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Importance of BI and Data warehousing in
Telecommunications
IT for BI, 2nd Year
VGSoM, IIT Kharagpur Submitted By:
Rohit Khandelwal 09BM8042 Archit Mehrotra 09BM8012
Contents Introduction ...................................................................................................................................... 4
Data Warehousing ......................................................................................................................... 4
Business Intelligence...................................................................................................................... 4
Growing Importance of Data Warehousing and BI ............................................................................. 4
Major BI Applications ........................................................................................................................ 6
Customer Relationship Management ............................................................................................. 6
Product Development .................................................................................................................... 6
Forecasting ................................................................................................................................ 6
Service Design and Delivery ....................................................................................................... 7
Service Fulfillment ..................................................................................................................... 7
Service Usage and Charging ....................................................................................................... 7
Finance and Asset Management .................................................................................................... 7
Budgeting .................................................................................................................................. 7
Asset Liability Management ....................................................................................................... 7
Profitability Analysis .................................................................................................................. 8
Reporting and Analysis............................................................................................................... 8
Human Resources .......................................................................................................................... 8
Human Resource Analytics ......................................................................................................... 8
Manpower Allocation ................................................................................................................ 8
HR Portal ................................................................................................................................... 8
Training and Succession Planning ............................................................................................... 9
Corporate Management ................................................................................................................ 9
Corporate Dashboards ............................................................................................................... 9
Statutory Reporting ................................................................................................................... 9
Key performance Indicators in Telecommunications Sector ............................................................... 9
Mapping of KPIs – an example ......................................................................................................... 11
CDRs (Call details record): ............................................................................................................ 11
Data Warehouse Mix ....................................................................................................................... 12
Data Modeling – Facts and Dimensional tables ................................................................................ 13
Customer billing process: ............................................................................................................. 14
Some Other Facts Table ............................................................................................................... 14
Some Dimensional Tables or Reference Tables ............................................................................ 15
Criteria for success – Sybase example .............................................................................................. 15
Sybase Technology: ..................................................................................................................... 15
Key Benefits................................................................................................................................. 16
Information Abounds, the Problem is Getting it All Processed ..................................................... 16
The First Incarnation of a Data Warehouse .................................................................................. 17
Defining the Criteria for Success .................................................................................................. 18
The Foundation of a Highly Functional Data Warehouse .............................................................. 19
Empowering - Sybase IQ, Faster, Deeper, and Broader Queries with Room for Growth ................ 19
Happy Users Enjoy Unforeseen Dividends .................................................................................... 20
Data Breeds Success and Success Breeds Data ............................................................................. 20
Major Players .................................................................................................................................. 21
Future of BI in telecom industry ...................................................................................................... 21
References ...................................................................................................................................... 22
Introduction
Data Warehousing
A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of
data in support of management's decision making process. A data warehouse (DW) is a
database used for reporting. The data is offloaded from the operational systems for reporting.
The data may pass through an operational data store for additional operations before it is used
in the DW for reporting.
A data warehouse maintains its functions in three layers: staging, integration, and
access. Staging is used to store raw data for use by developers (analysis and support).
The integration layer is used to integrate data and to have a level of abstraction from users.
The access layer is for getting data out for users. The means to retrieve and analyze data,
to extract, transform and load data, and to manage the data dictionary are also considered
essential components of a data warehousing system.
Business Intelligence
Business intelligence (BI) is a broad category of applications and technologies for gathering,
storing, analyzing, and providing access to data to help enterprise users make better business
decisions.
Business intelligence aims to support better business decision-making. Thus a BI system can
be called a decision support system (DSS). Though the term business intelligence is
sometimes used as a synonym for competitive intelligence, because they both support
decision making, BI uses technologies, processes, and applications to analyze mostly internal,
structured data and business processes while competitive intelligence gathers, analyzes and
disseminates information with a topical focus on company competitors. Business intelligence
understood broadly can include the subset of competitive intelligence.
