How Big Data Analytics Plays a Crucial Role in Transforming Your Business


Prit DoshiMarketing Executive at Rapidops Inc.

Friday, August 12, 2022

In a fast-paced and competitive market, companies are now looking for a faster decision-making process and better ways of strategizing and streamlining their business operations.

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How Big Data Analytics Plays a Crucial Role in Transforming Your Business
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One of the critical requirements which can help businesses overcome these challenges is the analysis of large volumes of data. Big data analytics plays a crucial role in processing, analyzing, and presenting the enormous amounts of data in a visually appealing format. Doing so allows the business to make data-backed decisions for:

  • Discovering new possibilities
  • Extracting growth opportunities 
  • Increasing product experience
  • Improving business operations
  • Satisfying consumers expectations

Big data analytics can be a powerful tool for businesses looking to transform their operations as companies can improve their efficiency, effectiveness, and bottom line by understanding and utilizing the right data.

How can big data help businesses transform?

There are three key ways big data analytics can help businesses with their overall transformation:

1. Improve customer service and support

By understanding the customer journey, big data can enable businesses to track customer behavior, and therefore provide better customer service and support. By identifying areas of friction in the customer experience, companies can address those issues and make improvements that lead to happier customers and increased loyalty.

2. Increase operational efficiency

Big data analytics can help businesses streamline their operations by identifying areas of inefficiency. They can gain a holistic view of how resources are being used and where bottlenecks are occurring, enabling them to make changes that lead to a more efficient operation.

3. Develop new products and services

Big data analytics can also identify new opportunities for product and service development. By understanding customer needs and wants, businesses can develop new offerings that address those needs and increase sales and revenue.

Combined with organizational digital transformation that focuses on gathering, analyzing and optimizing your company’s most important assets, the business will have confidence in the decisions it’s making and remain innovative while enjoying the perks of being an early adopter.

What is big data analytics?

Big data analytics examines large and complex data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other valuable insights that can help organizations make better business decisions.

In the past, businesses have relied on traditional data analysis methods, such as surveys and focus groups to gather insights about their customers. However, these methods are no longer adequate in today's fast-paced and globalized economy. 

Now, plenty of big data analytics tools are available that can process large amounts of data quickly to allow organizations to make more informed decisions.

Analysts, researchers, and business users can now utilize data that was previously inaccessible or unusable due to its size. 

7 key technologies that enable big data analytics

The dynamics of a competitive market are constantly changing, and this change occurs due to various factors. Below are 7 key technologies that play a significant role in enabling big data analytics for businesses.

1. Predictive analytics

The most commonly used form of big data analytics is predictive analytics which uses historical data to predict future events. Big data analysis and storage might help you analyze, evaluate, and deploy predictive scenarios with the use of predictive analytics hardware and software solutions.

2. Knowledge discovery tools

These technologies allow organizations to extract rich data (structured and unstructured) from a variety of sources. Potential sources might include: 

  • File systems
  • APIs
  • DBMSs

3. Data preprocessing

This data technique is generally performed before any predictive modeling, or machine learning algorithm is applied to the data. Data pre-processing is crucial as it can dramatically improve the performance of these algorithms by making the training process more efficient.

4. Data virtualization

Data virtualization enables applications to access data without technical restrictions, such as formats and locations. It’s used by Apache Hadoop and other distributed data storage for real-time or near real-time queries on various platforms.

5. In-memory data fabric

Kuznets uses a network-connected, geographically distributed architecture that allows for the distribution of massive amounts of data across system resources such as dynamic RAM, flash storage, or solid-state storage devices.

6. Data integration

Data integration involves combining data from multiple sources into a single dataset. This can take a number of forms, including:

  • Data warehouses use various techniques to store data, including column-oriented storage and partitioning.
  • MapReduce is a programming model that breaks down a big data problem into more minor issues, which are then processed in parallel by a cluster of computers.
  • NoSQL databases are designed to store and query large amounts of data by using various techniques to store data, including key-value pairs, columns, and documents.
  • Data mining is an extraction technique that can use classification, clustering, and association rules.

7. Big data security solutions

Security is a significant concern with big data, so the right security solutions are crucial to help businesses protect their data from breaches and cyberattacks. They can also ensure companies comply with legal regulations, such as GDPR.

How do businesses benefit from big data analytics?

With the right tools in place, businesses can start to reap the benefits of big data, but how exactly are organizations doing this? Here are a few prevalent use cases in operations, marketing, and sales that demonstrate how businesses can benefit from big data analytics.

1. Optimizing prices

In a world where online retail giant Amazon consistently has the lowest prices, other companies are turning to big data analytics to price their products more competitively.

Differentiation based on product features is not a new concept, but it has become more challenging to execute in recent years.

By analyzing customer data, businesses can identify patterns, trends, preferences, and buying habits, optimize pricing, and improve their margins.

Big data analytics can assist companies in setting up competitive pricing by understanding their competitor’s pricing strategy and comparing it to their own, helping them to compete for similar products by using insights to gain a price advantage.

2. Building better customer relations

The sales and marketing process has always been data-driven. However, this can become more accurate and insightful with the advent of big data, enabling sales and marketing professionals to better understand their customers through demographics, online behavior, and user intent. This also enables the marketing team to make the customer journey smoother and more efficient. 

3. AI + big data = automation!

The integration of AI into big data-related technologies is one of the most exciting and promising developments. Big data analytics tools that utilize AI can help businesses automate tasks, such as customer segmentation and target marketing. 

AI can also improve the accuracy of predictive analytics models, which use historical data to predict future events by finding patterns in data that would otherwise be missed.

4. Geo-targeting

Geo-targeting uses location data to target ads and marketing messages to people in specific geographic areas. It helps capture territories with more significant sales potential, helping to reduce their go-to-market costs.

5. Cutting organizational costs

Big data solutions can also help minimize production and operations costs for manufacturing companies based on inventory management and production insights. Managers can use the data to help improve their techniques, develop more effective strategies for enhancing agile working environments, and lower overall corporate expenses. 

6. Increasing workforce productivity and efficiency

By automating tasks, companies can free up employee time to focus on more critical initiatives. People will become more proactive and productive with their tasks if they’re given the means to make decisions based on quantifiable data and analytical insights.

7. Generating targeted marketing strategies

Demographics divide most markets, and knowing your target audience is critical to success. With big data, businesses can track customer buying patterns and preferences. This information can then be used to develop targeted marketing campaigns that are more likely to result in sales.

Final thoughts

The organizational benefits of big data analytics are vast and far-reaching. By understanding how to collect, store, and analyze large amounts of data, businesses can unlock new insights to help them run more efficiently and make smarter decisions. 

Any business looking to get ahead in the competitive landscape needs to consider investing in big data analytics tools and technologies. Having the right tools and technologies in place will help businesses extract significant insights from petabytes of data and allow you to analyze all those zeros without getting lost under their weight, giving meaningful insight into what matters most when making business decisions.

Prit Doshi

Prit Doshi is a marketing executive with expertise in writing about technology. He is skilled in SEO and helps brands to rank better with content. He is currently working at Rapidops Inc, a digital transformation company transforming your ideas into digital products.


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