What is Big Data? The History, Importance & Examples of Big Data

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What is Big Data?

Big data is the collection of complex data or databases. Large datasets are generated from text, video, audio, and image files and stored in data centers, data lakes, or data warehouses. 

The data and databases are complex due to their size and variable types, preventing common data processing software from being capable of processing and maintaining the data in real-time. 

The short answer to what is big data: a way to analyze, extract, and deal with enormous data sets.

How Does Big Data Work? 

Many companies desire to gain key insights from patterns in big data for a competitive edge. In order to make sense or detect these meaningful patterns from the raw information, large dataset technologies are applied to the data. 

These huge data collections can contain unstructured, structured, or partially structured data, and any of those data types are studied in order to obtain desired insight. 

It is open for discussion how much data is necessary to qualify as big data, but usually, it is a few petabytes (1 petabyte = 1000 terabytes), or sometimes, when a massive project is being worked on, even in the range of yottabytes (a trillion terabytes). 

Big data does not represent a unique technology, but rather a combination of newer and older technologies that help companies in various industries to analyze data.

3 vs of big data

3 Vs of Big Data

Large datasets can be represented by using the three Vs - volume, velocity, and variety.

1. Volume 

The sheer volume of data is what mostly comes to people’s minds when big data is mentioned. A lot of platforms and companies have data stored in the form of logs, but do not possess the technical capabilities to process them. This is where big data analyses, with their ability to process huge amounts of data, come into play.

2. Velocity

Data velocity relates to the constantly rising speed at which data is created, stored, processed, analyzed, and transferred from one place to another. 

3. Variety

As we mentioned above, data can be unstructured, structured, or partially structured. It is not always easy to classify information and form the structures of big data, and it is even harder to put large datasets into comparable databases. The variety of both unstructured and structured data increases the intricacy of difficulties of storage and analysis.

More than 90% of data is created in unstructured form. 

Besides the three Vs, veracity and value are two other representations of big data to be aware of.

While dealing with an extreme velocity, variety, and volume of data, it is impossible to have highly reliable data. There will always be dirty data, which is inaccurate, incomplete, or inconsistent data. The veracity of the source data plays an important role when data reliability is in question. 

Value is the most important aspect when looking at large datasets. Having access to large amounts of data is completely meaningless unless there is a strategic and structured analysis plan and research questions. Otherwise, there is a distinct possibility of falling into one of the most common statistical pitfalls: garbage in – garbage out (GIGO). The questions that the decision-making team asks need to be the right questions with high-quality data in order to get a quality outcome.

The History of Big Data and How It Began

The phenomenon of big data was first introduced to the computer industry in the 1990s. 

Today, big data applications are constantly spreading into new fields. The percentage of enterprises that started using big data has soared from 17 percent in 2015 to 59 percent in 2018. 

Businesses utilizing big data must navigate the political, legal, and ethical implications of obtaining valuable business insights while maintaining fair privacy practices to protect customers and employees from things such as government surveillance, discrimination, and profiling.

From social media platforms and Google analytics to medical records and biodiversity databases, almost anything that can be quantified, digitized, or statistically visualized can be stored into huge databases and used in datasets.

The Importance of Big Data

Big data is assisting companies in vital decision making. It is used to reorganize corporate campaigns, rework manufacturing methodologies, and improve products. 

One advantage of big data lies in advanced analytic applications such as predictive modeling and machine learning to improve machinery outputs in factories or to find new potential antibiotics in the already existing databases of fungal genetic material. 

The possibilities are endless. 

For example, massive companies such as Amazon are using large datasets to predict what products a specific user could buy next. This allows Amazon to ship those products in advance to the warehouses closest to the customer even before he or she has placed an order.

This is the power of big data analyses.

Another valuable application is improving customer service. Big data analytics could be correlated to a customer’s positive feedback, leading to fewer calls being made to customer services, therefore increasing both the customer's satisfaction and the cost efficiency of call centers.

