Big Data Analytics: How It Works and Its Benefits

Every day, people generate a wealth of data. Data is recorded whenever someone opens their mail, uses their mobile app, walks into a store, purchases something online, or speaks to a customer service representative. Moreover, employees, finance teams, marketing strategies, supply chains, and other facets of organizations generate abundant data.

Big data refers to these extremely large volumes of data that come from multiple sources and appear in diverse forms. Companies have realized the benefits of not only collecting this data but also analyzing it to put it to use for business decisions. That’s where big data analytics comes into play.

This blog offers a complete overview of big data analytics, discussing what it means, how it works, the required tools and technologies, its benefits and drawbacks, and real-world examples.

Let’s dive in!

In a nutshell, big data analytics refers to the process of discovering patterns, trends, and correlations among raw data to make data-driven decisions. Analyzing big data involves using familiar statistical analysis techniques - such as clustering and regression - and applying them to larger datasets with new-age tools.

Big data analytics has gone from being an early 2000s buzzword to a much-needed process to capitalize on substantial data. This analytics field is continuously growing as data engineers discover ways to integrate large amounts of data generated by networks, sensors, smart devices, transactions, and more. Today, big data analytics is used alongside new-age tech like machine learning to uncover and scale complex business insights.

Big data analytics typically works on a four-stage process involving collecting, preprocessing, cleaning, and analyzing data.

1. Data collection: Data collection comprises gathering data directly or indirectly through social media, surveys, purchase histories, and other relevant avenues. Using modern technologies, businesses can collect unstructured and structured data from target sources, including mobile applications, in-store IoT sensors, and cloud storage.
2. Data preprocessing: Data preprocessing involves transforming the collected data into well-organized sets to obtain accurate results via analytical queries. One of the most common processes used for preprocessing is batch processing, where large data blocks are observed over time.
3. Data cleaning: Once the data is preprocessed, it is thoroughly filtered to ensure maximum data quality. All irrelevant or incorrect data is either rectified or removed from the data set during this process.
4. Data analysis: The last stage is data analysis, where advanced analytics processes transform big data into actionable insights. Data mining is implemented during this stage to sort large datasets and uncover relationships or patterns through cluster creation and anomaly detection.

Data analysts may also use predictive analytics to utilize the company’s historical data and forecast upcoming opportunities and risks. If required, deep learning is also used to imitate human learning patterns through machine learning and AI to layer algorithms and identify patterns in complex data.

Companies can choose from various types of big data analytics, depending on their analytics goals. The most prominent ones include the following:

1. Predictive analytics: Predictive analytics aims to forecast future trends or events based on statistical modeling and historical data. This forecasting is done using techniques like time series analysis, machine learning algorithms, and regression analysis, allowing businesses to anticipate customer behavior, product demand, and potential opportunities or risks.
2. Descriptive analytics: Descriptive analytics is primarily the foundation for all other types of big data analytics. It involves summarizing historical data to clearly understand what has happened in the past. Using techniques like statistical analysis, data visualization, and data reporting, descriptive analytics enables companies to understand historical patterns for data-driven decisions.
3. Prescriptive analytics: Prescriptive analytics is designed to take predictive analytics further by recommending particular actions for optimal actions. This type of big data analytics suggests the best course of action to achieve desired objectives, using techniques like simulation models, decision support systems, and optimization algorithms.
4. Diagnostic analytics: Diagnostic analytics is the part of big data analytics that focuses on understanding why specific patterns or events occurred in the past. This type of analytics goes beyond defining what had happened and delves into what led to those events. Diagnostic analysis typically involves using drill-down reporting, root cause analysis, and other relevant techniques to determine factors that led to particular outcomes.
5. Text analytics: Text analytics focuses on obtaining insights from unstructured text data, such as social media posts, customer reviews, emails, news articles, surveys, etc. This type of analytics primarily relies on natural language processing (NLP) techniques to analyze and understand textual data, including topic modeling, sentiment analysis, and text classification. Today, text analytics has become a key tool for businesses to better understand customer opinions, assess brand sentiment, and detect emerging trends.

