5 Ways Big Data Analytics is Reshaping the Business Landscape in India

5 Ways Big Data Analytics is Reshaping the Business Landscape in India | Data Analytics | Emeritus

Why is data important for businesses? The Gartner Data and Analytics Summit 2022 India, held in Mumbai, highlighted that data alone is not valuable for businesses. They must make timely decisions, accelerate the decision process, and connect various decisions to add value to data. In addition, the Gartner Summit also acknowledged data and analytics as the key driver for organizations to navigate through uncertainty—like during the COVID-19 pandemic. This aptly explains why big data analytics is important in making effective business decisions that drive growth.

This blog discusses the following topics:

  1. What is big data analytics, and why is it important in India?
  2. What are the major challenges faced in managing and analyzing large datasets in India?
  3. How is big data analytics transforming the business landscape in India?
  4. What are some successful examples of big data analytics implementations in India?
  5. What steps can companies take to manage and analyze large datasets in India effectively?

ALSO READ: Big Data Vs. Data Analytics: Key Differences

What is Big Data Analytics, and Why is it Important in India?

Big data analytics can be broken down into two terms: big data and data analytics.  It refers to data sets that contain large volumes of complex data and rapidly grow in size.  Data analytics involves analyzing data using various techniques or tools to extract valuable information. According to IBM, this refers to using advanced data analytics techniques on big data sets comprising both structured and unstructured data.

Simply put, the question “’What is big data analytics?” refers to analyzing big data to discover relevant trends, patterns, correlations, and market insights. Therefore, its key purpose is to make effective data-driven decisions for business growth.

Relevance of Big Data Analytics in the Indian Business Environment

1. Strengthening Various Sectors

The Indian government, in collaboration with various IT startups, is leveraging analytics to resolve issues in various sectors and make them more efficient. For example, companies are analyzing data collected by newly installed smart meters to analyze power consumption and minimize power loss.

Analytics also plays a critical role in the advancement of the Banking, Financial Services, and Insurance (BFSI)  industry. It analyzes transaction patterns to detect fraudulent or unusual transactions. Thanks to this, companies can frame better policies to retain customers, and banks can predict loan recovery rates, reducing the risk of defaults.

2. Advancement in the Medical and Healthcare Field

It is also transforming the Indian healthcare sector by collecting and analyzing healthcare data from Electronic Medical Records (EMR) and wearable smart devices. This helps detect diseases at their early stage and plays a crucial role in preventive healthcare in India.

3. Effective Governance

Improving e-governance practices in digital India is one of the most significant advantages of analytics works. Predictive analytics provides valuable insights to the government about the state of infrastructure in the country, promotes transparency, and helps predict the performance of new projects.

ALSO READ: Scope of Data Analytics in the Future

What are the Major Challenges Faced in Managing and Analyzing Large Datasets in India?

1. Scalability and Storage Challenge

Managing and analyzing large data sets require ample storage, various resources and technologies, and a high budget. Since most big data tools collect and analyze real-time data, managing and storing such data poses a significant challenge for businesses. Moreover, scalability also becomes an issue if the data load increases exponentially.

2. Analysis of Unstructured Data

Businesses collect a significant amount of real-time unstructured data. One of the main reasons for this is India’s large population and diversity. Analyzing this unstructured data requires time and increases the risk of errors.

3. Data Security

In 2022, India had the second-highest number of data breaches in the world. If your business data falls into the wrong hands, it can spell a security disaster, not to mention the loss of reputation. This makes data security one of the biggest challenges for big data analytics today.

How is Big Data Analytics Transforming the Business Landscape in India?

Big data analytics is very effective for Indian businesses as it:

  • Provides market insights and forecasts that help businesses diversify and innovate
  • Helps reduce business risk by ensuring timely and accurate decision-making
  • Facilitates efficient allocation of resources by analyzing gaps in various processes
  • Determines tasks that take up a lot of time and resources. These can be automated to boost productivity
  • Allows businesses to analyze customer behavior and design strategies to retain customers

ALSO WATCH: Growing Importance of Data to Solve Real-World Business Programs

What are Some Successful Examples of Big Data Analytics Implementations in India?

1. Implementation in EdTech

Big data analytics helps analyze the learning patterns of individuals and prepares customized learning modules for an immersive learning experience, improving the quality of education in India.

2. Agritech

Another successful use case in India is the agritech or agricultural technology sector. It provides real-time updates on harvests and helps predict yields.

What Steps Can Companies Take to Manage and Analyze Large Data Sets in India Effectively?

The following are a few strategies for Indian businesses on how to handle large data sets:

1. Sampling

This statistical data analysis technique involves processing and analyzing a small subset from a large data set, called a sample. Since it is difficult for data analysts to analyze the entire set, the analysis received from the sample is considered uniform for the whole set. Hence, instead of surveying the entire population, data sampling facilitates the analysis of a small subset to determine valuable information.

2. Data Preprocessing

This technique involves cleaning or modifying data, also called data manipulation, before it is actually processed or analyzed. It helps organize data into a readable format for smooth processing. Moreover, it eliminates redundant information, thus enhancing the accuracy of the data analytics exercise.

3. Parallel Processing

In a nutshell, it refers to analyzing various sets of data simultaneously. Since multiple operations occur parallelly, the time to analyze large data sets can be reduced significantly.

4. Data Visualization

It involves organizing data analytics results into visual formats such as charts, graphs,  diagrams, tables, etc. This helps companies understand why big data analytics is important for better data-driven decisions.

5. Cloud Computing

This technology provides on-demand services for storing and processing data. A key benefit of using cloud computing applications is that they are easily scalable. Hence, companies can easily manage data volume on the cloud as data volume increases.

6. Data Warehousing

Businesses collect data from multiple sources, which eventually becomes difficult to manage. Creating a data warehouse where all data is stored is essential. It helps manage large volumes of data at a single location.

In addition to the above, companies can also practice the following steps to manage it efficiently:

  • Design a data roadmap and strategy that includes organizational goals, data requirements, and the resources required to achieve those goals
  • Build a solid data architecture that comprises various tools and techniques required to manage and analyze data
  • Eliminate data silos to ensure effective data integration and data accessibility
  • Set up frameworks to keep data secure

How Can Emeritus Courses Help You Master Big Data Analytics?

Emeritus’ data analytics courses use a backward-design methodology that focuses on goal-oriented and practical learning. Moreover, these courses are taught by industry leaders who offer insights into what is big data analytics, how big data analytics works, and why it is important for businesses and organizations. Emeritus has got you covered if you are wondering how to handle large data sets effectively. Additionally, you can explore these data science courses from Emeritus to advance your career.

By Sneha Chugh

Write to us at content@emeritus.org

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