Active Analytics: The Foundation of The Fourth Industrial Revolution
Active
Analytics: The Foundation Of The Fourth Industrial Revolution
Active analytics helps
companies, cities, and societies manage the shift from passive data to active
data as an asset that can help them react immediately
Mar 12, 2019, 06:07pm
Meeting the Data Demands of the IoT with AI,
Machine Learning, Location Intelligence, and Accelerated Analytics
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.
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