10gen telco white paper

7/29/2019 10gen Telco White Paper

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February 2013

A 10gen White Pape

Aiity i te Ae o As:How the Next Generation of Databases Can Create

New Opportunities for Telecoms


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Table of Contents

EXECUTIVE SUMMARY 1

What Is MongoDB? 1

Telecoms Adapt to Slow Growth 3

CUSTOMER CASE STUDIES 3

Outside the Box: Capitalizing on Online Video 3

Shared Experiences: Consumer Cloud Storage as a Way to Reduce Churn 4

Featured Case Study: H ow 02 Turned Cost into an Opportunity 4

One-Stop Shop: A Universal Product Catalog Across Multiple Channels 5

Small Sensors, Big Data: Building a Machine-to-Machine Platform 5

All in One Place: A True Subscriber Identity Management System 6

Know Your Customer Like Yourself: Customer Sentiment Analysis 6

MOngODB: SpEED, SIzE, STABIlITY 7

RESOURCES 8


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With consumers and businesses spending ever more

time connected to the Internet, telecoms can expect

growing demand for their services. But demand

doesnt always translate to prot, as competition,

commoditization, operational complexity, and network

investment costs threaten to turn telecommunications

providers into low-margin dumb pipes.

More than perhaps any other industry, telecommuni-

cations is disrupted every few years by market-shifting

innovations. To compete, telecoms need to develop

new services, and to do so rapidly, before their

competitive advantage is neutralized. Telecoms need

tools that allow them to adapt to changing needs

quickly and affordably, with the reliability they expect

from their long-lived legacy systems.

MongoDB, the leading NoSQL database, allows

telecommunication companies to develop new

applications quickly and adapt them as needs change.

It is highly scalable, perfect for an age in which

companies are capturing exponentially increasing

volumes of data. And in an environment buffeted by

many economic challenges, MongoDBs total cost of

ownership can be orders of magnitude less than that

of traditional databases like Oracle.1

WhAT IS MOngODB?

In traditional relational databases like Oracle and

MySQL, data are stored in linked tables organized

into rows and columns. Each row is associated with

a unique entity, often a customer account, and each

column is associated with a eld dening an attribute

of the account. Separate tables store different types

of information about an account (e.g., mailing address,

billing history), with common identiers linking tables

together. Schemas, or maps, that diagram these

links and dene allowable elds are locked in during

the database development period, and managed by

database administrators (DBAs), who must approve

any changes. This approval process creates a

bottleneck for developers trying to create and

modify applications quickly.

MongoDB uses a more exible data model. A

document database, MongoDB allows for varied data

types and rapid addition of new elds. While entities in

relational databases must pull information from elds

in multiple tables, a single MongoDB document can

store an entitys entire information. These documents

can contain as many or as few elds as necessary.

Creating new elds doesnt necessarily require admin-

1 See the 10Gen White Paper, A Total Cost of Ownership Comparison of MongoDB & Oracle.

http://www.10gen.com/sites/default/les/downloads/10gen.TCO%20-%20MongoDB%20vs.%20Oracle.pdf

Executive Summary


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istrative approval. Developers can create new elds

as they create new documents , write a script to

add the eld to all documents or batch-populate

documents with a new eld when sending a request

to the database. Since new elds can be added ad

hoc, MongoDB works well with unstructured, semi-

structured and polymorphic dataunlike relational

databases, which cant easily store different types ofdata and require data to be structured before they

can be mapped. With MongoDB, notes from customer

service calls can be stored rst, and organized later.

Because the documents in MongoDB are similar to

the objects used in most modern applications and

programming languages, developers nd MongoDB

easy to work with. Documents are described in the

Javascript Object Notation (JSON) format , familiar to

users of Javascript. Developers dont have to spend

much time learning MongoDBs syntax, or mapping

their application structure to the database structure.This contrasts sharply with relational databases. For

relational databases, developers and database admin-

istrators must ensure that their database schemas are

aligned across three layers: the application, the appli-

cation-database interface (object-relational map, or

ORM) and the database itself. The need to create and

maintain three separate, but consistent, data maps

creates administrative drag. With MongoDB, there is no

need for separate data maps or approval processes for

creating new elds. Developers can create new appli-

cations rapidly, and revise and enhance them quickly.

