Turning The Tide

Data warehousing is a strategy used to

empower employees with access to information about market,



customers, vendors, and so on. How relevent is it in telecom?

 

Competition drives innovation. And no industry

today is seeing competition as fierce and change as dramatic as

telecommunications. Deregulation, mergers and acquisitions, and

technological advancements have brought dramatic changes to the

telecommunications industry. Needless to say, this has emerged as

one of the hotly contested global markets. Traditional

telecommunications providers not only face competitive threats

from each other, but also from cable, wireless, satellite, and

private network providers. In response to the on going and

ensuing competition, telecommunications companies are rethinking

their fundamental business models. They are shifting resources,

exploring new market opportunities, and re-examining their

business processes and are re-engineering to increase revenues

and decrease costs.

They rely on new technologies and opportunities

to gather more and more information about each customer, while

reducing overheads in legacy, proprietary, and inflexible

mainframe environments. And more and more, they are implementing

cost-effective, enterprise-wide systems on parallel, scaleable,

and open database technologies.

In an industry like telecommunications, the

winners will be those that can adapt quickly to new market

opportunities. In this backdrop, telecommunications providers

need mission-critical informational technology solutions that can

combine performance and scale-ability with flexibility and

modularity.

Criticality Of Data

Warehousing In Telecom

In today’s fast-paced

deregulated telecom world, an operator’s business can no

longer revolve around the network, it must revolve around the

customer. When customers are free to pick and choose their

service provider at will, things tend to change. Improving

network infrastructure is no longer an end in itself, but a means

to retain profitable customers and attract new ones. Carrying out

this customer-driven view of the business as opposed to the

network-driven one is the whole point of data warehousing.

Data warehouse is a pivotal technology that

enables a carrier to gather and apply market intelligence on

customer behaviour and network usage. It is a strategy which

gives employees robust access to information about customers,

markets, suppliers, financial results, etc., which enable them to

strategically learn from the past, adapt to the present, and

position for the future.

A data warehouse is subject-oriented,

integrated, non-volatile, time variant, collection of data that

is used in the support of management’s decision process.

This is one of the commonly accepted definition of data

warehousing.

Subject-oriented, because information is

classified and stored in the data warehouse based on those

subjects that are of interest to the enterprise.

Integrated, as there should be

consistency in the naming convention, physical attributes of

data, etc. because the source of data going into the data

warehouse is from diverse segmens like customer care and billing

system, the F&A system, the MIS system, and third party

demographic data. The basic concept behind integration lies in

the fact that the data stored in the data warehouse should be in

a singular, globally-accepted fashion. So, when the Decision

Support System (DSS) analyst looks at the data warehouse, his

focus would be on using the data in the data warehouse, rather on

wondering about the credibility and consistency of the data.

Non-volatile, because unlike an

operational environment, where in regular updates, inserts,

deletes, and changes happen, the data that is once entered into a

data warehouse remains unchanged. Besides this, the information

in it is both detailed and summarized.

Time variant, because the data in the

data warehouse is historical in scope, often spanning a number of

years, to be used in identifying and evaluating trends. Systems Integration:

Key To Successful Data Warehousing

One thing that has to be

understood clearly is that data warehousing is not an of-the-self

product. It is an enabling technology, where the bulk of the cost

involved is in consultancy and implementation of the data

warehouse, which can go up to as high as 60 percent of the total

project cost. Any data warehouse is only as good as the people

who make it.

Systems integration is the key to data

warehousing project. Among the easier steps in the implementation

process is deciding on the software tools to use. Among the most

difficult things to do is to arrive at the "business

rules" that will drive the entire design process. All data

warehousing projects need and must start from the user, for it is

his requirements that the data warehouse is created to address.

For this to happen, the Systems Integrator (SI)

should, at a very basic level, understand the user’s

business both from the business and IT perspectives. The

integrator should have worked on and have an under-standing of

the clients operational systems.

Mostly, the SI will act as the user’s

one-point contact in data warehousing implementation. It is

normally not wedded to one tool vendor but maintains relationship

with a host of tool vendors and, as a result, gives the users an

unbiased feedback on the software tools available.

Deciding On What To

Warehouse In Telecom

Over the last two decades,

extensive computerization has led to the creation of large

volumes of transaction data in organizations, yet decision makers

have been unable to use this data effectively. With

liberalization of the global economies, the very survival of

organizations may depend on their ability to transform their past

and current transaction data into useful information in order to

make fast, well-informed, decisions.

In today’s competitive scenario,

technology must provide information (not mere data) that leads to

the development of a strong knowledge base. This information must

be accurate, timely, and supportive of the organization’s

mission. Information from various sources must be integrated to

enable efficient and effective decision making. It is around this

axiom that the Decision Support System (DSS) be built thereby

enabling organizations to achieve their goals and objectives.

