Business Intelligence paper 1
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ITS531_M1_Lecture.pptxITS 531: Business Intelligence
Week 1: Video Lecture
Lecture Objectives
During this lecture, we will:
Go over and introduce the class with tips on earning an “A” level grade.
Go over and introduce content for the class.
ITS 531: Earning Those “A” Level Grades
Getting Started (Read All Contents Within)
In the classroom, the course syllabus is located under “Content” and then under “Getting Started (Read All Contents Within).”
Other useful information is located in this area of the course including:
Biographical information about me
Place to post questions
As assignment calendar of due dates
Information about books and APA
And more.
As everyone explores introductory content, there will be “3” types of assessments in this including the following:
Graded Discussions
Graded Assignments
A Mid-Term Exam
A Final Exam
Let us now explore some tips on how to earn “A” level grades on all these types of assessments list above.
ITS 531: Discussions (Tips) --
During this course, there will be six discussion forums. Ideally, all students should always meet minimum expectations by doing the following:
Post an initial response and respond to at least two peers. Postings should be on time following the schedule provided in the classroom.
One or more postings should be supported with academic research and avoid postings that are generalized.
Exceeding minimum expectations will greatly increase chances earning “A” level grades when discussion forums are assessed for grading.
I want all my students to earn good grades so avoid incomplete or non-completed discussion boards within assigned discussion weeks.
ITS 531: Assignment (Tips) --
During this course, there will be six assignments allowing students to learn in both the theory and application approaches. Ideally, all students should always meet minimum expectations by doing the following:
Pay very close attention to page and research requirements when reading assignment instructions. This is important because I am very critical in grading when minimum expectations are not met.
Be illustrative by including any visualizations to support written content. Illustrations can account for approximately 25% of page requirements and at least 75% of content should be written.
When creating illustrations any drawing software can be used; however, it is recommended to use of “Smart Art” which is included in Microsoft Word. Other options could be Microsoft Visio.
Professionally format all assignments using APA including an APA cover page, abstract, body pages, and a reference Page.
Complete assignments by assignment due dates from the schedule provided in the classroom.
ITS 531: Exam (Tips) --
During this course, there will be two exams including a mid-term and final exam and the best way to prepare for these exams is by reading and reviewing both chapter readings and chapter PowerPoints.
These exams will have combinations of multiple choice and true/false questions.
Exam questions will be written based on content found in chapter readings and chapter PowerPoints.
Exams will be timed and must be completed in one sitting. Each student will have 120 minutes to complete at least 50 questions.
Multiple attempts will also be allowed up to the due date and these extra attempts are in place in case any student runs into any technical issues. It should be noted that exam results and grading will not be released until after the due date of the exam.
Exams should always be completed by the due dates, as documented, from the schedule of due dates found in the classroom.
ITS 531: Week 1 An Overview of Analytics, and AI
Week 1: An Introduction to Business Intelligence
As we get started with this class, the realm of analytics and business intelligence is basically the transformation of raw data from databases or other storage locations in efforts to transform and manipulate this data into usable business intelligence.
At the same time understanding this fact, there may be issues an organization or business may face in the data collection and data manipulation process. For example:
Data are not available. As a result, the model is made with and relies on potentially inaccurate estimates.
Obtaining data may be expensive and data may also be insecure.
Data may not be accurate or precise enough and data may be subjective in nature. In a way in these cases, data may also be qualitative in nature which can be more challenging to analyze.
There may also be too many data and most if not all of us is seeing more and more these days on the push for big data.
Week 1: An Introduction to Business Intelligence
Even with all the data collection issues just discussed, the motivation to get data and to transform data into usable business intelligence is present.
This is clearly evident because there are many benefits in the real of data analytics used for business and even artificial intelligence. For example:
An organization using aspects business intelligence will find significant reductions in the cost of performing work.
Another benefit of business an artificial intelligence is having information to help create ideas and processes where work can be performed much faster.
In a way, this improved efficiency will create more consistency in how any operational process in association with a work related tasks is performed.
Going even further, we could say that using business an artificial intelligence is a pathway toward environments that work smarter.
This as a return of investment would promote increased productivity and profitability as well as a competitive advantage are the major drivers.
Week 1: An Introduction to Business Intelligence
After understanding the motivation behind the needs for business intelligence, it becomes clear why we need this.
For example:
Management and leadership in various business environments are being exposed big data or data overloads.
Along with the data overload is the complexity of data also increasing making data analysis more challenging. For example, organizations today will experience graphical data in digital form, network structures, document sets, GPS measurement, and many other complex forms of data.
