1. A 10gen White Paper
Agility in the Age of Apps:
How the Next Generation of Databases Can Create
New Opportunities for Telecoms
February 2013
2. Table of Contents
E XECU TI V E SU M M A RY 1
What Is MongoDB? 1
Telecoms Adapt to Slow Growth 3
CUS TOM E R C A SE S T U DIE S 3
Outside the Box: Capitalizing on Online Video 3
Shared Experiences: Consumer Cloud Storage as a Way to Reduce Churn 4
Featured Case Study: How 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, Siz e, S tability 7
Resources 8
3. Executive Summary
With consumers and businesses spending ever more What is MongoDB?
time connected to the Internet, telecoms can expect
growing demand for their services. But demand In traditional relational databases like Oracle and
doesn’t always translate to profit, as competition, MySQL, data are stored in linked tables organized
commoditization, operational complexity, and network into rows and columns. Each row is associated with
investment costs threaten to turn telecommunications a unique entity, often a customer account, and each
providers into low-margin ‘dumb pipes.’ column is associated with a field defining an attribute
of the account. Separate tables store different types
More than perhaps any other industry, telecommuni- of information about an account (e.g., mailing address,
cations is disrupted every few years by market-shifting billing history), with common identifiers linking tables
innovations. To compete, telecoms need to develop together. Schemas, or maps, that diagram these
new services, and to do so rapidly, before their links and define allowable fields are locked in during
competitive advantage is neutralized. Telecoms need the database development period, and managed by
tools that allow them to adapt to changing needs database administrators (DBAs), who must approve
quickly and affordably, with the reliability they expect any changes. This approval process creates a
from their long-lived legacy systems. bottleneck for developers trying to create and
modify applications quickly.
MongoDB, the leading NoSQL database, allows
telecommunication companies to develop new MongoDB uses a more flexible data model. A
applications quickly and adapt them as needs change. document database, MongoDB allows for varied data
It is highly scalable, perfect for an age in which types and rapid addition of new fields. While entities in
companies are capturing exponentially increasing relational databases must pull information from fields
volumes of data. And in an environment buffeted by in multiple tables, a single MongoDB document can
many economic challenges, MongoDB’s total cost of store an entity’s entire information. These documents
ownership can be orders of magnitude less than that can contain as many or as few fields as necessary.
of traditional databases like Oracle.1 Creating new fields doesn’t necessarily require admin-
1
See the 10Gen White Paper, “A Total Cost of Ownership Comparison of MongoDB & Oracle.”
http://www.10gen.com/sites/default/files/downloads/10gen.TCO%20-%20MongoDB%20vs.%20Oracle.pdf
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4. istrative approval. Developers can create new fields the original server. Machines can be simple commodity
as they create new documents, write a script to servers found in cloud services like Amazon’s EC2.
add the field to all documents or batch-populate No expensive purpose-built hardware needs to be
documents with a new field when sending a request upgraded or replaced. Companies can therefore
to the database. Since new fields can be added ad quickly and cost-effectively scale their applications
hoc, MongoDB works well with unstructured, semi- in response to demand, and easily re-deploy system
structured and polymorphic data—unlike relational resources as needs change. Sharding brings perfor-
databases, which can’t easily store different types of mance benefits as well, as companies can place groups
data and require data to be structured before they of documents on machines closer to the geographic
can be mapped. With MongoDB, notes from customer source of those documents so, for example, customers
service calls can be stored first, and organized later. in a particular region can access their user information
more quickly.
Because the documents in MongoDB are similar to
the ‘objects’ used in most modern applications and MongoDB provides many of the features that will be
programming languages, developers find MongoDB familiar to users accustomed to relational databases.
easy to work with. Documents are described in the MongoDB uses a rich query language for searching
Javascript Object Notation (JSON) format, familiar to the database and supports indexing of documents on
users of Javascript. Developers don’t have to spend secondary fields. MongoDB provides an interface for
much time learning MongoDB’s syntax, or mapping interacting directly with the database, and drivers for
their application structure to the database structure. most popular programming languages, including Java,
This contrasts sharply with relational databases. For C++, C#, PHP and Python. To provide the high level of
relational databases, developers and database admin- uptime that users expect from relational databases,
istrators must ensure that their database schemas are MongoDB supports the automated replication of
aligned across three layers: the application, the appli- database shards on up to 14 back-up servers and
cation-database interface (object-relational map, or automated failover to back-ups when primary
ORM) and the database itself. The need to create and servers fail.
maintain three separate, but consistent, data maps
creates administrative drag. With MongoDB, there is no With users as varied as Viber, Disney and Cisco,
need for separate data maps or approval processes for MongoDB has proven its versatility as a general-
creating new fields. Developers can create new appli- purpose database. Unlike some other NoSQL
cations rapidly, and revise and enhance them quickly. databases, MongoDB allows analytics tools to record
and analyze changes to the database in real-time.
