Continue # Infosys Whitepaper 
 parties •   Framework reports incorrect data  and related information crossing the  thresholds  •  Capability to easily configure rules  and threshold values independently  •   Daily automated report generation,  post job completion, enables  independent operations for business  Data quality maturity model Think & Act Local Think Global &  Act Local Think Global &  Act collectively Think & Act  Global Matured Data  Governance model •   Limited awareness  of data quality •   Certain defined  rules for data quality  and integration  for a specific  module based on  production issues  encountered •   Duplication of data  across systems  with no established  system for data  quality validation •   Data inconsistency  across systems •   Organizations /  programs accepting  the impact of  inconsistent,  inaccurate, or  unreliable data •   Steps initiated to  identify corrupt  data •   Gains are defined  more at project  level •   Data inconsistency  across systems •   Organizations  / programs  accepting  the impact of  inconsistent,  inaccurate, or  unreliable data •   Steps initiated to  identify corrupt  data •   Gains are defined  more at project  level •   Establishment  of a well defined  and unified data  governance model •   Regular checks  and reporting  of established  business rules and  data quality on a  defined frequency •   Organization  shift toward  management of  data as business  critical asset •   System information  and data is trusted  •   Key metrics for  data quality is  tracked against the  defined variance  percentage •   Action items are  tracked to closure  for any variances  beyond the agreed  limit •   ROI for data quality  projects is tracked External Document © 2018 Infosys Limited 

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 Realizing the return on  investment (ROI) for data  quality Today, businesses need relevant data  to make informed decisions. Decisions  and communications based out of bad  data carries substantial risks to business  performance. For any data-driven  organization, it is important to ensure  that utmost standards of data quality are  met and the organization has scheduled  processes to validate quality along with the  data that is being pumped in and out of  the organization. We also need to ensure  that a structured methodology is being  followed in data quality metric definition  and its validation on regular intervals.  Few possible outcomes of successful  implementation of a strong data quality  framework are: Marketing: Accurate data helps drive more effective  campaigns for the intended target  audience Shipping: Cost savings and operational efficiencies  achieved with basic address validation  and order-related data quality checks Inventory: Faster turnover of stock Insurance: Complete information about client’s  risk exposure enabling more accurate             decisions on policy costs Banking: Ability to detect fraud patterns and         improved customer service Pharma: Gain more compliance as per FDA         regulations External Document © 2018 Infosys Limited 

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected   


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