For large companies saddled with complex legacy environments that were developed over many decades, finding the opportunity for disruption can
be a daunting challenge.
be a daunting challenge.
Unlike new economy firms, which have had the benefit of being able to
build their businesses and infrastructure from scratch in a “green-field” environment, most large corporations are saddled with disparate and
fragmented operational and analytical environments and processes,
characterized by business and data silos that limit the ability to operate
with agility, flexibility, and insight across customers and lines of business.
build their businesses and infrastructure from scratch in a “green-field” environment, most large corporations are saddled with disparate and
fragmented operational and analytical environments and processes,
characterized by business and data silos that limit the ability to operate
with agility, flexibility, and insight across customers and lines of business.
While institutional reputation, customer reach, and operational scale
provide many advantages to large corporations, these larger firms cansometimes be challenged when it comes to innovation and
responsiveness. Yet it’s still these large corporations that continue to be
the bulwark of the economy, accounting for the bulk of business and
consumer transactional activity. Now, Big Data approaches that were
developed by the new economy firms, which enabled the flexibility and
rapid growth of these firms, are being adopted by mainstream
corporations to overcome legacy challenges and introduce greater
corporate agility and speed.
provide many advantages to large corporations, these larger firms cansometimes be challenged when it comes to innovation and
responsiveness. Yet it’s still these large corporations that continue to be
the bulwark of the economy, accounting for the bulk of business and
consumer transactional activity. Now, Big Data approaches that were
developed by the new economy firms, which enabled the flexibility and
rapid growth of these firms, are being adopted by mainstream
corporations to overcome legacy challenges and introduce greater
corporate agility and speed.
A 2014 survey conducted by NewVantage Partners of 125 senior
corporate executives, representing 59 Fortune 1000 companies, showed
that more than two-thirds of executives reported that their organization
had a Big Data initiative in production. These same executives reported
that investments in Big Data are projected to grow dramatically in the
coming years — with 75% of executives reporting investments greater
than $10 million, and 28% reporting investments exceeding $50 million
by 2017.
corporate executives, representing 59 Fortune 1000 companies, showed
that more than two-thirds of executives reported that their organization
had a Big Data initiative in production. These same executives reported
that investments in Big Data are projected to grow dramatically in the
coming years — with 75% of executives reporting investments greater
than $10 million, and 28% reporting investments exceeding $50 million
by 2017.
For these corporate giants, operational process optimization can have
a huge business impact.
a huge business impact.
One such firm that’s benefiting from new Big Data approaches is
the financial services giant American Express (AMEX), which is
harnessing the power of its data to innovate and to streamline complex
operational processes. AMEX has opened its own new technology hub
in Palo Alto, led by former Google and PayPal executive Nik Sathe.
The new tech center “will focus on innovations in Big Data, cloud
computing, and mobile infrastructure,” according to AMEX CIO Marc
Gordon.
the financial services giant American Express (AMEX), which is
harnessing the power of its data to innovate and to streamline complex
operational processes. AMEX has opened its own new technology hub
in Palo Alto, led by former Google and PayPal executive Nik Sathe.
The new tech center “will focus on innovations in Big Data, cloud
computing, and mobile infrastructure,” according to AMEX CIO Marc
Gordon.
AMEX has taken steps to optimize and streamline its operational and
data processes end-to-end by migrating many traditional processes
from legacy mainframe environments to Big Data processing
environments, resulting in dramatic improvements in speed and
performance, significant reductions in cost, and notable increases in
responsiveness to customer needs.
data processes end-to-end by migrating many traditional processes
from legacy mainframe environments to Big Data processing
environments, resulting in dramatic improvements in speed and
performance, significant reductions in cost, and notable increases in
responsiveness to customer needs.
Ash Gupta, chief risk officer and president for Risk and Information
Management for AMEX, comments, “Fact-based analytics have always
been in our company’s DNA, yet I have never seen such an analytical
leap as the one we have made with Big Data.” He cites several areas
where AMEX is focusing its Big Data efforts, notably in addressing
service excellence, generating billings and receivables growth, and risk management. A few examples:
- Using Big Data platforms, AMEX has been able to dramatically reduce Management for AMEX, comments, “Fact-based analytics have always
been in our company’s DNA, yet I have never seen such an analytical
leap as the one we have made with Big Data.” He cites several areas
where AMEX is focusing its Big Data efforts, notably in addressing
service excellence, generating billings and receivables growth, and risk management. A few examples:
the time it takes to process the thousands of variables and billions of calculations needed to match customer card offers to card member
interests. It previously took years of processing time just to sift through
these massive volumes of data, but now it can be done in hours.
- AMEX has made similar strides in connecting merchants to card
members on personalized offers, taking a 3-day process and reducing
it to 20 minutes on their Big Data platform.
members on personalized offers, taking a 3-day process and reducing
it to 20 minutes on their Big Data platform.
- American Express uses Big Data analytics to detect and prevent fraud
in milliseconds and with greater precision, using machine-learning
models that leverage billions of historical transactions across millions
of card members, resulting in instant fraud alerts for customer protection.
in milliseconds and with greater precision, using machine-learning
models that leverage billions of historical transactions across millions
of card members, resulting in instant fraud alerts for customer protection.
Leading companies can learn from the example of AMEX and other
mainstream corporations that are adopting Big Data solutions and
approaches with success. Here are a few suggestions:
mainstream corporations that are adopting Big Data solutions and
approaches with success. Here are a few suggestions:
- Assess opportunities to migrate operational business processes from
mainframe environments to faster and cheaper Big Data approaches.
mainframe environments to faster and cheaper Big Data approaches.
- Create a Big Data Center of Excellence or Analytical Sandbox as a
testing ground to identify suitable applications for Big Data.
testing ground to identify suitable applications for Big Data.
- Adopt a “data first” approach that focuses on rapid identification of
data insights, rather than investing in large data warehouse initiatives.
data insights, rather than investing in large data warehouse initiatives.
- Develop a “culture of data” staffed by a next-generation of analytics
professional, whom are conversant in new Big Data approaches and
analytics techniques.
professional, whom are conversant in new Big Data approaches and
analytics techniques.
Mainstream companies will drive the future of Big Data investment. But
they must demonstrate a willingness and flexibility to tackle their
complex legacy environments and data infrastructure. For those firms
that make the commitment, as evidenced by the example of American
Express, the payback is likely to be high. Their ability to compete for
customer success in the coming years may depend on it.
they must demonstrate a willingness and flexibility to tackle their
complex legacy environments and data infrastructure. For those firms
that make the commitment, as evidenced by the example of American
Express, the payback is likely to be high. Their ability to compete for
customer success in the coming years may depend on it.
0 comments:
Post a Comment