Given how vast big data analytics is, it can’t be narrowed down to a single technology or tool. Instead, various technologies work together to facilitate big data collection, processing, cleansing, and analysis. Some of the most prominent technologies include the following:

1. NoSQL databases: NoSQL databases are non-relational data management systems that operate without a fixed scheme. Hence, these databases are ideal for raw, vast, unstructured data. MongoDB, Apache Cassandra, Couchbase, Elasticsearch, AmazonDynamoDB, and HBase are some common examples of NoSQL databases.
2. Hadoop: Hadoop is one of the most commonly used open-source frameworks that seamlessly processes and stores big datasets. Hadoop is free and efficiently handles large amounts of both structured and unstructured data, making it a go-to for most big data operations.
3. YARN: YARN, which stands for ‘Yet Another Resource Negotiatior’, is a part of second-generation Hadoop. YARN is a cluster management technology that enables resource management and job scheduling in data clusters.
4. MapReduce: MapReduce is another component of the Hadoop framework that serves a dual purpose. MapReduce helps with mapping, the process of filtering big data into various nodes within a cluster. Next, it facilitates reducing, which refers to organizing and reducing the results from a single node to answer a query.
5. Tableau: Tableau is a prominent big data analytics platform that helps companies prep, analyze, and communicate their big data insights. One of the biggest reasons behind Tableau’s popularity is its excellence in self-service visual analysis, enabling teams to know more about governed big data and seamlessly share that information across the company.
6. Spark: Spark is an open-source framework for cluster computing, using fault tolerance and data parallelism to offer a robust interface for programming complete data clusters. Spark is an integral tool for big data analytics, as it can handle both stream and batch processing for rapid computation.
7. Data lakes and warehouses: Data lakes are large storage repositories that store native-format raw data till it’s required for analytics. Examples of data lakes include Amazon S3, Azure Data Lake Storage, Cloudera Data Lake, and Google Cloud Storage. Data warehouses are also used alongside data lakes, which store large datasets obtained from multiple sources and only store data through predefined schemas. Amazon Redshift, Snowflake, Google BigQuery, Azure Synapse Analytics, and IBM DB2 are common examples of data warehouses.
8. Data integration software: Data integration software is vital to efficiently undertaking big data analytics. This integration software allows companies to streamline big data across various platforms like MongoDB, Apache, Hadoop, and Amazon EMR. Apache Kafka Connect, Streamsets, Informatica, SnapLogic, and Apache Camel are some of the most commonly used data integration software today.

Given how big data analytics is a new-age technology, it comes with a distinct set of pros and cons one must consider. Here are some of the most notable big data analytics benefits and drawbacks to know:

• It helps to analyze large volumes of data collected from disparate sources in multiple forms in a timely fashion.
• It results in cost savings due to enhanced optimization and efficiency of business processes.
• It helps make well-informed decisions quickly, allowing companies to successfully strategize on their desired objectives.
• It offers the ability to implement more informed risk management techniques after analyzing large data samples.
• It also helps gain a deeper knowledge of customer behavior, sentiment, and demands to create better solutions through robust product development data.

• The inability to maintain data accessibility, as storing and processing large volumes of data is a challenge.
• It can lead to poor data quality maintenance, as big data is sourced from various places and in different formats. This diversity necessitates more time, resources, and effort to maintain the data efficiently.
• The inability to maintain data security, as big data systems are complex and present unique security threats. Hence, companies often must invest hefty resources to address and resolve such complicated data security concerns.

As mentioned earlier, companies increasingly use big data analytics to leverage data and deploy innovative solutions. Here are some of the most prominent big data analytics applications and examples seen in the real world today:

Although gradual, big data analytics’ impact on the healthcare industry has been significant. This analytics sector helps companies with real-time alerting, strategic planning, telemedicine, better patient engagement, research acceleration, and enhancing the process of analyzing medical captures. Here’s a deeper dive into its real-world applications:

• Disease prediction: Companies like IBM Watson Health and Google Health have been working on disease prediction models using medical research and patient data to forecast disease outbreaks and enable early disease diagnosis.
• Personalized medicine: 23andMe has been actively using big data to provide personalized genetic reports. Other organizations like Novartis use genomics for advanced drug development tailored to individual patients and their diseases.
• Fraud detection: Big data analytics has also contributed majorly to healthcare fraud detection, such as prescription and insurance claim fraud. A key example is UnitedHealth Group’s subsidiary Optum, which uses data analytics to detect improper payments and healthcare fraud.

Big data analytics is also one of the key drivers behind the GPS smartphones used today for navigation. Here’s how data analytics has been transforming transportation and logistics:

• Route optimization: Companies like Lyft and Uber implement data analytics to use real-time traffic data and optimize routes for reduced travel durations.
• Demand forecasting: Analyzing big data helps logistics companies predict demand patterns and optimize cargo shipping accordingly. A great example of this is Maersk, one of today’s biggest shipping companies, which relies on data analytics for logistics demand forecasting.
• Fleet management: Big data has also helped companies manage and track fleets seamlessly, enabling them to monitor vehicle health and decrease maintenance costs. A prominent example is FedEx, which uses big data analytics to manage its global fleets and monitor package deliveries.

