What is Big Data? How Companies Use It

Every second, every single day, the world is generating an incredible amount of data. Every Google search, every social media post, every credit card transaction, every sensor reading from a connected gadget, every click on a website, every GPS ping from a smartphone, contributes to a worldwide data flood that presently runs to exabytes-millions of terabytes-a day. This is what is known as Big Data.

Big Data is not merely “more data”. It’s a new paradigm of how firms learn about the world, make decisions and build competitive advantage. Those companies that understand how to exploit Big Data can anticipate customer behavior in advance, uncover fraud within milliseconds, optimize supply chains in real time and make products at scale to individual tastes. Ignoring it is a sure way to be making gut feel and small sample decisions while your competitors are making decisions with data-driven precision.

In this tutorial we look at what huge Data is, how it is defined and quantified, where it comes from, how organizations are actually using it, the technology that support it and the huge challenges it brings.

What is Big Data?

Big data refers to data that is so large and fast-growing that it is difficult to process using traditional database management tools and data processing applications. The phrase is relative – the size of “big” data increases as processing power does – but the concept is characterized by attributes that set it different from traditional data management.

The most recognized concept is the Three Vs of Big Data, first presented by Gartner analyst Doug Laney, and later developed by others:

• Volume – The huge volume of data being generated and stored. We’re not talking about thousands of rows in spreadsheets, we’re talking petabytes and exabytes. Facebook processes almost 100 petabytes of data. Google processes about 20 exabytes every day. Almost 175 zettabytes will make up the world’s datasphere, or the total amount of data created, captured, copied and consumed globally.
• Speed – The speed at which data is generated, collected and processed. Stock markets handle millions of transactions per second. Hundreds of thousands of posts are churned out by social media every minute. IoT sensors are constantly transmitting data. Many Big Data use cases require value from having data handled in real time or near real time, not querying last month’s information.
• Diversity – The diversity of data types and sources. Traditional databases were about structured data in rows and columns. Big Data consists of structured data including databases and spreadsheets, semi-structured data such as JSON, XML and emails, and unstructured data such as text, images, video, audio and social network posts. Most Big Data (80-90%) is unstructured and new tools and methodologies need to be found to make sense of it.

Apart from the first three, two other Vs have been accepted widely:

• Truthfulness – Quality of truthfulness and uncertainty of data. There are many sources of Big Data and not all of them are credible. Sensor failures, human error in data entry, duplication of records and deliberate misrepresentation all reduce the quality of data. Working with Big Data is about that uncertainty – filtering noise, validating sources and building statistical tolerance for inaccurate data.
• Worth – The economic worth and business intelligence that can be extracted from the data. “Collecting massive volumes of data is not valuable in and of itself. “The true value is in turning it into decisions that improve efficiency, revenue or customer experience.” Value is often called the most important V because it’s the rationale for all the others.

Big Data: Sources of Big Data

This explains the extent and scope of Big Data. The source of Big Data is:

• Social Media – Facebook, Instagram, X (Twitter), TikTok and LinkedIn provide hundreds of billions of daily interactions – posts, likes, shares, comments, following, views and behavioral indications like how long someone watches a video before scrolling.
• IoT (Internet of Things) Gadgets – Billions of gadgets – smart thermostats, industrial sensors, connected cars, wearables, smart appliances – all connected and continually transmitting data about their condition, environment and usage.
• Transaction Data – For each e-commerce purchase, credit card transaction, banking transaction and point-of-sale interaction, a data record is generated. Similar transactions occur billions of times each day around the world.
• Web & App Behavior – Behavioral data includes every site view, click, scroll, search query, session length and navigation path. that’s billions of occurrences each day for big platforms.
• Machine and sensor data – The operational data from manufacturing equipment, aircraft engines, medical devices, meteorological stations and traffic sensors are being created in quantities that are beyond the ability of ordinary tools to process.
• Government & Public Records – Big data pools include census data, court and property records, health data, and regulatory filings.
• Scientific & Research Data – Big data infrastructure is needed to process the vast information generated by genome sequencing, particle physics investigations (CERN’s LHC produces 15 petabytes of data per year), astronomical surveys and climate modeling.

How Big Data Is Being Used by Companies

Big Data is only valuable when it’s applied. Here’s how the best companies across industries are actually using it:

E-Commerce & Retail Sector

• Recommendation Engines: Amazon’s recommendation engine, which accounts for a huge chunk of its revenue, looks at purchase history, browsing behavior, wish lists, search queries and what similar consumers bought. Netflix is recommending material based on behavioral data from over 200 million members. These recommendation systems must process massive amounts of data in real time to generate relevant recommendations.
• Dynamic Pricing: Airlines, hotels, ride-sharing services, and e-commerce platforms use Big Data to change prices in real time according to signals of demand, competitor prices, inventory levels and customer groups. Amazon’s pricing algorithm is said to change millions of prices daily.
• Inventory & Supply Chain Optimization: Big Data assists retailers in estimating demand at the SKU level at each location to reduce stockouts and overstocking. Walmart arguably has the most sophisticated supply chain in the world, using more than 2.5 petabytes of customer transaction data per week to maximize replenishment.
• Fraud Detection: Credit card companies watch transaction patterns in real-time for millions of accounts, flagging suspicious activity (unexpected locations of purchases, surprising categories of spending, unusual amounts) in milliseconds. That means scanning massive transaction flows against models built from billions of earlier transactions.

