Organizations who are successful have used data to improve their performance. Descriptive data, predictive data and prescriptive data are used to gather, organize, tabulate, and visualize analytics $($Jaggia$,2022).$ Each are used through graphs and tables to help identify variables and outcomes for strategic making decisions. Business Intelligence $($BI$)$ also known as descriptive data improves financial standings such as turnover rates and pricing strategies. On the other hand, predictive data oversees historical patterns and includes sales forecasts. A great example of this is $$identifying customers who are most likely to respond to specific marketing campaigns$($Jaggia$,2022,$ p$.5).$ And prescriptive data gives course of actions through algorithms from artificial intelligence programs. It helps meet demands of supply, marketing or financial goals. The goal for each is to be able to discuss and identify insights and value.

An example for one of these data is found in the article A Data$-$Model$-$Driven Predictive Method for PV Output Power $(2023),$ where they predict the photovoltaic $($PV$)$ power using a model$-$data$-$driven approach. Some of the parameters used to measure the modeling were solar irradiance, ideal output power and meteorological factors from nonlinear relationships between meteorological data and PV power output. With the comparison line chart they are able to use the x$-$ axis as the time of da and the y$-$axis as the power output. However, the model demonstrated reflects the Convolutional Neural Network $($CNN$)$ combined with Long Short$-$Term Memory $($LSTM$)$ networks to analyze the nonlinear relationships $($Zhen Rao et al$.,2023).$ This kind of frame works for historical purposes to capture pattern variations to predict the current and forecasted conditions. A line chart is used to plot the power output with the x$-$axis representing the time of day and the y$-$axis notes the power output in kilowatts. By using the predictive analysis, solar energy companies can optimize energy distribution and reduce costs. what is missing from the prompt? Differentiate between various types $($Descriptive$,$ Predictive, or Prescriptive$)$ of data an organization may use to assess organizational performance.

Provide an example for each data source.

Highlight the purpose of the data sources, the metric$($s$)$ it explains, and what kind of decision it would help justify.

Create a data visualization graphic that incorporates appropriate data sets for one of the three types.

Consider one of the data sets you have shared in question number $1$ of this workbook.

Evaluate the benefits of at least two different data analysis methods.

Share an example of each.

Explain how, when, and why these methods have been used in a business situation.

Justify a strategic choice based on a data analysis method.

Use the data analysis method in Week $3$ or another example of your choice.

Assess how big data can influence organizational performance.

You may consider using an example if you find that helpful to support your argument.

Consider how data can create insight into a business problem and provide a sense of decision$-$making justification.