They say that time is money, but Fortune 1000 executives polled in the
fourth annual Big Data Executive Survey conducted by NewVantage
Partners have boldly confirmed that reducing time-to-insight rather than
saving money is the primary driver for their Big Data business investment.
fourth annual Big Data Executive Survey conducted by NewVantage
Partners have boldly confirmed that reducing time-to-insight rather than
saving money is the primary driver for their Big Data business investment.
Conducted in November and December 2015, and published on January
11, 2016, the survey confirms that Fortune 1000 firms believe that Big
Data will deliver competitive advantage by enabling their firms to act
faster when it comes to analyzing data, gaining insights, making critical
decisions, and bringing new capabilities to market. The survey reflects
the evolving perspectives of chief data officers, business presidents,
chief information officers, and the heads of Big Data initiatives for
nearly 50 prominent Fortune 1000 firms.
11, 2016, the survey confirms that Fortune 1000 firms believe that Big
Data will deliver competitive advantage by enabling their firms to act
faster when it comes to analyzing data, gaining insights, making critical
decisions, and bringing new capabilities to market. The survey reflects
the evolving perspectives of chief data officers, business presidents,
chief information officers, and the heads of Big Data initiatives for
nearly 50 prominent Fortune 1000 firms.
Survey participants included Fortune 1000 top 50 mainstays such
as CVS Health, JPMorgan Chase, Bank of America, and Johnson
& Johnson. Large financial services firms were heavily represented,
and as an industry group, have long been at the forefront of investments
in data management solutions.
as CVS Health, JPMorgan Chase, Bank of America, and Johnson
& Johnson. Large financial services firms were heavily represented,
and as an industry group, have long been at the forefront of investments
in data management solutions.
As measured by investment and business adoption, it has taken just four
short years for Big Data to assert itself as an essential component of the
corporate mainstream. Among the Fortune 1000 firms surveyed by New
Vantage, 62.5% reported having Big Data initiatives in production or operationalized across the enterprise — nearly double the 31.4% of firms
at this stage in 2013. While only 5.4% of firms reported Big Data
investments in excess of $50 million in 2014, the number of firms that
project investments in Big Data of greater than $50 million leaps to
26.8% by 2017, a steep and rapid increase. For the first time, a majority
of firms (54%) reports having appointed a Chief Data Officer, up from
just 12% in 2012, providing further corroboration that data has become
a corporate priority.
short years for Big Data to assert itself as an essential component of the
corporate mainstream. Among the Fortune 1000 firms surveyed by New
Vantage, 62.5% reported having Big Data initiatives in production or operationalized across the enterprise — nearly double the 31.4% of firms
at this stage in 2013. While only 5.4% of firms reported Big Data
investments in excess of $50 million in 2014, the number of firms that
project investments in Big Data of greater than $50 million leaps to
26.8% by 2017, a steep and rapid increase. For the first time, a majority
of firms (54%) reports having appointed a Chief Data Officer, up from
just 12% in 2012, providing further corroboration that data has become
a corporate priority.
What is driving the sharp increase in Big Data investment? According
to the NewVantage survey, a clear pattern has emerged. Organizations
feel a need to learn quickly and act faster. While only 5.6% of firms
identified cost savings and operational reductions as the primary driver
of Big Data investment, 83.5% of survey respondents named factors
relating to speed, insight, and business agility as the primary reasons for
Big Data investment.
Of this total, 46.5% firms pointed to factors aimed at increasing speed and
reducing the time-to-insight. This is illustrated in the chart showing a
breakdown of Big Data investment factors relating to time-to-insight.
to the NewVantage survey, a clear pattern has emerged. Organizations
feel a need to learn quickly and act faster. While only 5.6% of firms
identified cost savings and operational reductions as the primary driver
of Big Data investment, 83.5% of survey respondents named factors
relating to speed, insight, and business agility as the primary reasons for
Big Data investment.
Of this total, 46.5% firms pointed to factors aimed at increasing speed and
reducing the time-to-insight. This is illustrated in the chart showing a
breakdown of Big Data investment factors relating to time-to-insight.
So, how will organizations respond and leverage Big Data investments to
accelerate the time in which it takes to capture and analyze data, identify correlations, derive insights, and validate their insights in the market?
accelerate the time in which it takes to capture and analyze data, identify correlations, derive insights, and validate their insights in the market?
