What is Big Data Analytics? Full Guide + How Businesses Can Use It

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Big data refers to large data sets that are difficult to manage due to their volume, velocity, and variety. But the complexity and scale of big data is also the reason for its potential: analyzing these data sets can unlock greater insights, better operational efficiency, and higher revenue growth. 

What is big data analytics?

Big data analytics means processing large volumes of raw data to extract insights on user behavior, create data visualizations, and understand market trends. While this sounds like a straightforward process, the reality is that a business will struggle to glean any valuable insights without a proper big data infrastructure.

Data analytics has a long history, from manual statistical analysis to the invention of relational databases to manage structured data. As businesses began collecting a greater variety of data, non-relational databases (NoSQL) emerged as a solution for unstructured data.

Today, distributed processing technologies (Apache Hadoop, NoSQL databases, massively parallel processing) help companies build a scalable big data infrastructure that supports high-speed, and often real-time data processing. 

Key components of big data analytics

Analyzing big data means accounting for its volume, velocity, and variety. Organizations need to handle the ingestion, processing, and storage of large data sets at scale, which can often include a variety of different data types (including structured, unstructured, and semi-structured). 

Volume

The majority of companies manage between one and five petabytes of data, according to a recent survey. These large amounts of data stream in from diverse sources – Internet of Things devices, payment equipment, social media, web apps, and more.

Almost four in five data experts report that the speed of data collection has outpaced their ability to extract value from their data, especially if most of the data is siloed.

Scalable data ingestion is necessary to forward all of this data to a repository (like a data warehouse or lake). From here, you can transform this data and share it with analytics tools. For example, Twilio Segment’s customer data platform (CDP) gathers data from fragmented sources, such as mobile, web, and the cloud. Then, it automatically transforms the data according to your data quality standards and uploads it to a database where it’s ready for analytics.

Velocity

Velocity refers to the speed of data generation. Big data is generated in real time (or near real time), so your ingestion engine must handle a constant data stream. This is important for big data analytics tools that rely on real-time data, like fraud prevention solutions.

Consider Camping World, a business that specializes in RVs. They used Twilio Segment’s real-time data collection capabilities to personalize customer interactions, resulting in a 12% increase in conversion rates.

Variety

Big data means diverse data: semi-structured, unstructured, and structured. Additionally, different teams may use different formats and naming conventions, which can degrade the quality of data (e.g., logging duplicate entries). 

For example, one team may use the nomenclature “Signed_Up” while another uses “sign_up.” This is the same event, but since it’s named differently, it’s tracked as two separate ones.  

Practical applications of big data analytics

Companies across industries use different types of analytics to transform their big data into fuel for decision-making. Here’s a look at how this is done in industries like finance, healthcare, marketing, and cybersecurity. 

Finance

The finance sector applies big data analytics to manage risk, automate investing, and detect fraud – among many other applications.

Machine learning models trained on various financial data can analyze the creditworthiness of a person or business. So, instead of relying on the credit score for risk assessment, a lender gets a more complete picture of an applicant’s ability to repay. Using these predictive insights, the lender may expand their customer base and generate more revenue without extra risk.

Big data analytics also allows robo-advisors (automated investing services) to make investments based on the client’s preferences. This has made investing accessible to anyone, not just people with a high net worth.

In fraud prevention, predictive analytics uses historical data to flag suspicious activity in real time. These solutions not only protect consumers but also allow financial service providers to save resources they would’ve spent on fraud investigations.

Healthcare

Big data supports predictive analytics in healthcare, allowing healthcare providers to maximize the use of existing resources. For example, predictive algorithms can forecast patient demand for a given period. If the algorithm predicts a patient surge, a hospital has enough time to allocate its resources and prevent staff overwhelm.

Access to big data analytics improves patient care in many ways – from preventing the development of chronic illness to detecting disease in its early stages. Healthcare practitioners can even develop personalized care plans for patients based on predictions of how they will respond to their treatment.

Marketing 

Without big data analytics, it wouldn’t be possible to deliver the high degree of personalization that consumers now expect. Sixty-two percent of business leaders say that personalization boosts retention, according to a Segment report, and businesses are setting aside more of their budget for this purpose. 

Big data tools enable marketing teams to understand what turns a website visitor into a customer and what kind of interactions retain them. 

Big data helps a business analyze which products attract specific customer segments, which allows the marketing team to target just the right audience. Data also uncovers what frustrates consumers and why they drop out of the sales funnel.

Learn more: Why Business Data is the Key to Unlocking Growth

Cybersecurity

Cybersecurity teams leverage big data to improve threat detection and prevent data breaches. 

Consider an employee whose credentials have been stolen by a threat actor, giving them full access to sensitive company information. With user and entity behavior analytics (a big data-powered cybersecurity solution), cybersecurity teams can quickly detect suspicious behavior and take action to contain the threat. 

Typically, detecting an insider threat takes an average of 85 days. However, analyzing big data allows cybersecurity teams to determine a baseline for non-suspicious user behavior, which illuminates suspicious actions in real time.

Best practices in big data analytics

Big data is complex, so it’s easy to make mistakes that lower the value of your data. Making sure it’s analytics-ready means you’ll need to strategically approach data quality, infrastructure scalability, security, and compliance.

Data quality and consistency

Regardless of what kind of analytics you’re running, you need reliable data to produce consistent and high-quality results. Reliable data is complete and accurate. But a big data system can’t produce it by default. It needs tools and processes that constantly clean and validate data. 

A major risk of poor-quality data is basing important business decisions on incomplete information. Additionally, you would lose out on all of the benefits of reliable data, including improved customer loyalty, faster product development, and revenue growth.

Scalability and efficiency

A big data system must be able to adapt to growing data volumes without hurting query performance. Building such data infrastructure from scratch is a resource-intensive process, so many businesses opt for a third-party solution to save time. 

As an example, take Retool, a platform for creating business apps. They used Twilio Segment to unify their customer data and scale their infrastructure, which saved them over 1,000 engineering hours per year. With Segment’s efficiency, everyone (from finance to product teams) could pull the data they need to understand user behavior and improve their experience.

Data security and compliance

Big data systems are a prime target for threat actors looking to steal sensitive data and cause damage to your business operations. Therefore, your data security measures should include encryption (in transit and at rest), regular penetration testing, and robust access controls to your data system.

Compliance with the CCPA, GDPR, and other privacy regulations is another priority. Consumers are becoming more aware of their data rights – the Twilio Segment platform saw a 69% increase in user deletion requests in 2022. Businesses must be able to easily comply with such requests and do so at scale, which is why automation is an ideal solution in a big data system.

Building a compliant and secure big data system is difficult and requires engineering resources that you could’ve spent on your product. As a result, many companies go for platforms that take care of security and compliance for them.

Simplify big data analytics with Segment

Twilio Segment’s CDP helps businesses in healthcare, finance, retail, and many other industries run big data analytics effectively and at scale.

Connections

Connections is Segment’s product for data unification. With just one API, Connections gathers first-party data from various sources and unifies it into a central hub. With the data in one place, you’ll get a complete customer view and an opportunity to analyze their behavior in-depth. You can then send this unified data to other business apps, such as marketing automation tools.

Protocols

Protocols is Segment’s data quality solution. It allows you to create a tracking plan for automatic data validation upon ingestion, so any issues are captured before they leave an impact on your analytics. You can quarantine any events that don’t conform to your tracking plan for later review. With Protocols, companies like Typeform improved their data governance and reduced the number of tracked events by 75%.