驾驭实时浪潮：数据策略和支持技术.pdf

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2022-06-12

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Best Practices Series

Vendia

PAGE 16

REAL-TIME DATA SHARING

Semarchy

PAGE 18

DATA PLATFORM

CAPABILITIES FOR

REAL-TIME STRATEGIES

DataStax

PAGE 19

REAL-TIME DATA:

THE TIME IS NOW

Kinetica

PAGE 20

BUILDING A SPEED LAYER

FOR REAL-TIME ANALYTICS

Molecula

PAGE 21

4 REQUIREMENTS TO REAL-

TIME ANALYTICS: LATENCY,

FRESH DATA, THROUGHPUT,

AND CONCURRENCY

GigaSpaces

PAGE 22

DIGITAL INTEGRATION

HUB: THE FAST TRACK

TO ACCELERATING

DIGITAL INNOVATION

Rockset

PAGE 23

BRINGING ROCKSET

REAL-TIME ANALYTICS TO

CONSTRUCTION LOGISTICS

AT COMMAND ALKON

FairCom Corporation

PAGE 24

HOW REAL-TIME

DEMANDS ARE CHANGING

THE RELATIONSHIP

BETWEEN DATABASES

AND MESSAGE QUEUES

CData

PAGE 25

ON-DEMAND DATA

MODERNIZES BUSINESS

DECISION MAKING

Denodo

PAGE 26

LOGICAL DATA FABRIC FOR

REAL-TIME VIEWS ACROSS

ENTERPRISE DATA

RIDING the

REAL-TIME WAVE:REAL-TIME WAVE:

Data Strategies

and

Enabling

Technologies

14 BIG DATA QUARTERLY | SUMMER 2022

ARE ORGANIZATIONS READY FOR THE REAL-TIME REVOLUTION?

Somewhat—but they are only just beginning to embrace it. Typ-

ically, business decision makers have had to analyze historical

data to assemble patterns that might predict upcoming events.

However, with the advent of real-time data and analytics, insights

on present events, and the use of leading-edge AI technologies,

inferences may be drawn about future events.

While there are many perceptions about what “real time” is, a

general deﬁnition includes the processing of data within millisec-

onds as well as the ability to provide access and insights within sec-

onds to users or consuming applications. This is rapidly becoming

a sought-after capability among today’s organizations. Seventy-

seven percent of respondents to a recent survey by the services

provider and consultancy Accenture indicated their adoption

of real-time data had increased over the preceding year. Things

are changing too fast these days—witness the disruptions of the

COVID-19 pandemic and continuing supply chain issues—to rely

on historical data or even current analytical models. In addition,

developments such as the proliferation of edge devices and the

Internet of Things have completely altered the data equation.

“To ﬁnd new patterns in data and better anticipate future

decisions, new data sets—including real-time data from across

the value chain—are being processed by new analytic approaches

based on artiﬁcial intelligence,” the Accenture report’s authors state.

This is “learning from the future,” as Accenture puts it. How-

ever, at this point, only 31% of the executives it surveyed said

they are completely conﬁdent in their ability to foresee, and

respond to, behavioral changes that affect demand. “Companies are

not being held back by technology,” the report states. “Improved

access to diverse sources of granular data and advances in mod-

eling techniques mean that the right predictive tools are increas-

ingly available. Instead, organizations are being held back by

mindset, cultural, and organizational constraints—and their

approaches to decision-making. Vast improvements in technol-

ogies have enabled greater connectivity and securely managed

information ﬂows, helping organizations overcome the con-

straints of distance. The pandemic—which triggered the biggest

experiment in remote working at scale—has shown that it is

indeed possible to collaborate well at a distance.”

Meeting the demands of the rapid evolution to real-time

business requires the following perspectives and approaches:

Here are seven recommendations to make the most of real-

time capabilities:

1. Adopt a “real-time first” mindset. Many applications and

requirements now coming down the road will likely have

real-time components to it—either in users’ ability to access

information on operational status, or in delivery of services

to customers. It’s key to think about ways to incorporate

KEY

CAPABILITIES

for REAL-TIME REAL-TIME

ANALYTICS

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