Big Data Analytics, Explained

Imagine yourself with a folder containing millions of gigabytes of data. If you were asked to process it with an Excel spreadsheet, you wouldn’t need to be a data expert to know that’s impossible. 

We refer to that amount of data as “big data”. Big data requires advanced techniques, tools, and methods beyond what regular data analytics entails — that’s where big data analytics comes in.  

Big data analytics is how organizations make sense of large volumes of data, coming in different forms, from various sources and at breakneck speed.

In this article, we’ll unpack the must-knows of big data analytics, focusing on its real-life applications, benefits, challenges and the tools big data analytics professionals use. 

What is big data analytics?

Big data analytics is the process of analyzing large volumes of data using advanced data analytics tools and techniques. This allows organizations to identify patterns, correlations, and insights that would otherwise go unnoticed if using traditional data analytics methods. Big data analytics involves the processes of collecting, processing, analyzing, and interpreting massive amounts of data in order to make informed decisions and predictions.

Big data, can be structured or unstructured based on their characteristics including the 6Vs:

• Volume: The amount of data being generated and collected.
• Velocity: The speed at which data is being created, processed, and analyzed.
• Variety: The different types and sources of data such as text, images, audio, video, social media posts, sensor data etc.
• Value: The insights and actionable information extracted from the data.
• Variability: The inconsistency or volatility of data over time.
• Veracity: The accuracy, reliability, and trustworthiness of the data.

Data is all around us — from our social media interactions, emails, traffic data or financial transactions. This data holds the potential to bring unique insight to businesses and organizations.

Big data analytics techniques

To make sense of all that data, analytics teams use advanced techniques and tools to extract unique insights, draw patterns, and predict trends. 

Some of big data analytics techniques include: 

• Machine learning: A subset of artificial intelligence that involves training machines to learn from data and make predictions or take actions without being explicitly programmed.
• Predictive analysis: as the name implies, uses historical data and statistical algorithms to forecast future events.
• Data mining involves searching through large datasets to unearth patterns, make connections and extract insights.
• Natural language processing uses computer programs to understand and interpret human language, whether as text or voice.

In big data analytics, you don’t only analyze data; you also develop prediction models, create visualizations, and communicate insights to stakeholders. 

Big data vs. small data (analytics)

Small data is traditional structured data that can be easily analyzed using tools like Microsoft Excel, Google Sheets, or SQL. It is usually generated in smaller volumes and follows a specific format, making it easier to manage and analyze.

On the other hand, big data is characterized by its volume, velocity, variety, and complexity. Advanced analytical techniques, algorithms, and tools — such as Hadoop, Spark, NoSQL databases, etc., — are required to extract meaning from the massive amounts of data being generated.

Big data analytics differs from traditional (small) data analytics in several ways:

1. Data size: As the name suggests, small data refers to datasets that are relatively smaller and can be easily processed using traditional methods. Big data, on the other hand, is massive in volume and requires advanced tools and techniques for analysis.
2. Variety: Small data is usually structured, and organized, and comes from well-defined sources such as databases or spreadsheets. Big data, however, comes from various sources and can be unstructured or semi-structured.
3. Velocity: Small data is static and doesn’t change frequently while big data streams in continuously at high speeds.
4. Sources: Small data typically comes from internal sources (e.g. customer databases) while big data can come from both internal and external sources (e.g. social media platforms). 
5. Insights obtained: With small data, you can easily draw insights from the data using basic statistical methods. Big data requires advanced analytics tools and techniques to extract meaningful insights.
6. Scope: Small data is usually focused on a specific problem or question while big data analytics aims to explore multiple questions, patterns, and correlations at once.

Real-world examples of big data analytics

You can find different big data analytics applications globally in different industries and sectors, with even more use cases still being discovered. Here are some examples of big data analytics at work:  

Government

Governments worldwide rely on all sorts of data from different sources to run their affairs. It helps in formulating policies, allocating resources, and keeping up with global and local trends.

Another instance is during the population census, where big data analytics can process citizens’ data according to demographics, gender, age, and other criteria. During elections, candidates also rely on insights from data to plan their campaign strategy — and some countries, like Estonia, even run their elections entirely via digital means.

Case study: In the first half of 2023, the Ministry of Communication, Telecommunications, and Digital Economy of the Republic of Senegal (MCTEN) signed an MoU with Presight AI, a big data analytics company powered by Artificial Intelligence. This is in a bid to achieve their goal of empowering the country digitally. The public-private partnership will focus on: 

• Improving the efficiency of the government’s operations. 
• Creating an enabling business environment. 

Supply chain and e-commerce

Supply chains and e-commerce companies have certainly felt the impact of big data analytics, which has, in turn, led to the rise of online shopping — and shipping! Some of these areas are personalized shopping experiences by building on the insights provided by customers’ purchase history, browsing patterns, and feedback. 

E-commerce marketing has gotten a big boost from big data analytics — just look at all the options for improved customer segmentation (especially with email marketing), ads, and marketing campaigns.  

Case study: The hardworking American apparel brand Carhartt produce millions of garments and accessories each year across four U.S. production facilities. With Splunk AppDynamics, all of Carhartt’s operations are now seeing benefits like:

• Enhanced customers trust in the purchasing process.
• Near-elimination of costly service or application downtime, avoiding millions of dollars in lost revenue.

(Related reading: web analytics.)

Cybersecurity

No doubt, there has been a rise in cybercrimes. Detection and prevention of those crimes wouldn’t be possible without big data analytics. For instance, advanced storage facilities can help store and process data from different sources, making it easy to send alerts if indicators of compromise (IOCs) and indicators of attack (IOAs) are detected. Insights from big data analytics contribute to building algorithms for threat detection and hunting, forecasting, and launching an incident response plan for use in the event of an attack.

