Basically, data analysis is used for business that involves collecting the different form of data from different sources and interpreting the data in systematic and planned manner . Today’s business depends upon data that helps to analysis the root cause of organizations development.
In layman’s language, Business Analysis is a set of tasks and techniques with help of tools for connection between clients. These help them to understand the business structure, policies, and operations and in return provide the recommend solutions to help the business to attain its goals. For this it is necessary to understand how the organizational goals connect to specific objectives. You will also have to make a detailed plan to help achieve the goals and objectives. In your business analysis, you will define how the client and different organizational department interact with each other. It is the duty of business analyst to analyse and prepare the report provided by the group of people who interact with the firm.
There are many types of data analysis process depending on the nature of the business and data involved. But the data analysis can be broadly classified into three types:
- Descriptive Analysis
- Diagnostic Analysis
- Predictive Analysis
Descriptive analysis is the process of analyzing data to explain the dataset. This explanation does not explain why it is happened or what will happen in the future. It reviews what really did happen and its current situation.
For example, consider a dataset of the monthly selling report of a distributor who sell electronics items over the last 10 years. When conducting descriptive analysis, the goal is not to understand the present revenues or what they will be in the future, but to describe what the revenues were in the past. An example finding might be that revenues dropped over the last two years.
Diagnostic analysis is a type of data analysis that involves diagnosing why things happened. It usually involves comparing two or more datasets, so as to identify any correlations, and potential causes of an event.
For example, of the distributor, as used above. The owner knows that revenues dropped over the last two years. Now, the owner wants to use descriptive analysis to find out reason. To do so, the owner can compare the revenue trend with other variables, such as the trend in local market, market consumption or the number of visitors to the shop. If a correlation is found, then use the statistical techniques can be used to confirm these links.
Predictive analysis is use data analysis techniques to make predictions as to what will happen in the future. It usually takes into account findings from both descriptive and diagnostic analysis.
In the distributor example, the owner knows how revenues have changed over the last 10 years and why. Now, the owner wants to know what revenues he can be expected in the coming years. By analyzing the past trends and the variables that influence them, the owner can predict the business.
Data Analysis Process
There are a number of steps for data analysis. Actually, analyzing the data is a small part of analysis, as the large amount of effort must go into collecting, processing, and cleaning (CPC) data so as to draw meaningful conclusions.
Data Collection
All data analysis starts with data collection. Data analysis works better with quantitative data, which can be represented numerically, than with qualitative data. As a result, data collection usually revolves around quantitative data, like the number or value of certain properties. This information can be collected from balance sheets (in the case of financial data), online analytics tools (such as Google Analytics), or even plain old counting (e.g. counting the number of people that order a certain electronics item per week.
- Qualitative: Individual project risk is analyzed.
- Quantitative: Analyzed the overall project risk, after individual risks have been considered.
Data Processing
Once data has been collected, it needs to be processed into a usable format. Often, this means inserting the data into a database or spreadsheet, where it can undergo further analysis.
Data Cleaning
An important step in data analysis is data cleaning. This involves reviewing the dataset to ensure that all information is correctly formatted, and no data is missing which should be discounted.
Data Analysis
After data collection, processing, and cleaning then the dataset actually be analyzed. This is the step in which the analyst looks to draw conclusions from the data, either by reviewing the dataset manually or using the statistical methods. In the case of diagnostic or predictive analysis, multiple datasets are combined to help understand what variables influence the dataset.
