Big Data

Big Data Analysis: The Basics for Beginners

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A company’s problems can begin precisely with the fact that competitors are moving forward with the world, and the company is still storing data in Excel. The culture of collecting and analyzing data should become one of the fundamental principles of the organization that is going to develop. This is the basic principle of modern business conduct. Let’s take a look at all of them:

  • proper high-quality data collection – for this you need to install at least any CRM-system,
  • fill in all the necessary columns in it on time,
  • carry out data analysis,
  • look for stable dependencies.

How does Yandex analyze data?

Collecting information for analyzing large amounts of data

The head of a company or marketing department needs to have at hand information on the following groups for further analysis of a large amount of data:

  • data on real and potential customers (their gender, age, cost of attracting, income level, education, geography);
  • hourglass conversion data is a better option than a sales funnel; the client is guided through the stages of outreach, capture, heating, up-sales, loyalty, affiliation and sales;
  • data on sales, including data on jointly sold products, in order to further take this into account;

– data on repeated purchases of customers in order to also take this into account when forming an offer.

Big data analysis and search for stable patterns

In order for the collected information to work, it must be visualized for better understanding (using programs such as Tableau, Qlik, Power BI) and the following big data tips for analyzing big data must be followed:

  • give maximum attention to quantitative research, since in practice they show greater results;
  • use a sample calculator, because the number of people in a survey can greatly change the result;
  • to carry out the calculation of Six Sigma – this is a technique that has won worldwide fame;
  • to carry out discriminant analysis, regressive and frequency – the most accurate types;
  • based on the research carried out, segment customers by loyalty (divide them by frequency of purchases, age and amount of purchases).

Big data analysis tools

It is better to provide information visualization to the analytical program:

Just as there are tools for collecting information (Excel, CRM, statistics counters on sites, various reporting forms), and tools for analyzing large amounts of data. According to professionals, it is best to use professional reporting and analytical systems with a focus on data visualization, such as Tableau.

Data visualization allows you to see information in a qualitatively new form and notice the trends that were hidden behind large volumes of numbers. In addition, this approach allows non-technical professionals to work with analytics, such as marketers. Such programs connect to tools for collecting information, if necessary, combine it from different sources. Various calculations can be made based on these data.