Case study: Carrefour, a leading retailer, relies on insights from Splunk Cloud Platform to centralize its data, especially security logs, boosting its response time to security threats three times over.

Even better? With shorter response times, the security teams are able to focus on value-adding work.

Healthcare

Medical diagnosis, research, and patient care improvement can be tied to big data analytics and the accompanying knowledge it brings. An instance of this is how big data analytics boosts the accuracy of diagnosis by helping medical professionals make better sense of patients’ medical history and effectively dig through previously reported or similar cases. 

Case study: Abbott is using big data to reveal patterns and trends in human behavior of their patients and their interactions.

Why is big data analytics important?

The importance of big data analytics lies in the insights it generates and the potential value such insights hold for businesses. This value is reflected within the organization, its profit margin, and how it keeps up with industry trends.

Organizations that use big data benefit in the following ways:

Gain competitive advantage

Competitive advantage comes from different areas. It could be first mover privilege, better marketing, or better product, all of which can happen courtesy of quality data. And with more data at our disposal, whoever harnesses such extensive data can figure out the secret to gaining a substantial chunk of the market share.

Provides unique business insights

Courtesy of the advanced tools and techniques used in big data analytics, organizations can expand their knowledge about business operations, customers' needs, and industry trends. These insights will subsequently contribute to the business’ growth and help forecast market direction so they stay ahead.

Strengthens product development

Without adequate data and research, figuring out customers’ needs and creating a product that solves them will be a herculean task. However, with big data analytics, you can better direct your product development process, improving your chances of creating a viable product. 

Improves marketing

Using big data analytics to analyze customer demographics, behavior, reviews, business offers, and market trends can help you nail customer targeting, messaging, and a business’ unique selling proposition, all of which are part of the formula for great marketing.

(See how product analytics also enhance your marketing strategy.)

Boosts operational efficiency

You can improve business operations by using big data analytics tools to store and process information about employee performance, business loopholes, and operational costs. Afterward, you use the information to reward high performers, motivate other workers, and figure out areas to cut costs or allocate more resources. 

Challenges of big data analytics

Big data analytics may sound promising, but having a proper implementation in your data infrastructure may come with a set of challenges.

Some of the challenges of big data analytics include:

Data quality

Data is as valuable as the insights it generates, making data quality a priority in big data analytics. However, despite the advancement in the big data analytics space, issues like data synchronization, the credibility of data sources, and the strength of data analytics tools have made data quality a far-fetched goal. 

Data accessibility and privacy concerns

The availability of numerous data sources, data breaches, and blurred lines around data sharing/use heighten data accessibility and privacy concerns. Now, companies have access to customers’ data without their consent but use them in ways that violate customer relations and data laws. 

(Related reading: customer data management.)

Budget constraints

The cost of implementing data analytics tools and hiring data professionals remains a stumbling block for organizations looking to make the switch. 

Big data analytics tools you should know

Big data analytics doesn’t start and end with one tool. Organizations that employ its techniques use various tools for storage, data cleanup, data classification, and analysis. Here are some of the big data analytics tools you should know: 

Splunk

Splunk is a data platform designed to help organizations centralize operations across data analytics, security, and IT observability. Splunk offers a whole suite of various software, apps, and APIs, plus a whole lot of flexibility. Our solutions help you mature your digital resilience, so you can adapt to anything.

Here are our most-well known solutions:

• Splunk Enterprise lets you search, analyze and visualize all of your data, providing insights you can act on.
• Splunk Cloud Platform which offers data search, analysis and visualization in the cloud (SaaS).
• Our Universal Forwarder is the most popular way of getting data into Splunk Enterprise and Splunk Cloud Platform.
• Our SIEM, SOAR and observability and monitoring solutions.

(See more of what Splunk does.)

Apache Airflow

Apache Airflow is an open-source platform to programmatically author, schedule and monitor workflows. It also offers a UI-based workflow editor for developers to map out their data pipelines. This tool integrates with various big data technologies such as Spark, Hadoop, and Presto.

MongoDB  

MongoDB is an open-source cloud-based NoSQL database for handling structured and unstructured data. MongoDB categorizes data into collections instead of regular rows and columns, stored in a document using a Binary JSON(BSON) format.

It has a more robust storage capacity, more scalability, and supports multiple programming languages like Python, PHP, and C++ making it a go-to solution for developers and enterprise data management.

Apache Spark

Apache Spark is a high-speed data processing engine that complements Hadoop’s MapReduce processing capabilities. Apache Spark is an open-source tool for real-time stream processing, handling large volumes of data in memory while still working with data stored on disk.

It's capable of handling various tasks like machine learning, SQL queries, and graph computation, which are common functions used in big data analytics.

Tableau

Tableau is a data visualization software for business intelligence and big data analytics. Its major strength lies in its user-friendly interface that allows users to view and make sense of large volumes of data. With it, you can store, prep, analyze, and even access data from multiple data warehouses without coding.   

Making the most out of big data analytics

Wrapping up, big data analytics offers endless possibilities for organizations to make data-driven decisions, improve operational efficiency, and boost revenue. However, big data analytics is only as valuable as the insights it generates.

Hence, investing in reliable tools and taking a proactive approach towards maintaining data quality can help you unlock its full potential.

Big data analytics has great potential to bring out the hidden value of your data in your organization. Without proper tools set in place, missed opportunities may arise from not extracting this value. Make the most out of big data analytics with Splunk Enterprise today.