Accelerate Time-to-Answer through
Test-and-Learn Processes
Test-and-Learn Processes
Business analysts have long been bound by the time it takes to capture,
organize, and make data available to non-technical users. Big Data
processes have consolidated the time it takes to engage in analytics by
reducing up-front data engineering and putting data into the hands of
business users faster. By starting with smaller sets of data, business
analysts can engage in iterative processes such as test-and-learn to
identify patterns and correlations that allow them to focus on the most
useful data quickly. This ability to accelerate the process of insight is
alternately referred to as time-to-answer, time-to-analytics, or
time-to-decision. The net result is the realization of greater insight faster.
organize, and make data available to non-technical users. Big Data
processes have consolidated the time it takes to engage in analytics by
reducing up-front data engineering and putting data into the hands of
business users faster. By starting with smaller sets of data, business
analysts can engage in iterative processes such as test-and-learn to
identify patterns and correlations that allow them to focus on the most
useful data quickly. This ability to accelerate the process of insight is
alternately referred to as time-to-answer, time-to-analytics, or
time-to-decision. The net result is the realization of greater insight faster.
Accelerate Speed-to-Market with
Data Discovery Environments
Organizations are employing new approaches to traditional data
management. These approaches include the deployment of analytical
sandboxes, Big Data labs, data hubs, and data lakes. All of these
approaches are designed to introduce greater flexibility and agility into
the process of taking data and transforming it into business insights.
The big breakthrough of Big Data comes from enabling firms to deploy
rapid analysis environments that facilitate data discovery. These more
nimble environments produce faster insights, which enable organizations
to move rapidly to action and accelerate the speed with which they can
bring new product and service capabilities to market. As a driver of
Big Data investment, “speed-to-market” experienced the greatest uptick
from previous years. Firms are looking for measurable results, ratified
in the marketplace.
management. These approaches include the deployment of analytical
sandboxes, Big Data labs, data hubs, and data lakes. All of these
approaches are designed to introduce greater flexibility and agility into
the process of taking data and transforming it into business insights.
The big breakthrough of Big Data comes from enabling firms to deploy
rapid analysis environments that facilitate data discovery. These more
nimble environments produce faster insights, which enable organizations
to move rapidly to action and accelerate the speed with which they can
bring new product and service capabilities to market. As a driver of
Big Data investment, “speed-to-market” experienced the greatest uptick
from previous years. Firms are looking for measurable results, ratified
in the marketplace.
With the emergence of a digital economy over the course of the past
two decades, leading firms have learned quickly that they must act
faster to respond to customer needs and competitive dynamics. Firms
can no longer wait days or months to analyze indicators of customer
interest and sentiment, or detect and respond to security threats or credit
breaches. Market leaders will not wait while their competitors uncover
critical insights that drive new product and service capabilities. Fortune
1000 firms have come to the conclusion that the ability to act faster
correlates with market survival and success. The need for faster
time-to-insight will be the driving force behind Big Data investment
for the years ahead.
two decades, leading firms have learned quickly that they must act
faster to respond to customer needs and competitive dynamics. Firms
can no longer wait days or months to analyze indicators of customer
interest and sentiment, or detect and respond to security threats or credit
breaches. Market leaders will not wait while their competitors uncover
critical insights that drive new product and service capabilities. Fortune
1000 firms have come to the conclusion that the ability to act faster
correlates with market survival and success. The need for faster
time-to-insight will be the driving force behind Big Data investment
for the years ahead.






