Benefits Of Big Data Analytics

With big data analytics, companies gain insights into customer behaviour, leading to improved marketing and increased revenue. It’s also a key to gaining a competitive edge. So, how can businesses make the most of big data analytics? In this article, we’ll explore its advantages and its current scenario.

Ready to dive into the world of Big Data Analytics? Join the Mastering Data Analytics course and master essential skills.

Table of Contents

What is Big Data Analytics?

Big data analytics is a process of examining large sets of data to discover significant trends, patterns, and connections. This analysis is done to make better decisions using data. It involves using mathematical techniques like clustering and regression on very big datasets with the help of modern tools and technologies.

The term “big data” became popular in the early 2000s when technology improved to handle vast amounts of unstructured data.

As companies expand, the volume of data they collect also grows. For instance, Amazon prioritises big data analytics by gathering data about customer preferences and trends through the measurements it collects on items listed for sale. This vast amount of data demands more than regular software for processing and analysis.

Issues arise in storing, moving, and processing such large datasets. To address this, high-performance computing platforms and cluster computing techniques are used. These technologies enable data scientists to run complex analytics on terabytes of data and find hidden patterns or insights in real time, allowing companies to stay ahead of the game.

Recommended Technical Course

How Big Data Analytics Operates?

Big data analytics involves collecting, processing, cleansing, and analysing extensive datasets to enable organisations to use their big data practically.

Gathering Data

Data collection varies for each organisation. Thanks to modern technology, organisations can amass structured and unstructured data from various sources, including mobile apps, cloud storage, in-store IoT sensors, and more. Some data gets stored in data warehouses, making it easily accessible for business intelligence tools. Raw or diverse unstructured data that doesn’t fit neatly into a warehouse may receive metadata and find a home in a data lake.

Processing Data

Once data is gathered and stored, it must be organised effectively to yield accurate results when dealing with extensive and unstructured datasets. With the ever-increasing volume of data, processing it poses a challenge for organisations. Batch processing, which analyses large data blocks over time, proves valuable when there’s a significant gap between data collection and analysis. Stream processing, on the other hand, examines small data batches simultaneously, reducing the delay between data collection and analysis for faster decision-making. However, stream processing is more intricate and often more costly.

Data Cleansing

All data, whether big or small, requires cleaning to enhance data quality and produce more robust results. Correct formatting is essential, and any redundant or irrelevant data must be either removed or appropriately accounted for. Unclean data can obscure insights and lead to misleading conclusions.

Data Analysis

Preparing big data for analysis is a time-consuming process. Once it’s primed, advanced analytics techniques can transform big data into valuable insights. Some of these methods for analysing big data include:

Data mining, which sifts through extensive datasets to identify patterns and relationships, pinpointing anomalies and forming data clusters.
Predictive analytics employs historical data to forecast future trends, uncovering potential risks and opportunities.
Deep learning mimics human learning patterns by utilising AI and machine learning to layer algorithms and uncover patterns within the most complex and abstract data.

Also read: What is Data Quality? Dimensions, Frameworks, Standards

Tools Required For Big Data

Big data analytics uses various tools and technologies to handle extensive data. Here are some important ones:

Hadoop: A free framework that stores and processes big data on regular computers, handling both structured and unstructured data.
NoSQL Databases: These databases are flexible and suitable for managing raw, unstructured data.
MapReduce: A part of Hadoop, it helps organise and analyse data.
YARN: It manages resources in Hadoop clusters.
Spark: A fast data processing framework that handles batch and real-time data.
Tableau: A tool for analysing and sharing big data insights visually

Recommended Technical Course

Examples of Big Data Analytics

Here are a few best examples of how firms use big data analytics:

Acquiring and Retaining Customers: By using consumer data, companies can refine their marketing strategies, responding to trends to boost customer satisfaction. For instance, personalisation engines like those used by Netflix, Amazon, and Spotify create better customer experiences and customer loyalty.
Target Advertising: Personalization data drawn from interaction patterns, past purchases, and product page views can fuel effective targeted advertisement campaigns, catering to individual users and broader audiences.
Supporting Development of Product: Big data analytics offers insights to guide decisions on product development, assess progress, and steer improvements in alignment with customer preferences.
Cost Optimization: Retailers employ cost models that leverage data from diverse sources to maximise revenue.
Risk Management: This field identifies new risks through data patterns, facilitating incredible risk management tactics.
Better Decision-Making: Relevant data insights empower organisations to make faster and more informed decisions.

Benefits of Big Data Analytics

Cost Efficiency

One of the significant benefits of Big Data technologies is their ability to lower the expenses associated with storing, processing, and analysing massive data volumes for businesses. Moreover, Big Data technologies can facilitate the discovery of cost-effective and efficient operational practices.

The logistics industry serves as an excellent example of how Big Data can reduce costs. In many cases, the cost of returned goods is 1.5 times the cost of delivery.

Big Data Analytics aids companies in mitigating product return costs by predicting which items are most likely to be returned. This enables businesses to take preemptive measures to minimise losses from returns.

Market Insight

Examining Big Data provides valuable insights into current market conditions. For instance, a company can analyse customer purchasing behaviour to identify popular products, helping to discern trends and customer preferences. This information can be leveraged to gain a competitive edge over rivals.

Also read: Everything You Need to Know About Big Data in Data Science

Case Study: Big Data Enhances Fast Food Efficiency

Have you ever wondered why your McDonald’s or Burger King orders arrive promptly or even ahead of time? Big Data plays a role in ensuring speedy food service at these fast-food chains. How does it work?

