The volume of information generated is growing quickly in the age of digital technology and social networks. If a company has a site, a smartphone application, queries and customer reviews is sent to its e-mail address or via immediate messages, the data can already be analysed. But how is the company going to benefit?

Large companies started asking themselves this question seven years ago, but then few saw the benefits of big data analytics. 

Companies from IT, banking and telecommunications business turned out to be pioneers in the implementation of big data. These sectors collect the largest amount of data from various sources. Banks collect the data through transactions, telecoms companies collect data through geodata, and search engines collect through query histories.

To understand the importance of the Data Analytics, Big data analysis is currently being used by all large firms. In the United States, this technology works for over 55% of organisations in a broad range of industries. Big data demand is slightly lower in Europe and Asia – around 53 percent. It turns out that companies have used large data three times during the past five years.

But the main thing that a business interested in implementing big data should understand is that the analyzed information will not give an answer to all the challenges that your company faces. Big Data finds solutions for businesses depending on what kind of data the company collects. 

In order to keep and manage all the data, data analytics companies require a team of skilled and professional data analysts who can handle the data analysis for multiple companies. Being a data analyst is also not an easy task as top-notch training from a multinational technology support company is great.

How an MCA/B.Tech student can become a data analyst?

In the field of data analysis, the modern labor market is just being formed. However, certain ideal competencies which a data analyst should strive for can already be identified.

A Data Analyst basically must need these 5 skills

  1. Good Mathematics knowledge, the school program at least. It helps to comprehend what the calculations are, in essence, of the procedures used by the analyst. Without this, the proper conclusions can hardly be drawn from your analysis.
  2. SQL language knowledge (used to work with databases). The work with data is 95 percent of the job of an analyst. To query and retrieve data from databases, you must be able to operate with SQL.
  3. Intermediate-level Excel Knowledge. One of the pillars on which data analytics is based is working with tablets. Excel’s analyst capabilities range from data processing to viewing.
  4. Skilled hands in Python or R. The new options open up to the analyst in programming languages: analysis, speed and efficiency.
  5. Knowledge of visualization tools: Tableau, Power BI, or visualization libraries in Python or R. This is generally requested by employers, since any analyst notion helpful in a simple language should be presented. Graphics and displays are one of the best techniques of communicating a concept.