Data Analytics is a complex process that is done in different stages. The stages of the analytic process are laid out as a framework and the framework is developed by considering several approaches such as DELTA framework, CRISP-DM, etc.
The data analytics lifecycle process includes data discovery, data aggregation, data model planning, & execution, and visualizing results. The analytic process is done not only by a single expert but a group of experts who has different roles such as data analyst, data engineer, project manager, business analyst. Let us see how the data analytic lifecycle process is performed by the key shareholders.
The first step in the analytic process is gaining valuable insights. Data discovery is the process of finding the relevant data to the problem. The problem needs to be specified first (i.e), “what the data analytics needs to solve?”. And based on the problem, the relevant data must be collected.
The relevant data discovery and collection is a complex task because of the sheer volume and the variety of data available in the business. The task is also equally important as the main analytic task, cause analyzing data on irrelevant data leads to undesirable results. Mainly a data engineer or sometimes even Artificial Intelligence performs the task of collecting relevant data.
The data is collected from a data warehouse or data repository of a business. There are many tools and software available to do the task of data collection. But it requires an expert to work with the tool to collect the relevant data.
During this phase, the raw data is converted into the desired form to be analyzed. The data discovered and collected is never accurate. It most certainly will contain irregularities like missing values, wrong entries, duplicate data, etc. It is cleansed in this phase by the data engineer.
Here, the quality of the data is improved by using various techniques and algorithms. During the data preparation, the raw data is converted into a standardized form for the analytic process to be executed. The missing values are filled, duplicate values are removed, outliers are detected, and finally, the data is reformatted.
This process is equally significant as the discovery or modeling process because the quality of the analytic model directly depends upon the quality of the data.
Modeling and Execution
As we have seen in the previous post, there are various analytic techniques available. The data analyst team will select one or more analytic techniques to be applied to the dataset based on the business problem.
The data model is created from the analytic process. Model is just an abstract representation of the insights such as patterns or predictions that are discovered from the data. There are various tools that execute different machine learning algorithms to create a model. This task is relatively easy for the analyst as it just needs to identify the right technique for the given problem to create the model.
After creating the model, it is deployed or executed. Execution of the model is nothing but addressing the business problem using the model. The problem may be predicting the profits, classifying the sales, or uncovering new patterns between the products. With the model and the data, the problem is resolved.
Visualizing the Results
The solution from the modeling stage is transformed into a form that could be understood by a business executive. The results are always in a mathematical form that is difficult to interpret apart from the analyst team. A graph or chart based output is created from the mathematical solution for the normal consumers or the business people.
Here too, various visualization tools are there to make this job easier for the analyst team. The summarized result is communicated to the business team to make decisions based on it.
The above-mentioned lifecycle is the general format that is executed in business around the world. The phases are not linear. Phases may intersect with each other. Phases may be revisited to clarify problems, etc.
Data analysts and the team execute the phase according to problems and it may vary according to the problems. As a data analyst, it is important to know the way of proceeding with the analytic lifecycle and to efficiently solve problems in the business.