Big Data is a term for massive volumes of unstructured, semi-structured, and structured data. This type of data is usually challenging to manage or process by conventional means. Instead, data scientists employ sophisticated methods involving multiple steps to utilize it.
First, Big Data is collected, processed, and cleaned before reaching a functional state. Once it’s useable, data scientists use the following analytics tools for insights and improved strategic business decisions:
- Data Mining Software: These tools work through large data sets to find trends and correlations and create data clusters.
- Predictive Analytics: Such modeling techniques analyze historical data to make accurate predictions, zeroing in on opportunities and risks for companies.
- Deep Learning: This broader type of machine learning employs 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.
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.
Finance
Data-driven financial companies utilize Big Data to enhance profits, reduce losses, boost supply management, and predict consumer demands.
- Stock Market: Deep learning tools analyze Big Data while factoring in political and socioeconomic trends to predict stock market values. Trade and exchange commissions leverage data analytics to identify illegal trades in the stock market.
- Risk Mitigation: Machine learning algorithms help credit card companies and other lenders mitigate the risk of fraud by detecting threatening patterns. Predictive analytics systems allow banks to make more intelligent lending and investment decisions.
- Supply Management: Systems backed by artificial intelligence predict consumer purchasing behavior to enhance supply management needs.
- Cash Flow: Mechanisms that analyze customer histories, market situations, and other patterns can accurately predict payment delays to optimize cash flow.
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.
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.
Other industries that leverage Big Data to enhance operations include automotive, public services, energy, retail, transportation, agriculture, sports, music, and entertainment. With the right tools, raw data can be a powerful resource for many types of industries.