The volume of data created by popular social, mobile

and video applications is immense. As relational

database deployments reach their processing and

storage limits, companies are faced with the expensive

and complex task of upgrading their purpose-built

servers and storage area networks (SANs). Upgrading

servers often means several hours or even days of

downtime. MongoDB, however, allows companies to

expand their processing power and storage capacity

easily across multiple off-the-shelf machines, on-site

or in the cloud, with no downtime.

With MongoDB, large databases can be partitioned

into groups of documents, known as shards. These

shards are distributed across different machines, with

both processing and storage occurring separately from

the original server. Machines can be simple commodity

servers found in cloud services like Amazons EC2.

No expensive purpose-built hardware needs to be

upgraded or replaced. Companies can therefore

quickly and cost-effectively scale their applications

in response to demand, and easily re-deploy system

resources as needs change. Sharding brings perfor-

mance benets as well, as companies can place groupsof documents on machines closer to the geographic

source of those documents so, for example, customers

in a particular region can access their user information

more quickly.

MongoDB provides many of the features that will be

familiar to users accustomed to relational databases.

MongoDB uses a rich query language for searching

the database and supports indexing of documents on

secondary elds. MongoDB provides an interface for

interacting directly with the database, and drivers for

most popular programming languages, including Java,C++, C#, PHP and Python. To provide the high level of

uptime that users expect from relational databases,

MongoDB supports the automated replication of

database shards on up to 14 back-up servers and

automated failover to back-ups when primary

servers fail.

With users as varied as Viber, Disney and Cisco,

MongoDB has proven its versatility as a general-

purpose database. Unlike some other NoSQL

databases, MongoDB allows analytics tools to record

and analyze changes to the database in real-time.There is none of the lag-time associated with batch

processing tools like Hadoop. MongoDB can be used

with a diverse set of applications, from content

management to data mining, allowing developers to

focus their time on application development, rather

than database maintenance and schema updates.

With its exible, simple and developer-friendly data

model, MongoDB empowers organizations to be agile,

to act like startups. They can get new applications to

market quickly, and revise, upgrade and expand them

as needs evolve. In many companies, administratorsof relational databases limit the number of changes

that can be made to the database structure to one or

two times per year. With MongoDB, theres no need for

such limits.


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TElECOMS ADApT TO SlOW gROWTh

Telecoms tackled the problem of big data before

many other industries. Telecoms implemented the

original operating support systems in the early 1970s

as a way to automate and speed up the massive

number of tasks they needed to do: taking orders,

assigning lines, conguring network components,collecting payments and so on. They were some of the

early adopters of relational databases, with Bell Labs

purchasing an Oracle machine as early as 1979, only a

year after Oracles commercial launch. Billing support

systems and operating support systems from the 70s

allowed for mass automation, but rules had to be

hard-coded and data relationships were xed. Getting

different legacy systems to speak to each other remains

an ongoing problem for many telecoms.

Today, telecoms face different challenges. In mature

markets, telecoms confront competition not onlyfrom companies offering similar technologies (e.g.,

two wireless operators) but from companies offering

the same applications over different technologies

(e.g., a landline and wireless operator), wholesale

operators with different cost structures and nimble

startups offering competitive applications over the

Internet. Opportunities for new subscriber growth are

limited, with mobile penetration in most rich countries

exceeding 100%, xed-line subscriptions falling and

pay-TV subscriptions at in many countries.

Telecoms are therefore turning to their existingsubscriber bases for revenue growth. They are

considering new revenue streamslike targeted adver-

tisingand additional value-added services, like

over-the-top video and consumer cloud storage, to

increase their revenue per user. Even if these appli-

cations cannot easily be monetized, they can help

strengthen brand loyalty, reducing customer churn and

therefore increasing the lifetime value of subscribers.

At the same time, increasing demands on telecoms

networks are creating a need for increased capital

investment. To maintain margins, telecommuni-cation service providers are looking for ways to reduce

their costs across all parts of the business, including

network operations, customer service and marketing.