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ReplyDeleteΠ₯Π°ΠΉ! ΠΠΎΡΠΌΠΎΡΡΠΈΡΠ΅ ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΡΡ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ². Π ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊ Π²ΠΎΡΠ»ΠΈ Π΄Π°Π½Π½ΡΠ΅ ΠΎ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅ ΡΠ΅ΠΌ ΡΠ΅ΠΌΠΈΡΡΠ°Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ ΠΌΠ΅Π΄ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ°Ρ , ΠΈΠ·Π³ΠΎΡΠ°Π²Π»ΠΈΠ²Π°Π΅ΠΌΡΡ Π½Π°ΡΠΈΠΌΠΈ ΠΈ ΠΈΠ½ΠΎΡΡΡΠ°Π½Π½ΡΠΌΠΈ ΡΠ°ΡΠΌΠ°ΡΠ΅Π²ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΠΌΠΈ. Π ΠΊΠ°ΠΆΠ΄ΠΎΠΌ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ΅ ΠΈΠΌΠ΅Π΅ΡΡΡ ΠΏΠΎΠ»Π½Π°Ρ ΠΈΠ½ΡΠ°: ΡΠΎΡΡΠ°Π² ΠΈ ΡΠΎΡΠΌΠ° Π²ΡΠΏΡΡΠΊΠ°, ΡΠ΅Π»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ, ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΈΡ ΠΊ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ, ΠΊΡΠΈΡΠ΅ΡΠΈΠΈ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ, Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ ΡΠΎ ΡΠΏΠΈΡΡΠ½ΡΠΌΠΈ Π½Π°ΠΏΠΈΡΠΊΠ°ΠΌΠΈ, ΠΏΡΠΎΡΠΈΠ²ΠΎΠΏΠΎΠΊΠ°Π·Π°Π½ΠΈΡ, Π²Π΅ΡΠΎΡΡΠ½ΡΠ΅ ΠΏΠΎΠ±ΠΎΡΠ½ΡΠ΅ ΡΡΡΠ΅ΠΊΡΡ ΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Π΄ΡΡΠ³ΠΈΠΌΠΈ ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΡΠΌΠΈ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΌΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΏΡΠΈ Π³ΡΡΠ΄Π½ΠΎΠΌ Π²ΡΠΊΠ°ΡΠΌΠ»ΠΈΠ²Π°Π½ΠΈΠΈ, Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΡΡΠΈ. Π ΠΏΡΡΠ΅Π²ΠΎΠ΄ΠΈΡΠ΅Π»Ρ Π²Π²Π΅Π΄Π΅Π½ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΠΊΠ°Π·Π°ΡΠ΅Π»Ρ, Π² ΠΊΠ°ΠΊΠΎΠΌ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊΠΎΠ΅ Π»Π΅ΠΊΠ°ΡΡΡΠ²ΠΎ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΏΡΠΈ ΠΊΠ°ΠΊΠΈΡ -ΡΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΡ , ΡΠΈΠ½Π΄ΡΠΎΠΌΠ°Ρ , ΡΠΎΡΡΠΎΡΠ½ΠΈΡΡ . ΠΡΡΠ΅Π²ΠΎΠ΄ΠΈΡΠ΅Π»Ρ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΎ Π½Π° ΠΌΠ΅Π΄ ΡΠ°ΠΉΡΠ΅. ΠΠ΅ Π±ΠΎΠ»Π΅ΠΉΡΠ΅! ΠΠ°ΡΡΠΈΠ΅Π²Π°Ρ ΡΠΎΠ»Ρ ΠΈΠΎΠΏΠΎΠ΄ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ, ΠΠΈΡΠΎΠ²Π°Ρ ΡΠΌΡΠ»ΡΡΠΈΡ, Π‘ΡΠΊΡΠΈΠΌΠ΅Ρ, ΠΡΠΏΠΈΡΠΎΡΠΈΠ½, ΠΠ½ΡΠ»ΠΈΠ½, ΠΠ΅ΡΠΈΠ΄ΠΈΠ½ / ΠΏΡΠΎΠΌΠ΅ΡΠ°Π·ΠΈΠ½,
ReplyDeleteΠ Π°Π·Π΄Π²ΠΈΠΆΠ½ΡΠ΅ ΠΎΠΊΠ½Π° ΠΈ Π΄Π²Π΅ΡΠΈ, ΠΈΠ½Π°ΡΠ΅ Π½Π°Π·ΡΠ²Π°Π΅ΠΌΡΠ΅ PSK-ΠΏΠΎΡΡΠ°Π»ΠΎΠΌ, Π²ΡΠ±ΠΈΡΠ°ΡΡΡΡ Π² ΡΠ΅Π»ΡΡ ΡΠΎΡ ΡΠ°Π½Π΅Π½ΠΈΡ ΡΠ²ΠΎΠ±ΠΎΠ΄Π½ΠΎΠ³ΠΎ, Π½Π΅Π·Π°ΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠ°. ΠΠΎΡΠΌΠ°Π»ΡΠ½ΡΠ΅ ΡΠΈΠΏΡ ΠΎΠΊΠΎΠ½ ΠΈ Π΄Π²Π΅ΡΠ΅ΠΉ ΠΎΡΠΊΡΡΠ²Π°ΡΡΡΡ Π²Π½ΡΡΡΡ Π΄ΠΎΠΌΠ°, ΡΡΠΎ ΡΡΠ΅Π±ΡΠ΅Ρ Π΄ΠΎΠ²ΠΎΠ»ΡΠ½ΠΎ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΠΎΠ±Π»Π°ΡΡΡ ΠΌΠ΅ΡΡΠ° Π² ΠΊΠΎΠΌΠ½Π°ΡΠ΅. Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ½ΡΠ΅ ΠΎΠΊΠ½Π° ΠΈ Π΄Π²Π΅ΡΠΈ ΡΠ°Π·ΡΠ΅ΡΠ°ΡΡ ΠΈΠ·Π±Π΅Π³Π½ΡΡΡ Π΄Π°Π½Π½ΠΎΠΉ Π½Π΅ΠΏΡΠΈΡΡΠ½ΠΎΡΡΠΈ, ΡΠ²Π΅Π»ΠΈΡΠΈΡΡ ΡΠ²Π΅ΡΠΎΠ²ΠΎΠΉ ΠΈ Π²ΠΎΠ·Π΄ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠΎΠΊ Π² Π·Π΄Π°Π½ΠΈΠΈ.
