The number of digital users is increasing day by day. So as devices, sensors, and apps that generate excessive data and require sharing the data over the internet frequently. Extensive data needs exceptional storage, processing, sharing, and reporting capabilities for effective utilization and meaningful interpretation. In the absence of the right technologies, tools, and skill set, it is almost impossible to find any valuable insights from data. In fact, all the efforts will be in vain. Big data analytics encompasses all the essential frameworks required to overcome the challenges with enormous data.
Big data analytics is quite helpful to big tech firms across industries as robust analysis can help them with developing quality products, improving existing products, and making data-backed decisions. To reap the exceptional benefits often requires a good amount of investment. Big tech firms are investing heavily in big data analytics fields in terms of tools, technologies, and skillsets to leverage the benefits. It is a space with soaring demands, a higher pay scale, and tremendous value, particularly professionals with good skill sets, tools proficiencies, and awareness of best practices. One can take a big data engineer certification to start a career in this domain.
This current article provides more insights on types of analytics, generic analytics process, a few essential tools, and big data analytics real-world applications.
Big Data Analytics Types
Descriptive analytics process raw data from different sources to identify patterns, trends, or insights from historical data. It is done regularly in organizations using simple tools such as excel, visualization tools. Some examples are balance sheets, cash flow statements, etc. It is mainly focused on what happened. It indicates if something is wrong without giving any reasoning. It is the primary step for organizations, and further analytics is necessary for improvements.
Diagnostics analytics concentrate on the root cause of the problem or why it has happened. One can get in-depth insights relevant to a particular issue with the help of diagnostics analytics. Some of the examples include finding the root cause of overall sales drop in a specific season. With analytics, you may drill down to one or multiple root causes for the sudden change in sales.
Predictive analytics is concerned about future events, and it answers questions such as what will happen in the future. Based on past data, mathematical models are built and trained using machine learning algorithms which help in making future predictions. It is an old technique; however it is now being used widely due to scores of data, increased performance capabilities, availability of user-friendly tools. Example – predicting late payments or defaults.
Prescriptive analytics is the most advanced and complex of all analytics types. It prescribes the actions required for eliminating a future problem. Prescriptive analytic usually require a lot of data, the latest tools, and technologies, along with exceptional skills.
Big Data Analytics Process
It is a lengthy process that starts with the objective of analysis and identification of suitable data sources with respect to the requirement. Once data is collected from various sources, it has to be cleaned in order to remove any unwanted information or bugs. It is a very crucial stage and may require good experience. The next step is to integrate data from various sources and transform it to the desired format. Aggregation of required parameters is accomplished on the formatted data before analyzing it for any trends, patterns, or insights. The final step is to visualize data and communicate findings to stakeholders.
This is a generic process followed for analytics. However, it usually depends on the type of analytics performed, and sometimes one needs additional steps such as mathematical modeling, training, testing, and deployment of models for predictive/prescriptive analytics.
Data experts perform all the steps for a variety of objectives, such as promoting innovation, performing risk analysis, enhancing customer experience, improving product quality, and aiding in informed decision making.
The data analytics process needs to be supported by various tools in each step. Some of the big data analytics tools are listed below.
Big Data Analytics Tools
Apache Hadoop
Apache Hadoop is a java based, open-source framework used commonly in the big data ecosystem. It enables firms to accomplish distributed processing of vast amounts of data over a cluster of machines. It works on the simple principle of splitting the tasks over several nodes so that each node can process a small amount of data parallelly. One can scale up processing power when required from a single machine to hundreds of machines.
MongoDB
It is an open-source document-oriented NoSQL database that can handle all types of data. It stores high volumes of data in JSON-like documents, which ensures higher flexibility and scalability.
Apache Cassandra
It is a high-performance, scalable, distributed database with high availability. It is best suited for critical applications with no single point of failure.
Apache Spark
Apache Spark is an advanced clustering system that helps to overcome some of the drawbacks of Hadoop. It supports features such as real-time processing, batch processing, and memory calculation, which makes it faster than Hadoop. Spark offers higher flexibility as one can work with a variety of data sources such as HDFS, Cassandra, and OpenStack.
Industrial Application for Big Data
Big data is key to many industries in the modern era. Some of the key industries using Big Data analytics are listed below.
Healthcare
The Healthcare industry primarily focuses on the prevention, diagnosis, and treatment of human impairments or diseases. Extensive data such as medical history, clinical data, along with other personal data, is recorded for each patient. This data is very crucial in times of emergency and can save precious lives. Big data analytics can help in predicting patients’ health issues in the future.
Entertainment
One of the critical examples in the entertainment industry is streaming platforms such as Youtube. It has millions of users that generate a vast amount of information every second. Using the collected information, YT platform provides video suggestions that are in line with the search history of users. Big data analytics has enabled such a platform to collect data of all users and provide personal recommendations.
Ecommerce
One of the crucial requirements for the ecommerce industry is to predict customer buying patterns. It helps pitch the right product at the right time and at the right price to boost sales and profit. At the same time,e customers enjoy world-class products at their doorstep at affordable prices.
Marketing
Big Data analytics plays a significant role in digital marketing fields. One can run marketing campaigns to improve sales and enhance their product reach.
