Forbes magazine states that Big Data is being used for everything from predicting disasters to exploring planets to preventing diseases. Big Data is being used by companies to analyze and improve specific operations and processes. Big Data analytics can reveal hidden trends, reduce internal expenses, identify new opportunities and generate business intelligence.
Better Risk Management
The insurance industry’s net premiums in 2015 totaled over one trillion dollars. Life and health insurance premiums accounted for 55 percent, while property and casualty accounted for 45 percent. The 5,900 organizations that make up the insurance industry contributed almost 3 percent to the country’s GDP or 450 billion dollars. The insurance industry is using Big Data analytics with artificial intelligence and machine learning to streamline their services and improve their risk management performance. Big Data can transform mountains of raw data into insightful information that leaders can use to adjust specific business processes. For example, insurance companies are using Big Data solutions to reevaluate credit check algorithms and client vetting procedures.
Coding and Classification
The industries that rely on complex codes for business identification purposes include banking, insurance, construction and health care. Organizations that must deal with hundreds of parts, contracts, suppliers, and services will likely use complex classification systems that make it difficult to maintain accuracy and standardization. For example, biotechnology and health care research companies use Big Data to analyze International Classification of Diseases (ICD) databases. ICD codes are used to record patient symptoms and diagnoses around the world, so Big Data is being leveraged to track global illness and treatment trends. These powerful insights can be used to guide and improve medical and biotechnology research.
Business Asset Management
Certain industries must continually replace or upgrade a variety of assets to remain efficient and competitive. For example, construction machinery and equipment experiences intense wear-and-tear during normal usage. Other industries rely on steady streams of B2B supplies to provide their services, such as the hospitality and healthcare industries. Industries that continually use large amounts of products will benefit from tracking and trending asset usage and consumption metrics within target environments. This may help with decreasing waste, synchronizing logistics and streamlining supply chains. Being able to identify the highest and lowest demand products may help with contractual negotiations. Standard metric categories, such as cost, speed and value added benefits, may help drive data collection and processing.
Business Process Management
Modern BPM platforms offer cloud-based solutions that gather key processes and parameters during operational execution. Big Data and BPM tools are mutually beneficial technologies that will help business leaders gain insights into the impacts of new activities, resources, and business rules. BPM platforms that use Big Data may provide metrics about how staff access and utilizes resources based on the time, location and unit. Process automation data can be used with predictive analysis to assess and improve the impacts of changes over time. Business leaders are free to focus on collecting person, location or business context data when mapping for BPM purposes. BPM concepts and Big Data technology helps to understand hidden processes and application logic in order to improve efficiency and respond faster to change.
A recent New York Times article states that students wondering if they will graduate should just ask Big Data. That is, predictive analytics is being used by colleges to identify academic performance red flags based on millions of student academic records. There are a limited number of data solutions companies that have created education industry software to monitor student progress and alert administration when students exhibit warning signs. These software solutions, which equally rely on Big Data and predictive analytics to work, do not need to know why a student fails a class, they just have to identify aggregate, historical patterns. For example, the most significant indicator of potential academic failure is when students earn poor grades in basic courses that prepare them for the higher-level work in their particular majors.
Many business leaders and organizations now consider data to be the new factor of production behind land, labor, and capital. Data solutions and platforms are needed to translate the overwhelming variety, velocity and volume of Big Data into simple, actionable insights.