Put a comment in those 3 post, the Professor asked us to comments to our classroom post on blackboard

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Initially I was not quite sure how to begin. However, I gave the random data generator a try. While I did get a data a set, I do not really know how to interpret this data into an analysis. So, here is what I did do, and I welcome any tips or pointers on what I should have done differently. I went to the data analysis tab and clicked random data generation. Then in the pop-up screen, I selected "normal", the textbook did not differentiate between the different types well so I kind of took a guess. Does anyone know what the difference between these selections are? I chose a normal distribution because it can "show how sampling from probability distributions can provide insights about business decisions that would be difficult to analyze mathematically" (Evans, 2020). With this generated data, I color coded the results as to easily find the probability of five or more customers in a day. With this data a 17% percent chance of five or more clients in a day was generated. If I read this correctly, this should mean that there is an 83% risk factor that the financial advisor will not meet his goal of five clients a day.

Evans, James R. (2020). Business Analytics: Methods, models, and decisions. (3rd edition). Boston, MA: Pearson.

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After tryer to do some research online on how to conduct a Random Number Generator for 100 samples in order to determine the number of customers for a financial consulate, I could not figure it out. The video I was watching was not that specific on generation 100 different samples. I was able to do the command for that generator but could not figure out proper formatting. I will be looking at other students' instructions to see if I can eventually figure this out and then further practice after that. However, I can research and discuss probability distributions and explain the difference between discrete and continuous variables. In mathematics probability distribution can be described as a statistical function describing all possible values and likelihoods for a random variable that can fall within a given range. When constructing a probability distribution the range is set between a minimum and maximum values, however the lotted value is codependent on numerous factors depending upon where it falls. Factors can include standard deviation, skewness, and kurtosi(Hayes, 2021). Depending upon the type of experiment that is conducted, some outcomes are numerical, and some are not.In this situation all outcomes should be numerical, when they aren't then a numerical value is assigned to a specific outcome. Doing this, a random variable is described as"a numerical description of the outcome of an experiment. Formally, a random variable is a function that assigns a real number to each element of a sample space"(Evans, 2020). Random variables have two options of being discrete or continuous. Discrete variables are one in which the number of possible outcomes can be counted. While a continuous random variable outcome is over one or more continuous intervals of real numbers"(Evans, 2020). Probability distributions come into play by taking the random variables/values, discrete or continuous, and developing a probability distribution curve or frequency. This depiction shows all the outcomes that have been achieved and graphs to be visually observed. There are various different types of distributions that can be used, however probability is frequently used in business for scenario analysis. Managers can calculate current sales and construct a distribution curve, and thus further predict future sales in order to make more effective decisions.

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With data intelligence, you can quickly find data. And it also helps you to figure out how to use the data.

Data governance helps youto deal with various challenges related to data. It addresses multiple questions from different points of view. So a user can figure out whether a dataset is fit to use from the compliance, regulatory, and business perspectives. Data governance formalizes responsibility and authority around data.

It clearly defines roles in regards to who can do what concerning the data. It also guides users to the best data and its most appropriate use. So data governance supports trust and transparency.

Now the spontaneous question comes, how does data intelligence support governance? Here, metadata plays a key role. If you look at the past practices, businesses used data governance as a defensive tool to enforce compliance to ensure the passage of audits. Althoughcompliance is essentialto the regulated industries, the command and control method used in practice created barriers between people and data.

But data governance can play a dominant role in servicing a data strategy that is both offensive and defensive.

Today, leaders are prioritizing data democratization to ensure people get access to the data they need.

Also, data catalog plays a crucial role in the process. It can integrate compliance at the consumption point to alert people of sensitive data. Metadata plays a key role in data intelligence. And what is metadata? It is data about data.

Data intelligence is based on metadata. So you can come across high-level metadata categories in a data catalog. And that can include:

• Behavioral:The records that show who is using data and how they are using it
• Technical:They show schema or table definitions.
• Business:They show the policies on appropriately handling different kinds of data.
• Provenance:They show the relationship between two data object versions formed when a new dataset version is created.