Big data isn’t all bad
A quick search for “Google and Facebook privacy concerns” reveals growing backlash against buying and selling data. It’s little wonder when ads seem to follow you across the internet and data theft and misuse regularly makes headlines.
But I want to talk about another side of data: Data for good. You see, data collection isn’t inherently bad. It’s all in how you use it (and protect it). Many folks do amazing things with data — things that make our lives better. And real-time stream processing can help those folks do even more amazing things.
Using streaming data processing to deliver better experiences
When I was at McLaren, I worked on a Formula One fan engagement project using real-time stream processing to help curate each fan’s race experience. During a race, there are so many things to see. Fans can easily get caught up in something happening on one side of their screen and miss the action elsewhere.
The project used stream processing to monitor race data from hundreds of sensors and deliver real-time alerts to users based on what they wanted to see. We could enhance the experience and leave fans delighted by what they saw instead of disappointed by the highlights they missed.
Delivering a great Formula 1 race experience might not change the world, but it showed me how stream processing could help companies give people the right experience at the right time. With Quix, I can help put the power of data stream processing into the hands of people who can make lives better — whether it’s a better retail experience, faster gaming, or improving access to healthcare.
Solving challenges in the social sector with machine learning
I’m not the only one excited by the potential of AI, machine learning, and data to make lives better. The authors of Stanford Social Innovation Review’s “Can Machine Learning Double Your Social Impact?” identified two common challenges in the social sector, that machine learning — which relies on quality data — is particularly suited to solve.
Prevention problems: Machine learning and predictive algorithms can help strategically deploy interventions, such as peacekeeping missions to places where violent conflict is likely to occur, or health interventions to prevent disease in places where outbreaks are likely.
Data void problems: Nonprofits and governments often lack the data they need to identify the places that would benefit most from services. With machine learning, they can derive better insights from existing data and use those to prioritize and coordinate efforts for greater impact.
Four ways stream processing big data solves big problems
Data degrades fast. In the time it takes a system to collect, store, process and return data, the information has often changed. With AI, machine learning, and real time stream processing, we can use data in ways that would otherwise be impossible.
Stream processing opens up opportunities for us to respond in real time and make changes as a situation unfolds. We can improve outcomes now, not just in the future.
Helping busy doctors deliver more personalized healthcare
Doctors are busy and, like all humans, they occasionally miss things or make mistakes. While machines won’t replace them, machine learning combined with real time stream processing can help reduce the strain and provide a second set of eyes.
In intensive care settings, predictive algorithms help doctors monitor patients and analyze risk in real time, enabling them to prioritize patients at highest risk of adverse events. The University of Chicago Medicine credits a predictive algorithm powered by streaming data with reducing the number of cardiac arrests in the hospital by an estimated 15% to 20%.
Outside the hospital setting, stream processing helps doctors monitor patients in real time to improve outcomes. Wearable devices can transmit vitals, monitor progress after joint replacement surgery and recommend changes based on patient data and treatment protocols. This digital assistance saves doctors time and enables them to provide better care to more people.
Using sensor data to make cities safer and more efficient
Managing a city is a big job, especially in older cities that weren’t built with today’s challenges in mind. Smart cities are experimenting with data and technology to make city management more effective in a digital world.
In much the same way that manufacturing companies use sensors to monitor and improve processes, cities are gathering data to improve traffic, reduce emergency response times, coordinate city services, improve parking and reduce crime.
Coral Gables, a community in Florida, reduced crime by 40% in two years using predictive analytics, artificial intelligence and other tools at their community intelligence center, Chief Innovation Officer Raimundo Rodulfo, explains in State Tech Magazine’s “8 Smart Cities to Watch in 2020 and Beyond.”
Matching people to the resources they need
Distributing resources to maximize access and minimize waste is a vexing problem for many companies. It’s also a problem on a global scale. We already grow enough food to feed everyone on the planet, according to the Food and Agriculture Organization of the United Nations. The challenge is getting that food to the people who need it.
Ayazona and Ignite Labs are using data and machine learning to tackle food security in Africa while aiming for zero waste. Their technology enables food partners to make excess food that would ordinarily be wasted available to middle- and low-income households at half the cost. Machine learning and AI tools turn that data into a weekly list of groceries designed to fit the budgets of these households.
Improving efficiency to tackle transportation challenges
The pandemic has indelibly changed our world in ways that many of us couldn’t imagine. A global labor shortage is forcing companies to be more efficient with the resources that they do have. Using real time stream processing to optimize processes and increase efficiency is one way to help a small labor force accomplish more.
As many states in the US face busing shortages, Karros Technologies is aiming to become the world leader in student transportation through route optimization, real time GPS tracking and predictive machine learning that reconciles planned routes with live data.
What excites me about Quix
Solutions are born when a person sees a need and has the tools to solve the problem. Quix puts the problem-solving power of real time stream processing into the hands of more people.
We make it easy for any Python developer to stream data and process it with real time machine learning models. You get the same quality and reliability as the world’s leading machine learning organizations — without any of the hassle, time and investment needed to set up the underlying infrastructure.
In other words, we remove the hurdles so that you can run with your idea and make an impact sooner. Want to try it for yourself? Sign up for a free trial, which comes with $240 per year in free Quix credit to get your PoC off the ground. If you have any questions, jump in our community Slack.
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
Mike Rosam is Co-Founder and CEO at Quix, where he works at the intersection of business and technology to pioneer the world's first streaming data development platform. He was previously Head of Innovation at McLaren Applied, where he led the data analytics product line. Mike has a degree in Mechanical Engineering and an MBA from Imperial College London.