Reducing customer acquisition costs is a particular

focus of many rich world telecoms.

Customer Case Studies

OUTSIDE ThE BOX: CApITAlIzIng On

OnlInE VIDEO

As consumers have watched increasing amounts of

video online, pay-TV providers have had to adapt.

Many providers have pursued TV Everywhere strat-

egies that enable their customers to watch content

on devices other than their TVs. A few have pursued

standalone Internet-based video services to compete

directly with Netix, NOW TV, LOVEFiLM and other

streaming video providers.

A major pay-TV provider recently launched an online

video site that allows users to subscribe on a monthly

basis or order movies a la carte. Users can choose

from a catalogue of more than 1,500 lms, and pause,

rewind or fast-forward programs easily. Users can mark

lms for future viewing, and automatically receive

recommendations for other lms they might like.

The provider chose MongoDB to power its system

because of its exibility and scalability. The company

wanted a system that could support 70,000 concurrent

users during peak hours, with users making constant

calls to the database to search, browse, rewind, pause

and fast-forward lms. The database also stores data

on where exactly in a lm viewers pause watching so

they can return to the content later.

The ease of adding new elds to documents in

MongoDB permits developers to rapidly add new

meta-tags to characterize lms in a variety of ways,

alongside the traditional tags like actor, director and

genre. Over time, the recommendation engine becomes

smarter, as it leverages a growing base of content,

meta-tags and information about user behavior.

In the future, MongoDBs support for mixed hierar-

chies will allow the provider to add new content

types like TV show collections and even live events.

MongoDBs ability to support documents nested insideother documents means that developers wont have

to categorize individual episodes of TV shows at the

same level as standalone lms. Users will be able

to access a particular content typefor example, a

football teams seasonand nd programs organized

in an intuitive way.


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ShARED EXpERIEnCES: COnSUMER ClOUD

STOR AgE AS A WAY TO REDUCE ChURn

For years, a major European mobile operator was

ahead of the curve in offering its subscribers the

ability to store photos, music and video in the cloud.

But recently, the operator found that the MySQL

database it had built 13 years ago was reaching thelimits of scalability, and did not allow for the kind of

exible access controls that users are accustomed to

on social networking sites.

The operator chose MongoDB to enable more exible

sharing. Subscribers can now share videos, photos,

photo albums and mixed media albums with particular

users or categories of users. The content itself is

stored in a separate le system, while MongoDB is

used to store metadata about the content, such as

viewing permissions, location data and timestamps.

Because adding new elds to the previous relational

database system was such a time-consuming process,

much of this type of data was previously stored in text

form or discarded. MongoDB, however, can automat-

ically turn this data into new elds, so users can see

where and when their photos or videos were taken.

By tying document storage to mobile subscrip-

tions, the operator increases the stickiness of it s

paid service and defends against churn in a market

threatened by increasing competition. Improving the

available features allows the operator to keep pace

with standalone consumer cloud storage sites like

Dropbox, social networking sites and online photo

album services.

How O2 Turned a Cost into an Opportunity

O2 uses customer movement data to offer location-specic local offers.

By necessity, wireless operators need to track the locations of their customers. Rational network investment hinges

on knowing which cell sites require more capacity, or where more cell sites are needed. But where other operators

saw a cost, O2, the United Kingdoms leading wireless operator, saw an opportunity. What if you could get businesses

to pay to offer your subscribers location-specic special offers?

O2s Priority Moments provides businesses a way to reach potential customers when theyre in the vicinity of one of

their locations. O2 subscribers install a free mobile application and receive notications about discounts and other

special offers in their area. Deals are delivered by location, so its quick and easy to nd the offers and experiences

they want, said O2s Andrew Pattinson.

Traditional relational databases are ill-equipped to handle the complex volumes of data generated by millions of

subscriber movements. Nor are they par ticularly adaptable if the applications functionality needs to change.

Selecting MongoDB as our database platform was a no-brainer, said Pattinson, as the technology offered us theexibility and scalability that we knew wed need.