ReplyDeleteΠΠ°ΠΊΠ»ΠΎΠ½Π½ΠΎ-ΡΠ΄Π²ΠΈΠΆΠ½ΠΎΠΉ ΠΏΠΎΡΡΠ°Π» – ΡΡΠΎ ΡΠΈΡΡΠ΅ΠΌΡ, ΡΡΠΎ ΠΌΠΎΠ³ΡΡ ΠΏΠΎΡ Π²Π°ΡΡΠ°ΡΡΡΡ ΡΠΈΡΠΎΠΊΠΎΠΉ ΠΏΠ»ΠΎΡΠ°Π΄ΡΡ ΡΡΠ΅ΠΊΠ»ΠΎΠΏΠ°ΠΊΠ΅ΡΠ°. Π Π°Π΄ΠΈ ΡΠ΄Π²ΠΈΠ³Π° ΡΡΠ²ΠΎΡΠΎΠΊ ΠΌΠΎΠ½ΡΠΈΡΡΡΡΡΡ Π½ΠΈΠΆΠ½ΠΈΠ΅ ΠΈ Π²Π΅ΡΡ Π½ΠΈΠ΅ Π½Π°ΠΏΡΠ°Π²Π»ΡΡΡΠΈΠ΅, Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΠΊΠΎΡΠΎΡΡΠΌ ΠΌΠ΅Ρ Π°Π½ΠΈΠ·ΠΌ Π»Π΅Π³ΠΊΠΎ ΠΈ Π±Π΅ΡΡΡΠΌΠ½ΠΎ ΡΠΊΠΎΠ»ΡΠ·ΠΈΡ.
Π Π°Π·Π΄Π²ΠΈΠ³Π°Π½ΠΈΠ΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Ρ ΠΎΠ΄ΠΎΠ²ΡΡ ΡΠΎΠ»ΠΈΠΊΠΎΠ², ΡΡΠΎ ΠΈΠΌΠ΅ΡΡ Π² ΡΠ²ΠΎΠ΅ΠΌ ΡΠΎΡΡΠ°Π²Π΅ ΠΎΠΏΠΎΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² Π°ΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΌ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎ ΡΠ°Π²Π½ΠΎΠΌΠ΅ΡΠ½ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ ΠΌΠ°ΡΡΡ ΠΏΠΎ ΠΏΠΎ Π²ΡΠ΅ΠΌΡ ΠΎΠΊΠ½Ρ. ΠΠ½ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΡΠ²ΠΎΡΠΊΠ΅ ΠΏΠ΅ΡΠ΅Π΄Π²ΠΈΠ³Π°ΡΡΡΡ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ ΡΠ΅ΠΆΠΈΠΌΠ°Ρ (ΡΠ΄Π²ΠΈΠ³Π°, Π½Π°ΠΊΠ»ΠΎΠ½Π°).
