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An introduction to data analysis

Data analysis is the process of analyzing, and then extracting useful information from data. The primary goal of data analysis is to discover actionable information that can be utilized to help businesses grow by increasing their productivity, helping their customers, and taking better decisions.

According to, the industry has grown by about 5% per year over the last decade. It is not a surprise, considering the fact that data analysis involves understanding what happened in the past, what happens right now, and what steps needs to be taken to positively shape what will happen in the future.

The process begins by choosing the analysis objectives. The data researcher will begin by asking himself two important questions: what indicators will be used to evaluate performance, and what metrics need to change to help the business grow? Then, he will start to gather as much data as possible, using a great variety of sources. The gathered data needs to be cleaned using dedicated computer applications and human operators who will supervise the process. As you can guess, human operators are necessary because the data records may be incomplete, or they may contain errors. Often, the gathered data may include duplicate records as well.

It's time to focus on data modeling, drawing conclusions that can help managers take decisions. However, this step may require additional data cleaning, and even additional data sets. Each major decision should be followed by another data analysis process, which will validate it (or not).

Inferential statistics are used to determine the relationship between certain variables. As an example, a skilled data scientist can run an analysis that will help determine if there is any correlation between the amount of money that was allocated for advertising in a certain month, and the resulting company profits.

Here are the key factors that must be considered when it comes to data analysis.


Data scientists should be highly trained; otherwise, their data collection methods, analysis and conclusions will not be accurate. Always pick a company which employs researchers who have already demonstrated their expertise.

Effective data recording methods

Results can be greatly influenced by the chosen data collection methods. Often, analysts record audio data, and then use transcribing services to convert the information to text files. Surveys can be open ended, providing a greater flexibility, or closed ended, simplifying the researchers' job, but providing less value.


Data reliability

Most researchers utilize computers to evaluate how many people have answered "Yes" to a specific question. Still, this method may introduce errors if the transcribed data was also including similar answers, such as "Sure". It's just a very simple example, but it clearly demonstrates the importance of an underlying variable analysis.

Efficient data presentation

Raw data analysis results aren't always easy to understand, so they need to be turned into actionable information. A skilled data scientist should also have the ability to communicate its findings clearly, in an easy to understand form.

Often, companies require data in several formats, which can then be sent to the team of decision makers. Data researchers will frequently use tables to show the managers the relationship between specific numbers, while charts will provide a visual representation of the data variation.