Growing Importance of Data Warehousing and BI
The total wireless subscriber base {GSM, CDMA and WLL (F)} in India stands at 584.32
million on 31st March 2010. The number of wire-line subscribers on 31st March 2010 was
36.96 million. On an average, more than 15 million telephone subscribers are added every
month during the financial year which makes 5.8 users to be added per second which
indicates to a huge volume of data to be handled.
Profit margins are thin in this highly competitive industry and even small degrees of
efficiency improvements add up and fund investments in services and basic infrastructure. In
turn, service and quality become key differentiators between competing telecommunications
companies. Players needs to be ready to counter their competition's offers and be able to
provide more options without endangering profitability and keeping their costs to a
minimum. With more data they can model different scenarios.
The data warehouse needed to address the requirements of the marketing, finance, customer
service, sales, and engineering departments. However, the sheer volume of daily input data
began swamping the original system. Concurrent users from multiple departments slowed the
system with memory usage reaching unacceptable levels. Moreover, due to heavy load the
system to be unavailable to concurrent users. Hence, simultaneous use of data from different
departments requires efficient data management.
Also, there are certain stringent information reporting requirements imposed by
Telecommunications Regulatory Authority of India that are designed to elevate
telecommunication operational standards and enhance the country's infrastructure
With Upcoming technologies like mobile number portability and 3G, there is an ever
increasing emphasis on data management
The data when analyzed intelligently leads to effective segmenting, targeting and positioning
for telecommunication companies. They can understand customer usage patterns by
analyzing call detail recording (CDRs). They can do trend analysis for a particular area, time
or product. Also, it assists to estimate revenue, volumes and margin of profit. Customers
could be categorized into segments according to their behavior and demography for customer
relationship management. Companies can launch accurate marketing campaign with
improved ability to target new customers. Also, the performance of campaign within a period
of time could be tracked for improving marketing skills. Also, some companies constantly
monitor the behavioral changes of customers so that they can immediately response with
adequate measures.
Major BI Applications
Customer Relationship Management Telecommunications vendors are rapidly acquiring significant product development
capabilities as technology changes drive consumer demand. However, they lag behind in
understanding the customer. This has led to a significant churn as products are developed and
discarded in an attempt to drive new business and retain existing customers.
Telecommunications vendors need to analyze their customers’ needs and tailor their business
processes in the value chain to effectively meet their customers’ unique requirements and
increasing demands. Telecommunication companies have the ability to turn large volumes of
data pertaining to their customers and services into actionable information. Business
intelligence systems can significantly help in almost all aspects of the value chain to achieve
this objective. Telecommunication value chain is as shown below:
The CRM process in a telecommunications company has three steps:
1. Identify the most profitable or potentially profitable customers for future
interaction.
2. Understand their needs and buying patterns, and
3. Interact with them so as to meet all of their expectations.
Product Development
Forecasting
To plan their networks, telecommunications service providers perform forecasting that helps
operators to make key investment decisions. These decisions affect all aspects of the business
including product development, launch, advertising, and pricing. Effective forecasting helps
to ensure that the company will make a profit and that capital is invested wisely. BI solutions
that use forecast data can help network planners decide how much equipment to purchase and
where to place it to ensure optimum management of traffic loads.
Service Design and Delivery
In response to fierce competition, telecom service providers must develop new products in
order to offer a wide range of new value-added services faster and more cost efficiently.
Design of effective services is enhanced through the use of BI solutions that provide
information regarding the adoption and profitability of existing products and services.
Service Fulfillment
Business Intelligence solutions can help telecommunications service providers improve
customer retention and satisfaction through the effective analysis of service fulfillment
systems. Information regarding installation, upgrades and repairs to customer’s service can
help the business reduce the cost associated with service fulfillment.
Service Usage and Charging
Effective analysis of service usage and charge metrics can help telecommunications service
providers prevent fraud, increase collections and provide the basis for new product
development. In addition, this analysis can help in the planning for preventative maintenance
of the infrastructure and for planning for network upgrades. Data from existing billing and
receivables systems can be leveraged in BI solutions to help contain expenses as well as grow
the business.
