Big Data – Simply Explained

The quantities, signs or symbols of operations performed by a computer, which can be stored and transmitted in the form of electrical signals and recorded on magnetic, optical or mechanical recording media, shall be called data. But what is big data then?

What exactly is big data?

Big data is also data, but with a huge size. Big data describes a collection of data that has a huge volume and yet grows exponentially over time. In short, such data is so large and complex that none of the traditional data management tools can store or process it efficiently.

Well-known examples of big data

Here are some examples of huge amounts of data:

  • The New York Stock Exchange generates about one terabyte of new trading data per day.
  • Social media: Statistics show that more than 500 terabytes of new data are added to the databases of the social media site Facebook every day. This data is generated mainly in the form of photo and video uploads, message exchanges and comments.

The different types of big data

These amounts of data can be found in three forms:

  1. Structured
  2. Unstructured
  3. Semi-structured

1. Structured

All data that can be stored, retrieved, and processed in a fixed format is called “structured data.” Over time, IT talents have achieved greater success in developing technologies that process such data (known formats) and derive value from it. Today, however, we see problems when the size of such data increases sharply. Typical sizes are several zettabytes.

Did you know? One zettabyte equals one billion terabytes!

If you look at these numbers, it’s easy to understand why the name “mass data” is given and imagine the challenges associated with its storage and processing.

2. Unstructured

All data in an unknown structure or shape is classified as unstructured data. Unstructured data is not only huge, but also poses a variety of processing challenges to derive value from it. The classic example of an unstructured record is the heterogeneous data source, which contains a mix of simple text files, images, and videos.

Today, companies have a wealth of data, but unfortunately they don’t know how to derive value from it. This data is available in its raw form or in unstructured format.

3. Semi-structured

The semi-structured data sets can consist of both data forms. We can see semi-structured data as structured data, but it is not actually clearly defined. The typical example of semi-structured records is data that is represented in an XML file.

The properties of Big Data

  1. Volume The term “big data” stands for data volumes of enormous size. The data size is the focus when determining the value of data. Whether or not certain data can actually be considered big data also depends on the volume of data. Therefore, volume is a feature that must be taken into account when dealing with big data.
  2. Diversity The next important feature of big data is its diversity. This aspect describes the heterogeneous sources and the form of structured and unstructured data. In earlier days, databases and spreadsheets were the only data sources considered by most applications. Today, information in the form of e-mail, photos, videos, surveillance devices, PDFs, or audio is also included in the analytics applications. When storing, mining, and analyzing data, large amounts of unstructured data can cause certain problems.
  3. Speed The speed here refers to data generation. The actual potential of the information determines how quickly the data is generated and processed to. The so-called big data velocity deals with the speed at which information from application protocols, business processes, sensors, networks and social media sites or mobile devices arrives. The data flow is massive and continuous.
  4. Variability This refers to the inconsistency that can sometimes be displayed by the data, which hinders the process of effectively handling and managing the data.

What are the benefits of big data processing?

There are several benefits to the ability to process big data:

Companies can use external information when making decisions

By accessing social data from search engines and websites such as Twitter and Facebook, companies can optimize their business strategies.

Improved customer service

Ordinary customer feedback systems are replaced with new applications with big data technologies. These new systems use large data and processing technologies in natural language to read and evaluate customer responses.

Better operational efficiency

You can use big data technologies to create a deployment area or landing zone for new data before you determine what information to move to the data warehouse. In addition, such integration of data warehouse and big data technologies helps an organization outsource information that is infrequently accessed.

The Life Cycle of Big Data Analysis

Now let’s review the lifecycle of big data analysis:

  1. Business Case Assessment The life cycle of big data analysis begins with a business case that defines the reason and goal of the analysis.
  2. Identification of data A variety of data sources are identified here.
  3. Data filtering All identified data from the previous stage is filtered here to remove corrupted information.
  4. Data extraction Data that is not compatible with the tool is extracted and then converted to a compatible form.
  5. Data aggregation In this phase, data is integrated with the same fields across different records.
  6. Data analysis The data is evaluated using analytical and statistical tools to identify useful information.
  7. Visualization of data With tools like Tableau, Power BI, and QlikView, big data analysts can create graphical visualizations of analysis.
  8. Final result of the analysis This is the final step in the life cycle of big data analysis, in which the final results of the analysis are made available to business stakeholders who will take action.

The different forms of big data analytics

There are four types of big data analysis:

Descriptive analytics

This summarizes past data in a form that is easy to read. The process helps create reports such as a company’s profit and revenue. It also supports the tabulation of social media metrics.

Use case: The Dow Chemical Company analyzed its previous data to increase the utilization of facilities in its office and laboratory space. Using descriptive analyses, the Dow Chemical Company was able to identify underutilized space. This consolidation has enabled the company to save almost USD 4 million per year.

Diagnostic Analytics

This is done to understand what caused a problem in the first place. Techniques such as drill down, data mining, and data recovery are examples. Organizations use diagnostic analytics because they provide detailed insight into a specific problem.

Use case: An e-commerce company’s report shows that sales have declined even as customers put products in their shopping carts. There may be several reasons why the form was not loaded correctly, shipping costs are too high, or there are not enough payment options available. Here you can use diagnostic analysis to determine the reason.

Predictive Analytics

This type of analysis examines historical and current data to make predictions for the future. Predictive analysis uses data mining, artificial intelligence, and machine learning to analyze current data and make predictions about the future. For example, it works to predict customer trends and market trends.

Use case: PayPal determines what precautions they must take to protect their customers from fraudulent transactions. Using predictive analytics, the company uses all historical payment and user behavior data and creates an algorithm that predicts fraudulent activity.

Prescriptive Analytics

This type of analysis dictates the solution to a specific problem. Perspective Analytics works with both descriptive and predictive analytics. Mostly, it is based on artificial intelligence and machine learning.

Use case: Prescriptive Analytics can maximize an airline’s profit. This type of analysis is used to create an algorithm that automatically adjusts airfares based on a variety of factors, including customer demand, weather, destination, vacation times, and oil prices.

Conclusion

Big data is defined as data sets that are very large. It is a term used to describe a collection of data that is huge and yet grows exponentially over time. Well-known examples of generating the huge amounts of data include exchanges and social media sites. The collected big data can be structured, unstructured and semi-structured. Important features are volume, variety, speed and variability. In summary, this means improved corporate governance, better operational efficiency and improved decision-making. However, these are just a few of the benefits of big data.

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