Big Data refers to massive amounts of different types of unstructured, semi-structured, and structured data and is often difficult to handle or process using conventional methods. Instead, data scientists employ sophisticated methods involving multiple steps to use it.
First, Big Data is collected, processed, and cleaned before reaching a functional state. Once it’s usable, data scientists use different tools to get the insights for improved strategic business decisions.
For example, data scientists use Data Mining Software and work on large data sets to find trends, correlations and create data clusters. And use predictive analytics to analyze historical data to make accurate predictions, zeroing in on opportunities and risks for companies.
Artificial neural networks to isolate patterns in complex data in a supervised, semi-supervised or unsupervised environment.
But where does Big Data come from? There are several different sources:
- Databases
- Transaction processing systems
- Social networks
- Documents
- Emails
- Medical records
- Vehicles
- Internet of Things (IoT) devices
- Appliances
- And more
In addition to gaining insightful market intelligence, many industries use Big Data to enhance productivity, safety, marketing, customer service, and innovation.
1. Real Estate
The real estate industry took a long time to modernize and leverage Big Data. Finally, it’s catching up with its peers. According to Regan McGee, the founder of disruptive real estate marketplace Nobul, “The great thing about the real estate industry’s recent embrace of Big Data and all things digital is that we’re now able to be so much more current with our customers.”
Modern homebuyers are using apps that offer up-to-the-minute data on market conditions. Such insights are critical in a competitive environment where homes sell rapidly.
“That matters tremendously when the market is as hot as it is right now. But, more importantly, customers have come to expect that kind of immediacy,” McGee shared in an interview with Digital Connect Mag. “They get it with Uber, which can tell you within minutes when your driver is coming to pick you up. And they get it with Amazon, which can tell you almost exactly when your package is going to arrive.”
Rental property owners are also benefiting from Big Data analysis in fascinating ways. Deep learning models can help them predict changes in rent with surprising accuracy. Similarly, investors use artificial intelligence and machine learning to gain market insight for property selection.
2. Finance
Data-driven financial companies are using Big Data to increase profits, reduce losses, boost supply management, and predict consumer demands. Global big data market is expected to grow at 10.6% CAGR and surpass $229.4 billion by 2025 and therefore there is a surge in big data investment in banking institutions.
Companies use deep learning tools to analyze Big Data while factoring in political and socioeconomic trends to predict stock market values. Trade and exchange commissions use data analytics to identify illegal trades in the stock market.
For example, Machine learning algorithms can help credit card companies and other lenders mitigate the risk of fraud by detecting threatening patterns and predictive analytics allow banks to make more smart lending and investment decisions.
Also artificial intelligence to predict consumer purchasing behavior to enhance supply management needs and also analyze customer histories, market situations, and other patterns to accurately predict payment delays to optimize cash flow.
3. Education
The education sector is leveraging Big Data to develop the quality of education delivery. Data analytics tools can identify neighborhoods that require more investment from educational bodies, school subjects that are challenging for students, and schools with inadequate teaching methods.
Big Data can also help teachers develop their teaching strategies and isolate student age groups that require innovative teaching methods.
4. Health Care
From hospitals and clinics to pharmacies and manufacturers, Big Data analysis is beneficial to different parts of the healthcare industry. The healthcare industry is reducing fraud, isolating counterfeits, boosting the speed of recalls, and optimizing medical research by applying machine learning software.
It’s also offering better health care services by factoring in the needs of local demographics and other conditions.
For example, in an outbreak, AI can predict communities that may require urgent attention based on historical data, average age numbers, racial demographics, and inoculation levels.
5. Telecommunications
Telecommunications companies use big data to improve their services and manage vast cable networks, satellites to transmit data smoothly, and analyze huge amounts of data to find and fix problems quickly.
By looking at data, telecoms can identify busy times and places and upgrade their networks where it’s needed most. Big data also helps telecom companies to find issues like dropped calls or slow internet speeds from millions of devices, and fix it faster when something is wrong.
Telecoms can also find the trends by looking at how people use their phones and internet to understand what new technologies and services might be popular and invest in the right areas.
6. Entertainment
Entertainment companies like movies, and games use big data to understand what people like and how to improve their services. For example companies like Netflix or Spotify, use big data to give recommendations on movies, or songs.
Companies are using different behavior patterns to analyze the data of what users watched or listened to in the past, and suggest similar content to them. Companies also look at how people are engaging on social media and predict the most popular movies, or music for the future.
Also companies show ads that are more likely to interest specific groups of people. For example, if someone often watches action movies, they might show some action related ads.
Conclusion:
Other industries that are using Big Data to enhance operations include automotive, public services, energy, retail, transportation, agriculture, and sports. With the right tools, raw data can be a powerful resource for many types of industries.
