Ready for Data Analysis? Answer these 5 Key Questions First
Before starting any data analysis, the most crucial step is data preparation. Many companies spend millions and billions of dollars in collecting the data and analyzing it using various analyzing tools which may not always turn out as profitable – the most hindering part is improper data preparation. Data preparation involves a lot of steps like data integration, data cleaning etc. Knowing that data analysis is very expensive and that’s why data preparation becomes important which should be done in an efficient way.
Here are few questions which you should ask yourself while preparing the data and starting any data analysis :
What do you want to find out from the data?
Understanding the business requirement is a key to success. It’s very important to have a clear understanding of business problems and that’s why this question becomes very crucial. It’s important to know what kind of KPIs you are intending to measure. It will help to find the right kind of data you are looking for and data analysis you will need to perform. Having the best understanding of business requirements can help not only in performing best analysis but also it can help to meet what business expects and can generate best results.
Where will the data come from?
Now, you have an understanding of what is the business requirement. It’s crucial to identify the data source to get all the relevant data. The data source could be from any simple excel spreadsheet or from any data lakes or data warehouse. Its important it should be relevant data and can help to answer business questions. Here, few things to figure out here that, once you have the data, you should also have a mechanism to store the data. You should have the right software and hardware to crunch the data and more.
How will you ensure the data quality?
High quality of data is very important because it will help to perform better analysis and helps to generate better insights. Incorrect or low-quality data may give a distorted view of reality. It is, therefore, necessary to clean the data and discard outdated information. It is important to make sure that data is accurate, complete and up to date. If there is data inconsistency, it may result in redundant values, hindering the final results significantly..
What Are The Different Statistical Analysis Or Visualization Methods You Can Apply To Your Data?
Once we have the right data in place, now it’s time to transform, join and measure the data with statistical techniques to get the desired results. There are many statistical methods that can be used,but some common and important analysis – Regression Analysis, Cohort Analysis, Predictive Analysis and Prescriptive Analysis. Regression analysis is used for estimating the relationships and correlations among variables. It is mostly used to predict on historic data so that better decisions can be made. Cohort analysis gives a quick and clear insight into customer retention trends. Predictive and Prescriptive analysis is based on analyzing current and historical datasets. It is used to predict future possibilities, including alternative scenarios and risk assessment. Client/Stakeholders usually understand graphical or pictorial representation, hence, a better visualization of analysis is must.
Who will be end users?
One of the crucial steps that every data scientist/analyst should have an answer to is who will be the end user, who will be consuming the report on which they are working. It is important to keep in mind their technical skills, their mind so that it will be easy to present or visualize the information.The visualization should be easy to understand, explicitly, mentioned the insights from it, easy to use and understandable. Hence, you should always ask this question ‘Who will be the end users’ before starting any data analysis.
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