Finance and Asset Management
The role of financial reporting has undergone a paradigm shift during the last decade. It is no
longer restricted to just financial statements required by law. Increasingly, it is being used to
help in strategic decision making. Many companies, in an attempt to improve financial
reporting and decision making, have integrated their financial data in a data mart or data
warehouse.
Budgeting
Data warehousing facilitates analysis of budgeted versus actual expenditure for various cost
heads like promotion campaigns, product development, infrastructure maintenance,
investments, commissions, etc. BI tools can provide drill down capabilities whereby the
reasons for cost overruns can be analyzed in more detail. It can also be used to allocate
budgets for the next financial period. Various activity based costing models can be developed
for better cost control and allocation.
Asset Liability Management
Models can be developed using BI tools to measure the company’s exposure to various risk
factors like changes in interest rates. These models can be used to predict the performance of
the portfolio under different economic scenarios and predict future liquidity needs of the
insurer.
Profitability Analysis
This includes profitability of individual products, product lines, and investments. A major
component of profitability analysis is a thorough analysis of costs incurred during product
development which can be a major factor in reducing the overall profitability of
telecommunications companies.
Reporting and Analysis
Swift decision making requires ready access to financial data via an intuitive interface.
Increasingly companies are providing concerned executives web-based access to financial
data.
Human Resources
Business Intelligence can significantly help in aligning the HR strategy to the overall
business strategy. It can present an integrated view of the workforce and help in designing
retention schemes, improve productivity, and curtail costs.
Human Resource Analytics
HR analytics can be generated to support an integrated view of the workforce. Various
analyses include staff movement and performance, workforce attrition by department,
workforce performance by department, compensation and attrition and absenteeism. The HR
data can be integrated with benchmark figures for the telecommunications industry and
compared to help identify areas for improving profitability.
Manpower Allocation
This includes allocating manpower based on new product launches and for major upgrade or
expansion projects. According to increased requirements, personnel can be deployed in
specific areas where demand projections are high or likely to increase.
HR Portal
Employers need to maintain accurate employee data which can be vied by the employees for
information about compensation, benefits, retirement plans and other HR related information.
Payroll data can be integrated with other HR information in an HR data mart and then be
made visible within the organization through an HR portal.
Training and Succession Planning
Accurate data about the knowledge, skills and abilities of the workforce can be leveraged
using Business Intelligence solution to aid in succession planning. In addition, BI can help
identify skills gaps and design training programs to bridge those gaps.
Corporate Management The top management of any telecommunications company has its own business intelligence
requirements. The IT department is typically responsible for providing all the reports to them.
It is also responsible for providing statutory reports to various outside agencies as well as
meeting other information requirements within and outside the company. This may include
information given to customers in the form of statements and other reports. A BI environment
that leverages data collected across the value chain is possibly the only effective solution for
IT.
Corporate Dashboards
Performance measurements like product line profitability, overall development costs, and
ROI can be presented in dashboard reports to top management to facilitate the decision
making process. Also alerts can be triggered if any performance measure reaches a pre-
defined threshold level. These reports can incorporate Telecommunications industry
benchmarks provided by third party researchers.
Statutory Reporting
Telecommunications companies have to provide statutory reports to outside agencies. These
reports can easily be generated from the Business Intelligence environment.
Key performance Indicators in Telecommunications Sector Some of the key performance indicators in Telecommunications Sector are as described
below:
• Systems and Network Performance Analysis / Capacity Planning
– Grade of service
Grade of service is the probability of a call in a circuit group being blocked or
delayed for more than a specified interval, expressed as a vulgar
fraction or decimal fraction. This is always with reference to the busy
hour when the traffic intensity is the greatest
– Service life of equipment
– Downtime / Time out of service
– Call completion ratio
The ratio of successfully completed calls to the total number of attempted calls
– Average call duration (ACD)
Average Call Duration (ACD) is a measure based on a call record
(or CDR) sample to determine traffic demand and forecast call volumes,
serving also as a tool for infrastructure monitoring
– Answer-Seizure Ratio (ASR)
Answer/Seizure ratio (ASR) is the number of successfully answered calls
divided by the total number of calls attempted (seizures) multiplied by 10
– Idle time on network
– Dropped calls
• Quality / Usage (Airtime): Analysis of the volume of successful calls
– Mean Opinion Score
Mean opinion score (MOS) provides a numerical indication of the perceived
quality of received media after compression and/or transmission.
