Analyzing big data helps companies answer critical questions, test hypotheses, and ultimately improve business outcomes. Well-managed big data also allows organizations to identify the location and proliferation of sensitive data and track its use so companies can spot and act on a potential data breach.
Big data projects may be focused on delivering a specific business benefit—for example, using financial transaction data for real-time fraud detection, building a 360-degree view of customer data for deeper customer understanding, or using predictive analytics to detect and replace mechanical components before they fail. Or they may take the form of broader, enterprise-wide modernization initiatives—for example, building a centralized data lake to store all enterprise data for big data analytics, moving data into a cloud-based data lake, or migrating to a cloud data warehouse.
Health-related systems and devices generate an immense volume and variety of data, far beyond the information captured in electronic health records and the content of claims and other transactions. Big data in healthcare also includes data from medical and pharmaceutical research, smartphone apps and wearable devices, equipment tracking sensors, public records, government agencies, and far more—so much data that it has an estimated compound annual growth rate between 2018 and 2025 of 36 percent according to the IDC Data Age 2025 Report.
There are many opportunities to use big data in healthcare. Sophisticated analytics monitor the pharmaceutical industry to ensure that drugs are safe, effective, and high-quality throughout their life cycle. Big data from research trials and medical records can be analyzed to look for situations where medications are incompatible or where a medication may have a new, useful application—both of which can improve patient outcomes and spark new, more effective treatments. AI and machine learning technologies rapidly screen medical images and flag potential issues for human review. Streaming data from medical devices and wearables allows healthcare organizations to spot danger signs and intervene early enough to prevent poor patient outcomes. Natural language processing (NLP) can extract nuanced insights from unstructured text notes in patient records, with many potential uses such as identifying lifestyle, geography, social, and other data that indicate patients who need intervention to avoid slipping through gaps in the system.
And of course, only big data analysis can handle the gargantuan, distributed datasets involved in tracking and investigating the human genome. Big data has already allowed us to create new drugs and therapies that are tailored to specific genetic requirements for efficiency. It also lets us identify genetic patterns that predict cancer and other conditions with sufficient accuracy to be clinically useful. Tomorrow, it may enable us to create therapies and clinical trials for rare diseases that currently have no treatment. Read our blog for more big data healthcare insights.
Government agencies collect vast amounts of data about the constituents they serve, from who and where they are to the services they need and use and the environment they live in. When government agencies analyze, augment, aggregate, correlate, and consolidate data across silos and organizations, data can lead to deeper insight and greater efficiency in every aspect of operations, from providing citizen self-service offerings to predicting the impact of natural disasters and developing response plans.
One representative use case is San Diego's 2-1-1 service, a free 24/7 resource and information hub that connects residents in San Diego, California with community, health, and disaster services by phone or online. The city uses big data to create and maintain a 360-degree view of callers across 1,200 different agencies and providers. This enables the 2-1-1 center to make appropriate recommendations, know which agencies are serving which callers, and proactively guide callers to services they may not know they need. Big data also gives San Diego city workers greater visibility into the effectiveness and outcomes of referrals so it understands whether people are finding the help they need—and if not, what the city can do to improve their service. Learn more about San Diego 2-1-1 and its data management requirements.
Another example is the United States Geological Survey (USGS), which runs the National Water Quality Assessment Program to collect and interpret data about the quality of America’s major groundwater and surface water systems. By integrating its big data sources, the agency built the largest available dataset about water quality, which it uses to support scientific research, manage natural resources, and minimize health risks from tainted water. Learn more about the USGS and its large-dataset challenge.
As vehicles increasingly include telematics—telecommunications and monitoring systems, including GPS diagnostics and other smart or connected systems—the automotive industry is actively developing ways to use this big data resource for vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything communications. This has ramifications for every aspect of building, driving, and servicing vehicles, from the factory assembly lines to the open road.
Automobile manufacturers can turn real-time streaming diagnostics data into improved automotive designs and more prompt, predictive maintenance alerts for car owners, service centers, and factories. City planners and transportation authorities can leverage big data about traffic flows, parking needs, and road maintenance requirements to make commutes more efficient and pleasant. Insurance companies can analyze data about driving behavior to manage premiums more accurately and provide truly personalized quotes and service. Rental agencies can use both behavioral and diagnostic data to schedule maintenance, determine pricing, control liability, and even ensure that renters report accidents and traffic violations accurately.
Regardless of your industry, big data analytics can help your organization better understand past performance and current trends so you can make confident decisions for the future. Data—and therefore data management—is moving to the cloud, and the questions that businesses ask of their data now require increasingly advanced analytics and new, AI- and ML-enabled technologies to deliver answers.
In response, big data is moving away from on-premises Hadoop to multi-cloud environments in Spark-serverless mode that increase agility, innovation, and cost-effectiveness. As a result, we're seeing the rise of data engineering, a discipline that enables enterprises to build intelligent, end-to-end data engineering pipelines for AI and ML projects and advanced analytics. Data engineering spans data integration, data quality, cataloging, streaming, masking, and data preparation to deliver faster, better insights.