The volume of data created by popular social, mobile There is none of the lag-time associated with batch
and video applications is immense. As relational processing tools like Hadoop. MongoDB can be used
database deployments reach their processing and with a diverse set of applications, from content
storage limits, companies are faced with the expensive management to data mining, allowing developers to
and complex task of upgrading their purpose-built focus their time on application development, rather
servers and storage area networks (SANs). Upgrading than database maintenance and schema updates.
servers often means several hours or even days of
downtime. MongoDB, however, allows companies to With its flexible, simple and developer-friendly data
expand their processing power and storage capacity model, MongoDB empowers organizations to be agile,
easily across multiple off-the-shelf machines, on-site to act like startups. They can get new applications to
or in the cloud, with no downtime. market quickly, and revise, upgrade and expand them
as needs evolve. In many companies, administrators
With MongoDB, large databases can be partitioned of relational databases limit the number of changes
into groups of documents, known as ‘shards.’ These that can be made to the database structure to one or
shards are distributed across different machines, with two times per year. With MongoDB, there’s no need for
both processing and storage occurring separately from such limits.
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5. Telecoms Adapt to Slow Growth
Customer Case Studies
Telecoms tackled the problem of ‘big data’ before
many other industries. Telecoms implemented the
original operating support systems in the early 1970s Outside the Box: Capitalizing on
as a way to automate and speed up the massive Online Video
number of tasks they needed to do: taking orders,
As consumers have watched increasing amounts of
assigning lines, configuring network components,
video online, pay-TV providers have had to adapt.
collecting payments and so on. They were some of the
Many providers have pursued ‘TV Everywhere’ strat-
early adopters of relational databases, with Bell Labs
egies that enable their customers to watch content
purchasing an Oracle machine as early as 1979, only a
on devices other than their TVs. A few have pursued
year after Oracle’s commercial launch. Billing support
standalone Internet-based video services to compete
systems and operating support systems from the ‘70s
directly with Netflix, NOW TV, LOVEFiLM and other
allowed for mass automation, but rules had to be
streaming video providers.
hard-coded and data relationships were fixed. Getting
different legacy systems to speak to each other remains A major pay-TV provider recently launched an online
an ongoing problem for many telecoms. video site that allows users to subscribe on a monthly
basis or order movies a la carte. Users can choose
Today, telecoms face different challenges. In mature
from a catalogue of more than 1,500 films, and pause,
markets, telecoms confront competition not only
rewind or fast-forward programs easily. Users can mark
from companies offering similar technologies (e.g.,
films for future viewing, and automatically receive
two wireless operators) but from companies offering
recommendations for other films they might like.
the same applications over different technologies
(e.g., a landline and wireless operator), wholesale The provider chose MongoDB to power its system
operators with different cost structures and nimble because of its flexibility and scalability. The company
startups offering competitive applications over the wanted a system that could support 70,000 concurrent
Internet. Opportunities for new subscriber growth are users during peak hours, with users making constant
limited, with mobile penetration in most rich countries calls to the database to search, browse, rewind, pause
exceeding 100%, fixed-line subscriptions falling and and fast-forward films. The database also stores data
pay-TV subscriptions flat in many countries. on where exactly in a film viewers pause watching so
they can return to the content later.
Telecoms are therefore turning to their existing
subscriber bases for revenue growth. They are The ease of adding new fields to documents in
considering new revenue streams—like targeted adver- MongoDB permits developers to rapidly add new
tising—and additional value-added services, like meta-tags to characterize films in a variety of ways,
over-the-top video and consumer cloud storage, to alongside the traditional tags like actor, director and
increase their revenue per user. Even if these appli- genre. Over time, the recommendation engine becomes
cations cannot easily be monetized, they can help smarter, as it leverages a growing base of content,
strengthen brand loyalty, reducing customer churn and meta-tags and information about user behavior.
therefore increasing the lifetime value of subscribers.
