BUS610 Study Guide

5a. Explain the difference between describing and analyzing data

• How is data analytics used to study data?
• How do we draw conclusions and support managerial decisions?

Data analytics is the "thinking" part of BI. Once the information has been mined, organized, and stored, the analyst must access it through structured queries. The analysis process applies rigorous methodologies to study information and interpret the results. Using these methodologies allows the analyst to determine how the information relates to the needs of their management team. Data analysis is often done using tools like dashboards, such as Tableau.
 
Analytics is where information becomes intelligence. It is transformed from disparate data points that can be described in terms of data sets into patterns resulting from the analysis. This is where the real brainwork of the analytic process takes place. The methods are myriad and are highly dependent upon both the available inputs and the requirements for your particular project.
 
Managing and extracting valuable meaning from big data is not only a science challenge but, more than anything, a leadership challenge. Becoming a big-data-enabled organization requires a culture of empowerment, trust, transparency, and inquiry. These qualities allow analytics to be woven throughout an organization's fabric, which elevates the investment and commitment to analytics. Across the literature, it is acknowledged that big data's managerial and leadership challenges outrank the technical challenges associated with utilizing big data to solve business goals.
 
Conclusions are based on an analysis of the data. Descriptive data is beneficial for presentation and facilitating conclusions but is not a conclusion itself.
 
To review, see Data Analysis.

5b. Apply various analytic techniques to various datasets to make analytic estimates

• How do you use analytic techniques to find meaning in data?
• What are some of the more commonly used classes of analytic techniques?
• What are the criteria for selecting the best graphics to display data to a particular audience?

Descriptive analytics collects historical data from reporting, scorecards, clustering, and various other sources of information, enabling managers to highlight trends and identify opportunities and risks. It is also one of the lower levels of analytics focusing on the past and moderate benefits and complexity.
 
Predictive analytics leverages statistical models and machine learning to enable managers to predict future outcomes with varying degrees of statistical confidence. Descriptive analytics collects historical data from reporting, scorecards, clustering, and various other sources of information, enabling managers to highlight trends and identify them. Decision analytics uses data-driven models and visualizes outcomes of specific organizational behaviors allowing managers to visualize the various results of different strategic approaches. Prescriptive analytics uses optimization and simulation, enabling managers to produce recommended decisions through analytical modeling.
 

1. Prescriptive – What we should do based on the data
2. Diagnostic – Identify causal relationships
3. Cognitive – Use artificial intelligence and machine learning
4. Descriptive – Summarization and aggregation of data
5. Predictive – Determine the likelihood of future behavior

It is important to know how many times a value appears when organizing data. Questions like the number of hours students study or the percentage of families with multiple pets. Frequency (also called absolute frequency), relative frequency, and cumulative relative frequency are measures that answer questions like these.
 
The absolute frequency is the number of times a value occurs in the data. The relative frequency is the ratio of the number of times a value occurs in the total number of values. The cumulative relative frequency is the summation of all relative frequencies and adds up to 1 (or 100%).
 
Analytics has defined stages of development, from descriptive analytics to cognitive analytics. As an organization moves along with analytics development, the benefits of the outcomes and implementation complexity increase.
 
Many analytic techniques can be applied to the attributes of a particular decision problem space. It is important to understand these techniques and under what circumstances each is most appropriate.
 

1. Clustering - Used to group related attributes in sets that have common characteristics
2. Classification - Identifying attributes and assigning them to sets based on their characteristics
3. Prediction - Using the past values of an attribute to assign a future value
4. Profiling - Searching for attributes that have preselected characteristics
5. Smoothing - Taking the average of an attribute over time

Decision trees are one technique that can be structured to solve many different types of problems. Applying the correct tree type to the problem under consideration is important.
 
When displaying data to an audience, it's important to choose the right choice to help them quickly understand the point being made. Some simple charts that can be used include:
 

1. Line charts for comparing trends, multiple datasets over time, or correlations
2. Area charts for comparing change over time from two or more variables
3. Column charts for showing frequency distribution and comparing datasets
4. Bar charts for ranking datasets or comparing datasets
5. Pie charts for comparing datasets as percentages of a whole.

To review, see Prediction and Inference in Data Science.

5c. Determine what kinds of scenarios and simulations would be most useful for your business case

• How is scenario construction used in decision-making?
• How are simulation techniques used to model real-world systems?

Scenarios place analysts in the role of the decision-maker or other figure whose decisions, influences, agendas, and profiles the analyst is attempting to model or forecast. Just as in the military, these games often also include "Red Teaming", which means trying to anticipate what your adversary will do given certain conditions. In real military war games, the physical "red team" is given a challenge, and along the way, key options or needed equipment or sources or something they expected to rely upon to win is taken away. The value of the exercise is to see how adaptive the military unit, or in this case, the analyst team, can be when environmental challenges present themselves and all requirements, timelines, and other elements of the process remain the same.
 
Simulation of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or proposed system. Simulations are similar to scenarios, although today, simulations often take the place of computational models representing some problem to be solved that might be too expensive or dangerous to attempt in the real world. These computer simulations enable analysts to see what happens in a given situation, then ask, "What happens if X is changed?" Simulations are often used to experiment with environmental conditions or to predict behavior, such as consumers in a marketplace when a new competitor is introduced. The simulation process should follow a defined procedure.
 
Once the outcomes from the decision are captured, it is important to return to determine if they were supported by the analysis. The results can then be utilized to better inform the analysis for future uses.
 

Unit 5 Vocabulary

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

• area chart
• bar chart
• classification
• clustering
• cognitive
• diagnostic
• frequency
• frequency distributions
• line chart
• prescriptive
• prediction
• profiling
• scenario
• simulation
• smoothing