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What Is Big Data Analytics?

According to IBM, 90% of all the data that exists has been created in the last two years. To download all the data from the internet today, it would take one 181 million years, according to Unicorn Insights. These statistics about data suggest the huge upsurge of the information that is being produced every day. Industries are utilizing the power of big data. For example, Netflix analyses what its subscribers are watching to give them recommendations based on that. American Express Company analyses different variables to predict that 24% of accounts will close within 4 months, accurately. Amazon groups customers with similar interests to offer better product recommendations. One-third of all purchases made at Starbucks are online and using this information, they learn more about the purchasing habits of their customers.

The process of discovering patterns, trends, and correlations from huge heaps of raw data is called big data analytics. Since the early 2000s, big data has become a buzzword. Databases such as Hadoop, NoSQL, and Spark have been created for storing and processing a huge amount of data. 

Importance of Big Data Analytics

Big data analytics is important for many reasons –

  1. Decision making – With big data analytics, companies can analyze information almost immediately, enabling them to make quicker and better business decisions, based on their findings. 
  2. Cost reduction – Big data analytics is beneficial as it provides cost-effective solutions for storing large amounts of data.
  3. Product development – Organizations can use advanced data analytics services to analyze the efficiency of their products and make changes if there are any improvements to be made.
  4. Improving customer experience – By using big data analytics, companies can observe data and solve problems of customers, to improve their experience as well as build good relations with them.

Let’s look at how big data analytics works – 

  1. Data collection – The first step of big data analytics for any organization is data collection. There are various sources from where organizations can gather data that is structured or unstructured, such as mobile applications, cloud storage, IoT sensors, etc. 
  2. Data processing – After data collection and storage, it needs to be organized properly. It can be complicated, owing to the amount of data available and its growth. It can be done by batch processing, wherein huge blocks of data are processed over time, or stream processing that considers short data blocks. Data processing is necessary to achieve accurate results.
  3. Data cleaning – To improve the quality of data and get better results, it needs scrubbing. This includes formatting it correctly and eliminating data that is duplicate or irrelevant, as this can lead to flawed insights and inaccurate results.
  4. Data analysis – Now that data is ready to be used, big data analysis methods will be used to draw actionable insights from it. These methods include data mining, predictive analytics, and deep learning.

Applications of Big Data Analytics

Nowadays, organizations from almost all sectors and domains are harnessing the power of data to derive useful information and make better business decisions. Some of these areas are –

  • Manufacturing – By using big data analytics in manufacturing, defects can be tracked and energy efficiency can be increased. The quality of products can also be improved and mass-customization becomes possible.
  • Entertainment and Media – The entertainment and media industry uses big data analytics to predict what their audience wants to watch, target ads, develop new content, and schedule optimization.
  • Healthcare – The benefits of big data analytics in the healthcare industry are great. With prescriptive analytics and personalized medicine, treatments can be tailored to suit each patient as well as reduce costs. With big data analytics, it can be analyzed which treatment is more suitable for a particular condition and it is also possible to predict disease outbreaks by analyzing geographical data sets.

Tools and Technologies

Big data analytics uses several tools and technologies for all its processes. Some of the most famous big data tools and technologies include –

  • Hadoop
  • MapReduce
  • Spark
  • NoSQL
  • YARN
  • Tableau

A Career in Big Data Analytics 

A career in big data analytics is highly sought-after since the demand for big data professionals is huge. A big data analyst is responsible for performing all the above-mentioned big data processes. Their responsibilities include –

  1. Understanding the goal of the organization – A big data analyst needs to understand the end goal of their organization to work accordingly. 
  2. Writing queries – As a big data analyst, you will have to write SQL queries and scripts for gathering, storing, manipulating as well as retrieving information from databases.
  3. Data mining – Big data analysts are responsible for mining data from different sources and organizing it to gain information from this data. 
  4. Data cleaning – Another responsibility of a big data analyst includes cleaning data that has been collected since it would usually be unstructured and raw. 
  5. Data analysis – As the name suggests, data analysis is the main goal of a big data analyst. They analyze clean data using different tools and technologies to gain valuable insights from it. 

How to Become a Data Analyst?

If you want to become a data analyst, you can follow these steps –

  1. Educational qualification – You will need a degree in a relevant field such as computer science, information technology, or mathematics. 
  2. Programming skills – To become a successful big data analyst, you need to work with programming languages including Java, C++, Python, and R. 
  3. Data analysis tools – You need to be able to work with databases as well as data analysis tools such as Microsoft Excel, Matlab, SQL, Hadoop, and Spark. You can pursue Data Analytics free courses to acquire these skills and learn more about these tools.
  4. Knowledge of machine learning algorithms and statistics – You will have to learn machine learning and statistical concepts such as hypothesis testing, classification and clustering techniques, probability distributions, and regression analysis.
  5. Data visualization tools – To create business reports, you will have to utilize data visualization tools such as Power Bi and Tableau. You should also possess presentation and communication skills to become a successful big data analyst.
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