– Grade of Service
Grade of service is the probability of a call in a circuit group being blocked or
delayed for more than a specified interval, expressed as a vulgar
fraction or decimal fraction. This is always with reference to the busy
hour when the traffic intensity is the greatest.
• Coverage
– % of land covered with services
– % of population covered with services
– Average land unavailable to services
– Average population unavailable to services
– Access to customer service
• Faults and complains (Trouble tickets analysis)
– % of open and level of escalation priority required
– % closed
– Mean time to resolved
– Work in progress
– Customer service level statistics
• Customer Analysis
– ARPU (Average Revenue per User)
– Customer segmentation
– Analysis of subscriptions
– Top N customers
– Churn rate
Fraction of Subscriber who stopped using Services or left particular network
and it is expected to increase due to number portability
Mapping of KPIs – an example
CDRs (Call details record):
Call details record stores the data for all the calls made or tried and messages sent and
received. Such data can be used to track call density and therefore the stress on a particular
transmission tower in a locality. In a CDR one could look for following dimensions:
Attempted vs. completed calls
Type of call – long distance or local
Length of call
Originating and terminating number
Time of call
Attempted calls vs. Completed calls can give an idea about network traffic, unreachable areas
and also whether to give promotional offers to which customers, so his attempted calls
reduces compared to completed calls, because the customer might not be interested in paying
higher call charges.
Type of call analysis can be used to make promotional and advertising decisions based on the
customer usage patterns. For example – If a customer is making long distance calls (STD and
ISDs) he could be offered a promotional package with STD calling cards. Also, another way
to look at it could be to normalize the network congestion, so as the usage pattern is evenly
distributed.
Length of calls and time of calls can be used to analyze and design and extend various offers
like night calling and to figure out the esteemed customers who can be potentially converted
to the post paid customers. It is important from the Customer Relationship Management and
Marketing division.
Originating and terminating numbers for every call can be used to monitor traffic and can
help in making decisions like where to set up network towers and deploy more sales team.
The Call records process is of great use to the decision makers by giving us a lot of insights.
Many questions can be addressed by analysing the data for the various calls made at a
particular time of the day on a certain locality. It can be judged which area encountered
maximum traffic density or which week day experienced maximum calls.
The records of calls, SMSs and MMSs can be used to create valuable reports by conducting
operations like joins etc so as to get the result as required. For Example, on days like
Valentine’s Day or New Year, message traffic would be very high irrespective of the regions
and hence promotional offers for these days can be effective in maintaining as well as getting
new customers.
Data Warehouse Mix
Considering the above figure, we see that the data stored can be used by various functions.
This stresses on the importance of saving data in correct format in appropriate tables. For
example, if we see in a Customer Billing process, the fields like Date, Customer Information,
Product, rate Plan, Sales Channel, Service Line Number are important and actually define this
process. In the process of Call Detail Traffic again few of these fields are repeated and are
used to define such a process. So consistency between these two processes should be
maintained for the common fields in terms of their linkages and formats. This should be kept
in mind while designing the Databases and using the Data warehousing and BI tools for
maintain a healthy Data Warehouse mix.
Data Modeling – Facts and Dimensional tables
We explain the facts and dimensional tables with the help of a billing process. Facts tables
are the transaction tables, which are updated with every transaction that is processed from the
fields in reference or dimensional tables and other facts tables. A Dimensional table is not
updated as frequently as a Facts table. For example the Customer Information table
containing the details of a customer like Name, Customer ID, City, billing address etc. is a
dimensional table. A Billing information table will have information of the billing for a
particular customer id, for a particular month, information about the sales representative
(from the Sales Representative, dimensional table) for that particular bill generated, the call
rates (from the Rate Plan dimensional table), the call details (from CDRs, a facts table) etc.
Customer billing process:
This process is mainly required to keep the track of the call charges applicable to a particular
customer and provide a monthly bill for the customer against his usage. Each month, the
operational billing system generates a bill for each phone number, also known as service line.