In other words, the complexity of data and the increasing need for shorter data analysis methods creates an environment that is getting bigger, more complicated, and faster.
In a way, this sounds good for productivity and efficiency; however, most of the digitally stored data in organizations relies on the relational database model, which is great for storing transactional data and not so good for analytical purposes.
As a result, we are seeing more aspects data warehouses and data lakes, for example, to store and manage internal and external data sources.
In this process to store and manage data, structuring and turning this informational data into knowledge or business and artificial intelligence is using aspects of predictive analytics.
Week 1: An Introduction to Business Intelligence
Other drivers and motivation behind the needs for business intelligence and even artificial intelligence include and is not limited to:
The interest and push for smart machines and artificial brains.
The correlation of reduced cost when using intelligent applications versus the high cost of manual labor. Of course, this will always create room to have good debates on concerns where technology is used to take away actual jobs.
The need of large technology companies to capture competitive advantages and market share of the intelligence market with the ability and will to invest pretty much any monetary value needed.
The pressure on management to increase productivity and speed. Furthermore in this case, using technology to replace manual labor could decrease liability as a result human labor. Again, good area for debate and discussion.
The availability of quality data contributing to the progress of business and artificial intelligence.
The increasing functionalities and reduced cost of computers in general
The development of new technologies, particularly cloud computing to support business and artificial intelligence.
Any others depending on custom circumstances and need.
Week 1: An Introduction to Business Intelligence
Other more specific drivers and motivation behind the needs for business and artificial intelligence is based on models associated with:
Streamlining processes, including minimizing waste, redesigning processes, and using business
Business Process Management (BPM)
Outsourcing certain business processes, including going offshore
Using intelligence in decision making by deploying artificial intelligence and technology driven analytic processing systems.
Replacing human tasks with intelligent automation.
Digitizing customersʹ experiences
Any others depending on custom circumstances and need.
Week 1: An Introduction to Business Intelligence
After understanding the continued need and motivation behind analytical data analysis or data analytics, we can see many skill sets need.
For example a data scientist or data analyst would ideally need skills in areas below not limited to and in no specific order:
Statistical Inference
Operational Research
Regression and Time Series Analysis
Social Network Analysis
Complex Event Processing
Data Mining
Test Mining
Relational Database Management Systems
Big Data and Hadoop
SQL Databases
No SQL Databases
Business Activity Monitoring
Business Case Preparations
SDLC
Any many others as focused to specific work environment.
Week 1: An Introduction to Business Intelligence
The skill sets just covered are very important especially for business intelligence because components of a BI system include the following.
A data warehouse, with its source data
Business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse
Business performance management (BPM) for monitoring and analyzing performance
And a user interface like a user friendly digital dashboard.
Week 1: An Introduction to Business Intelligence
Business Intelligent Systems or BI will typically support the following types of analytics and other methods as needed.
Descriptive or reporting analytics which refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences.
Predictive analytics which aims to determine what is likely to happen in the future. This analysis is based on statistical techniques as well as other more recently developed techniques that fall under the general category of data mining.
Prescriptive analytics which recognizes what is going on as well as the likely forecast and make decisions to achieve the best performance possible.
Week 1: An Introduction to Business Intelligence
When thinking about an overview of analytics and artificial intelligence, we need to understand the need for computerized support of managerial decision making.
In this effort, we need to be aware that computer support can be used for structured, semi structured, and unstructured decisions. For example we have:
Structured Decisions: Structured problems, which are encountered repeatedly, have a high level of structure. It is therefore possible to abstract, analyze, and classify them into specific categories and use a scientific approach for automating portions of this type of managerial decision making.
Semistructured Decisions: Semistructured problems may involve a combination of standard solution procedures and human judgment. Management science can provide models for the portion of a decision-making problem that is structured. For the unstructured portion, a DSS can improve the quality of the information on which the decision is based by providing, for example, not only a single solution but also a range of alternative solutions, along with their potential impacts.
Unstructured Decisions: These can be only partially supported by standard computerized quantitative methods. It is usually necessary to develop customized solutions. However, such solutions may benefit from data and information generated from corporate or external data sources.
Week 1: An Introduction to Business Intelligence
In summary, I want to again welcome everyone to this class and as everyone starts learning about any aspect of data analytics whether used for business or artificial intelligence now that:
The business and organizational environment is continuously changing, and it is becoming more and more complex.
Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate.
Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex.
Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support.
As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways.
This is why we so much motivation for both business or artificial intelligence.
This is cool
stuff!
“A” Level Grades
Earn Those