With more than 20 million subscribers, O2 required a database that could scale as usage grew. Deployed on Amazon

Web Services cloud, the Priority Moments database can easily expand due to MongoDBs support for database parti-

tioning. MongoDBs native geospatial support made MongoDB a natural t , while MongoDBs exible data model will

allow O2s developers to tweak the application as subscribers and advertisers needs evolve.

O2 was so satised with its experience with MongoDB that O2 and its parent company, Spain-based Telefonica, have

started using MongoDB for other next-generation applications. Said Pattinson, Were very excited about MongoDB

and look forward to more project s in the near future.

Deals are delivered by location, so its quick and easy to nd the

offers and experiences they want. -Andrew Pattinson, O2

Featured Case Study


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OnE-STOp ShOp: A UnIVERSAl pRODUCT

CATAlOg ACROSS MUlTIplE ChAnnEl S

A large European mobile operator was nding it

difcult to maintain a consistent product catalog

across all its channels: stores, telesales and the web.

Because of the lag time in updating the catalogs, a

user could nd an offer online, go into a store andnd that the offer was not available yet. The operator

needed a system that allowed it to update offers once

and have those offers be instantaneously available to

consumers searching the catalog in any channel. They

also needed the ability to add and change products

quickly to respond to shifting market demands.

The operator initially chose Oracle as the database

to power its new omnichannel product catalog. But

after spending more than $2 million and a year of

work, the operator found it was getting nowhere. The

database required an enormously complex schema,with 250 tables required to describe a single product.

The schema had to be reproduced in object-relational

maps (the database-application interface) and the

application itselfundermining the original goal of

developing a catalog that could be updated quickly.

Oracle simply could not cope with the variations of

payment options, devices, contract lengths, bolt-on

services and bundles the provider was offering.

However, MongoDBs highly exible data model and

economical approach to licensing allowed the operator

to develop a true omnichannel product catalog withinsix months and for a substantially smaller investment.

The product catalog includes an array of prepaid and

postpaid products, a growing selection of devices

(smartphones, tablets, wireless modems, SIMs) and

bolt-ons, such as data top-ups and international calling

packages. Different product types are organized in

different hierarchies, and some products are simulta-

neously available in different sections of the site. In

addition, the operator has found it easy to add new

product detail to product listings, such as specica-

tions and regulatory-required safety notications.

SMAll SEnSORS, BIg DATA: BUIlDIng A

MAChInE-TO-MAChInE plATfORM

With the number of mobile subscriptions exceeding

the size of the population in most mature markets,

operators have looked to alternative sources for

subscription growth. One highly promising area is

machine-to-machine (M2M) communication in enter-prises, with estimates of future M2M connections

running into the tens of billions. Analyzing a constant

stream of readings from a large number of sensors

allows businesses to create efciencies and identify

pain points in their infrastructures. But how do you

enable companies to store, process, analyze and

quickly act upon all this data?

While investigating database options for its M2M

enablement platform, a European mobile operator

realized that using an Oracle database would be

cost-prohibitive. The operator needed a system thatcould take in up to 10 billion sensor readings for a

single customer, with each reading a separate record

or document. But typical M2M use cases, such as

eet tracking systems for shipping companies, do not

generate enough return to justify the large investment

required for an Oracle system that could handle the

desired data volumes.

The operator chose MongoDB due to its lower total

cost of ownership, exible data model, scalability and

support for real-time analytics. The operators beta

customer is a power company collecting readingsfrom electric meters every few minutes, eliminating

the need to send out technicians and allowing the

company to keep a closer eye on household-level

usage in its distribution network. MongoDBs support

for real-time analytics allows the customer to set up

alerts that can be triggered when specied perfor-

mance or utilization benchmarks are breached.

MongoDBs exibility will also allow the operator to

easily adapt the platform for other types of sensor

readings, such as temperature, speed and acceleration.

And MongoDBs scalability permits the platform to

grow as more customers use the operator for M2Msolutions, and as their needs grow.


