access be detrimental to businesses?
- The previous article highlighted trends in information technology change with quick adoption and rejection, often seen almost overnight. This article provides insight into some trends that have gained traction during the 2020 COVID-19 global pandemic due to their ability to bolster societal resilience. Did these trends affect your COVID-19 experience? What other trends do you see growing out of this time that will be here to stay?
- The first 33 minutes of this video highlight the trends showing why BI tools will no longer be needed to provide status quo data but actionable insights, as now all applications provide data. Do you understand the stance taken here? How will this view will likely affect industry in the future?
1.3.1: Adapting Business Models to Globalization and Technology
Is globalization changing the world, or is the world changing because of globalization? Either way, globalization was often viewed as a negative in the past. However, as we move forward in a world with access to technology becoming more widely dispersed, it is clear that business models are adapting. The cross-pollination of knowledge happens faster now, allowing countries (and businesses) previously waiting for new information to leapfrog those considered advanced with adaptations to processes that allow for robust, relevant regional outputs. This customization of factors such as quality infrastructure, sufficient skilled labor, access to finance, and reliable managerial support with robust organizational practices has long been stagnant. Still, recent advances in technology have brought unforeseen changes. As you progress through this lesson, consider what changes you have seen.
- This section explains the nuances of global integration and local responsiveness balance when responding to cultural differences for global management via the four global business strategies. After reading the article, take the quiz to check and gauge your understanding.
- This research paper examines and pitches a new approach to the construct of business models and the need for business model innovation (BMI). The same historically designed models can no longer be applied within the international marketplace for running current businesses or developing new businesses for growth. Will globalization increase, or will systemic shocks like the pandemic and trade costs cause small businesses to look for local options, thus segmenting the global marketplace?
1.3.2: Maintaining the Firm-Centric Approach
How you develop and use BI depends on your business needs, but many factors must be considered. As previously noted, the average employee can now use most BI tools. This rise in the focus on user/firm-centric tools is not a negative. It greatly enhances the potential for more positive viable outcomes. BI being placed at the center of your business processes maintains and allows for delivering comprehensive data that can be easily accessed, interpreted, and provide actionable insights.- This article highlights the changing dynamics needed for models to achieve feasibility within an organization by proposing a new process challenging both ETL (extract, transform, load) and ELT (extract, load, transform) by modifying with ETLA (extract, transform, load, and analyze). To challenge your thinking, apply the concept by putting dirty dishes into a dishwasher for context.
1.3.3: Incorporating Data from the Internet of Things (IoT)
There have been many technological advances in how data is used since the term business intelligence was coined. These articles explain new types and potential implications for everyday life. You should develop an understanding of the challenges and key issues of the Internet of Things (IoT), different architectures, important application domains, and terms such as quality of service and interoperability. This section should help you understand and tie together how new types of data are obtained through IoT.- This paper is comprehensive, provides a view into the taxonomy of IoT technologies, and clarifies the interconnectivity of devices, processors, and cloud computing. As the paper points out, "IoT is not a single technology; rather it is an agglomeration of various technologies that work together in tandem". Figure 3 presents a detailed taxonomy of research in IoT technologies.
- Read this paper to capture a more contemporary perspective on data architecture. It provides a detailed and in-depth challenge to the existing architecture. Also, it proposes a new architecture for the Cognitive Internet of Things (CIoT), which adds the human brain and big data to the mix.
- This paper provides a case study highlighting the difficulties of building and implementing analytics in IoT using equipment data in an industrial setting. How do you see these same issues applying to other industries? What are similar issues that may exist in your industry?
- To challenge your thinking, watch this brief video on causality and how other factors can influence a relationship between two components. This is very important in your assessment and data analysis.
Read this article on privacy, security, and ethical concerns with integrating IoT in the business intelligence cycle. As the section overview states, IoT is a collection of different technologies working together. Still, it is also an amalgamation. This article will help you understand in a detailed discussion how IoT fits into everyday life and its potential from both the technological and sociological perspectives. How can the connected devices you own be utilized with others?
Study Guide: Unit 1
We recommend reviewing this Study Guide before taking the Unit 1 Assessment.
Unit 1 Assessment
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Take this assessment to see how well you understood this unit.
- This assessment does not count towards your grade. It is just for practice!
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