Features and Analysis

Introducing the Analytics Big Data Repository

At TM Forum Live! last year, Catalyst participants showed how existing big data analytics technology can be applied in real-life use cases. A follow-up project, which was demonstrated at Digital Disruption in December, laid the groundwork for creation of a common data repository to eliminate the need for multiple copies of data within service providers’ organizations. During that project, service providers and suppliers vetted 15 use cases ranging from churn prediction and marketing analytics to fraud detection and revenue assurance.

Now during a third phase of the project, which will be demonstrated at TM Forum Live! 2015 in Nice in June, participants are developing an innovative Analytics Big Data Repository (ABDR) to help digital and communications service providers improve customer experience and achieve business growth by avoiding data replication, saving on extract, transform and load (ETL) and hardware costs, and enabling faster implementation of new big data analytics use cases.

TM Forum Editor Dawn Bushaus recently caught up with Amir Gefen, Director of Industry Relations, cVidya and Marketing Leader for the Catalyst, to find out more about the project.

DB: Who are the sponsors and participants in this Catalyst and what are their roles?

AG: This big data Catalyst project brings together an impressive team of leading sponsors including China Mobile, Mediacom and Orange. This is in addition to a strong team of participating suppliers that includes Comverse, cVidya, Etiya, ParStream and Spirent. [More service providers and suppliers are in the process of joining the Catalyst team, so for an updated list please visit the TM Forum Live! Catalyst page.] The role of the sponsors is to define the industry challenges and then review and validate the proposed solutions of the suppliers as being relevant and viable for properly addressing the challenges.

DB: What will this Catalyst be demonstrating in Nice?

AG: The Catalyst team is developing and will demonstrate a highly innovative new concept called Analytics Big Data Repository (ABDR), and Nice 2015 will be the third phase of the project. ABDR provides a unified layer that can support multiple use cases and multiple analytics systems while successfully addressing the challenges that prompted this Catalyst, such as avoiding data replication, reducing ETL costs and time, saving in hardware costs (storage and processing power), and shortening the time to implement new use cases.

DB: What are your expectations about the outcome?

AG: The outcome of phase 3 of this Catalyst is expected to include further expansions and implementation best practices for ABDR that will be documented as part of TM Forum’s Big Data Analytics Guide Book (GB 979). The team will produce a new ABDR annex for the guidebook that includes a data dictionary of the data entities used for the demonstrated use cases. In addition, further contributions will be made back to the big data analytics team through a dedicated ABDR sub-team.

DB: Is this Catalyst likely to be continued in the future?

AG: Yes, being a new and innovative concept of ABDR, we will look to continue and expand it, helping service providers to implement it, track the results and further improve ABDR based on filed implementation inputs. In addition, we would like to see other TM Forum teams adopting and using ABDR such as the teams focused on customer experience and small and medium business.


About The Author

Managing Editor

Dawn Bushaus began her career in technology journalism in 1989 at Telephony magazine, which means she’s been writing about networking for a quarter century. (She wishes she didn’t have to admit that because it probably gives you a good idea of how old she really is.) In 1996, Dawn joined a team of journalists to start a McGraw-Hill publication called tele.com, and in 2000, she helped a team at Ziff-Davis launch The Net Economy, where she held senior writing and editing positions. Prior to joining TM Forum, she worked as a freelance analyst for Heavy Reading.

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