Medical Care

• Disease Prediction and Prevention: Healthcare companies may examine patient records, genetic data, lifestyle factors and environmental data to predict which individuals are at high risk of developing particular diseases even before the onset of symptoms, allowing for preventive interventions.
• Medication Discovery: Big Data allows pharmaceutical companies to analyze genomic data, protein interactions, clinical trial findings, and academic research to accelerate the identification of drug candidates that might otherwise take decades to identify using conventional approaches.
• Real-Time Patient Monitoring: Data streams of vital signs are generated by ICUs and remote patient monitoring devices. Big Data infrastructure can provide real-time anomaly detection that can alert clinicians about deteriorating patients before conventional monitoring would do so.
• Epidemiology: During global health crises, analysis of Big Data on mobility patterns, test positive rates, hospital capacity and genome sequencing data informed public health measures in ways that were not possible with traditional monitoring methods.

Finance Services

• Algorithmic Trading: Big Data infrastructure is the key competitive advantage for high-frequency trading enterprises analyzing market data, news sentiment, social media signals and macroeconomic indicators at millisecond speeds.
• Credit Scoring: Conventional credit scoring relies on a small set of features. Alternative credit scoring algorithms are using Big Data – thousands of various components are taken into account, such as behavioral signals, utility payment history, rental records and mobile usage habits, to assess creditworthiness for those who don’t have a conventional credit history.
• Anti-Money Laundering (AML): Banks look at transaction networks that include millions of accounts to find tendencies of money laundering that would be undetected in any one account but clear when relationships across accounts are looked at at scale.
• Risk Management: Financial institutions use Big Data analysis to model systemic risk, market exposure and liquidity demands, by processing market conditions across global portfolios in real time.

Tech & Media

• Search Engines: Google’s search engine processes more than 8.5 billion searches a day, all of which are located in a constantly updating index of hundreds of billions of pages available online. It is made feasible thanks to the ranking, relevance and personalization algorithms which are basically the limits of what Big Data infrastructure can give.
• Content Moderation: Social media businesses apply Big Data technologies to detect hazardous content. They analyze billions of posts, photographs, and videos almost in real-time to spot violations of platform rules.
• Targeted Advertising: Digital advertising platforms employ behavioral profiles built on Big Data collected across the web to match individual users with relevant ads in milliseconds as pages load. This is the bedrock of the digital advertising economy.

Manufacturing and Industries

• Predictive Maintenance: Constant performance data from sensors on the industrial machinery. Machine learning algorithms trained on this data can predict equipment breakdowns days or weeks in advance, enabling repair to be scheduled before the breakdown, rather than after, considerably reducing the costs of downtime.
• Quality Control: vision systems and sensor arrays generate huge data streams during manufacturing. Large Data analysis finds little patterns in this data that are related to product faults so quality control may be done in real time at speeds and volumes that are not possible with human inspection.
• Energy Optimization: Power grids, industrial facilities and data centers use Big Data to optimize energy use, in real time, by matching generation with demand, identifying inefficiencies and predicting load patterns.

Transport & Logistic

• Route Optimization: UPS’s ORION system uses Big statistics from GPS devices, package scanners, traffic statistics and client delivery windows to optimize driver routes, saving the company tens of millions of driving miles and millions of gallons of gasoline each year.
• Traffic Management: Cities leverage real-time traffic data from connected cars, sensors and mobile applications to optimize signal timing, manage congestion and dispatch emergency vehicles.
• Autonomous Vehicles: Self-driving vehicles produce and consume huge amounts of sensor data – LIDAR, cameras, radar, GPS and HD maps – that require Big Data infrastructure to process and learn from.

Technologies Behind Big Data

The core technologies that enable Big Data analysis are:

• Apache Hadoop: Apache Hadoop was the first Big Data technology – a distributed computing platform for storing and analyzing massive datasets on clusters of commodity devices. Its distributed file system (HDFS) and processing model (MapReduce) enabled cost-effective examination of petabyte-sized data. Hadoop is widely used but is being largely supplanted by new technologies.
• Apache Spark: Apache Spark took Hadoop’s processing approach and did it better, allowing for in-memory computing that made some workloads 100x faster. This is the most popular big data processing engine. It does batch processing, real-time streaming, machine learning and graph processing all in the same framework.
• Cloud Data Warehousing: Amazon Redshift, Google BigQuery and Snowflake enable organizations to analyze petabyte-scale data volumes using conventional SQL on elastic cloud infrastructure. These services have made Big Data analysis democratic – organizations who could not afford dedicated Hadoop clusters may now run Big Data workloads by the query.
• NoSQL Databases: Big Data is so varied and voluminous that standard relational databases are hard to use. Unstructured or semi-structured data at massive scale is handled by NoSQL databases such as MongoDB, Cassandra, DynamoDB and Redis, which are optimized for different access patterns than standard SQL databases.
• Stream Processing: Apache Kafka and Apache Flink are for real-time data streams – ingesting, processing and routing millions of events per second. They are the infrastructure layer that enables real time fraud detection, live recommendation engines and operational monitoring.
• Artificial Intelligence and Machine Learning: Big Data and machine learning are closely related. Big Data and machine learning algorithms offer insights that are not possible with traditional analytics. Deep learning models for recognizing images, natural language processing, recommendation systems, etc. demand vast datasets, hence Big Data infrastructure is required.

