Data Quality v2 - Cognizant · PDF fileInformatica Data Quality (IDQ), will help set up a ... customer data) —Establishing a Data Quality oriented 'Governance Committee' to...

Cognizant’s Data Quality Ring

Cognizant offers Data Quality services using its

Data Quality 'Ring' methodology. The 'Ring'

methodology helps the business establish a logical

and sustainable process to monitor Data Quality

and apply quality controls. The 'Ring'

methodology, when used with best-in-class tools

such as Informatica Data Explorer (IDE) and

Informatica Data Quality (IDQ), will help set up a

high quality information environment.

Some of the best practices incorporated as a part

of the Ring methodology are:

— A cross-domain approach and technique for

improving Data Quality (not restricted to

customer data)

— Establishing a Data Quality oriented

'Governance Committee' to oversee the Data

Quality program

— Identifying and establishing ongoing metrics to

monitor and communicate the state of data

quality to key stakeholders across the

organization

— Attention to data coming in from outside

organization boundaries, e.g., POS, EDI inputs,

etc.

The Quality Ring to Safeguard your Enterprise Data

• Cognizant Solution Overview

Solution Overview 2016|

Perspectives on Data Quality in the organization

are still mostly based on assumptions and notions

rather than on facts and measures. Very few

businesses have matured to a stage where metrics

are used to constantly assess and communicate

the state of Data Quality in the organization. It has

been observed that poor Data Quality has costed

organizations 10-30 percent of their revenue.

With time, businesses have realized that Data

Quality issues are no minor irritants for IT but

glaring problems that impact strategic initiatives

and prevent achievement of goals and efficiency.

The quality of enterprise data is now tightly linked

with the success of other key initiatives including

Governance, Risk and Compliance Management,

Master Data Management and Customer Data

Integration. The need for error-free data has

never been stronger or clearer.

Investment Justification

QualityMonitoring

Cleansing Project Kickoff

Data

Cleansing

Building

Business Case

3

1

2Assessment

Challenges for an Organization

Some of the key data-related challenges faced by

organizations include:

Missing or incorrectly referenced data

Conflict between metadata standards

and actual stored data

Records that have out-of-date values

Repeated records or attributes

Same entity with two different values

in different instances

Missing or unreliable dataCompleteness

Conformity

Consistency

Accuracy

Duplicates

Integrity

Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process

outsourcing services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in

Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry

and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100

development and delivery centers worldwide and approximately 221,700 employees as of December 31, 2015,

Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked

among the top performing and fastest growing companies in the world. Visit us online at or flow us www.cognizant.com

on Twitter: Cognizant.

Completeness Conformity Consistency

Accuracy Duplicates Integrity

Modify unusabledata or sourcemissing data

Ensurestandard

data format

Identify valueswith incorrectinformation

Treatment ofincorrect/

out-of-date dataDe-duplication

Re-referencingof data

Step 1: Data Quality Assessment with

IDE / IDQ

Scoping &Data Profiling

Rule/Transformation

Building

DataVerification

AssessmentFeedback

Build Quality Rules

Repository (QRR)

This step is used to unearth all data related issues.

It is not only “bad” data that always constitute

data issue. Non-compliance with the business

rules of the organization can also constitute a

quality challenge. Hence, Cognizant begins the

assessment by building a Quality Rules

Repository (QRR) to use as a benchmark for

assessing conformity and consistency of data.

Finally, the quality of data is assessed using QRR

and presented using the Informatica Data Quality

(IDQ) tool.

Step 2: Building Business Case

investments for the data cleansing and iterative

quality control initiative. Here, Cognizant helps set

up the required governance committee policies to

guide the Data Quality initiative on an ongoing

basis.

Step 3: Data Cleansing

In Step 3, Cognizant undertakes the necessary

steps to rectify the errors highlighted during the

assessment phase. Once the current data errors

have been corrected the processes to re-assess

data on an ongoing basis are set.

Sustenance:

The rules and metrics needed to sustain an error-

free information environment were established in

Step 1. These can now be used to develop the

reports or dashboards required for ongoing

monitoring. If any quality issues are identified in

the future, the Ring methodology can be used

again to rectify them.

Cognizant’s Data Quality Expertise

— Well trained and experienced Data Quality

professionals, including IDQ professionals and

IDQ certified consultants

— Projects across the globe

— Expertise in leveraging Informatica Data

Quality for assessment and rectification of

data problems

World Headquarters

500 Frank W. Burr Blvd.

Teaneck, NJ 07666 USA

Phone: +1 201 801 0233

Fax: +1 201 801 0243

Toll Free: +1 888 937

Email: [email protected]

European Headquarters

1 Kingdom Street

Paddington Central

London, W26BD UK

Phone: +44 (0) 207 297 7600

Fax: +44 (0) 207 121 0102

Email: [email protected]

India Operations Headquarters

#5/535, Old Mahabalipuram Road

Okkiyam Pettai, Thoraipakkam

Chennai, 600 096 India

Phone: +91 (0) 44 4209 6000

Fax: +91 (0) 44 4209 6060

Email: [email protected]

About Cognizant

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