Big Data Analysis for the Clueless and the Curious

In 2011, we generated 2.5 billion gigabytes of data per day. A decade later, we create more than 2.5 quintillion gigabytes of data daily. As the volume of data increased, so did its usage in different fields. Businesses, governments, and other entities have different use cases for the data they collect, and one such use is big data analytics.

Big data analytics has been a hot topic for quite some time now. But what exactly is it? Ask anyone, and depending on their sources of information, you will get many answers. Some may refer to it as Silicon Valley giants using user data to sell ads, while some may talk about its importance in tech and medicinal research. Ask a dystopian person, and the discourse will turn to cyber espionage and how Google showing you an ad for the product you searched on Amazon is outright scary.

And if you search the Internet, most literature is either for business leaders or IT professionals. But if you are new to big data analysis or a casual tech blog reader, we have got you covered. Here is your big data analytics 101. So, let's dive straight in.

What is Big Data Analytics?

Before we move on to data analytics, let us discuss what exactly big data is. Some Internet Pundits call it the new oil, while others call it a gold mine of information. Whatever the adjective they may use, they all point towards big data's vitality in different industries.

Big data is information - it could be about anything or anyone – emails, social media, consumers of a business, citizens of a country, or data from sensors of vehicles and devices. However, not all data is big data.

Big data has:

• Volume – a large amount of data
• Variety – many sources and different formats and data sets
• Veracity – data is inconsistent
• Velocity – collected at high speed in real-time
• Value – offers meaningful insights

Extracting value from big data is where big data analytics comes into the picture. Big data analytics is an advanced technique used to analyze voluminous and diverse data sets to reveal underlying patterns and trends and identify correlations between different variables.

What is the Origin of Big Data Analytics?

Most Gen Z and Millennials only refer to big data analytics in reference to the Internet, smart devices, and corporations. And rightly so. Technology and consumerism have widened the discourse around big data and its usage. However, the genesis of big data analytics lies way back in the 17th century.

In 1663, John Grant used enormous data to study the bubonic plague. The European statistician was the first to use statistical data analysis. Then, about 150 years later, data collection and analysis became a mainstay in the field of statistics. Fast forward to 1965, the US Government established the world's first data center to store tax records and fingerprint data.

Big data analytics as we know it today started shaping after the invention of the Internet, taking off in the late 1990s. While the volume of data was increasing faster than ever, computers became more powerful and accessible. Organizations turned to computers and programming to make sense of the data they were collecting from their operations.

Whether multinational corporations, small businesses, manufacturers, or service providers, all are in the race to use big data analytics to gain meaningful insights from raw data. While companies use analytics to analyze consumer behavior and market trends, governments use it to make policies and enhance the effectiveness of administration.

How Does Big Data Analytics Work?

Big data analytics is not a magic wand that one swings and gets answers to their questions. It is not even an entirely automated process, although it heavily uses Artificial Intelligence and Machine Learning.

An organization needs an expert team of programmers, data scientists, and data analysts who can operate analytics tools and collect, organize, and extract actionable insights from data.

There are four steps involved in big data analytics. They are:

1. Data Collection

Each organization has many sources of data – IoT devices, business software, consumer records, marketing campaigns, and more. So, the first step is to identify all relevant data sources and implement a system to collect data from these sources.

2. Data Storage and Processing

The sources either provide structured data (data in tabular format) or unstructured data (data that do not follow any data model). So, depending on the diversity and complexity of data, organizations store data in cloud data warehouses and data lakes. Then dedicated servers process the raw data into computer-readable formats.

3. Data Cleaning

Data cleaning is a quality control measure where data scientists remove duplicate, obsolete, and irrelevant data to ensure data analytics provides accurate results.

4. Data Analysis

At last, the data analysts use different techniques and types of analytics to find patterns, correlate variables, and find meaningful insights from the data. They use programming, analytics, and business intelligence tools, AI and ML, to attain desired results.

Writing codes, creating algorithms, and training AI to look for trends is a complex and lengthy process. So, data analysts are turning to no-code tools. It is free to use an online data-science workbench that processes raw data automatically into a tabular format. So, using formulae and applying filters to extract insights becomes much more convenient.

What are Some Types of Big Data Analytics?

Big data analytics has many users, and they all have different questions they need answers to. Depending on the answers they want, they use different types of big data analytics. At present, there are four primary types of big data analytics:

1. Descriptive Analytics

We always want to know what is happening or what happened. Even when we have some idea of what is happening, we ask that question just to be certain. Businesses rely heavily on descriptive analytics to analyze their performance.

When corporations want to know what is happening in their businesses, they use descriptive analytics to get their answers. Descriptive analytics includes simple measurements and mathematical calculations based on data. Companies commonly use it to analyze financial metrics, month-over-month sales and revenue growth, the number of customers, and more to understand what is happening in their business.

2. Diagnostic Analytics

Diagnostic analysis helps one answer the questions like why something is happening or happened. The analysis uses data to identify reasons for an event, behavior, and patterns. For example, a company's bestselling product's popularity declines, and the management wonders why it is so and turns to diagnostic analysis to diagnose the reasons for declining sales.

They will analyze the data collected from marketing campaigns, sales calls and emails, consumer data from websites, and market information in general. They will perform a diagnostic analysis to find and establish a relationship between causes and declining sales.

3. Predictive Analytics

Making predictions and foreseeing the future fascinates us, humans. We predict the weather, the competition's winner, the horoscope, and even the world's end. But we all can agree that weather forecasts are more trustworthy than an old calendar predicting doomsday. Why? Because predictions such as weather forecasts are the result of intensive predictive analytics.

The name makes it clear that predictive analytics helps predict future outcomes. It uses statistics, modeling techniques, and machine learning to analyze historical and recent data to predict future events.

Predictive analytics finds patterns in existing data that are likely to occur again in the future. Weather forecast is just one example; businesses use this analytical technique to forecast cash flows, manufacturers use it to predict equipment failure, and insurance companies use it to detect fraud.

4. Prescriptive Analytics

If you can identify what is happening and what may happen in the future, you can surely use data to plan your best possible course of action. That is where prescriptive analysis comes into action.

Organizations combine results from descriptive and predictive analytics to improve their decision-making process. They use data and techniques like simulations and machine learning to design the best strategy to move forward.

Netflix is a perfect example of prescriptive analytics done right. It recommends different content to different individuals. First, it uses Descriptive analytics to analyze what content a user is watching.

Then, it uses predictive analytics to predict what that user may watch next. Finally, it combines findings from the two analytics into prescriptive analytics and recommends content that matches the viewer's taste 8 out of 10 times.

To Conclude

Big data analytics is a powerful technique that most businesses and governments are trying to harness. It allows organizations to look minutely at the masses, understand behaviors and choices and help improve decision-making. Whether administering citizens, creating policies, or gaining an edge over the competition, big data analytics is the only way forward.