Big Data Analytics – Why You Shouldn’t Miss This Trend And How It Can Benefit You

The increasing digitization in the last few decades has resulted in companies and public institutions generating and storing vast amounts of data. We’re talking about 2.5 trillion bytes per day worldwide (this corresponds to a storage capacity of 36 million iPads). The explosive growth of digital data becomes particularly clear when one realizes that 90% of the data available worldwide were generated in the last two years.

With software systems constantly evolving and an increasing number of Internet and social media users around the world, this growth trend will continue in the future.

How to deal with the amount of data is a crucial question for companies nowadays. With the right analysis, important insights can be gained that serve as the basis for strategic company decisions. Companies can benefit from the amount of data available to them in order to remain competitive in the digital economy.

Big data are large amounts of data that are produced every day by companies and private individuals. At the consumer level, this data includes information on online, search and purchasing behavior. 

 

There are 3 characteristics that summarize the properties of Big Data:

  • Volume – describes the enormous volume of data or the huge amount of data.
  • Velocity – describes the speed at which data is created. More and more data are being generated in less and less time.
  • Variety – describes the different sources and forms of data. Data can be structured or unstructured. And they can be available as an audio or video file.

 

According to Jules Berman, the complex characteristics of big data become particularly clear when you look at how big data differs from small data in practice.

The differences between big data and small data are

1.Goals: While small data is being used for a specific goal, the use of big data often goes in an unexpected direction.

2.Place: Small data is basically in one place (in a computer file); Big data is often spread over many files on multiple servers in different countries.

3.Data structure: Small data is usually structured in a straight line; Big data can be unstructured, contain many file formats from different disciplines and refer to different sources.

4.Data preparation: Small data is prepared by the end user for his purposes; Big data is often prepared by one group, analyzed by a second, and used by a third party. Each group can have a different purpose.

5.Longevity: Small data is usually stored for a limited period of time (approx. 5-7 years) after the project has been completed. With big data, the data remains stored indefinitely, since the data projects are transferred to further projects.

6.Measurements: Small data is recorded with a single log in fixed units of measurement within a short period of time. Big data comes from different places, times, organizations and countries. This entails complex conversions.

7.Reproducibility: In the event that something goes wrong, small data can usually be fully reproduced. Big data comes in so many forms and sources that it is impossible to start over if you have problems.

8.Risk: If there are analysis problems with small data, the project remains financially manageable. Similar difficulties with big data can cost hundreds of millions of dollars in financial damage.

9.Introspection: (to what extent can the data describe itself meaningfully): The data set in small data everything is well organized and the meanings are clear. Big data is more complex and can contain information that is unidentifiable or meaningless. This can reduce the data quality.

10.Analysis: Small data can be analyzed from a single computer file in a single process. With big data, the extensive data has to go through extractions, tests, reductions, normalizations and transformations, among other things.

 

Big Data Analytics

By consciously or unconsciously collecting big data, the question arises for companies whether and how one can use this information for themselves. There are no business benefits to just owning this data. This is where big data analytics comes into play and makes it possible to analyze large amounts of data from various sources.

The aim is to read useful information and, in particular, patterns and correlations from the data volumes. With the knowledge gained, company processes can be optimized and competitive advantages achieved. In most cases, the optimization measures derived help reduce costs, save time and optimize products and services.

Big data analytics aims to use data to help companies make better business decisions. This goal is achieved by data scientists new hardware and software-based processes to structure large, unstructured amounts of data. In this way, the large amount of data that is available in a company is made understandable for the management and used productively.

According to surveys, big data is mainly used to get to know the customer better, to evaluate internal information in a more targeted manner and to build a better data landscape.

 

Industries for which big data analytics offers great advantages

 

  • Social media

Big data analytics can already bring a decisive advantage during development. The evaluation of social media channels or customer ratings can reveal social trends and market gaps at an early stage.

  • Production

As production is getting smarter and smarter, it is not surprising that big data also plays a major role here. The numerous processes are monitored by sensors and generate large amounts of data. This data can ensure preventive maintenance and prevent production delays or failures.

  • Distribution and logistics

Sensors are also increasingly being used in the supply chain to measure fuel consumption or to record the position data and the condition of wear parts. The structuring of this data means that costs can be sustainably minimized by planning transports promptly, changing routes and loads, or minimizing downtimes and maintenance costs.

  • Marketing and sales

You can greatly improve the relationship with your customer through data analysis. You have to know the needs of your customers more precisely and can even address each individual customer directly with personalized offers.

  • Finances

With the help of big data analytics, reliable predictions or risk calculations can be made in the financial sector. Therefore, it is possible to react more quickly to market developments or falling prices in banking and finance.