In the telecom scenario, there is a large

amount of data available in a variety of systems. While these are

required for billing and other operational functions, they do not

really meet the management’s information needs. A data

warehouse provides the user the key information that he would

like to have in order to affect better decisions, such as, call

usage analysis, call volume analysis, handset usage analysis,

network utilization and down-time analysis, customer

segmentation, billing patterns, churn and its reasons, customer

satisfaction and dealer analysis, receivable and fraud analysis,

and profitability analysis. Consider the scenario, where marketing heads of

telcos in the metros have problems as under.

• Unable to meet marketshare objectives.

The primary reason for this could be that the

person does not have information needed to understand either what

the customers want or how they use the services.

He needs to be able to analyze detailed current

and historical customer data in order to fully understand how

customers are using the service and use the information to

develop customer segments and profiles. To realize this

information, he will require metrics such as customer value by

services used overtime and historical usage patterns.

• Poor customer services image.
  
  

To address to this alarming situation, he needs

to analyze customer data to understand how customers are

currently being serviced and develop strategies for optimizing

service to high-value customers.

For this kind of information to be generated,

he will require certain critical metrics such as number of

customer complaints, number of customer complaints from

high-value customers, customer satisfaction measures, industry

customer satisfaction measures, and marketshares.

• Unable to offer new services as these
  
  cannot be justified.

In this circumstance, the executive requires

the analysis of how customers use its service, identify patterns

of usage, predict future trends of usage, and then identify the

underlying capabilities needed to support the development of new

services. He might require some further inputs on backlog of

customer demands for enhanced services, number of new services

launched by the competitors, etc.

The same executive could face many more issues,

like:

• Loosing high value customers to the
  
  competitors
• Not meeting revenue-per-customer objective
• Ineffective promotional campaigns
• Unable to bring new services to market
  
  quickly

Besides this, the other decision making

executives, like the CEO, top network operations executive, and

top information systems executive might face a whole set of other

issues to contend with.

Possible issues faced by the CEO

• Not meeting the profitability objective
• Not meeting revenue objective

Possible issues faced by the top network

operations executive

• Unable to make optimal decisions for
  
  deployment of expensive resources
• Poor customer service
• Unable to meet demand from marketing to
  
  provide new services
• Increasing network equipment cost

Possible issues faced by top IS executive

• Cannot respond to information demands from
  
  marketing
• Current systems environment severely
  
  limits access to information

In order to succeed in the changing telecom

marketplace and be able to address questions arising out of any

of the aforesaid issues, the operator needs to plunge into his

operational databases and come up with information that will

assist management in their decision support activity.

The paradoxical thing in the whole scenario is

that the carrier in most cases is generating and sitting on huge

volumes of data which he can barely manage and provide

information on. Needless to say, the carrier operates in an

industry where the number of customers are huge and the number of

transactions even larger. For example, one of India’s metro

cellular operators has a base of around 100,000 active customers

which must be generating an average 500,000 Call Data Records

(CDR) per day.

This is where an operator needs the means to

completely analyze detailed call records to identify historical

usage patterns. If an operator could fully understand how

customers were using existing services, it would be able to

define customers’ needs and predict acceptance of new

services.

The primary

data warehouse applications implemented include customer

profiling, call behavior analysis, marketing product

development, customer retention, and churn

revenue/profitability analysis.

In an increasingly

competitive market, in which customers’ expectations

are also on the rise, businesses will no longer be able

to offer "one size fits all" products. Data

warehousing underpins Modi Telstra’s strategy of

developing products and value-added services that must

meet individual customer needs.

This is where a data warehousing solution would

come into significance. There is no denying the fact that

information and knowledge is the only sustainable competitive

advantage.

The Indian Scenario

Indian telcos are witnessing

stiff competition and companies have to keep their

competitiveness at global levels. As per the current National

Telecom Policy, a rapid acceleration of telecom services is

visualized that would require supplementing the resources

allocated to this sector. In its policy document, DoT has

recognized that improving telephone services would be crucial for

the development of the national infrastructure and to lure

foreign direct investment. Which is why DoT has opened the doors

to independent service providers for both basic telephone

services and value-added services–like cellular telephones

and paging. As a sequel, this has brought in organizations with

high-capacity handling capabilities into the Indian scenario that

generate large volumes of data overtime. Therefore, there is a

concomitant demand for data warehousing in the telecom industry.

Data warehousing is still in a nascent stage

but is going to be a big market in future. Its architectures have

become popular as a mechanism to integrate dispersed data for

user-friendly queries and reports. Data warehousing

implementation is currently being undertaken on a pilot basis.

Vendors have been trying to actively promote awareness of data

warehousing concepts and solutions in India. These vendors are

entering into partnerships with leading Indian SIs–like

DEIL, Tata-IBM, Tata Infotech, and Wipro Infotech which are

actively formulating strategies for targeting this market.