Since the wireless company has millions of service lines, this represents a significant amount
of data. Each service line is associated with a single customer. However, a customer can have
multiple wireless service lines, which appear as separate line items on the same bill; each
service line has its own set of billing metrics, such as the number of minutes used and
monthly service charge. There is a single rate plan associated with each service line on a
given bill; this plan can change as customers’ usage habits evolve. Finally, a sales rep (and
his or her respective sales organization and channel) is associated with each service line in
order to evaluate the ongoing billing revenue stream generated by each rep and channel
partner.
Some Other Facts Table
Some other possible facts table important from a telecommunications industry perspective
could be as follows:
• Customer Transaction
• Services
– DnD (Do Not Disturb)
– Caller Tune etc.
– GPRS usage
– Call Waiting, Conferencing, forwarding
• Billing Info
• Recharge
• Online payments
• CRM – Customer complaints and service
Some Dimensional Tables or Reference Tables
Various Dimensional tables or reference tables important from a telecommunications industry
perspective could be:
• VAS (type of service, charges, usage)
• Customer information table (corporate or retail, prepaid or postpaid, customer first
name, customer last name, billing address, address proof, Sim number)
• Sim Info (MSISDN no, other attributes)
• Promotions info (which offers are currently available, full talk time, night talk time
etc.)
• Circle master, Zonal master (Information regarding the circles and network zones)
• Payment information (Available Payment options like credit card, Online banking
details etc.)
Criteria for success – Sybase example
Sybase Technology:
Spice Telecom, is providing services across the Indian states of Punjab and Karnataka, using
a host of Sybase technologies including Sybase IQ, ASE, Power Designer, and Replication
Server to populate a data warehouse with raw mobile and landline call data. The query speed
and data compression of Sybase IQ has been the key to implementing the data warehouse
successfully.
Spice Communications Ltd. (Spice) is Punjab's second leading Telecommunications
Company, and with over three million current subscribers, Spice is one of the region's fastest-
growing cellular phone service providers. Spice offers a wide range of voice and non-voice
cellular services to subscribers on a post-paid or prepaid basis. Using Sybase IQ, Spice
maintains a 12 terabyte data warehouse populated with three months of customer call data.
Spice runs advanced multi-dimensional analytics on the information to identify sources of
revenue loss and uncover gaps in its service offerings. The data warehouse has lowered costs,
improved data quality, increased their competitive edge, and fostered exceptional growth.
Key Benefits
10X improvement in performance
Analyzes data sets not previously possible
Decreases query times from hours and days to minutes
Reduces storage needs by 50%
Creates new ad hoc query possibilities
Opens data warehouse access to all departments
Information Abounds, the Problem is Getting it All Processed
In the past decade India has undergone a phenomenal technological transformation,
increasing mobile capabilities and rapidly expanding user bases. In fact, a new Spice cellular
subscriber is added each second of every working day.
Through the course of doing business, phone companies collect enormous quantities of raw
information on customer use patterns. The bulk of the data originates from Call Detail
Records (CDRs) that are created for customer billing and financial transfers between telecom
companies. Every call by each of Spice's more than three million subscribers adds to the data.
Most of the information comes from Spice's own customers, but they also receive data from
the other telecommunications companies as those calls pass through the Spice infrastructure.
Profit margins are thin in the highly competitive telecommunications industry and even small
degrees of efficiency improvements add up and fund investments in services and basic
infrastructure. In turn, service and quality become key differentiators between competing
telecommunications companies.
Modelling of different scenarios with dynamic industry environment is crucial. Few such
models are:
Sybase IQ
Sybase Adaptive Server Enterprise (ASE) 15
Sybase Replication Server
Sybase Replication Server Option for Oracle
Sybase PowerDesigner®
SQL Anywhere
Sybase Partner Business Objects
The First Incarnation of a Data Warehouse
Spice Telecom's initial foray into data warehousing was built on traditional OLTP database.
