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News


ANALYTICS, AUTOMATION AND AI WILL FUEL FUTURE OF BUSINESS




ORGANIZATIONS THAT SPEED THEIR DECISION INTELLIGENCE ABILITIES WILL BE THOSE
THAT SURVIVE AND THRIVE, WHILE THOSE THAT DON'T DIGITALLY TRANSFORM WILL FAIL.

Share this item with your network:

 * 
 * 
 * 


By
 * Eric Avidon, News Writer

Published: 01 Jun 2022

The combination of analytics, automation and augmented intelligence will drive
the future of business.

That was the message delivered by Ray Wang, founder and analyst at Constellation
Research, who spoke recently during the Graph + AI Summit, the spring edition of
the biannual open conference hosted virtually by graph data and analytics vendor
TigerGraph.



According to Wang, nearly two-thirds of the companies that made up the Fortune
500 in 2000 are now gone, having been acquired, merged with another company or
gone bankrupt. And by 2040, 80% will have disappeared.

Meanwhile, tech vendors such as Amazon, Apple, Facebook and Microsoft have
quintupled their market capitalization in the last five years alone.

The difference is data -- specifically, decision velocity, which is the use of
data to rapidly make informed decisions -- according to Wang.

And what will play out going forward, what will drive the future of business and
determine the companies that survive and thrive versus those that fade into
oblivion, will be a battle for data supremacy.

"Never before have companies been able to quintuple their market cap [so
quickly], and that is done because these are companies that are built on data,"
Wang said. "These are companies that are built on new business models. Data is
the foundation for these next set of businesses."


THE 3 A'S -- ANALYTICS, AUTOMATION AND AI

That foundation for the future of business starts with analytics, according to
Wang. It begins with the ability to ask business questions, such as whether to
add staff to the marketing team or spend money on a marketing campaign to
increase sales, and be able to answer those questions.

Ray Wang

And the data used to inform those decisions can come from anywhere rather than
just the data captured through traditional sales channels. It can be as varied
as information related to supply chains and data related to the weather, and it
has to be captured and analyzed in real time.

Beyond analytics, Wang said the foundation for the future of business includes
automation and AI to create feedback loops that add context, eliminate false
positives and negatives to result in precision, and improve decision velocity.

Through the combination of analytics, automation and AI, organizations can
vastly improve decision accuracy. In addition, while humans can take weeks or
months to collectively make a strategic decision, machines can make hundreds or
even thousands of decisions per second.

So it will be the companies that combine analytics, automation and AI that make
up the future of business, while those that continue to rely on human
decision-making will ultimately fail, according to Wang.

"That asymmetry will be the difference between companies that are going to
succeed in this world and the companies that are going to fall behind," he said.

Another key to the future of business will be the deployment of graph databases,
Wang continued.

Graph databases enable data points to simultaneously connect to multiple other
data points rather than to just one other data point at a time, as within a
traditional relational database. And by connecting to multiple data points at
once, users can more easily discover relationships between data -- for example,
information about a single customer captured by disparate sources or multiple
customers who might be part of the same family -- resulting in speed and
accuracy.

"Behind decision velocity ... is the business graph," Wang said. "Interactions
over time form the business graph, and that's how we go from data to decisions
-- how we go from the ability to take insight to action."


EARLY WINNERS

Some of the companies already effectively using analytics, automation and AI --
and using graph technology to build networks -- to drive growth are in the food
delivery industry.

> Never before have companies been able to quintuple their market cap [so
> quickly], and that is done because these are companies that are built on data.
> Ray WangFounder and analyst, Constellation Research

Domino's Pizza's stock price has gone from about $3 per share in 2008 to a high
of more than $550 in late 2021, and opened trading on June 1 at $364.97.

The company now offers multiple ways to order -- for example, in person, online,
over the phone, in the app and through Amazon Alexa. Every step of the
preparation and delivery process is tracked. And when the food is delivered,
customers can upload pictures to an AI-powered engine to give quality control
feedback.

All of that creates data that Domino's uses to build a feedback loop that
informs decisions ranging from how to market to specific customers to measuring
the performance of each franchise.

"Domino's won the digital transformation battle," Wang said.

Similarly, companies such as Grubhub, DoorDash and Uber Eats have thrived
throughout the pandemic as a result of their use of analytics, automation and
AI.

With many small businesses unable to deliver during the COVID-19 pandemic, food
delivery companies took over. As they did, they collected transaction data and
built large networks of optimal delivery routes and drivers.

"This is the story of what is going on in this next stage of digital
transformation," Wang said. "They built networks and used digital monetization
models."


GETTING STARTED

While the future of business will be built on analytics, automation and AI,
organizations must go through a process to get from ad hoc decision-making to
autonomous decision-making, according to Wang.

And technology is not at a point where fully autonomous decision-making is
optimal, meaning that organizations must begin by balancing human
decision-making with machines, he continued. As they digitally transform,
organizations will need to understand when to rely on machine automation, when
to augment machine intelligence with human decision-making, when to augment
human decision-making with machine intelligence and when to fully rely on human
judgment.

Prime candidates for automation include repetitive tasks and those with a high
volume of nodes that need to be connected, while some that should still be done
by humans include those in which creativity is critical and a physical presence
is required for oversight.

Organizations also need to assign outcomes, making sure they understand what
questions they want answered with their analytics, automation and AI. Among the
outcomes are notifications, suggestions, predictions and prevention.

"Right now, we're in an era where data and digital require [humans] to get to
automation, this level of being fast and work in real time," Wang said. "We're
in the middle of a digital transformation that's much bigger than what we've
seen in the past, and this digital transformation runs on data."



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