The Data Analytics Process
The Data Analytics Process:
The application of data analytics is going to involve a few different steps, rather than just analyzing the data that you have gathered, particularly on some of the more advanced projects of analysis, much of the required work is going to take place upfront, such as with collecting, integrating, and preparing data. Then we can move on to the part where we develop, test, and review the analytical models that we have to ensure that they are producing results that are accurate.
The data analytics process is going to start from the very beginning, where we work on collecting data. This is where the data scientist and their team will identify the information that they need to find and gather for a particular analytics application, and then they will continue to work on their own, or with some IT staffers and data engineers to assemble all of that gathered data for human consumption. Related article: Why Use PyTorch with the Data Analysis?
Data from different sources can sometimes be combined, with the help of a data integration routine, transformed into a common format, and then loaded up into what is known as an analytics system. There are a number of these systems available for you to choose from including a data warehouse, NoSQL database, and a Hadoop cluster.
In some other situations, the collection process is going to be a bit different. In this case, the collection process could consist of pulling the relevant subset out of a stream of raw data that flows into your storage, and then moving it over to a second, and separate, partition in the system. This can be done in order to allow for an analysis of the information, without any of the work that you do affecting the set of data overall.
Once we have been able to gather up the data that we need and we have gotten it into place, the next step that we need to work on is to find and then fix any of the quality problems that are in the data. We want to clean up any of the quality problems that could potentially affect the accuracy of our applications as we go along. This can include a number of different processes including data cleansing and data profiling to ensure that the information in our set of data is as consistent as possible and that duplicate entries and errors can be eliminated.
In addition to what we have been able to do so far, there is some additional work for data preparation that we need to focus on. This work is important because it is going to manipulate and organize the data that you plan to use in the analysis. You should add in some policies of data governance in order to help the data stay within the standards of your company, and that everything is done according to industry standards.
When you reach this point, the process of data analytics is going to get started in earnest. The data scientist is going to build up an analytical model working with some tools of predictive modeling or analytics software. There are a number of programming languages that we are able to focus on as well, including SQL, R, Scala, and Python, to get the work done. The model is initially going to be run against a partial set of data because this is one of the best ways to check out the amount of accuracy that is present in that model.
Of course, the first test is not going to be as accurate as you would like, which means that the data scientist has to revise the model as needed and test again. This is a process that is known as training the model, and we continue working with it until get can get all of the parts together, and the model functions as we intended.
Finally, we are going to run the model on what is known as the production model. This means that the model is going to be run against the full set of data. This is going to be done once because it is going to help us address a specific need in information. Then there are times when it is going to be done on an ongoing basis, any time that we update the data.
In some cases, the applications of the analytics can be set up in a manner that will trigger business actions automatically. For example, we may see this happen with some of the stock trades that a financial services firm is going to use. Otherwise, the last step of this process of data analytics is communicating the results generated by the analytical models that you used to business executives and other end-users to aid in how they make their important decisions.
There are a few different methods that you can use to make this happen, but the most common technique to work with here is data visualization. What this means is that the data scientist and any team they are working with will take the information they gathered out of the model, and then turn this into a chart or another type of infographic. This is done to help make it easier to understand the findings.
One more thing that we need to take a look at here is that we have to consider the variety of statistical methods that we have available with our data analysis, and then decide how we can use each one. There are a few that are really good at getting this done, but often it will depend on what we would like to accomplish with all of this. But first, we need to take a look at them and how they will be able to help us get the results that we want. Some of the best statistical methods that you may want to consider for your project will include:
The general linear model. This is going to be a generalization of the linear regression to the case of having two or more of the dependent variables that you need to rely on.
Generalized linear model. This one may sound like the other model, but it is a bit different. It is going to be an extension and works best when your dependent variables are more discrete.
Structural equation modeling. This particular type of modeling is going to be usable when you would like to assess some of the latent structures that were measured from your manifest variables.
Item response theory: With these types of models, they are going to be used to help us assess just one of the variables that are latent from the variables that are binary measured.
Depending on the kind of information that you have presented, and what your final goal in the process is, there are a few different approaches that you are able to use in order to help get the data analysis done. Some of the most common ones that you can choose from (and again, look to your data and what you are trying to figure out from that information to help make the decision), are going to include the following:
- Cross-cultural analysis
- Content analysis
- Grounded theory analysis
- Discourse analysis
- Hermeneutic analysis
- Constant comparative analysis
- Phenomenological analysis
- Narrative analysis
- Ethnographic analysis