# Infosys Whitepaper 
Title: A framework to increase ROI through quality data 
Author: Infosys Limited 
Format: PDF 1.7 

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 WHITE PAPER A FRAMEWORK TO INCREASE ROI   THROUGH QUALITY DATA Kuriakose K. K., Senior Project Manager 

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 External Document © 2018 Infosys Limited 

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 The perception of data across  organizations is changing. Data is no  longer just one of the components of  business. It has turned out to be ‘the  business.’ Today, information is viewed as a lifeline  by senior management and by many  other departments for decision making,  customer interactions, and optimum  operations. An organization’s success  heavily depends on how it is able to  understand and leverage its data.  Unfortunately, there continues to be a  high amount of inaccurate data within  enterprises today, despite developing  multiple solutions/ systems to counter  the same. Nowadays, a major portion  of data for decision making is collected  from external sources through a variety of  channels. This often results in poor data  quality, which has a negative effect on an  organization’s ability to take decisions,  run business, budget, market, and gain  customer satisfaction. Organizations  which fail to control the quality of data, is  unable to sustain in today’s data-centric  world. Any data-driven effort needs to  have strong focus on data quality, which  implies that organizations looking for  success are mandated to prioritize data  accuracy and accessibility. It is essential for  them to interact with consumers, vendors,  suppliers, and third parties in countless  ways, by exploring diverse new methods  of communication. Information is the  key for areas like inventory management,  shipment, and marketing. The objective of  this paper is to analyze principle challenges  with data across few key business functions  and discuss a framework which can bring  down the erroneous data that is getting  pumped in and out of an enterprise  system.<|endoftext|>Marketing: If we have accurate information on who  our customers are and what their needs  are, we have hit gold in Marketing terms.  This is more easily said than done as today  we neither have an accurate nor enough  information about customers. We can  surely gather information about customers  from various sources like- website, physical  store, mobile application, call center, face  to face, catalogues etc. But, one can never  be sure if they are same or different set of  people consuming your services. There’s no  surety of the information being accurate as  most of these channels accept data directly  with limited to no validations.  Now let’s assume that we have done all  possible validations and found out the  target group of customers but there’s still  no defined method of reaching them--  should it be through emails, telephonic  conversations, social media, physical  address etc. Let’s drill this down into one  of the mediums as--physical address. The  catch- the customer has many addresses  like for credit card, savings bank account,  driving license, and for office purposes.<|endoftext|>Shipping The current status of shipments is  constantly added to enterprise systems  through shipping vendors like DHL  Express, DHL Parcel, United States Postal  Service (USPS), United Parcel Service (UPS),  FedEx, Canada Post, LaserShip, OnTrac,  and Hermes. Most of these vendors do  not even share shipment history, hence  organizations are forced to store and link  this continuous flow of information. Many  times incorrect data gets fed into system  through these external sources. We see  data like order shipment data being before  order book data etc. This results in: •   Order lost in transit •   Incorrect shipping address •   Order sent to wrong address •   Shipping wrong items •   Late shipments External Document © 2018 Infosys Limited 

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 Inventory Inventory management can help a  manufacturer / supplier in improving  accuracy, cost savings, and speed. This in  turn will help organizations have better  control on operations and reduce cost of  goods. Today, most of the manufacturers  are facing challenges in inventory  management systems. Few challenges  listed below:  •   Limited standardization in  management systems, business users,  inventory integration, and movement  checkpoints  •   Limited inventory reconciliation on  regular intervals •   Data discrepancies between demand  planning and inventory planning  systems •   Improper logging of inventory  information •   Inaccurate data fed into forecasting  systems Banking Financial organizations are required to  meet regulatory compliance requirements  according to the law of the land to avoid  instances such as housing crisis. At the  same time, data quality issues lead to  transparency and accountability problems.  Hence, quality of data for banking needs  to be measured along the dimensions  of completeness, accuracy, consistency,  duplication, and integrity. There is also a  need to ensure information that is being  shared complies with information privacy  and protection laws and regulations.<|endoftext|>Pharma Pharmaceutical industry gets warnings  on regular intervals for falsifying, altering,  or failing to protect essential quality data  on their drug manufacturing process and  its validation, resulting in huge business  risks. According to US Food and Drug  Administration (USFDA) regulations,  pharma companies are mandated to  maintain manufacturing and drug testing  data. Many times, issues occur due to  human data entry errors and machine  errors like data recording failures.  These regulations have even resulted in  shutdown of plants causing huge losses.<|endoftext|>Today’s state  Many organizations are constantly  investing in data quality to improve their  efficiency and customer interactions  through data insights. Majority of the  companies are suffering from common  data errors like incomplete or missing  data, outdated information, and inaccurate  data. This level of inaccurate data  jeopardizes business that relies on business  intelligence for taking key decisions. An  organization’s current level of maturity can  be assessed from data quality maturity  model given below: Insurance When it comes to insurance industry, data  not only helps run operations, but also  helps them ensure that claims have the  required and correct information. Gener- ally, the following issues are found in the  claims data:  •   Invalid diagnosis codes •   Incorrect pin codes of hospitals •   Incomplete forms with missing crucial  data like gender, patients’ stay in  hospital, etc.<|endoftext|>•   Inaccurate age data  External Document © 2018 Infosys Limited 

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 Automated data quality  framework: This calls for a need of a strong quality  framework, which can validate standard  business rules against the processed  data coming from external sources into  enterprise systems. This framework should  be able to report incorrect data and related  information.  A framework which has easily configurable  rules and threshold values can be set by  business using simple text through a user  interface directly into framework. The  framework can connect to almost all kinds  of data sources — mainframes, file systems,  relational database management system  (RDBMS) systems, analytical databases  such as columnar, massively parallel  processing (MPP), in-memory data base,  NoSQL databases, Hadoop, web services,  packaged enterprise applications, OLAP  applications, software as a service, and  cloud-based applications.<|endoftext|>The details of common business rules  are also collected by our subject matter  experts (SMEs) in retail, consumer  packaged goods (CPG), logistics,  manufacturing, banking, healthcare,  insurance, life sciences, capital markets,  financial services, cards and payments,  energy, utilities, communications, and  services. This helped in the creation of  a backbone for our standard quality  framework where one can add / remove  rules according to the specific business  need.<|endoftext|>The user can pick a set of business rules  and schedule it according to their need.  An automated report gets generated  which is emailed to the concerned  parties. It is recommended to go with  open source solution to bring down the  cost of development and maintenance  of the tool. We have used a combination  of tools--Talend and Python scripts for  the development. This framework can be  based out of other open source solutions  like KETL, Pentaho Data Integrator - Kettle,  Talend Open Source Data Integrator,  Scriptella, Jaspersoft ETL, GeoKettle,  CloverETL, HPCC Systems, Jedox,  Python, Apatar. The framework can also  be enhanced further to carry out data  profiling and data cleansing on an “as- needed” basis.<|endoftext|>Infosys Automated data quality framework  consists of a configurable data quality (DQ)  framework with built-in, reusable rules  across domains with the following: •   Standard business rules which can  validate the processed data and  information from third