What Real-Time Analytics Looks Like for Real-World Businesses
Real-time analytics. Streaming analytics. Predictive analytics. These buzzwords are thrown around in the business world without a clear-cut explanation of their full significance. Each approach to analytics presents its own distinct value (and challenges), but it’s tough for stakeholders to make the right call when the buzz borders on white noise.
Which data analytics solution fits your current needs? Our new series of posts aims to help businesses cut through the static and clarify modern analytics solutions. Starting with real-time analytics, we will define each variety of analytics solutions, share use cases, and provide an overview of the players in the space.
- Real-time or streaming analytics allows businesses to analyze complex data as it’s ingested and gain insights while it’s still fresh and relevant.
- Real-time analytics has a wide variety of uses, from preventative maintenance real-time insurance underwriting to improving preventive medicine and catching sepsis faster.
- To get the full benefits of real-time analytics, you need the right tools and a solid data strategy foundation.
What Is Real-Time Analytics?
In a nutshell, real-time or streaming analysis allows businesses to access data within seconds or minutes of ingestion to encourage faster and better decision making. Unlike batch analysis, data points are fresh and findings remain topical. Your users can respond to the latest insight without delay.
Yet speed isn’t the sole advantage of real-time analytics. The right solution is equipped to handle high volumes of complex data and still yield insight at blistering speeds. In short, you can conduct big data analysis at faster rates, mobilizing terabytes of information to allow you to strike while the iron is hot and extract the best insight from your reports. Best of all, you can combine real-time needs with scheduled batch loads to deliver a top-tier hybrid solution.
Streaming Analytics in Action
How does the hype translate into real-world results? Depending on your industry, there is a wide variety of examples you can pursue. Here are just a few that we’ve seen in action:
Next-Level Preventative Maintenance
Factories hinge on a complex web of equipment and machinery working for hours on end to meet the demand for their products. Through defects or standard wear and tear, a breakdown can occur and bring production to a screeching halt. Connected devices and IoT sensors now provide technicians and plant managers with advanced warnings – if they have the real-time analytics tools to sound the alarm.
Azure Stream Analytics is one such example. You can use Microsoft’s analytics engine to monitor multiple IoT devices and gather near-real-time analytical intelligence. When a part needs a replacement or it’s time for routine preventative maintenance, your organization can schedule upkeep with minimal disruption. Historical results can be saved and integrated with other line of business data to cast a wider net on the value of this telemetry data.
Real-Time Insurance Underwriting
Insurance underwriting is undergoing major changes thanks to the gig economy. Rideshare drivers need flexibility from their auto insurance provider in the form of modified commercial coverage for short-term driving periods. Insurance agencies prepared to offer flexible micro policies that reflect real-time customer usage have the opportunity to increase revenue and customer satisfaction.
In fact, one of our clients saw the value of harnessing real-time big data analysis but lacked the ability to consolidate and evaluate their high volume data. By partnering with our team, they were able to create real-time reports that pulled from a variety of sources ranging from driving conditions to driver ride sharing scores. With that knowledge, they’ve been able to tailor their micro policies and enhance their predictive analytics.
How about this? Real-time analytics saves lives. Sepsis, an excessive immune response to infection that threatens the lives of 1.7 million Americans each year, is preventable when diagnosed in time. The majority of sepsis cases are not detected until manual chart reviews conducted during shift changes – at which point, the infection has often already compromised the bloodstream and/or vital tissues. However, if healthcare providers identified warning signs and alerted clinicians in real-time, they could save multitudes of people before infections spread beyond treatment.
HCA Healthcare, a Nashville based healthcare provider, undertook a real-time healthcare analytics project with that exact goal in mind. They created a platform that collects and analyzes clinical data from a unified data infrastructure to enable up-to-the-minute sepsis diagnoses. Gathering and analyzing petabytes of unstructured data in a flash, they are now able to get a 20-hour early warning sign that a patient is at risk of sepsis. Faster diagnoses results in faster and more effective treatment.
That’s only the tip of the iceberg. For organizations in the healthcare payer space, real-time analytics has the potential to improve member preventive healthcare. Once again, real-time data from smart wearables, combined with patient medical history, can provide healthcare payers with information about their members’ health metrics. Some industry leaders even propose that payers incentivize members to make measurable healthy lifestyle choices, lowering costs for both parties at the same time.
Getting Started with Real-Time Analysis
There’s clear value produced by real-time analytics but only with the proper tools and strategy in place. Otherwise, powerful insight is left to rot on the vine and your overall performance is hampered in the process. If you’re interested in exploring real-time analytics for your organization, contact us for an analytics strategy session. In this 2-4 hour session, we’ll review your current state and goals before outlining the tools and strategy needed to get you achieve your goals.
Fred Bliss is the CTO at Aptitive. He brings over 15 years of experience solving complex business problems through data solutions including cloud integration, data warehouse modeling, ETL, and front-end reporting implementations.