Big Data Challenges Enterprises are Facing in 2021

Big data is becoming implemented in most businesses today but is not always according to plan. In fact, most companies didn’t manage to maintain big data analyses right after their implementation into the business. Here are six challenges you may face when implementing big data analyses:

1. Data Strategy

The amount of data that needs organizing, analyzing, and integrating is incomprehensible. One of the most important challenges is setting a careful and precise, step-by-step database strategy, which must consist of clear sets of questions. 

2. Data Sources

Almost anything can be expressed as some form of data, therefore, the optimization of data collected from various kinds of sources is sometimes an extremely hard endeavor. It is crucial to integrate data, as data integration plays an important role in all future analyses. 

Popular data integration tools you may consider are: 

  • IBM InfoSphere
  • ArcESB
  • Microsoft SQL
  • QuilkView

3. Data Growth

Considering that large data analyses take thousands of terabytes of data, storage represents a challenge. A lot of data has intrinsic high-performance issues. Datasets are constantly growing and expanding, leading to significant problems for the business which wants to make use of them. Data compression, avoiding duplication, and tiering help greatly in storage space reduction and costs. 

In order to deal with this problem, companies use data tools such as: 

  • Spark
  • Hadoop
  • NoSQL

Some of the solutions may include migrating data to a cloud data warehouse that is designed for optimal performance and improving the data warehouse design.

4. Data Validation

On a large-dataset level, data validation usually represents a difficult task. Different datasets could possess similar data points on different pages. Organizing data and checking the usability, security, and accuracy is data governance, a complex process that uses different technologies and policies to arrive at the best possible outcomes.

5. Real-Time Insights

Data sets are pointless if they are unable to provide an insight in real-time. Using big data tools requires the expertise of data scientists, computer scientists, analysts, data professionals, and engineers. The lack of people with desired skill sets is becoming a challenge, because of the constantly growing demand. 

6. Data Security

The biggest and most costly challenge for all businesses is security, especially if datasets contain sensitive data points such as personal information. That type of data represents the most common target for malicious hackers and cyberattacks. 

The approximate cost of a single data breach in 2021 is 4.24 million US dollars. Security problems could be solved by employing more cybersecurity professionals, data encryption, real-time activity monitoring, using security tools, access authorization, and identity control.

5 Big Data Use Cases of Today

There are plenty of examples of how big data can be used to propel businesses to new levels. Here are the top five use cases in 2021:

1. Fraud Prevention

Big data has an incredible usage for fraud detection and prevention of fraudulent transactions for credit card users. It can help track customer’s spending habits in order to notice and put a stop to any possible suspicious activity. In some cases, even the tiny difference in purchases carried using the same credit card could be flagged and analyzed as a potential fraud attempt.

2. Cybersecurity

Numerous companies are using huge amounts of generated and stored log data in order to detect or block malicious hackers.

3. Price Optimization

Companies are using big data analytics in order to optimize their prices with the goal to maximize their income. Transactional data plays an important role in price optimization.

4. Social Media Analysis

Social media platforms such as Facebook and Instagram are some of the most prominent big data examples. These companies are keeping track of what their customers are saying about services or products. Using that data, they are able to construct predictive models and show specific ads containing products that certain customers could possibly buy in the future.

5. Healthcare

In healthcare, big data is being used for almost everything. It is used for highlighting threats and trends in patterns and creating predictive models for a broad range of research, from improving profitability to designing personalized treatments and saving lives.

Start Using Big Data Today

In the past few years, new technologies have enhanced our abilities and reduced the costs of collecting, storing, and analyzing data. Private companies can now collect huge amounts of data from social platforms, blogs, and audio and video files. 

Open source big data technologies for the optimization of data storage and analyses have been developed. These technologies are enabling big data analysis to be efficiently carried out in real time, making big data the all present, yet completely invisible, aspect of our everyday lives.

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