In the future, MongoDB’s support for mixed hierar-
At the same time, increasing demands on telecoms’ chies will allow the provider to add new content
networks are creating a need for increased capital types like TV show collections and even live events.
investment. To maintain margins, telecommuni- MongoDB’s ability to support documents nested inside
cation service providers are looking for ways to reduce other documents means that developers won’t have
their costs across all parts of the business, including to categorize individual episodes of TV shows at the
network operations, customer service and marketing. same level as standalone films. Users will be able
Reducing customer acquisition costs is a particular to access a particular content type—for example, a
focus of many rich world telecoms. football team’s season—and find programs organized
in an intuitive way.
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6. Shared Experiences: Consumer Cloud viewing permissions, location data and timestamps.
Storage as a Way to Reduce Churn Because adding new fields to the previous relational
database system was such a time-consuming process,
For years, a major European mobile operator was much of this type of data was previously stored in text
ahead of the curve in offering its subscribers the form or discarded. MongoDB, however, can automat-
ability to store photos, music and video in the cloud. ically turn this data into new fields, so users can see
But recently, the operator found that the MySQL where and when their photos or videos were taken.
database it had built 13 years ago was reaching the
limits of scalability, and did not allow for the kind of By tying document storage to mobile subscrip-
flexible access controls that users are accustomed to tions, the operator increases the stickiness of its
on social networking sites. paid service and defends against churn in a market
threatened by increasing competition. Improving the
The operator chose MongoDB to enable more flexible available features allows the operator to keep pace
sharing. Subscribers can now share videos, photos, with standalone consumer cloud storage sites like
photo albums and mixed media albums with particular Dropbox, social networking sites and online photo
users or categories of users. The content itself is album services.
stored in a separate file system, while MongoDB is
used to store metadata about the content, such as
Featured Case Study
How O2 Turned a Cost into an Opportunity
O2 uses customer movement data to offer location-specific 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 Kingdom’s leading wireless operator, saw an opportunity. What if you could get businesses
to pay to offer your subscribers location-specific special offers?
O2’s Priority Moments provides businesses a way to reach potential customers when they’re in the vicinity of one of
their locations. O2 subscribers install a free mobile application and receive notifications about discounts and other
special offers in their area. “Deals are delivered by location, so it’s quick and easy to find the offers and experiences
they want,” said O2’s Andrew Pattinson.
Traditional relational databases are ill-equipped to handle the complex volumes of data generated by millions of
subscriber movements. Nor are they particularly adaptable if the application’s functionality needs to change.
“Selecting MongoDB as our database platform was a no-brainer,” said Pattinson, “as the technology offered us the
flexibility and scalability that we knew we’d need.”
With more than 20 million subscribers, O2 required a database that could scale as usage grew. Deployed on Amazon
Web Service’s cloud, the Priority Moments’ database can easily expand due to MongoDB’s support for database parti-
tioning. MongoDB’s native geospatial support made MongoDB a natural fit, while MongoDB’s flexible data model will
allow O2’s developers to tweak the application as subscribers’ and advertisers’ needs evolve.
O2 was so satisfied 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, “We’re very excited about MongoDB
and look forward to more projects in the near future.”
“Deals are delivered by location, so it’s quick and easy to find the
offers and experiences they want.” -Andrew Pattinson, O2
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7. One-Stop Shop: A Universal Product Small Sensors, Big Data: Building a
Catalog Across Multiple Channels Machine-to-Machine Platform
A large European mobile operator was finding it With the number of mobile subscriptions exceeding
difficult to maintain a consistent product catalog the size of the population in most mature markets,
across all its channels: stores, telesales and the web. operators have looked to alternative sources for
Because of the lag time in updating the catalogs, a subscription growth. One highly promising area is
user could find an offer online, go into a store and machine-to-machine (M2M) communication in enter-
find that the offer was not available yet. The operator prises, with estimates of future M2M connections
needed a system that allowed it to update offers once running into the tens of billions. Analyzing a constant
and have those offers be instantaneously available to stream of readings from a large number of sensors
consumers searching the catalog in any channel. They allows businesses to create efficiencies and identify
also needed the ability to add and change products pain points in their infrastructures. But how do you
quickly to respond to shifting market demands. enable companies to store, process, analyze and
quickly act upon all this data?
The operator initially chose Oracle as the database
to power its new omnichannel product catalog. But While investigating database options for its M2M
after spending more than $2 million and a year of enablement platform, a European mobile operator
work, the operator found it was getting nowhere. The realized that using an Oracle database would be
database required an enormously complex schema, cost-prohibitive. The operator needed a system that
with 250 tables required to describe a single product. could take in up to 10 billion sensor readings for a
The schema had to be reproduced in object-relational single customer, with each reading a separate record
maps (the database-application interface) and the or document. But typical M2M use cases, such as
application itself—undermining the original goal of fleet tracking systems for shipping companies, do not
developing a catalog that could be updated quickly. generate enough return to justify the large investment
Oracle simply could not cope with the variations of required for an Oracle system that could handle the
payment options, devices, contract lengths, bolt-on desired data volumes.
services and bundles the provider was offering.