ΠΡΡΠΎΡΠ° ΡΡΠ²ΠΎΡΠΊΠΈ ΠΎΠΊΠ½Π° Π΄ΠΎΠ»ΠΆΠ½Π° Π±ΡΡΡ Π½Π΅ Π±ΠΎΠ»Π΅Π΅ 2360 ΠΌΠΌ, Π° ΡΠΈΡΠΈΠ½Π° ΠΎΠΊΠ½Π° Π±ΡΠ΄Π΅Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡΡΡ ΠΎΡ 67 ΡΠΌ Π΄ΠΎ 1600 ΠΌΠΌ. Π‘ ΡΡΠ΅ΡΠΎΠΌ ΡΡΠΌΠΌΠ°ΡΠ½ΠΎΠ³ΠΎ Π²Π΅ΡΠ° (ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΡ 100 ΠΊΠ³ Π΄ΠΎ 200 ΠΊΠ³), Π½Π° PSK-ΠΏΠΎΡΡΠ°Π» ΠΌΠΎΠ½ΡΠΈΡΡΠ΅ΡΡΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½Π°Ρ ΡΡΡΠ½ΠΈΡΡΡΠ° Π΄Π»Ρ Π³Π°ΡΠ°Π½ΡΠΈΠΈ ΠΈΡΠΏΡΠ°Π²Π½ΠΎΠ³ΠΎ ΠΎΡΠΊΡΡΠ²Π°Π½ΠΈΡ - Π·Π°ΠΊΡΡΠ²Π°Π½ΠΈΡ ΡΡΠ²ΠΎΡΠΎΠΊ, ΠΏΡΠΎΠ΄Π»Π΅Π½ΠΈΡ ΡΡΠΎΠΊΠ° ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ. Π¨ΠΈΡΠΈΠ½Π° ΡΠ²ΠΎΠ±ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΌΠ° Π²ΠΏΠΎΠ»Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ Π΄ΠΎΡΡΠΈΠ³Π°ΡΡ 2000 ΠΌΠΌ.
ΠΠ°ΠΊΠ»ΠΎΠ½Π½ΠΎ-ΡΠ΄Π²ΠΈΠΆΠ½ΡΠ΅ ΠΏΠΎΡΡΠ°Π»Ρ ΠΎΠ±Π»Π°Π΄Π°ΡΡ ΠΎΡΠ»ΠΈΡΠ½ΡΠΌΠΈ Π³Π΅ΡΠΌΠ΅ΡΠΈΡΠ½ΡΠΌΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°ΠΌΠΈ, Π²ΡΡΠΎΠΊΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ Π·Π²ΡΠΊΠΎ-, ΡΠ΅ΡΠΌΠΎΠΈΠ·ΠΎΠ»ΡΡΠΈΠΈ, ΠΏΡΠ΅Π΄Π»Π°Π³Π°ΡΡ Π΄ΠΎΠ»ΠΆΠ½ΡΠΉ ΡΡΠΎΠ²Π΅Π½Ρ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ²Π·Π»ΠΎΠΌΠ½ΠΎΡΡΠΈ, Π·Π°ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎ ΠΏΠΎΠ΄Ρ ΠΎΠ΄ΡΡ Π΄Π»Ρ Π²ΡΠ΅Ρ Π²ΠΈΠ΄ΠΎΠ² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΠΏΡΠΎΡΠΈΠ»Π΅ΠΉ. ΠΡΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΊΠ΅ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΠΎΠΏΠΎΡΡΡΠ²Π°ΡΡΠΈΠ΅ ΡΠ΅ΡΠΎΡΠ½ΡΠ΅ ΡΠΏΠ»ΠΎΡΠ½ΠΈΡΠ΅Π»ΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΡΡΠΈΠΌΠΎ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΈΡΡΡΡ ΡΠ°Π±ΠΎΡΡ ΡΡΠ²ΠΎΡΠΎΠΊ.
