LONDON–(BUSINESS WIRE)–A global data analytics and advisory firm, Quantzig, that delivers
actionable analytics solutions to resolve complex business problems has
announced the completion of their latest big
data analytics engagement for a leading Belgian telecommunication
services provider.
During the course of this engagement,
the big data analytics experts at Quantzig helped the telecom giant
re-engineer their fraud detection process by developing custom user
interfaces that focused on reducing fraud.

Owing to the huge volumes of data generated from a number of sources
including- social platforms, connected consumer devices, and call data
records it has been estimated that the data generated by telcos will
grow by 5x in the next few years. Considering the surge in data volumes
that are generated, telcos should expect to encounter a plethora of
challenges associated with data management. However, to transform these
challenges into new market opportunities, telecom industry players will
have to leverage big data analytics and develop strategies to harness
and integrate new sources of data in real-time.

The Business Problem: The
client is a European telecommunication industry player, catering to the
needs of clients across all business sectors through their customized
internet and network-based ICT service offerings. Their failed efforts
to defraud customer groups posed several data management roadblocks.
This is when they approached Quantzig to leverage its big data analytics
expertise to validate data and prevent fraud.

Our big data analytics expertise will help you understand the
trends and big data technologies behind the data-driven
telecommunications revolution.
Get
in touch with our experts
to know more about our
customized big data analytics solutions.

“The unexpected surge in data volumes, though a game changer, will
soon become a major roadblock for telecom industry players, only if not
utilized to its maximum potential,”
says a big data analytics
expert from Quantzig.

The Solution Offered: Leveraging
big data analytics to identify and minimize the impact of fraud was a
major focus area in this case study. To help the client tackle their big
data challenges, we adopted a comprehensive three-step approach, the
initial phase of which revolved around data modeling to gain real-time
insights on potential risk factors. Quantzig’s big data analytics
solutions not only helped them manage risks but also enhanced the
overall value of their business in regards to revenues, service
optimization, and customer satisfaction.

With Quantzig’s customized big data analytics solutions, you can
go beyond understanding what happened and why to unravel insights about
the future of telecom.
Request
a free proposal
to know how the effective usage of big
data analytics can turn you into a market leader.

Quantzig’s big data analytics solutions helped
the client to:

  • Enhance the security and privacy of their subscribers
  • Develop a big data strategy to effectively manage risks from
    cyber-attacks
  • Request
    a free demo
    to know how you
    can apply such big data methodologies to address similar business
    requirements.

Quantzig’s big data analytics solutions offered
predictive insights on:

  • Analyzing and minimizing the impact of fraud
  • Creating custom user interfaces that focused on reducing fraud
  • Wondering how big data analytics solutions can help you discover
    various opportunities for fraud detection, churn detection, and risk
    analysis?
    Request
    for more information now!

About Quantzig

Quantzig
is a global analytics and advisory firm with offices in the US, UK,
Canada, China, and India. For more than 15 years, we have assisted our
clients across the globe with end-to-end data modeling capabilities to
leverage analytics for prudent decision making. Today, our firm consists
of 120+ clients, including 45 Fortune 500 companies. For more
information on our engagement policies and pricing plans, visit:
https://www.quantzig.com/request-for-proposal

(Excerpt) Read more Here | 2019-02-01 16:20:00
Image credit: source

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