By 2022, the global big data and business analytics market is predicted to rise to USD 274.3 billion says Statista. Also, with more than 2.5 quintillion bytes of data generated daily, organizations will need more skilled specialists acquainted with data analysis.
More so, if you’re looking to seek a career in data analytics perhaps now would be the right time to do so.
Before a data scientist could predict information obtained from the data, it must first undergo analysis. Data analysis is defined as the process through which the data is cleaned, transformed, and analyzed in order to derive useful and relevant information beneficial to the organization.
Data analysis tools and techniques are used to process and manipulate the data or detect any type of correlations between data sets and further used to interpret future predictions.
Moving further we will talk briefly about certain stages toward the data analysis process. They are:
Understand the data
We’re living in the era of data. Most often we hear about data being misused or data privacy breaches. However, if used properly, it could be a powerful tool for organizations. Data helps you transform information into actionable insights thus, giving organizations the privilege to make better business decisions. Data analysis is one of the basic skills even a big data analyst needs to possess.
Clean the data
While more enterprises are interested in using data to boost productivity, it is also important to know whether the data is error-free. The only way to gain maximum efficiency through data is by first cleaning the data and removing unwanted inconsistencies and errors.
If the organization is looking to gain maximum business growth using data, then they need to make sure the data they’re using is accurate.
Such problems can be easily detected using data cleaning tools to identify incomplete and unreasonable data. To name some of these tools would be Xplenty, DemandTools, RingLead, and Tibco Clarity.
Analyze the statistical summary of the data
Once the data is free of any errors and anomalies, the data professionals need to take a look at the data’s statistical summary. In this, the specialist needs to be aware of the describe function. This function helps compute the statistical summary as required by data frame columns.
Data visualization is crucial to represent data in the form of graphs, charts, tables, or illustrations. These visualizations make it easier for the data scientist to explain the results to business stakeholders. It gets easier for them to understand using visual representations.
Some of the best tools include Qlikview, Tableau, D3, Microsoft Power BI, E Charts, Fusion Charts, Sisense, and Plotly.
Data visualization is considered one of the most important skills for a big data analyst or a data scientist.
Some of the important data visualizations used cases also include the SWOT analysis.
The SWOT analysis is used to basically determine the strength and weakness of a certain organization – strengths, weaknesses, opportunities, and threats. This can only be possible only once the data points have been established. A SWOT analysis can also be presented in a 2D or a 3D square which is divided into four major areas. In the hands of the right specialist, a visual presentation of the information can help strike out the factors affecting organizational growth.
Drawing conclusions/inferences based on the results from the data
Making inferences determines the data to the user. However, an inference is based on a certain number of attributes. To obtain precise and in-depth results we will need to consider more attributes. Using inferential statistics, we can easily draw conclusions from any sample data in order to estimate the parameters of a significant population.
Let’s take an example here, a data scientist wants to understand how a variable within the experiment behaves. What would they do next? Perhaps, the data scientist might gather all the data i.e. population for a certain variable. Thus, this might get challenging. This is why only a small sample of the population needs to be considered. This makes it easier for them to perform statistical inferences in a small sample size. The major aim of a data scientist is to generalize about the population from even a small sample size while seeking the degree of uncertainty. Such analyses help them draw conclusions while also make propositions about the overall population.
The aforementioned steps need to be followed whenever you intend to work on a dataset. Although it is always not mandatory to exactly follow the same procedure, these are basic steps that can garner better insights for your next project.
Simply said, data analysis is a simple process designed to determine meaningful insights that will benefit the organization. As a result, the company tends to make better business decisions.