Big Data Analytics: Definition, Features, and Importance

According to Gartner, the definition of Big Data –

This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations.

Basic Tenets of Big Data

However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:

• It refers to a massive amount of data that keeps on growing exponentially with time.
• It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
• It includes data mining, data storage, data analysis, data sharing, and data visualization.
• The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.

There were 800,000 petabytes of data in the world in 2000. It is predicted to reach 35 zettabytes by 2020. Every day, half a million books' worth of data is created on social media alone.

Key Features of Big Data

Big data is large, quick, unstructured, and diverse. There are various distinguishing features:

• Variety: Data comes in many forms, including structured and unstructured data. Numeric and text fields make up structured data. Images, video, music, and a variety of other data formats are examples of unstructured data. There are also numerous data sources. Data from ERP systems and other operational systems are typical sources of structured data. Unstructured data sources include social media, the Web, RFID, machine data, and others. Unstructured data exists in a variety of sizes and resolutions, and it can be analysed in a variety of ways. Video files, for example, can be labelled and played, but video data is often not computed, and audio data is the same. For network distances, graphic data can be evaluated. Sentiment analysis can be performed on Facebook texts and tweets, but they cannot be directly compared.
• Velocity: The Internet substantially enhances the speed with which data may move, from e-mails to social media to video files. Cloud computing enables rapid and easy sharing from any location. People can rapidly share their data with one another thanks to social media programmes. Mobile connectivity to these applications further accelerates data generation and access.
• Volume: Websites have evolved into excellent data stores. The clickstreams of users are recorded and saved for future use. Users of social media programmes such as Facebook, Twitter, Pinterest, and others have become data prosumers (producers and consumers). The number of data shares has increased, as has the size of each data element. High-definition videos can increase the total amount of data shared. There are autonomous data streams of video, audio, text, and data from social networking sites, websites, RFID applications, and other sources.
• Data sources: There are various data sources, including some new ones. Data from sources outside the organisation may be incomplete or of poor quality.

⮚People: All Web and social media activity are considered stores and are accessible. The first big source of new data was e-mail. People can generate data for one another through Google searches, Facebook posts, Tweets, YouTube videos, other social media, and blogs.

⮚ Organizations: Business and government organisations are key data generators. ERP systems, e-commerce systems, user-generated content, web-access logs, and a variety of other data sources create useful information for enterprises.

⮚ Machines: The Internet of Things is taking shape. Many machines are linked to the network and create data independently, without human intervention. RFID tags and telematics are two prominent applications that produce massive volumes of data. Phones and refrigerators, for example, generate data on their location and status.

⮚ Metadata: There is a massive amount of data on data itself. Web crawlers and web-bots scour the Internet for new web pages, their HTML structure, and metadata. Many programmes, including web search engines, make use of this information.

The data also covers a range of data quality. Data originating within the organisation is more likely to be of higher quality. Some trustworthy data would be included in publicly available data, as would less trustworthy data.

The History of Big Data

Although the idea of big data is relatively new, the beginnings of enormous data sets can be traced back to the 1960s and 1970s, when the world of data was just getting started with the establishment of the first data centres and the creation of the relational database.

People began to discover how much data users created through Facebook, YouTube, and other online services around 2005. That same year, Hadoop (an open-source framework designed primarily to store and analyse large data collections) was created. During this time, NoSQL was also gaining prominence.

The development of open-source frameworks such as Hadoop (and, more recently, Spark) has been critical to the emergence of big data since they make enormous data easier to deal with and store. The volume of big data has exploded in the years afterwards. Users are still generating massive volumes of data, but it is not simply humans doing it.

With the introduction of the Internet of Things (IoT), more products and gadgets are being connected to the internet, allowing data about customer usage patterns and product performance to be collected. The rise of machine learning has resulted in even more data.

While big data has gone a long way, its use is really just getting started. Cloud computing has increased the potential for big data even more. The cloud provides true elastic scalability, allowing developers to create ad hoc clusters to test a fraction of data.

Why is big data analytics important?

Big data analytics enables businesses to harness their data and use it to discover new opportunities. As a result, smarter company decisions are made, operations are more efficient, earnings are higher, and consumers are happier. Tom Davenport, IIA Director of Research, interviewed more than 50 businesses for his paper Big Data in Big Companies to see how they exploited big data. He discovered that they received value in the following ways:

Cost reduction: When it comes to storing massive volumes of data, big data technologies like Hadoop and cloud-based analytics provide significant cost savings – and they can also find more effective methods of doing business.

Decisions are made more quickly and with greater accuracy. Businesses can analyse information instantaneously – and make decisions based on what they have learned – thanks to the speed of Hadoop and in-memory analytics, as well as the capacity to analyse new sources of data.

New items and services are available. The ability to measure client requirements and satisfaction through analytics gives you the ability to give customers what they want. According to Davenport, more organisations are developing new solutions to fulfil the needs of their customers using big data analytics.

What is the process of big data analytics?

Data analysts, data scientists, predictive modellers, statisticians, and other analytics experts collect, process, clean, and analyse increasing volumes of structured transaction data as well as other types of data that traditional BI and analytics systems do not use.

The four steps of the data preparation process are summarised below:

1. Data professionals collect data from a variety of different sources. It is frequently a mix of semi-structured and unstructured data. While each company will use different data streams, the following are some frequent sources: internet clickstream data;

• web server logs;
• cloud applications;
• mobile applications;
• social media content;
• text from customer emails and survey responses;
• mobile phone records; and
• machine data captured by sensors connected to the internet of things (IoT).

2. Data is analysed. After collecting and storing data in a data warehouse or data lake, data professionals must correctly organise, arrange, and segment the data for analytical queries. Analytical queries perform better when data is processed thoroughly.

3. The data is sanitised to ensure its quality. Data experts use scripting tools or business applications to scrub the data. They seek for faults or inconsistencies in the data, such as duplications or formatting errors, and organise and tidy it up.

4. Analytics software is used to analyse the data that has been collected, processed, and cleaned. This covers resources for: data mining, which sifts through data sets in search of patterns and relationships

• predictive analytics, which builds models to forecast customer behavior and other future developments
• machine learning, which taps algorithms to analyze large data sets
• deep learning, which is a more advanced offshoot of machine learning
• text mining and statistical analysis software
• artificial intelligence (AI)
• mainstream business intelligence software
• data visualization tools

Types of Big Data Analytics

a) Descriptive Analytics: It entails asking the question, "What is going on?"

It is an early stage of data processing that results in a collection of historical data. Data mining methods organise data and aid in the discovery of patterns that provide insight. Descriptive analytics forecasts future probability and patterns and provides insight into what may occur in the future.

b) Diagnostic Analytics: This entails asking the question, "Why did this happen?"

Diagnostic analytics seeks to identify the underlying cause of an issue. It is used to figure out why something happened. This kind seeks to discover and comprehend the underlying reasons of events and behaviours.

c) Predictive Analytics: This involves asking the question, "What is likely to happen?"

It forecasts the future by analysing past data. It all comes down to forecasting. Predictive analytics analyses current data and creates scenarios based on techniques such as data mining and artificial intelligence.

d) Prescriptive Analytics: This involves asking the question, "What should be done?"

It is focused to determining the best course of action to take. Descriptive analytics gives historical data, whereas predictive analytics forecasts what may occur. These parameters are used by prescriptive analytics to discover the optimum solution.

Also Read - Data Mining Explained : Methods, Tools & Real Uses