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Big Data

Analyzing large amounts of data is only a part of what distinguishes Big Data analysis from any other data analysis. Read on to find out what the other aspects are. There is data and then there is Big Data. So, what’s the difference?

What is Big Data?

Big Data generally refers to datasets that are so large in scope and so complex that traditional data processing software products are unable to retrieve, manage, and process data within a reasonable amount of time. These large datasets can include structured, unstructured, and partially structured data, each of which can be overwritten for insights. How much data actually represents “Big Data”? That question is debatable, but usually can be more petabytes – and for the largest projects in the scope of exabytes.

Big data is characterized by 5 aspects:

  1. Volume – Develop a plan for the amount of data that will be in play, and how and where it will be stored.
  2. Variety – Identify all the different sources of data in an ecosystem and acquire the right tools for processing
  3. Velocity – Speed is critical in modern business. Research and deploy the right technologies to ensure the big data picture is being developed in as close to real-time as possible.
  4. Veracity – Very important thing is to make sure the data is accurate and clean.
  5. Value – Not all information collected is equally important, it is important to build an environment of big data and easily understand business intelligence.

The data storage consists of Big Data that can come from sources including websites, social media, desktop and mobile applications, scientific experiments, and – increasingly – sensors and other devices to the Internet of Things (IoT).

The Big Data concept contains a set of related components that enable organizations to use data for practical needs and solve a variety of business problems. This includes the IT infrastructure needed to support Big Data, data analytics, the technology needed for Big Data projects, related skill sets, and the actual cases that Big Data is required to use.

Analytics and Big Data

What really represents the value of all the Big Data that organizations collect is the analytics applied to the data. Without analytics, it would be just a bunch of data with limited business use.

Analytics can refer to basic business intelligence applications or more advanced, predictive analytics such as those used by scientific organizations. Among the most advanced types of data analytics is data mining, where analysts evaluate large datasets to identify relationships, patterns and trends.

Data analytics can include exploratory data analysis (which identifies patterns and relationships in the data) and confirmatory data analysis (which applies statistical techniques in order to determine the validity of any assumption about a particular data set).

Another difference is quantitative data analysis (analysis of numerical data that have quantifiable variables that can be compared statistically) as opposed to qualitative data analysis (that analyses non-numeric data such as video, images and text to identity patterns).

IT infrastructure for Big Data support

To make the concept of Big Data to work, organizations must have available the infrastructure for the collection and storage of data, providing access to and provision of information while in storage and in transit. At a high level, this includes Big Data storage and server systems, data management and integration software, business intelligence and data analytics software, and Big Data applications.

Most of this infrastructure is likely to be on the organization’s premises as companies want to continue to leverage their investment in data centers. But more and more organizations started relying on cloud computing services to handle most of their Big Data requirements. Many of them – such as web apps, social media, mobile apps and email archives – already exist. But as IoT becomes increasingly established, companies may need to use sensors across all devices, vehicles and products to collect data, as well as new applications that generate user data.

To store all incoming data, organizations must have adequate data warehouses. Storage options include traditional data warehouses, data lakes, and cloud storage. Security infrastructure tools can include data encryption, user authentication and other access controls, monitoring systems, network barriers, enterprise mobility management, and other system and data security products.

Big Data specific technologies

In addition to the IT infrastructure mentioned above for data in general, there are several Big Data specific technologies that your IT infrastructure should support:

  • The Hadoop ecosystem

Hadoop is one of the technologies closely related to Big Data. The Apache Hadoop project develops open source software for scalable, distributed computing.
The Hadoop software library is a framework that enables distributed processing of large datasets across computer clusters using simple programming models. It is designed to scale from a single server to thousands of servers, each offering local computing and storage.

  • Apache Spark

Part of the ecosystem Hadoop, Apache Spark is a framework for open source computing cluster that serves as an engine for processing Big Data Hadoop within. Spark has become one of the key environments for distributed data processing and can be deployed in different ways. It provides native connectivity for Java, Scala, Python (especially for Anaconda Python distro) and R programming languages (R is especially suitable for Big Data), and supports SQL, data streaming, machine training and graph processing.

  • Data Lakes

Data lakes are repositories that hold extremely large amounts of raw data in its original format until needed by business users. Digital transformation initiatives and IoT growth are helping to drive the growth of the data lake. Data Lakes are also designed to make it easy for users to access large amounts of data when the need arises.

  • NoSQL Database

Conventional SQL databases are designed for reliable transactions and ad hoc queries but have limitations such as a rigid schema that make them less suitable for some types of applications. NoSQL databases address these constraints by storing and managing data in a way that allows high speed of operation and great flexibility. Many have been developed by companies looking for better ways to store content or process data for large websites. Unlike SQL databases, many NoSQL databases can scale horizontally across hundreds or even thousands of servers.

  • Databases in memory

In-memory data base (IMDB) is a database management system that relies primarily on main memory, and not disk, for storage. In-memory databases are faster than disk-optimized databases, which is important for Big Data analysis and the creation of warehouses and data marts.

  • Predictive analytics

Software or hardware solutions that enable companies to discover, evaluate, optimize and deploy predictive models by analyzing large data sources to improve business performance or mitigate risk.

