SOURCE: Guavus Inc.

Guavus Inc.

December 08, 2015 08:00 ET

Customer Care Pressures Creating Appetite for Big Data Analytics Amongst CSPs

Heavy Reading Survey Reveals Data Integration and Skills Shortage Are Biggest Barriers to Adoption

SAN MATEO, CA--(Marketwired - December 08, 2015) - Guavus, Inc., a leading provider of big data applications for operational intelligence, today released the findings of a global survey* of network operators exploring their big data analytics strategies. The research shows that big data has moved from hype to reality, with 87% of respondents either having completed or currently implementing a big data analytics strategy. The primary drivers behind this increase in adoption include: revenue maximisation (66%), customer experience and loyalty (61%) and opex reduction (61%). Within these areas, the main uses cases that operators are looking to address in the next two years are:

  • Proactive customer care (57%)
  • Revenue assurance (48%)
  • Targeted offerings (47%)
  • Service assurance (44%)

"In a world of mounting competition, providing a seamless customer experience holds the key to safeguarding operator revenue streams," said Anukool Lakhina, founder and CEO, Guavus. "It's no surprise that proactive customer care is the top area for investment in big data. This is where we see the greatest interest from service providers that we work with, as they seek to gain an end-to-end view of subscribers' experience so they can quickly intervene to remedy any service degradations, prevent churn and raise customer satisfaction for increased loyalty and ultimately to grow revenue."

Implementation and Challenges

The research further revealed that operators are focusing their initial efforts on combining disparate siloes of data across their organisation. The biggest efforts are going on combining online consumer data, such as web browsing habits, with offline consumer data, such as billing history (31%). The secondary area of focus is on combining customer data with network operations data (27%) to enable a correlation of factors, such as the relationship between network faults and subscriber behaviour for contextual insights. Shedding further light on these sentiments, the research identified that the key barriers operators are struggling to overcome as they implement their big data analytics strategies are:

  • The inability to integrate disparate systems of data (28%)
  • Poor data quality and management (25%)
  • Finding people with the right skill set to handle big data projects (22%)

Despite the hype around data lakes as an effective way of integrating data, operators exhibit a clear lack of confidence in this method; only 22% of respondents see data lakes as crucial to analysing disparate sources of data. Added to this, 68% of operators remain unsure or are waiting to see if data lakes will emerge as more than just hype.

"As big data strategies mature, the focus is shifting from simply trying to collect and analyse large data sets, to being able to derive actionable, operational intelligence," said Lakhina. "The findings show that operators have realised that the ability to fuse data streams and bridge the gap between business and operational data is essential to achieving this goal. However, it's also vital to strip out only the most valuable nuggets of data for analysis, as trying to store everything will increase costs, delay time to insight and devalue the quality of the analytics provided. The limited confidence in data lakes shows that operators have realised a more effective approach is needed to derive value from big data. Streaming real-time analytics, with use-case specific applications that have data science built-in will put operators in a much better position. These analytics applications have data science embedded in them, so can also simultaneously address the shortage of data scientists, by removing the need for them."

*The survey of 81 qualified respondents working across a range of functions for 65 network operators was conducted by Heavy Reading as part of a multi-client study.

About Guavus

Guavus solves the world's most complex data problems. Proven across Fortune 500 enterprises, Guavus provides a new generation of analytically powered big data applications to address specific business problems for next-generation service assurance, next-generation customer experience management and the Internet of Things. The Company uniquely breaks down the barriers between Operational Support Systems and Business Support Systems to enable customers to more efficiently plan network capacity, improve service operations and deliver a better customer experience. Guavus' operational intelligence applications correlate and analyze massive amounts of streaming and stored business, operational and sensor data from multiple, disparate source systems in real time. Guavus products currently process more than two Trillion transactions per day.

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