The data warehouse needed to address the requirements of the marketing, finance, customer
service, sales, and engineering departments. However, it did not take long for the sheer
volume of daily input data to begin swamping the original system. Query response times
increased and it became painful to extract even simple operational counts. Concurrent users
from multiple departments slowed the system even further while memory usage reached
unacceptable levels; it was not unusual for the system to be unavailable to concurrent users.
The inability to analyze data limited the company's decision-making process. Not only was
ad-hoc, “what-if” queries out of the question, the standard queries that monitoring the pulse
of the company were becoming unwieldy. Call traffic CDRs were large and increasing daily
and multi-dimensional analysis had become a distant dream. Around that time, to even
further raise the stakes, the Telecommunications Regulatory Authority of India imposed a
stringent set of information reporting requirements that were designed to elevate
telecommunication operational standards and enhance the country's infrastructure. It was
clear that key changes were required in order to continue to be successful.
In addition to the performance issues, Spice Telecom needed to address the system's
significant storage constraints. The data warehouse needed a lot of information, but also
needed to take up less room. Maintenance functions like backups, transfers, and redundancy
were a drag on the entire architecture and increased the number of potential failure points.
Temporary fixes included creating smaller, summary samples of data that -while not as good
as the real data – were capable of being processed in a timely manner. However, the process
of creating the smaller samples was itself a drain on both processing and storage resources.
The system, with its associated workarounds, was essentially collapsing under its own
weight.
Defining the Criteria for Success
Determined to create a successful data warehouse, Spice assembled a list of features and
considerations for their next generation data warehouse. Based on what the IT team learned,
they identified these criteria for the new data warehouse:
Extreme Performance - Performance was given the highest priority. Having a huge
data store without an ability to perform reasonable analysis means the essential
function of the data warehouse is lost. Minimally, the solution must have sufficient
performance to run basic daily reporting. Ideally, it would do much more.
Small Load Window - The ETL process (extract, transform, and load) from the
staging repository into the data warehouse needed to be fast. With the previous
implementation, the window of time to run the ETL kept increasing. In some cases it
was taking so long there was concern it would not complete before it was time to load
the next batch of data.
Support Existing Tools - Spice wanted to avoid proprietary, locked solutions and try
to preserve as much of their existing architecture as possible, while delivering on the
performance demands of the business.
Use Existing Hardware - Spice already had a significant investment in powerful HP
servers, the data warehouse solution would need to maximize the existing HP
hardware.
Storage Space -On a large terabyte scale, disk storage itself becomes a significant
cost factor. Spice needed to find a solution to reduce the storage requirements and
attendant costs.
The team also wanted the solution to help them manage database growth and lower the
overall maintenance costs and administrative efforts.
The Foundation of a Highly Functional Data Warehouse
After evaluating potential solutions, the team chose an architecture powered by Sybase IQ -
developed specifically for advanced analytics. Chosen because of its reputation for
performance and data compression, Spice anticipated a significant boost in analytics with
Sybase IQ.
Sybase IQ represented a break from the traditional RDBMS, which -given the problems they
were facing - was exactly what Spice needed. Sybase IQ is a column-based analytics server,
which is extremely good for query analysis and loading. The learning curve is not steep and
Sybase IQ let us easily experiment with data. It is so simple to run that operations work has
reduced significantly, and database maintenance is almost negligible.
The project took about four months with another month spent testing before it was ready for
production. The team used the Business Objects platform for portions of the ETL and
reporting processes, and Sybase PowerDesigner for data modelling.
Spice uses the latest version of Sybase ASE to augment the data warehouse. Sybase ASE, a
traditional RDBMS, is used as a data repository for both reporting and, in conjunction with
Sybase Replication Server, loads external source data in real-time into the Sybase IQ
analytics server. Spice Telecom also uses SQL Anywhere to maintain connectivity to Sybase
IQ from local data sites.
Because this external data is maintained in third party applications running on an Oracle
database, Spice uses Sybase Replication Server Option for Oracle along with Replication
Server to immediately capture data changes in the Oracle system and move them into Sybase
IQ. Replication Server's flexibility also lends itself to a number of other uses, including
extending the life of the OLTP database.