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All In OnE plACE:

A TRUE SUBSCRIBER IDEnTITY

MAnAgEMEnT SYSTEM

Over a customers lifetime, operators collect

enormous amounts of data about their subscribers:

billing histories, usage patterns, total usage, location

(for mobile operators), contract changes, servicecall histories and more. But a patchwork of legacy

systems, some decades old, collects this data in

different databases, many of which dont commu-

nicate with each other. To monetize this data and

improve internal operations, operators need a single

system that is scalable and exible enough to incor-

porate new types of data.

A major wireless operator chose MongoDB as the

database for its subscriber identity management

system. Trying to aggregate customer data from a

variety of systems was proving a bottleneck for devel-opers, who had to create numerous object-relational

maps to get their applications to read from existing

relational databases. The operators new person-

alization server will aggregate data from dozens

of systems in one place, eventually allowing both

customers and internal personnel the ability to see

all data about a customer in a single location.

An improved subscriber identity management system

improves call center efciency by reducing the amount

of time customer service representatives need to pull

data on customers. MongoDBs support for real-timeanalytics enables a live dashboard that shows trending

customer service issues, which can help customer

service representatives determine whether customer

complaints are an isolated issue or part of a larger

pattern. This complete view of a customers needs will

improve customer satisfaction and increase retention.

A single source of customer data also allows devel-

opers to build business intelligence systems more

rapidly. Licenses to access these systems can be sold

to retailers and others looking for data on subscribers

movements and Internet usage.

KnOW YOUR CUSTOMER lIKE YOURSElf:

CUSTOMER SEnTIMEnT AnAlYSIS

UberVu, a social media analytics company, uses

MongoDB to aggregate and analyze data from social

networks for clients seeking insight into customer

sentiment on their products. Mentions of particular

terms are annotated with pertinent data, such assource (Twitter, Facebook, etc.), language, sentiment

and time, and indexed in MongoDB. UberVu can easily

lter these streams by attribute (language, gender, etc.)

to produce segmented cuts on customer sentiment.

MongoDBs exibility and scalability allows UberVu to

add new sources and sentiment attributes over time,

and grow its storehouse of data as social networking

use grows.

Telecommunications companies looking for insight

into their products can use MongoDB in a similar way.

They can aggregate data from social networks, blogs,bulletin boards and media websites to answer tough

marketing questions, such as, are available download

speeds affecting brand perception? MongoDB can help

reduce the lag-time and expense involved in tradi-

tional market research, by greatly reducing the need

for focus groups and customer surveys. MongoDBs

ability to support rapid customer sentiment analysis

allows companies to change course quickly if marketing

campaigns prove ineffective, as well as anticipate

emerging customer needs more rapidly. MongoDBs

support for varied data types allows telecoms to store

a mix of external and internal data (customer servicecalls, corporate website usage history, etc.), and

determine how best to annotate, analyze and use

the data at a later date.


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MongoDB:

Speed, Size, StabilityMongoDB enables telecoms to expand their customer

bases, increase their revenue per user and improve

their customer acquisition and retention. MongoDB

doesnt require expensive licenses or proprietary

hardware, making it a natural t for greeneld deploy-

ments with unknown demand, like geo-targeted

mobile advertising. Its cost-effective scalability and

quick time-to-market makes it equally suitable to

time-sensitive company-wide deployments, like an

omnichannel product catalog. And its exible data

model provides companies with the agility to change

applications like an M2M platform in response to

customer demand. In addition, its support for real-time

analytics makes it a great tool for improving internal

operations, from customer sentiment analysis toincreasing call center efciency.

For much of the last decade, telecoms have felt left

behind by hardware and software vendors in the race

for innovation, hamstrung by their reliance on legacy

systems. With its agility and scalability, MongoDB

allows telecoms to couple the resources of a multina-

tional with the speed of a startup.


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MongoDB Downloads www.mongodb.org/downloads

Free Online Training education.10gen.com

Webinars and Events www.10gen.com/events

White Papers www.10gen.com/white-papers

Case Studies www.10gen.com/customers

Presentations www.10gen.com/presentations

Documentation docs.mongodb.org

Additional Info [email protected]

For more information on 10gen and MongoDB, please visit www.10gen.com and www.mongodb.org.

Resources


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Published by 10gen, Inc. / Feb 2013