The importance of data in the world of today can not overstate. Though data has formed the backbone of all research for centuries, today, its use has spread to businesses – both online and offline, governments, think tanks which help in policy formulation, and professionals.
With the surge is collection and dissemination of data, the importance of data analysis has grown as well. While data collation is vital, it is just the first step in the process of using it. The ultimate use of data is to draw meaningful insights from which can then be put to use to practice. Data analysis helps in doing this by transforming raw data into a human or machine-usable format from which information is being drawn.
Also Read: What is Data Analysis and Who Are Data Analysts?
Some ways in which data analysis can be distinguished are as follows:
- Organizing data: Raw data collected from single or multiple sources may be disorganized, or present in different formats. Data analysis helps in providing a form and structure to data and makes it useful so that other tools can be used to arrive at findings and interpret the results.
- Breaking down a problem into segments: Working on data collection from an extensive survey or transaction and consumer behavior data can become very challenging due to the sheer volume of data involved. Data analysis techniques can help segment the data thereby reducing a massive, seemingly insurmountable problem, into smaller parts which can be relatively easily tackled.
- Drawing insights and decision-making: This is the aspect which is most readily associated with data analysis. Tools and techniques from the field applied to pre-organized and segmented data assist in drawing meaningful insights which can either help in concluding a research project or support business in understanding consumer behavior towards their products better.
Further, through data analysis in itself is not a decision-making process, it certainly does help policymakers and businesses make decisions based on insights, information, and conclusions drawn while researching and analyzing data.
- Presenting unbiased analysis: The use of data analysis techniques helps ensure that unwarranted biases – human or statistical – are reduced at least or eliminated at best. It helps ensure that top quality insights can be extracted from the data set which can help in taking effective policy actions or decisions.
Some people misconstrue data analysis to be just the presentation of numbers in a report based on which researchers support their thesis or managers take decisions. This is far from being true. More than merely data collection, data analysis helps in cleaning raw data, dissecting it, and analyzing it. It can also assist in presenting the insights drawn or information received from this exercise in a format which is compact and easy to understand.
In companies, there are data analysts and data scientists who are responsible for conducting data analysis. They can play a crucial role in harvesting information and insights from the data collection and study cause and effect relationships by understanding the meaning behind figures in light of business objectives. They are trained to process technical information and convert it into an easily understandable format for management.
Some data analysis methods that they use include:
- Data mining: This studies patterns in large data sets – also known as big data – by applying statistical, machine learning, and artificial intelligence methods.
- Text analytics: It processes unstructured information in text format and derives meaningful information from it. It also converts this information into the digital format for use by machine learning algorithms.
- Business intelligence: This method draws insights from data and converts it into actionable information which is used by management for strategic business decisions.
- Data visualization: This method uses data analysis tools to present trends and insights visually, thus making data more palatable.
Companies like Amazon and Google have made pioneering efforts in using data analysis by applying machine learning and artificial intelligence to create end-user experience better. Given that we are living in the information technology age, the use of data analysis is expected to increase manifold in the future and enhance its scope.