Analyzing big data has helped bring innovative changes to the manufacturing industry, allowing its companies to streamline processes through analytics. Here are some key examples:

• Predictive maintenance: Using sensor data and analytics helps manufacturers predict equipment failures and schedule proactive maintenance. General Electric is one such manufacturer that actively uses big data analytics to predict maintenance needs for its industrial equipment.
• Quality control: Data analytics also helps to identify product defects in real time during manufacturing processes. Ford Motor Company, for example, uses big data to implement rigorous quality control for its automobile manufacturing processes.
• Supply chain optimization: Analyzing logistics, demand, and supplier data also helps companies to optimize their supply chains efficiently. Procter & Gamble is a manufacturer that heavily implements big data analytics to optimize its supply chains.

The finance industry has also been actively using big data in various areas like risk management, fraud detection, personalized marketing, and customer relationship optimization. Here are some examples of the same:

• Risk assessment: Many banking and financial institutions analyze big data to evaluate credit risk and detect fraudulent transactions. A significant example is JP Morgan Chase, which uses data analytics for credit risk assessment. Another example is PayPal, which has been using the same to implement robust fraud detection protocols.
• Customer churn prediction: Big data has also been helping financial organizations assess customer behavior, allowing them to forecast and prevent customer churn. An example of this is American Express, which uses big data analytics to reduce customer churn on its vast range of financial products, including debit and credit cards.
• Algorithmic trading: Big data also plays an important role in the stock market, allowing companies to make high-frequency trading decisions through algorithmic trading systems. Virtu Financial and Citadel Securities are some notable examples that use algorithms and real-time data for high-frequency trading.

Advertisements have always been personalized to target specific consumers. Big data takes this a step further by equipping companies with accurate data to understand the intricacies of what their customers search for, prefer, and purchase. Here are some examples:

• Behavioral targeting: Analyzing customer data allows marketers to target ads to individuals based on their online behaviors. Facebook and Google are two of the most notable examples, as the two companies frequently utilize user data for targeted advertising.
• Social media analytics: Big data analytics also allows companies to monitor their social media platforms more effectively, helping them understand customer engagement and sentiment. Sprout Social and Hootsuite are two great examples of this, as the two companies offer in-depth social media analytics for businesses driven by big data.
• A/B testing: Analyzing big data from marketing campaigns has also helped companies optimize their strategies for catering to their consumers. Airbnb is an excellent example, as the travel and lodging company uses A/B testing driven by big data for website and mobile app experience optimizations.

One of the earliest real-world applications of big data has been in the eCommerce landscape. Retail and eCommerce companies have implemented big data analytics for many years for customer segmentation, user experience enhancements, understanding market trends, and building recommendation systems. Here are some notable examples:

• Recommendation systems: Countless eCommerce platforms today use big data analytics to implement recommendation algorithms for product suggestions based on user preferences and browsing history. Spotify and Amazon are two of the biggest examples, as the two giants use big data to segment consumers and personalize their product recommendations.
• Customer segmentation: Analyzing big data helps retailers understand customer purchase history to segment them and personalize marketing campaigns. Prominent retail companies like Alibaba, Amazon, and Walmart use customer data for customer segmentation and personalize their offerings accordingly.
• Inventory optimization: Another key contribution of big data analytics is inventory optimization, allowing companies to streamline inventory levels, reduce overhead costs, and ensure that their products are always well-stocked. Zara, one of today’s biggest fashion retailers, employs big data analytics to optimize its inventory management at a global scale, ensuring that trending items are always available.

Data is the new king, and big data is no exception. Although complex, big data analytics has consistently helped organizations make better decisions, optimize processes, and equip themselves better for the future. Besides saving resources and managing risk, big data allows companies to deploy future-proof products, maintain a competitive edge, and drive innovation to increase market dominance.

At Turing, our big data analytics services offer customized, end-to-end solutions to harness the possibilities of big data. Our data experts offer strategic guidance, optimize data infrastructure, and interpret complex data to extract key insights for highly informed decisions. Over 100 fast-scaling companies have trusted Turing with their data needs, and our in-house data experts have delivered customized solutions to help realize business value.

Talk to an expert today and share your big data analytics needs!