The increasing digitization in the last few decades has resulted in companies and public institutions generating and storing vast amounts of data. We’re talking about 2.5 trillion bytes per day worldwide (this corresponds to a storage capacity of 36 million iPads). The explosive growth of digital data becomes particularly clear when one realizes that 90% of the data available worldwide were generated in the last two years.

With software systems constantly evolving and an increasing number of Internet and social media users around the world, this growth trend will continue in the future.

How to deal with the amount of data is a crucial question for companies nowadays. With the right analysis, important insights can be gained that serve as the basis for strategic company decisions. Companies can benefit from the amount of data available to them in order to remain competitive in the digital economy.

Big data are large amounts of data that are produced every day by companies and private individuals. At the consumer level, this data includes information on online, search and purchasing behavior. 

 

There are 3 characteristics that summarize the properties of Big Data:

  • Volume – describes the enormous volume of data or the huge amount of data.

 

  • Velocity – describes the speed at which data is created. More and more data are being generated in less and less time.

 

  • Variety – describes the different sources and forms of data. Data can be structured or unstructured. And they can be available as an audio or video file.

 

According to Jules Berman, the complex characteristics of big data become particularly clear when you look at how big data differs from small data in practice.

The differences between big data and small data are

1. Goals: While small data is being used for a specific goal, the use of big data often goes in an unexpected direction.

2. Place: Small data is basically in one place (in a computer file); Big data is often spread over many files on multiple servers in different countries.

3.Data structure: Small data is usually structured in a straight line; Big data can be unstructured, contain many file formats from different disciplines and refer to different sources.

4.Data preparation: Small data is prepared by the end user for his purposes; Big data is often prepared by one group, analyzed by a second, and used by a third party. Each group can have a different purpose.

5.Longevity: Small data is usually stored for a limited period of time (approx. 5-7 years) after the project has been completed. With big data, the data remains stored indefinitely, since the data projects are transferred to further projects.

6.Measurements: Small data is recorded with a single log in fixed units of measurement within a short period of time. Big data comes from different places, times, organizations and countries. This entails complex conversions.

7.Reproducibility: In the event that something goes wrong, small data can usually be fully reproduced. Big data comes in so many forms and sources that it is impossible to start over if you have problems.

8.Risk: If there are analysis problems with small data, the project remains financially manageable. Similar difficulties with big data can cost hundreds of millions of dollars in financial damage.

9.Introspection: (to what extent can the data describe itself meaningfully): The data set in small data everything is well organized and the meanings are clear. Big data is more complex and can contain information that is unidentifiable or meaningless. This can reduce the data quality.

10.Analysis: Small data can be analyzed from a single computer file in a single process. With big data, the extensive data has to go through extractions, tests, reductions, normalizations and transformations, among other things.

 

Big Data Analytics

By consciously or unconsciously collecting big data, the question arises for companies whether and how one can use this information for themselves. There are no business benefits to just owning this data. This is where big data analytics comes into play and makes it possible to analyze large amounts of data from various sources.

The aim is to read useful information and, in particular, patterns and correlations from the data volumes. With the knowledge gained, company processes can be optimized and competitive advantages achieved. In most cases, the optimization measures derived help reduce costs, save time and optimize products and services.

Big data analytics aims to use data to help companies make better business decisions. This goal is achieved by data scientists new hardware and software-based processes to structure large, unstructured amounts of data. In this way, the large amount of data that is available in a company is made understandable for the management and used productively.

According to surveys, big data is mainly used to get to know the customer better, to evaluate internal information in a more targeted manner and to build a better data landscape.

Industries for which big data analytics offers great advantages

  • Social media

Big data analytics can already bring a decisive advantage during development. The evaluation of social media channels or customer ratings can reveal social trends and market gaps at an early stage.

  • Production

As production is getting smarter and smarter, it is not surprising that big data also plays a major role here. The numerous processes are monitored by sensors and generate large amounts of data. This data can ensure preventive maintenance and prevent production delays or failures.

  • Distribution and logistics

Sensors are also increasingly being used in the supply chain to measure fuel consumption or to record the position data and the condition of wear parts. The structuring of this data means that costs can be sustainably minimized by planning transports promptly, changing routes and loads, or minimizing downtimes and maintenance costs.

  • Marketing and sales

You can greatly improve the relationship with your customer through data analysis. You have to know the needs of your customers more precisely and can even address each individual customer directly with personalized offers.

  • Finances

With the help of big data analytics, reliable predictions or risk calculations can be made in the financial sector. Therefore, it is possible to react more quickly to market developments or falling prices in banking and finance.