Whenever we talk about data, there are always some questions that arise. How to handle data? How to work with it in a way that gives us some fruitful results? You must have heard the phrase data is everything. In reality, data is nothing but garbage, if you do not know how to handle it and mold it for your favor. Data, if controlled and managed rightly, has the power to transform your business.

Data is everywhere, and everything contains some data. Sometimes it is in a small amount, and we use our brain to work through it and find some results. Whenever there is something wrong in your business and your products are not selling, there it is, data for the rescue. You use the existing data and conduct some new surveys and feedback panels to know what people think is going wrong and what they want. It is the most reliable way to know the audience and target their needs. Even after collecting the dataset, you will need something, some ways to pull out the results from data.

To pull out some fruitful results from the data, there are some techniques and practices under the names of Big Data, Data Science, and Data Analysis. We need them to work through our data and bring out the real value of it. We are using all of these methods not only for the marketing purpose but Data Analysis is also used for some crucial stuff like health management. These methods have become an essential part of our lives, as we are getting more and more data every day.

What is Data Analysis?

The process in which we transform, clean, or model the data to pull out critical insights for making decisions, is called Data Analysis. It is simply the process of working through the data to extract useful information. Based on this information, we make decisions for the betterment of our business.

There are usually two methods of data analysis, Qualitative Analysis, and Quantitative Analysis. In the former one, we find the answers to why and what, and how. Usually, it consists of narratives and texts. The latter one is the analysis that belongs to the number game.  It focuses more on statistics and its manipulation.

The 12 Step Method

We cannot overestimate the true potential of data analysis. Until now, what we do is we pull out the insights from the data to predict the behavior of our clients, or we pull out some results to improve our business. There are many more great outcomes we get from data analysis. The best we are doing it, we are using data analysis in the healthcare department and by analyzing clinical data collected regularly, we are making better decisions. There is a method of 12 steps for stronger data analysis. Let's discuss those steps here. Meanwhile, if you want to practically learn how to do stronger data analysis, you can join our training program.

  1. Set Objective

Setting the objective is the first and the most important thing. Because knowing the destination helps you decide the journey. At this stage, all you need to do is, set the primary goal and the subjective goals to reach it. For example, if you are a car dealer, your main goal will be to sell more cars. To achieve that, you will have to achieve some sub-goals.

  1. Prioritize Use Cases

The second thing is to go through the prioritization process, which is usually in two instances. One is urgency, and the other is value. You need to prioritize user cases based on these two.

  1. Source Data

The second thing is to go through the prioritization process, which is usually in two instances. One is urgency, and the other is value. You need to prioritize user cases based on these two.

  1. Connecting the Dots

It is the process of finding common ground among different data sources. We can easily do it by analyzing all the available datasets. This is the process in which we tend to find the missing elements of datasets. After this process, we have the idea of what data we have that is manageable.

  1. Determine Data Architecture

This is the process of designing the data architecture according to the data you have. And there should be room for future addition of data connectors as well.

  1. Data Modeling

It is the process where we make to models of all the data we have. It is a way of understanding the data better with the visual representation. In data analytics training online, you can learn more about data modeling.

  1. Building Data Fusion Module and Data Integration

It is the process of merging all the data, and for that, we will have to build a fusion module by writing code.

  1. Build an Analytic Engine

It is the process of merging all the data, and for that, we will have to build a fusion module by writing code.

  1. Present and Visualize

It is the step of developing a layer of visualization that stakeholders can also access and have a look at. This can be in the form of charts, tables, or displays.

  1. System Review

In this step, we review our system and find out the loopholes if there are any, and try to overcome them.

  1. Enhance Data

In this step, we try to enhance the quality and quantity of the data. For that, we try to find new data sources to put the data in and test the system.

  1. Fortify Data Architecture

In this step, we check our architecture if that has enough power to undergo future changes in data or not. And, we can also redesign the data architecture, if there is a need. Test the architecture by integrating additional data into the module.

Well, there you go with a plan to do more effective data analysis. If you follow these 12 steps for data analysis, you will get the results that can take your business to places. If you need any other information regarding data analysis, feel free to contact our experts at any time.