Meeting the Data Demands of the IoT with AI, Machine Learning, Location Intelligence, and Accelerated Analytics
The Fourth Industrial Revolution has only lived up to part of its promise. We’ve seen smart home security devices, ride-sharing, personalized e-commerce, and an explosion of apps that make our individual daily lives more efficient, and that is a good start. But we have yet to see the big picture evolution towards things that have a macro-level societal impact, such as autonomous driving, smart grids, and smart cities.
The promise of the data-driven economy has been touted for a long time, and the benefits to society, whether industry-specific like distributed power generation, or larger-scale like smarter, safer, more efficient cities, can only be realized when data is shared.
McKinsey finds that between 2011 and 2016, “progress in capturing value from data and analytics has been uneven.” The problem is, to date, most data still ends up in a giant swamp.
But the Extreme Data Economy thrives on the hive where information is continuously shared across the ecosystem, and where decisions are also made at the node and the edge. Signal data is coming at us at insane velocity from an infinity of sources, from wearables to cars, smart devices to connected infrastructure, and must be interpreted as it flows so we can interact with it effectively. The Extreme Data Economy is premised on shared signal data and networked infrastructure and demands not just analysis, but immediate action: a process called active analytics.
Take autonomous vehicles as an example. Deploying AV at scale is a monumental signal data challenge, from safety, driver experience, regulatory, and smart city perspectives. Which roads at which points in time are safe for autonomous operation with millions of drivers and millions of cars? If every event and decision has to come back to the data swamp, AV won’t work.
Instead, the vehicles need to communicate with each other, with municipal infrastructure, and with other people, making dynamic decisions end-to-end. If we are thinking about large-scale adoption by millions or even billions of people in cities all over the planet, all with their own regulatory authorities, unless we think about data-sharing machine-to-machine, machine-to-city, and machine-to-driver, level 4 autonomy will not be enough. We aren’t just solving an autonomous operation problem, but a global data problem that impacts our society, economy, and environment.
To address these challenges, Kinetica built the world’s first active analytics platform as an enabler for this data-driven economy. The goal of active analytics is to help companies, cities, and societies manage the make-or-break shift from using data as a passive asset to glean insights into using data as an active asset that can help them react immediately.
We all know that risk is dynamic. Every trade and every interaction in the market has an impact on risk, and as a consequence, risk changes by the millisecond. Yet knowing that, why is it that banks only measure risk once or twice a day as a batch process? Wouldn’t it be better to take the entire corpus of historic data and current market signals to measure risk as changes occur in the market? Accurate risk calculation responsive to instant market fluctuations keeps savings and investments more secure, but also ensures the stability of the financial system as a whole.
Traditional retailers, faced with enormous competitive pressure, are looking for every opportunity to transform and streamline their operations. In this world of consumer-driven commerce, the need to optimize delivery and move towards on-demand logistics and replenishment are essential not just from a customer experience perspective, but also a cost and operational efficiency perspective. Retailers with active analytics in place are delivering goods faster, saving on fuel and labor, reducing perishable waste, and increasing customer satisfaction.
In healthcare, institutions that use AI to automate decisions about which data is relevant are making huge strides in science and pharmaceutical research, reducing R&D time by years. With active analytics, not only do life-saving medications get to patients faster, but the companies performing the drug trials can take on more trials and more research as the time per trial decreases and the rate of success increases.
To return to the autonomous vehicle example, in order to make large-scale adoption a reality, we need to ensure that active analytics shapes smart mobility as a whole, from ride-sharing to journey planning to route optimization, all tied into smart city infrastructure and initiatives. As live signal data interactions are woven into the way a city operates, our cities become cleaner, less congested, safer, and more efficient. The end result is a revolutionary shift in the way we live and work.
The World Economic Forum explains why: “Data grows ever more connected and valuable with use. Connecting two pieces of data creates another piece of data and, with it, new potential opportunities.” It is this dynamic growth in connection, opportunity, and ultimately value that makes data the defining asset of this industrial era.
Active analytics is thus the backbone of the data-driven Fourth Industrial Revolution. Every aspect of our modern society is impacted by this shift, and the promises of a more livable city, society, and planet will be ours for the taking if we are willing to put our data on the line.
Paul Appleby is CEO of Kinetica. You can follow him on Twitter @Appleby_Paul. This article was originally published in Forbes.
