CASE STUDY OF BIG DATA & DATA ANALYTICS

Big Data Re-engineering a Telecom Market

Share Analytical Application

OBJECTIVE

The aim of telecom industry is a highly competitive one, providing consumers with numerous options and the industry with many challenges. Given this competitive environment, market share mong the Mobile operators is continually in flux, changing by the second. Mobile operators anddevice manufacturers rely on our customer, a Global provider of Telecom Industry analytical solutions, to provide them with the analytics they need to understand and analyze their market share, across the US and Canada, by the minute. These analytics are provided via a telecom market measurement product that our customer sells to the telecom industry.

PROBLEMS

Though our customer’s telecom market measurement product is an industry leader, it suffered from quality issues that resulted in late deliverables, frequent restatements and significant extra effort, including frequent rework. Given the time spent on quality issues, our customer was also unable to evolve their solution to meet the highly dynamic needs of the telecom industry.  And our customer needed to completely re-engineer their market-leading telecom product from the ground up to provide their customers with trusted and accurate data in a timely manner. The system also needed to be more reliable, scalable, flexible and robust given that their many customers had diverse requirements.

SOLUTION IDEA

The consumer data services team sought a fit-for-purpose solution that would consolidate all information requirements in a single environment, and enable reliable, ad hoc analysis and end user self-service. This would accelerate the delivery of critical business performance information to the point of need, in a timely enough for that intelligence to be useful and actionable

• A fragmented, inconsistent, semi-automated solution

• Sub-par data quality which required numerous manual adjustments to the data to meet expected

data quality standards

• An inability to consistently meet agreed upon

Service Level Agreements

• Data not available at a low enough level of

METHODOLOGY

 

• New user interfaces to automate scheduling, collection and metric calculation processes to greatly improve the overall system’s effectiveness

• An architecture that supports rule based processing of data as well as UI-driven adjustments

• Quality control reports and processes

• An architecture to scale up the system for planned future enhancements

• Automation of 10 major and 15 minor processes

• Key new functionality for end users that provides customers with the ability to use both existing market definitions and to create their own custom market definitions

MODEL

• Type of Project:

Data Integration, Data Warehouse,Design, Reporting and Custom UI Developmen

• Team Size:

30 InfoCeptians, 15 Customer Associates
Our Roles:

Project Management, Solution ,Architecture, Database Architecture,

development in Java/Tibco, Cognos, and Quality Assurance

•  Users:

Business Analysts,Senior Management

MEASUREMENT

Our customer was able to earn new customers and retain existing ones with a financial impact of $9 million

·         The number of staff hours required to manage the production cycle dropped by over 40%

·         Manual QC reports were replaced with Cognos reports that greatly reduced the time spent by the production team in data quality related tasks

·         The average metric calculation time was reduced on average from eight hours to forty minutes

·         The number of markets where the data had to be corrected based on customer feedback went from 70 at its highest to 0

·         Severity 1 production incidents dropped from four per month to zero

·         New user interfaces for production planning, data adjustments and metric calculations increased the productivity of internal and external users

·         Alignment of the solutions technologies with our customer’s core strategic technologies enabled our customer to more easily manage their product Our combined team, based in multiple geographies turned a product which was routinely late with low quality deliverables, into a high performing product with high quality and on-time delivery. These improvements were achieved while simultaneously reducing person hours by 55%. To top it off, the project was completed on time and within budget.

ACCURACY

The consumer data services business unit of one of the largest mobile network operators in Europe – one of the top five mobile phone service providers in the world  is responsible for developing and managing advanced data services provided to its domestic customers and other key countries in Europe. These services include the packages that provide mobile Internet access on a range of devices, and applications including mobile email, instant messaging, Google search, news and sports updates, and weather and traffic reports.The result was a new product that delivered high quality deliverables to our customer’s clients while simultaneously reducing the volume of support needed by 55%.

EVALUATION

We helped a leading data analytics provider for the telecom industry in the US to re-engineer their market measurement product. The effort included re-engineering of data integration processes, workflow automation, developing new custom user interfaces and building a set of IBM Cognos reports . These analytics are provided via a telecom
market measurement product that our customer sells to the telecom industry.