Empowering - Sybase IQ, Faster, Deeper, and Broader Queries with Room
for Growth
Spice stores 12 terabytes of data that -if it were not compressed by Sybase IQ - comprises
about 25 terabytes of information. Sybase IQ gives Spice the ability to analyze trends across
the entire data warehouse. Before Sybase IQ, Spice was spending time and processing cycles
to filter the data down to summary tables just to make it manageable. Now Sybase IQ gives
Spice the ability to analyze trends across the entire data warehouse.
A useful data warehouse primarily depends on having a large enough data sample to identify
trends, but the queries that sift the data must also run to completion within a reasonable
timeframe. The benefits of a data warehouse can range from the daily and weekly reports
used to steer the organization, up to advanced trend analysis that seeks patterns in the
information, patterns subtle enough to escape all but the most rigorous analytics.
Competitive CDR traffic also flows through the Spice system. With Sybase IQ, Spice has
enough analytics power to collect and analyze more of this additional data. The business is
now able to look at and analyze dimensions that were previously out of reach. Analysis that
used to be a pain and took hours or even days to complete has been reduced to minutes and
hours. The space savings is huge. Load windows have reduced from hours to minutes.
Querying is a cakewalk and a pleasure. Users are now able to drill down to very fine granular
levels without any issues. Data quality has also improved as users are now able to easily
reconcile discrepancies.
Happy Users Enjoy Unforeseen Dividends
With the Sybase IQ-based data warehouse, the problem of concurrent users dragging down
the system or being knocked off the system has evaporated. The data warehouse is readily
available to the people who most require the information. In fact, the rejuvenated system
capability has spawned a new batch of “what-if” questions –something that was previously
impossible.
Data Breeds Success and Success Breeds Data
Spice Telecom is riding a wave of explosive growth in a competitive market. Rich business
intelligence is one of the driving factors behind their growth. Spice adds a new customer
every second of every business day. However that very growth adds to the overall volume of
data to be analyzed, creating a massive store of useful information. Spice turned to Sybase to
help it avoid being crushed under the weight of this success. The results have been a 10X or
better improvement in speed coupled with 40-50% data compression. This means the deep
analytics required to keep the business on course will continue to create new customers and
fuel the positive feedback loop now and into the foreseeable future.
Major Players
In Data Warehousing for Telecommunications sector the few major players who have made a
name for themselves are:
• Teradata Corporation
• MAIA Intelligence
• Binary Semantics Ltd
• IDC India Ltd.
• TechAxes
• Business Intelligence software ElegantJ BI’s Integrated Business Intelligence and
Reporting Software capabilities
– Corporate Performance Management, Operational Business Intelligence and
Enterprise Data Management System
• Ingres corporation is a leading provider of open source database management
software for Telecom service providers
Future of BI in telecom industry
Telecom carriers worldwide, including wireline, wireless and cable operators, spent $4.4
billion on BI software, services and system integration in 2010. That figure is expected to
rise by 18.2% to $5.2 billion in 2011 (According to the Yankee Group Report). But only a
few companies are in the advanced stages of BI initiatives. The recent introduction of BI
appliances, such as Ingres ICE Breaker BI Appliance, offers a cost effective solution that
can reduce the typical BI implementation schedule from months to weeks
As per the 12th five year plan (2012-2017), the total number of telecom subscribers is
projected to grow from the present 780 million to 1,200 million during the five year
period. About 25 per cent (roughly 300 million) would be 3G/4G subscribers, which
would require scaling up the infrastructure. The total investment in the pan-India
broadband rollout is expected to be US$ 16.79 billion, while another US$ 9 billion will be
invested in augmenting the transmission network.
Keeping in mind the growth and competition in this sector, tons of data is generated every
day, and companies need to be in a position to analyze this data and use it for better decision
making. Thus, BI plays a very critical role in the Telecommunications industry
References 1. Business Intelligence for the telecommunications Industry Improving the Bottom line
and controlling expenses - Ingres
2. TRAI annual Report
3. Customer Success Stories – Sybase
4. Report on Business Intelligence for the Telecommunications Industry - Improving the
bottom line and controlling expenses by Ingres
