What is What-if Analysis?
What-if analysis, also known as sensitivity analysis, is a form of decision-making in data analytics in which an individual or business reviews all possible choices based on limited information. This form of analysis provides a way to hypothesize about various scenarios and values in order to receive a range of possible outcomes. What-if analysis is commonly carried out in situations in which there is limited data and a company must make the most informed decision based on the information they do have available. Software such as Microsoft Excel can be used to enter various numbers into cells in order to gain an understanding of how variables affect outcomes.
What-if analysis is a form of predictive analysis. Predictive analysis leverages techniques like data modeling, data mining, statistics, artificial intelligence, and machine learning to forecast the likelihood of an outcome. It relies on historical data to offer predictions about what may transpire at some future point. What-if analysis is an important tool for helping Data Scientists, Researchers, Scientists, and Data Analysts learn more about the effects of different outcomes in a statistical model that is often used alongside risk assessment.
Benefits of Using What-if Analysis
What-if analysis is a powerful tool that provides Data Analysts with helpful data insights and perspectives about how to prepare for future events. The following are some of the benefits of what-if analysis:
- It helps with business planning. One real-world business planning example pertains to situations where a user needs to know how many dividends they can declare in a year with regard to a company’s possible performance outcomes. In years when performance is strong, a user may elect to declare additional dividends than in the past. With the help of what-if analysis, it’s possible to figure out the amount of cash that can be declared if things go well, and how little cash would need to be withdrawn in a worst-case scenario. It is a powerful tool for helping users plan for expenses and finances.
- In project management, there are several benefits to using what-if analysis. The overall predictability of a project is improved when this analysis technique is used. Because what-if analysis requires speculating about future problems, it involves asking questions of data in order to anticipate problems. The more questions that are asked, the more predictable the project outcome tends to be. This allows project managers to make more informed decisions.
- With regards to financial planning, what-if scenario analysis provides an effective way to calculate the costs involved in both best-case and worst-case scenarios. This not only makes it less likely that a project will go over budget, but also helps to ensure that resources will be properly allocated during the project’s duration.
- Inventory planning is more precise thanks to what-if analysis since it provides insights into possible outcomes. Calculating what-if scenarios provide a visual indication of what is necessary to ensure smooth operations. It can be applied to situations in which it is important to decide if inventories should remain the same or be expanded so that the inventory never piles up or runs low.
- What-if analysis can be used to answer a variety of business questions, such as if it is wise to change a pricing structure or run a marketing campaign.
Performing What-if Analysis in Tableau
Tableau is the fastest-growing platform for visual analytics on the market. It allows users to simplify raw data into a format that’s easy to access and understand by those working at any level of an organization. Even non-technical Tableau users can create customized dashboards and worksheets with the help of this versatile tool. It’s relied on by teachers, students, Data Scientists, Analysts, executives, and business owners for their end-to-end analytics needs.
When performing what-if analysis in Tableau, users have the benefit of this platform’s flexible front end and its input capabilities. Tableau makes it possible to perform quick modifications to calculations, as well as to test various scenarios. The following features are available on Tableau to help with the what-if analytic process, and to help users progress from theories and questions into professional-grade dashboards:
- Parameters are one of Tableau’s most helpful features. Parameters function similarly to wildcards, which can be changed at any point as needed. These values can stand in for constant values in Tableau, and are helpful for analyzing and interacting with data. When performing what-if analysis in Tableau, the values of parameters are typically changed in order to provide insights into how these alterations may subsequently affect data outcomes in a Tableau worksheet. For example, a what-if analysis could be created in Tableau to present a picture of what would happen if sales jumped from 0 percent to 100 percent.
- Drag-and-drop segmentation helps Data Analysts to effectively segment data. Segmentation is useful for learning more about who customers are, what they are purchasing, what patterns are apparent in their behavior, and what forms of communication may best reach them. Those interested in creating segments can drag dimensions into rows or columns. In addition, dynamic sets and clustering can be applied for more advanced calculations.
- Regression models in Tableau indicate how strongly related two variables are. They are used to monitor how actions (independent variables) affect outcomes (dependent variables), information that is then helpful for anticipating future impact. Some of these models are straightforward and rely on one independent and one dependent variable; others are more complex, multiple linear regressions that include at least two independent variables. One powerful application of regression is that it can be used for what-if analysis if the relationship between the variables is defined. In addition, those working with what-if analysis can enter new independent variables to gain an understanding of how they would influence the outcome. One example of applying a regression model to a real-world scenario would be using it to learn how various qualities of a product can influence the probability of it being purchased. Using this model, it would be possible to learn if there’s a correlation between the color of a shirt and how well it is selling.
Learn Data Analytics & Tableau with Hands-On Classes
Noble Desktop’s data analytics classes are open to students with no prior coding experience. These full-time and part-time courses are taught by top New York Data Analysts and provide timely and hands-on training for those wishing to learn more about topics that extend beyond data visualization, such as Python, SQL, Excel, or data science, among others.
If you’re interested in learning how to create stunning and engaging data visualizations, maps, charts, and graphs, Noble Desktop’s Tableau classes are a great starting point. These small group classes are available in-person in NYC, as well as in the live online format. Tableau-focused courses teach students skills like how to spot the most optimal datasets to connect to, as well as how to analyze, filter, structure, and visually represent data.
In addition, a variety of live online Tableau courses are also currently available from top training providers. These interactive classes are taught in real-time and provide all learners with access to an instructor who is live and ready to provide feedback and answer questions. Courses are offered for novice Tableau users, as well as those with more advanced data visualization skills. Classes range from seven hours to five days in duration and cost $299- $2,199.
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