Some fast-food establishments utilise Big Data analytics to monitor their drive-through lanes and adjust their menu offerings accordingly. When the order queue is long, the menu displays only items that can be prepared and served quickly. Conversely, if the queue is short, the menu highlights items that require a bit more preparation time. As a result, these real-time menu updates are visible on restaurant LCD screens.

Enhanced Competitive Understanding

Big data analytics offers another valuable advantage: improved competitive intelligence. Without big data, companies might rely solely on publicly available information for insights into their competitors. With big data, businesses can gain deeper insights into their competitors’ operations, market dynamics, and customer preferences. This positions them to maintain a competitive edge.

Real-Time Insights

Big data analytics also equips companies with real-time insights into their customers. This live information enables swift adjustments and enhancements to enhance customer service. Advanced streaming methods like Apache Kafka facilitate the rapid ingestion and analysis of vast data volumes. For instance, big data can help a company determine the optimal time or location for displaying signage based on foot traffic and other customer behaviour patterns. Consequently, companies can increase sales by ensuring their offerings are promoted during peak times and at popular locations.

Effective Marketing

Big data helps companies create ads and offers that suit their customers. This saves money by not showing ads to people who aren’t interested. For instance, if someone buys a lot of sports gear online, they’ll see ads for sports equipment rather than items they’re not likely to buy.

Innovative Products

Big data aids companies in improving their current products and coming up with new ones. They can understand what their customers like by analysing all the data available. Viewing today’s situation, it’s not enough to rely on guesswork. Businesses can keep track of customer feedback, product performance, and what other companies are doing using this data.

Big data technologies support research and development (R&D), leading to the creation of novel goods and services. Data that has been cleaned, processed, and refined can sometimes become a valuable product itself. For example, the London Stock Exchange generates significant revenue from providing data and research, surpassing its earnings from equities trading.

Adaptive Supply Chain Management

Today’s supply chains can have unexpected problems, like running out of stock or shipping delays. Usually, we only notice these problems when they’ve already caused big issues. With its predictions and real-time analysis, big data helps keep supply chains running smoothly. It looks at what people buy online and in stores, supplier info, prices that change in real-time, and even things like shipping and the weather. This kind of information helps big companies make decisions. It can help smaller online stores, too, help them figure out how much stuff to keep, how to avoid risks, and when to hire more workers.

Detecting Fraud and Unusual Activity

In businesses like banking and healthcare, it’s essential to identify problems just as much as it is to spot successes. Big data, along with AI and machine learning, can swiftly spot incorrect transactions, signs of fraudulent behaviour, and unusual patterns in data that could signal various existing or potential issues.

This capability can help banks and credit card companies identify stolen cards or suspicious purchases even before the cardholder notices.

When a company’s security team gets real-time alerts, they can take quick action. Detecting errors early and understanding the causes helps prevent more significant issues. Promptly addressing issues benefits customer service and the company’s reputation.

Boosting Revenue

Big data analytics helps companies make more money by understanding how customers act.

For instance, companies that use this data have an edge over rivals because they offer what their customers really want. This means they make more money.

Understanding customer behaviour also matters for pricing. For instance, discounts on popular stuff sell more, and cutting prices on things people don’t like saves money. This data also helps companies guess what customers will buy, making more money.

Big Data in the Current Scenario

Big data analytics revolutionises how companies and governments navigate the data landscape, leading to smarter, well-informed choices.

Entertainment: Think of Spotify and Netflix – they’ve transformed the entertainment industry by offering personalised movie and music recommendations based on individual tastes.
Education: Schools and educational tech firms use big data to craft new curricula and improve existing ones, ensuring they align with students’ needs and preferences.
Healthcare: Doctors can monitor patients’ medical histories through big data, helping to detect and prevent diseases.
Government: Big data gathers information from sources like CCTV cameras, satellites, body cameras, emails, and more to aid in efficient public sector management.
Marketing: Customer data and preferences drive targeted ad campaigns, delivering a high return on investment (ROI).
Banking: Data analytics is invaluable for tracking and preventing illegal money laundering

Also read: Top 25 Big Data Interview Questions and Answers

Conclusion

Big data analytics is no longer a luxury; it’s a necessity in today’s business landscape. It unlocks insights into customer behaviour and gives you an edge in the market. If you’re interested in delving deeper into this field, there are courses available to help you build your knowledge and skills. The Mastering Full Stack Data Analytics course can teach you the skills required in Big Data Analytics like Tableau, MySQL, etc.

PW Skills Provide Various Platform

FAQs

What are the differences between traditional data analytics and big data analytics?

Traditional data analytics typically deal with structured data in smaller volumes, while big data analytics handle large volumes of structured and unstructured data. Big data analytics often use distributed computing and advanced algorithms to process data.

What are the key challenges in implementing big data analytics?

Implementing big data analytics can be challenging due to issues like data security, data quality, and the need for skilled data professionals.

What is the role of data privacy and compliance in big data analytics?

Data privacy and compliance are critical in big data analytics, especially with regulations like GDPR and HIPAA.

Can big data analytics predict future trends accurately?

Big data analytics can make informed predictions based on historical data and trends. While it's not infallible, it provides valuable insights that help companies anticipate and adapt to future market shifts.

What are the primary data sources for big data analytics?

Big data analytics can draw data from various sources, including structured databases, unstructured data from social media and text, sensor data, logs, and more. The choice of sources depends on the specific use case.