As data increases, digital transformation is no longer a choice!

Although Big Data is seen as a new concept for many people, it dates back to the 1970s, when relational databases were developed. However, this term, which describes a large amount of data, whether structured or not, gained popularity in the early 2000s when data generated by users began to be researched and analyzed via social media.

Today, Big Data has become capital. As it directly affects every industry area, data analysis even affects social life. In addition to increasing customer satisfaction, which is the most common use, it is now possible to predict new disasters with advertising trends, early diagnosis of diseases, strategy development of law enforcement, and even advanced analytical interpretation of natural disasters. The possibilities offered by Big Data are virtually limitless. Massive amounts of data have become properties to buy and sell. However, the data used is very complex and not particular. Only a few thousand people in the world know how to make sense of this data and use specialized software.

Big Data, primarily by examining companies’ customer behavior, makes significant contributions to making the right decisions and developing strategies. After the data is made the most straightforward and most workable, their relations are examined using the comparison method. Furthermore, the reactions to different choices can be seen with the simulations created by changing places of various points in the data.

Thanks to extensive data analysis, organizations can accurately evaluate data based on actual customer behavior and turn it into a highly beneficial tool.

Since big data is entirely based on accurate data analysis, it allows making the right decisions in many different areas, such as reducing costs, spending on the proper channels, saving labor, and developing products that meet expectations. In this way, Big Data can be used in every field, especially in sectors such as banking and retail, where customer behaviors are of great importance and need to be followed. In recent years, the states understood the importance of big data and started to be used in various fields. For example, in health; Big Data, which is used for early diagnosis of diseases or drug development, can also prevent crimes or improve the education system.

What are Big Data Analytics Components?

1. Value: The most crucial component of big data is value. For this reason, it is a priority to construct the analysis.
2. Variety: Most of the data produced has different formats. From phones, tablets, computers; Data from other operating systems or languages ​​lead to various forms.
3. Velocity: Technological opportunities that increase day by day cause the amount of data obtained, the number and variety of transactions to be made to grow in the same way.
4. Volume: In the past ten years, the amount of data has increased more than 40 times, but the expenses for data storage have increased by 1.5 times. This situation reveals that excellent editing is required to correctly and efficiently store the obtained data.
5. Verification: Another issue that has come to the fore in recent years regarding data has been data security and accuracy. 

If big data is designed and used correctly, it provides essential benefits in the decision stages of companies and provides companies with a competitive advantage. Companies aware of this situation; benefit from Big Data in many areas such as marketing, sales, and production.

How Should Big Data Analysis Process Be?

Using Big Data for many businesses, accessing information is not a problem. The main point is to analyze the data and create value. The steps to be followed in this process to increase your Data Analysis performance and make the right decisions are as follows:

1. Define Your Questions: You should always start data analysis with the right questions. Questions should be measurable, clear, and short. In addition, the questions you ask should be specific to a particular problem or opportunity.
2. Set Your Measurement Priorities: Decide what you will measure and how. You should know in advance what qualities you will measure the answers to the questions you ask.
3. Data Collection: Now that you have clearly defined questions and measurement priorities, you can start collecting and organizing your data. First, look at what can be gathered from existing databases. Next, create file storage and naming system to save time and avoid data duplication. Finally, log the data you have collected with the dates.
4. Analyze the Data: After this stage, you will make conclusions and inferences by manipulating the data you have collected. Analysis of trends, variations, and outliers will get you where you want to be.
5. Interpret Results: As you begin to interpret, you should ask yourself these key questions: Does the data answer the questions? Does the data help you defend an answer against any objections? How?

There are six types of data analysis methods most commonly used globally. It can be listed as A/B Testing, Data Fusion and Data Integration, Data Mining, Machine Learning, Neurolinguistic Programming (NLP), and Statistics.

To remain competitive in their markets, companies are trying to discover new ways to find and analyze growing information about customers, products, and services.