Real-Time Analytics At Work Inside Telcos - InformationWeek

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Real-Time Analytics At Work Inside Telcos
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SeanBowen PUSH Technology
SeanBowen PUSH Technology,
User Rank: Author
3/10/2014 | 5:25:30 AM
Re: Real-time Analytics at Work Inside Telco
This example is of course extreme, but it highlights one very real problem for mobile providers: With mobile data consumption growing at the rate it is, networks are more heavily congested than ever before. Operators are finding it increasingly difficult to provide the optimal quality of service to their users.

But, this is not the only issue that mobile providers are facing right now. Not only do they have to deal with the explosion in data consumption and growing bandwidth demand, but they also need to work out how to recover revenue from over-the-top (OTT) services, and how to make money from the cloud. They must consider how to build new business models that lessen the strain on the network by reducing the amount of data sent across it. They also need to consider how to climb the value stack by offering new cloud services.

What's needed is an intelligent solution that optimizes how data is sent over the network. Some telco providers have already turned to our real-time data distribution platform to gain back network control. Some of the world's leading telcos are choosing Diffusion to:

- Radically improve performance of real-time conversational applications and services

- Build smarter data services that reduce the amount of data sent across the network

- Significantly save bandwidth

- Reduction in hardware footprint and associated infrastructure

- Increase customer retention

- Develop new high performance cloud services

One example is our cooperation with Telefonica Digital. Combining Diffusion and Telefonica's infrastructure-as-a-service, offers the first high performance, scalable data distribution solution from the cloud to any device. This agreement creates an entirely new method of building scalable mobile and web services, and means customers can deliver real-time applications at low cost.
User Rank: Ninja
2/28/2014 | 8:44:13 PM
Digital Data
Organizations which create and store more transactional data in digital form will be able to collect more accurate and detailed performance information.
User Rank: Ninja
2/28/2014 | 8:39:07 PM
Re: Realtime vs. Analyze Later
@brain I agree with your comment. Real time data analysis will definitely help you to take effective decisions.
User Rank: Ninja
2/25/2014 | 1:35:28 PM
End Game
I'm wondering what came from realizing what the cab companies were doing? Did they, or could they, shut that traffic down? This is the Netflix dilemma in a nutshell. Does this carrier now go out and triple it's capacity, with all it's customers paying higher monthly charges, because this cab company wanted to create a TV service out of something that was never intended to be that?

Where does this all end?
D. Henschen
D. Henschen,
User Rank: Author
2/25/2014 | 1:19:11 PM
Event processing technology?
I'm not really clear on the technology in play here. Event processing and analysis of streaming data (while it's still streaming) has been around for a long time. How does Guavus compare with the event-processing technology offered by the likes of IBM, Oracle, SAP and others that has been used from everything from financial trading floors to network security to national intelligence (including the likes of the NSA)?

The in-memory database camp is also addressing real-time needs, with legacy products like Oracle TimesTen and IBM solidDB used by telcos and financial services to quickly store and analyze data without having to wait for overnight batch data loading. SAP Hana is not horning in on this space for more mainstream data warhousing and enterprise app needs. An there's a raft of NoSQL and NewSQL database players like Aerospike, VoltDB, MemSQL, Pivotal GemFire and others exploiting RAM to analyze data and trigger decisions within milliseconds.

Maybe Guavus can tell us more about where it fits in this mix?

User Rank: Strategist
2/25/2014 | 10:08:01 AM
Realtime vs. Analyze Later
Great article.  It appears to me that this need to analyze data in realtime falls under more of an operational business intelligence model (if there is such a term).  The need to store now and analyze later (i.e., looking at monthly trends at the end of a fiscal year) offers a higher-level view of data for making more long term decisions (not sure what the term for that is).  The former allows you to analyze a few trees quickly and the latter allows for a larger view of the forest.  Both are valuable but they each offer different kinds of value.  Agree?  Disagree? 

Anyway, this is a great, great article, thanks so much for sharing.

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