The operator chose MongoDB due to its lower total
However, MongoDB’s highly flexible data model and cost of ownership, flexible data model, scalability and
economical approach to licensing allowed the operator support for real-time analytics. The beta customer is
to develop a true omnichannel product catalog within a power company collecting readings from electric
six months and for a substantially smaller investment. meters every few minutes, eliminating the need to
The product catalog includes an array of prepaid and send out technicians and allowing the company to
postpaid products, a growing selection of devices keep a closer eye on household-level usage in its
(smartphones, tablets, wireless modems, SIMs) and distribution network. MongoDB’s support for real-time
bolt-ons, such as data top-ups and international calling analytics allows the customer to set up alerts that
packages. Different product types are organized in can be triggered when specified performance or
different hierarchies, and some products are simulta- utilization benchmarks are breached. MongoDB’s flexi-
neously available in different sections of the site. In bility will also allow the operator to easily adapt the
addition, the operator has found it easy to add new platform for other types of sensor readings, such as
product detail to product listings, such as specifica- temperature, speed and acceleration. And MongoDB’s
tions and regulatory-required safety notifications. scalability permits the platform to grow as more
customers use the operator for M2M solutions, and
as their needs grow.
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8. All in One Place: Know Your Customer Like Yourself:
A True Subscriber Identity Customer Sentiment Analysis
Management System
UberVu, a social media analytics company, uses
Over a customer’s lifetime, operators collect MongoDB to aggregate and analyze data from social
enormous amounts of data about their subscribers: networks for clients seeking insight into customer
billing histories, usage patterns, total usage, location sentiment on their products. Mentions of particular
(for mobile operators), contract changes, service terms are annotated with pertinent data, such as
call histories and more. But a patchwork of legacy source (Twitter, Facebook, etc.), language, sentiment
systems, some decades old, collects this data in and time, and indexed in MongoDB. UberVu can easily
different databases, many of which don’t commu- filter these streams by attribute (language, gender, etc.)
nicate with each other. To monetize this data and to produce segmented cuts on customer sentiment.
improve internal operations, operators need a single MongoDB’s flexibility and scalability allows UberVu to
system that is scalable and flexible enough to incor- add new sources and sentiment attributes over time,
porate new types of data. and grow its storehouse of data as social networking
use grows.
A major wireless operator chose MongoDB as the
database for its subscriber identity management Telecommunications companies looking for insight
system. Trying to aggregate customer data from a into their products can use MongoDB in a similar way.
variety of systems was proving a bottleneck for devel- They can aggregate data from social networks, blogs,
opers, who had to create numerous object-relational bulletin boards and media websites to answer tough
maps to get their applications to read from existing marketing questions, such as, are available download
relational databases. The operator’s new person- speeds affecting brand perception? MongoDB can help
alization server will aggregate data from dozens reduce the lag-time and expense involved in tradi-
of systems in one place, eventually allowing both tional market research, by greatly reducing the need
customers and internal personnel the ability to see for focus groups and customer surveys. MongoDB’s
all data about a customer in a single location. ability to support rapid customer sentiment analysis
allows companies to change course quickly if marketing
An improved subscriber identity management system campaigns prove ineffective, as well as anticipate
improves call center efficiency by reducing the amount emerging customer needs more rapidly. MongoDB’s
of time customer service representatives need to pull support for varied data types allows telecoms to store
data on customers. MongoDB’s support for real-time a mix of external and internal data (customer service
analytics enables a live dashboard that shows trending calls, corporate website usage history, etc.), and
customer service issues, which can help customer determine how best to annotate, analyze and use
service representatives determine whether customer the data at a later date.
complaints are an isolated issue or part of a larger
pattern. This complete view of a customer’s 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.
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9. MongoDB:
Speed, Size, Stability
MongoDB enables telecoms to expand their customer
bases, increase their revenue per user and improve
their customer acquisition and retention. MongoDB
doesn’t require expensive licenses or proprietary
hardware, making it a natural fit for greenfield 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 flexible 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 to
increasing call center efficiency.
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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10. Resources
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 info@10gen.com
For more information on 10gen and MongoDB, please visit www.10gen.com and www.mongodb.org.
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