When digital transformation started gaining momentum in the mid-2000s, it was a buzzword describing how companies would use technology to transform their business processes and customer experiences. Many dismissed it as just a wave that would pass. Even when Roger Mougalas coined the term “big data” in 2005, not so many were quick to embrace the disruptive technology and run away with it.

Nonetheless, big data analytics has become an integral part of our lives today. We’re surrounded by sensors collecting information about us at all times. Our smartphones are constantly sending us notifications from apps like Facebook and Twitter. We can also track our movements using GPS or Wi-Fi signals. The amount of data being collected is growing exponentially every year.

Let’s start by understanding big data analytics before looking at the top 5 must-know things about this disruptive technology.

What Is Big Data Analytics?
In simple terms, big data refers to any large volume of structured and unstructured data that can be analyzed for insights. It includes social media posts, web pages, emails, text messages, videos, images, sensor data, financial transactions, medical records, and more. Thus, big data analytics refers to using advanced analytics techniques to extract valuable insights from this massive data pool.

5 Must-Know Things About Big Data Analytics
If you’re contemplating pursuing a career in big data analytics or are just interested in exploring the topic, we bet you want to know more about this field. Below, we’ve compiled five must-know things about big data analytics that every aspiring analyst should know. We’ll cover:

● How big data analytics works

● Key tools and technologies

● Big data analytics real-world uses and examples

● Benefits and career opportunities

● Lastly, trends for 2022 and beyond

1. How Does Big Data Analytics Work?
Big data analytics comprises several processes: data collection, processing/preparation, cleansing, analysis, and visualization. Here’s a closer look:

● Data Collection. The first process involves capturing a mix of structured, semi-structured, and unstructured data from various sources, such as websites, mobile phones, sensors, and other devices. These data sources are then stored in databases, also known as data warehouses or data lakes.

● Data Processing/Preparation. After data collection, the next step is to prepare the data for further analysis. For example, if your company collects sales figures from its customers, it will need to organize, partition, and configure these numbers for reporting purposes. Plus, it may need to convert them into a format sensible for statistical analysis.

● Data Cleansing. The cleansing process involves removing irrelevant data, such as duplicate entries, erroneous data, and incomplete data. For example, if you collect customer feedback on a website, you might need to remove comments posted by bots instead of actual human users.

● Data Analysis. Data analysis involves applying analytical methods to the cleaned data to identify patterns, relationships, and correlations between different variables. For instance, you could analyze how many people visit your site daily and compare those results with the number of visitors during specific holidays.

● Data Visualization. Visualizing data helps analysts understand the meaning behind the information they’ve collected. It can be used to create charts, graphs, maps, and diagrams, helping you make better business decisions.

2. Key Tools & Technologies
To conduct big data analytics effectively, you must use key tools and technologies. Let’s take a closer look:

● Hadoop Distributed File System (HDFS). HDFS is a tool enabling you to store large amounts of data across multiple servers. HDFS stores files in blocks called “hdfs blocks” distributed across all nodes in the cluster.

● Predictive analytics software and hardware. Predictive analytics software allows you to build predictive models based on historical data. This type of modeling can predict future events, such as whether a particular product will sell well or when an employee will leave the organization. Hardware includes specialized computers, storage systems, and networking equipment for running complex algorithms and performing advanced data mining tasks.

● Spark Streaming. Spark streaming is a framework that lets you write code in Java, Python, Scala, R, or any other language compatible with Hadoop. The framework provides APIs for real-time data streams, which means you can continuously query data without waiting until the entire dataset has been loaded.

Other essential big data analytics tools and technologies include data integration software, NoSQL databases, in-memory data fabric, data virtualization, a data lake, and more.

3. Big Data Analytics Real-World Uses and Examples
The following sections provide examples of applications in various industries:

● Healthcare Industry. Healthcare organizations often use big data analytics to improve patient care. They can use this technology to monitor patients’ vital signs, detect health issues early, and prevent diseases before they become serious.

● Supply Chain Management. Supply chain management companies use big data analytics to track inventory levels, optimize supply chains, and reduce costs.

● Financial Services. Financial services firms use big data analytics to manage risk, increase revenue, and enhance customer experience.

● Manufacturing. Manufacturing companies use big data analytics for quality control, production scheduling, and optimization.

● Retail. Retailers use big data analytics to develop marketing strategies, target customers, and offer personalized shopping experiences.

Big data analytics also play a crucial role in:

● Customer acquisition and retention. Companies use big data analytics to identify new customers and retain existing ones. For example, retailers can determine how much money consumers spend per visit, what products people buy most frequently, and where shoppers shop online.

● Product development. Companies use big data analysis to design new products, test them, and find ways to improve their performance.

● Targeted ads. Advertisers use big data analytics tools to create targeted advertisements by analyzing consumer behavior.

4. Big Data Analytics Benefits and Career Opportunities
There are many benefits of using big data analytics. Some of these benefits include:

● Improved decision-making. Using big data analytics, businesses can make better decisions about their operations and finances.

● Increased productivity. Businesses can use big data analytics to streamline processes and save time.

● Enhanced customer service. Customers can receive customized offers, promotions, and information based on their preferences.

● Reduced operational expenses. By using big data analytics, companies can cut down on unnecessary spending.

● Fraud detection and prevention. Fraudsters often try to hide their activities from big data analytics systems. This makes it easier for business owners to detect cyber attacks and stop them from happening.

If we’re talking career opportunities, these analytics provides the following job titles:

● Data Scientist. The term “data scientist” describes someone who uses big data analytics to solve complex problems.

● Business Analyst. Business analysts help companies analyze data and translate it into useful insights.

● Software Developer. Software developers build apps that run on computers or mobile devices. Further, they typically work with database administrators (DBAs) to integrate big data analytics solutions into applications.

Other careers include:

● Data Security Analyst

● Data Architect

● Business Intelligence Analyst

● Database Developer

● Lastly, Big Data Engineer

5. Trends for 2022 and Beyond
The future of big data analytics looks bright. Here’s a look at some of the trends:

● Edge computing for faster analysis. Edge computing refers to the process of storing data closer to users than traditional cloud servers. This then allows for faster access to data and more efficient processing.

● Data fabric. Organizations adopting big data analytics must develop a robust infrastructure to support it. That’s why many are turning to data fabrics, which provide a secure, scalable platform for storing, retrieving, and analyzing massive amounts of data.

● IoT-driven data. Internet of things (IoT) refers to the network of physical objects connected to the internet. These objects collect data and send it back to the server, where it can get analyzed.

● Engineered decision intelligence. Data is one of the most resourceful assets in any organization. Do you know what else is valuable? Time! Engineered decision intelligence understands this perfectly and leverages AI, conventional analytics, and complex adaptive system apps to gain quick insights into large data volumes for optimized decision-making.