Challenges of Big Data

key Data provides lots of opportunities but there are also key problems that companies have to solve:

• Data Quality: Input=Output; GIGO Garbage in, Garbage out. Quality is not a substitute for Big Data. Bad data, duplicate records, inconsistent formats and missing values add errors to analytical models. Data quality management is an expensive and perpetual challenge.
• Privacy & Compliance: Big Data contains personally identifiable data that may be subject to GDPR, CCPA, HIPAA and other privacy laws. The gathering, storage and analysis of personal data in Big Data scale carries with it significant compliance duties and danger of breach.
• Storage and Processing Costs: Cloud costs are still dropping, but petabyte-scale storage and the compute needed to handle it is still pricey. Organizations need to assess the value of retaining data against the cost of storage, and most throw away or archive stuff that they are unlikely to have value.
• Talent Gap: There are few and expensive data engineers, data scientists and ML engineers with Big Data infrastructure understanding. The persistent problem of organization is the gap between Big Data ambitions and Big Data capacities.
• Security: Big data repositories are a target. Sophisticated security solutions are necessary for securing Big Data infrastructure, controlling access, encrypting data at rest and in transit, and auditing usage.
• Prejudice in Models: Models constructed using machine learning trained on historical Big Data may embed and amplify prejudice from the past. Algorithms used to make recruiting judgments, which are educated on data from previous employment decisions, can wind up perpetuating discrimination. Bias in big data driven models is a technological and ethical problem to be conscious and minimize.

Big Data vs. Conventional Data Analysis

This is not a difference of scale, but a difference of analytical paradigm:

• The traditional analytics deals with structured data that is historical and of a reasonable size. The classic analytics approach is to run a weekly sales report out of a transactional database. It is useful but limited in scope, speed and the queries it can answer.
• Big Data analysis can evaluate massive amounts of data, usually unstructured data, at a tremendous scale, often in real-time, to solve problems that traditional analytics cannot. What will one consumer do in the next 24 hours? What equipment on the assembly line is going to break in the next 72 hours. How will a certain user be engaged tonight?

What happened? What will happen? (traditional analytics) “what do we do about it? (predictive analytics) and (prescriptive analytics) – all enabled by Big Data.

Conclusion

Big data is not a technology or passing trend. It is a fundamental capacity that is changing the way that businesses in every industry understand and respond to the world around them. Over time, companies that can effectively gather, process and analyze data at scale will have compounding benefits in personalization, efficiency, risk management and innovation.

For consumers, an understanding of Big Data gives context as to why the apps and services you use know you as well as they do – and why data privacy is as crucial as it is. For companies operating below petabyte scale, the concepts of data-driven decision making are becoming more and more important competitive tools for enterprises of all sizes.

FAQ (Frequently Asked Questions)

1. So what is Big Data in simple terms?

Big data is so large, quick or complicated that it is difficult or impossible to process using typical data processing applications. It is defined by Volume (huge volumes), Velocity (produced and processed at fast speed) and Variety (many different data types}. Firms can look at data in ways that smaller, simpler data sets cannot, and find patterns, predict what will happen and make conclusions.

2. What is the difference between large data and normal data?

Regular data is manageable with ordinary procedures – a database with millions of records that a SQL query can handle in seconds. Big Data, as the name suggests, is beyond the capacity of existing technology — data sets of billions or trillions of records, real-time streams of data with millions of events per second, or unstructured data like video and text that do not fit into regular database schemas. Big Data requires special distributed computer infrastructure for storing and processing it efficiently.

3. What are the top industries affected by Big Data?

The sectors that are most impacted by the capabilities of Big Data include financial services (fraud detection, trading, risk management), healthcare (disease prediction, drug discovery, patient monitoring), retail (personalization, pricing, supply chain), technology (search, advertising, content recommendation), manufacturing (predictive maintenance, quality control), and transportation (route optimization, autonomous vehicles).

4. Does Big Data Matter To Small Businesses?

Small companies don’t usually need Big Data infrastructure to scale, but they do benefit from Big Data indirectly trough the AI capabilities embedded in the SaaS services they use (recommendation engines, fraud detection, predictive analytics in their CRM). Cloud data warehouses and BI tools are increasingly accessible, allowing small firms to do data-driven decision making at their scale without the infrastructure big data at scale requires.