5 Step Guide for Data Analysis

Data Analysis, also commonly called as data analytics or analysis of data is a technique of inspecting, purging, modifying, and modeling the data with the aim of discovering relevant and useful information, supporting decision making and implying or indicating results. There are some approaches and facets to data analysis, which encompass an array of techniques under different names, businesses, social science domains, and science domains.

Hence, data analysis is nothing but a method of collecting and organizing the data so that you can derive the helpful and valuable information from it. The primary purpose of data analysis is to understand what the data is trying to tell. For instance, what does the data do or show? What doesn’t it do or show?

There are various ways of collecting data. Given below are the forms defined for data collection. These are categorized based on the kind of research one is conducting.

Observations:

This category of data collection includes watching and observing someone or something.

Interviews:

This type of data collection comprises a group of people who talk with other people. At the time of the interview, the interviewer asks the questions to the candidate, and these issues usually are posed by the researchers so that they can come to some conclusions.

Surveys:

A series of problems is called a survey. Given below is an example of the survey questions:

1. How satisfied were you with your online shopping experience at xyz website?
2. Would you be shopping at this store again?
3. ETC.

Measurements or Records:

This system of data collection lets the researchers’ measure something or collect the records of measurements, which have been conducted previously already.

Data Mining& Business Intelligence- Techniques for Data Analysis

Data Mining is a particular method for data analysis. It mainly focuses on the knowledge discovery and modeling instead of the exclusively descriptive purposes. On the other hand, Business Intelligence is all about data analysis which depends ultimately on focusing and aggregation of the business information.

Process of Data Analysis

The shortfall of data is not a very big problem for most of the government agencies and businesses. Moreover, it is, in fact, the opposite. There is often just way too much information available so as to make a clear and definite segregation. With so much data to be sorted properly, you need to get something more from your data, as listed below:

• You need to know whether it is the correct data to be able to answer your questions.
• You should be able to draw accurate and precise results from your data.
• Data should be good enough to help you in your decision-making process.

All in all, you are in need of a better data analysis.

Given below is a 5-step guide to proper data analysis:

1. Defining your questions- You need to start with the right questions in your business or organizational data analysis. The questions should be concise, clear, and measurable. Draft your questions so that they can either disqualify or qualify the potential answers to your particular opportunity or problem.
2. Collecting the Data–Once you have decided on the type of data required for your study, it is the time to figure out whether the data that you already have would suffice, or more information from other sources will be required.
3. Summarizing and showcasing the data– Making graphical display is the next step. It is a powerful tool for persuasion and teaching. A picture is worth a thousand words because more people can understand the pictures better than lectures.
4. Growing a data science team- It is not easy to hire data scientists. Hence, it is not a bad idea that you build a team of data scientists with advanced degrees in stats to focus on the data modeling and the predictions. The other members of the team can simultaneously build the necessary data collection infrastructure, data products, and data pipeline, which enable the streaming of the data through models while showing the results to the business in the form of dashboards and reports.
5. Communication- The conclusions of the data analysis are reported in a format as needed by the users for supporting their decisions and for further action. Additional analysis may be required after receiving the feedback from the users.