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Clean Architecture of Analyzing Data

Rahulraj Singh

Rahulraj Singh

Last updated 21/07/2021

Clean Architecture of Analyzing Data

Irrespective of what profession you come from, if your business or service has a customer then you are definitely analyzing data. This analysis is the root of your confidence for future developments. In this article, I will deep dive into the process of analyzing data and ways in which most can be brought out of it. Let’s deep dive.

1. Questions/Requirements Gathering

Understanding the requirement is key. It is important to find out what is kind of business problems that you are looking to answer and the KPIs that you are intending to measure. The next step is to plan the way forward. A very useful tactic that I would recommend is taking some time to figure out the data pointers from the information given to you. These data pointers are the performance indicatives that will form the set of information you need to focus on. It is important to ask specific questions. This phase builds a foundation for your data analysis solution as well. Note down the key areas from the data that you think would make a difference in decision making. Once, that is done and understood you could now move to Data Exploration and collection.

2. Data Acquisition

A major part of Data Acquisition gets covered during the loading up the data and performing the first two steps. But, acquisition as a process is a lot more than just taking the data from a resource to your workspace. There has always been a terminological confusion regarding the acquisition. First, “data acquisition” is sometimes used to refer to data that the organization produces, rather than (or as well as) data that comes from outside the organization. This is a fallacy because the data the organization produces is already acquired. So, we take into consideration only the data that we will capture from other sources and processes.

3. Data Wrangling/Data Preparation

According to a report by Aberdeen Group, data preparation “refers to any activity designed to improve the quality, usability, accessibility, or portability of data. This step starts with the collection of data and importing it to your workspace. From here on, since there is a possibility of actually showing you up with data, I am using a data-set to explain further steps. It will start by taking the data in formats that most code will understand. If you are using a Query-based program to work with data, any SQL syntax should work fine. I will proceed further with Python on a CSV file. One important thing to note here is, data is CSV, and TSV formats are easier for Python, R to understand and interpret. At the same time, these formats can be easily run on Excel to visualize, but at the same time, an XLS formatted document is difficult to understand and run. There are various statements you could use to load the data onto your Python environment.


4. Data Cleaning

The Data Cleaning portion initializes some of the Visualization libraries to check for data discrepancies. This is important because outliers can wildly cause misinterpretation of the analysis we make. There are some common libraries within Pandas, Matplotlib, and Seaborn that can be used for identifying these outliers. Let us look at some of the code that would determine if there are values in the data that need cleaning. I am explaining two parts of realization here, one is finding missing values and another is finding outliers.

5. Data Exploration

Exploratory analysis of data is not just fascinating but it is also one of the best forms to gather the architecture and dependencies within the Data. This phase can or at times cannot be associated with the primary problem at hand, but nonetheless still has a place in my clean architecture of an analysis solution. The primary reason for this is that use cases formed from the business requirements more often than not answer all the questions, although at times there might be some portions of data that we miss out. This data may or may not be required for solving the problem at hand but will be very useful to grasp the structure of that data set.

6. Predictions and Conclusions

We are slowly coming towards the end of the process since most of the analysis has now concluded, but the solution is still not complete. One important aspect to consider here is that for any data analysis solution; the delivery of the solution will often more than not, not be given to another data analyst. So, the conclusions that we draw from the data needs to come in a language that most people understand. This can be perfected during the visualization phase but nonetheless, the easier things are to understand, the better presentation will be. Coming to predictions made from the data, make sure the prediction is made only and only based on the requirements from the first step. Seldom analysts like to visualize additional information, assuming that lots of data would be a good prediction. Theoretically, that might prove that you have done a great job during the analysis but at the end of the day if the consumer does not need the additional data it will not be of any use. So, one important point to remember is to keep things as simple and crisp as possible.

7. Data Visualization

We now come to the last sections in the architecture and data visualization is more like adding an additional layer of validation to the communication that your data is going to give. Now, visualization in itself is a tremendous step, encompasses many concepts, and could also have an architecture of its own. The depth of this can be understood by realizing that Data Visualization in itself is a full-time job, unlike all the other steps of the architecture diagram.

8. Communication (Story-Telling)

Although data visualization covers most of the graphical message of the analysis the last portion of the architecture is the art of storytelling. Once the analysis, predictions, and visualization is completed we return to base 1 of the mission that is gathering requirements and understanding the question that we need to answer. Communication involves giving the end-user that answer. In the path of reaching this conclusion, we might have come across various segments of data that need not be put into the conclusion. The best form of giving the result in my opinion is a one-page or to a maximum a two-page reporting dashboard. [2] This report would contain precise, to-the-point answers to the question that we were solving and only the data that the end-user needs. The language used during this communication should be built with the standpoint of the business user and not a data scientist. There a few tools that can be used to convey this message in a creative manner — tableau, adobe analytics are some really great dashboarding tools. I will be creating a separate article for how the art of storytelling should be commenced. But, in the mean-time let me show you a few examples of great dashboards.


Web Analytics dashboard image on Unsplash by Luke Chesser

Web Analytics dashboard image on Unsplash by Luke Chesser

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About Author

Rahulraj Singh is a Software Engineer currently working in the Data Analytics and Machine Learning domain for a Fortune 500 company. His professional aim is to bridge the gap between collection of data and the ease of extracting insights from it. he also take part in creating tutorials for aspiring Data Scientists and Engineers



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