  • Data virtualization

New technology that delivers information from a variety of data sources in a visible format, including large data sources such as Hadoop and distributed real-time and near-real-time data stocks.

  • Stream Analytics

Sometimes the information that an organization must process can be stored on multiple platforms and in multiple formats. Stream analytics software is very useful for filtering, aggregating and analyzing such big data. Stream analytics also allows you to connect to external data sources and integrate them into your application flow.

  • Data Integration

The biggest challenge for most companies handling big data is processing terabytes (or petabytes) of data in ways that can be useful for customer deliveries. Data Integration Tool enables companies to streamline data through numerous big data solutions. Some of these tools are: Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB, and Couchbase.

  • Data Quality

A very important parameter for big data processing is the quality of that data. Data quality software can perform cleaning and enrichment of large datasets using parallel processing. These software programs are widely used to obtain consistent and reliable results from big data processing.

Big Data skills

Big Data and Big Data analytics require specific skills, whether within the organization or through external experts. Many of these skills are related to key components of Big Data technology such as Hadoop, Spark, NoSQL databases, in-memory databases and analytics software. Others are specific to disciplines such as data science, data mining, statistical and quantitative analysis, data visualization, general programming and data structure and algorithms. There is also a need for people who have comprehensive management skills to lead large data projects successfully.

While the analysis of Big Data projects becomes more prominent, the scarcity of people with the specific skill set can make finding experienced professionals one of the biggest challenges for organizations and companies.

Big Data Usage Cases

Big Data and analytics can be applied to many business problems and usage cases. Here are some examples:

  • Customer Analytics.

Businesses can examine customer data to improve customer experience, improve conversion rates, and increase retention.

  • Operational analytics

Improving operational performance and better utilization of corporate assets are the goals of many companies. Big Data analytics can help businesses find ways to run their business more efficiently and improve performance.

  • Fraud prevention

Data analysis can help organizations identify suspicious activities and patterns that could indicate deceptive behavior and help mitigate the risk.

  • Price optimization

Businesses can use Big Data analytics to optimize the prices they charge for products and services, which helps increase revenue.

  • Sentiment Analysis

Sentiment analysis offers powerful business intelligence to enhance customer experience, revitalize a brand, and gain competitive advantage. The key to successful sentiment analysis lies in the ability to mine multi-structured data pulled from a variety of sources into a single database.

  • Ad-hoc Analysis

Big data ad-hoc analytics can help in the effort to gain greater insight into customers by analyzing the relevant data from unstructured sources, both external and internal.

  • Real-time Analytics

Systems that offer real-time analytics quickly decrypt and analyze data sets, providing results even as data is being generated and collected. This high-velocity method of analytics can lead to instant reaction and changes, allowing for better sentiment analysis, split testing, and improved targeted marketing.

  • Multi-Channel Marketing

Multi-channel marketing creates a seamless experience across different types of media like company websites, social media, and physical stores. Successful multi-channel marketing requires an integrated big data approach during all stages of the buying process.

  • Customer Micro-Segmentation

Customer micro-segmentation provides more tailored and targeted messaging for smaller groups. This personalized approach requires analysis of large sets of data collected through customers’ online interactions, social media, and other sources.

  • Clickstream Analysis

Clickstream analysis helps to improve the user experience by analyzing customer behavior, optimizing company websites, and offering better insight into customer segments. With big data, click stream analysis helps to personalize the buying experience, getting an improved return on customer visits.

Interesting Big Data statistics

  • Big data holds the key to our incredible future. It reveals patterns and connections that make our lives better. Big data makes medical treatments more effective, makes secure self-driving cars a possibility, and due to big data, the weather forecast is expected to be more reliable, which in turn can lead to better yields in agriculture.
  • Data volumes have dramatically jumped, from 2017. to 2019. The data generated during this period was more than that was generated  in the entire human history.
  • Big data needs as much computing power as you can invest in it. In the next decade, engineers will aspire to reach the processing capability of the human brain for their CPUs.
  • Statistics show that big data revenue is constantly growing. In 2015, it was responsible for $ 122 billion in worldwide profits. It is expected to generate $ 189.1 billion in 2019 and as much as $ 274.3 billion in 2022.
  • In only a year, the accumulated world data will grow to 44 zettabytes (44 trillion gigabytes accumulated). For comparison, today estimates about 4.4 zettabytes.

Benefits of Big Data

  • Better Decision-Making

Analyzing what people buy helps companies plan ahead and produce what their customers want. Big data enables organizations to better understand the constantly changing market trends.

  • Reduced Cost

Big data software can help companies improve their processes and customer service. This increased efficiency can have a major impact on reducing costs in companies.

  • New Product Development

BDA enables companies to keep up with trends and create successful products. Furthermore, it can help a company pull ahead faster than its competitors.

  • Increased Productivity

The very high speed at which BDA tools work enables businesses to make quality decisions quickly.

  • Control online reputation

Big data tools are capable of doing sentiment analysis. Therefore, you can get feedback on who is talking about your company and how. If you want to track and improve your business presence online, then big data tools can help you achieve that.

Conclusion

We have seen that Big Data has a very wide and successful application in today’s world of technology. It is up to us to follow the trends and learn the new technologies to keep up with the world.

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