Π ΡΠ»ΡΡΠ°Π΅, ΠΊΠΎΠ³Π΄Π° Π²Π°ΠΌ Ρ ΠΎΡΠ΅ΡΡΡ ΠΎΡΠΎΡΠΌΠΈΡΡ Π³ΠΎΡΠΎΠ΄ΡΠΊΡΡ ΠΊΠ²Π°ΡΡΠΈΡΡ, Π±ΠΎΠ»ΡΡΠΎΠΉ Π·Π°Π³ΠΎΡΠΎΠ΄Π½ΡΠΉ Π΄ΠΎΠΌ, Π±Π°Π»ΠΊΠΎΠ½ ΠΈΠ»ΠΈ Π»ΠΎΠ΄ΠΆΠΈΡ ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΌΠΎΠ΄Π½ΡΠΌ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡΠΌ Π² ΡΡΠΈΠ»Π΅, ΡΠΎ ΡΠΈΡΠΌΠ°-ΠΈΠ·Π³ΠΎΡΠΎΠ²ΠΈΡΠ΅Π»Ρ Π‘Π ΠΠΊΠ½Π° ΡΠ΄Π΅Π»Π°Π΅Ρ ΠΈ ΡΠ΄Π΅Π»Π°Π΅Ρ ΠΌΠΎΠ½ΡΠ°ΠΆ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΠΎ-ΡΠ°Π·Π΄Π²ΠΈΠΆΠ½ΡΡ ΠΎΠΊΠΎΠ½ ΠΈ Π΄Π²Π΅ΡΠ΅ΠΉ.
Π₯ΠΎΡΠΈΡΠ΅ Π²ΡΡΡΠ½ΠΈΡΡ, Π² ΠΊΠ°ΠΊΠΎΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ-ΠΈΠ·Π³ΠΎΡΠΎΠ²ΠΈΡΠ΅Π»Π΅ ΡΠ°Π·ΠΌΠ΅ΡΡΠΈΡΡ Π·Π°ΠΊΠ°Π· Π½Π° ΠΎΠΊΠ½Π° ΠΈΠ· Π΄Π΅ΡΠ΅Π²Π°? ΠΠΎΠΆΠ΅ΠΌ ΠΏΠΎΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°ΡΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠ΅ Π΄Π΅ΡΠ΅Π²ΠΎΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π‘Π ΠΠΊΠ½Π°, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ ΠΎΠΊΠ½Π° Ρ 1995 Π³ΠΎΠ΄Π°. ΠΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΠ΄Π°Π΅Ρ ΡΠΈΡΠΎΠΊΠΈΠΉ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ Π΄Π΅ΡΠ΅Π²ΡΠ½Π½ΡΡ ΠΎΠΊΠΎΠ½Π½ΡΡ Π±Π»ΠΎΠΊΠΎΠ² ΠΎΡ ΡΠΊΠΎΠ½ΠΎΠΌ Π΄ΠΎ ΡΠΊΡΠΊΠ»ΡΠ·ΠΈΠ²Π½ΡΡ . ΠΠ°ΠΆΠ΅ ΡΠ°ΠΌΡΠΉ ΠΏΡΠΈΡ ΠΎΡΠ»ΠΈΠ²ΡΠΉ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»Ρ Π²ΡΠ±Π΅ΡΠ΅Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΠΊΠ½Π° ΠΈΠ· Π΄Π΅ΡΠ΅Π²Π°. Π Π½ΠΎΠΌΠ΅Π½ΠΊΠ»Π°ΡΡΡΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΡΡ ΠΎΠΊΠΎΠ½ Π½Π°Π»ΠΈΡΠ΅ΡΡΠ²ΡΡΡ Π±ΡΠ΄ΠΆΠ΅ΡΠ½ΡΠ΅ Π΄Π΅ΡΠ΅Π²ΡΠ½Π½ΡΠ΅ ΠΎΠΊΠ½Π° ΠΈΠ· ΡΠΎΡΠ½ΠΎΠ²ΠΎΠ³ΠΎ Π±ΡΡΡΠ°, Π΅Π²ΡΠΎΠΎΠΊΠ½Π° ΠΈΠ· Π΄ΡΠ΅Π²Π΅ΡΠΈΠ½Ρ Π΄ΡΠ±Π°, Π΄Π΅ΡΠ΅Π²ΠΎ-Π°Π»ΡΠΌΠΈΠ½ΠΈΠ΅Π²ΡΠ΅ ΠΎΠΊΠ½Π°, Π΄Π²Π΅ΡΠΈ-ΠΏΠΎΡΡΠ°Π»Ρ, ΡΠΊΠ»Π°Π΄Π½ΡΠ΅ ΠΎΠΊΠ½Π°-Π³Π°ΡΠΌΠΎΡΠΊΠΈ.
ReplyDeleteThe low code movement is the convergence of low code, no code and DIY app development platforms that enable business users, subject matter experts, and other non-technical users to build, run, and manage their own apps. Low code platforms, likeMendix low code offer the same agility, speed, and scalability as do popular low code development platforms, but in a more accessible, easier to use format that businesses of almost any size can benefit from.
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