Data management

B | Data management

Agenda1

2

3

4

Overview

Approach and methodology

Key benefits

Selected credentials

3Data management |

Data management: Overview

4 | Data management

DataA double edged sword

Data is the basis of management. Poor data quality leads to serious business problems and puts an organization at risk. Moreover, the volume of data is increasing rapidly, and organizations are having a difficult time in effectively managing it. The ability to maintain reliable data is becoming increasingly

important for a business to make strategic decisions.

Typical data issues Typical management challenges Potential business implications

• Poor data capture

• Duplicate master data

• Data inconsistencies

• Obsolete data

• Inaccurate source data

• Inappropriate data capture

• Inaccurate financial reporting

• Inability to identify fraud

• Inefficient business processes

• Poor business insights

• Sub-optimal business decisions

• Loss of market share

• Loss of customers

• Litigation/regulatory fines

• Financial instability

• Financial restatements

• Solvency issues

• Incorrect tax determination

Poor data quality and availability

5Data management |

Data managementThe starting point

Our data management offering aims at ensuring that your data is in a position to be used effectively in enterprise systems and business analytics platforms. An independent review on data quality and reliability of data can provide you with reasonable assurance on data management, and helps in developing frameworks to ensure sustenance of quality of information.

Organizations, as well as regulators, across the world are collecting more and more data about their customers, the market and competition. Once stored, the data is often forgotten on servers where it remains as an untapped source of customer and market insight. Analytics can help harness the power of data stored in financial systems.

Organizations could also be facing data issues, such as:

• Having difficulty integrating multiple legacy systems

• Planning to implement new Enterprise Systems– Business Intelligence, ERP, CRM etc.

• Lack of master data creation processes, etc.

6 | Data management

Approach and methodology

7Data management |

Our approachData Quality Assessment

• Understand the “as-is” ERD, database schema and table structures in ERP• Understand from client any additional data requirements, mapping and transformations• Define and agree on data profiling objectives based on criticality of data tables• Identify the master tables and transaction tables that would require profiling

• Design customized data profiling tests• Identify data gaps between the as-is state and the requirements of the migration blue

print/data field mapping• Perform data profiling to check for data completeness, accuracy and data fitment to

future state • Identify dependencies and collate exceptions

• Report data quality issues/gaps to client team

Understand

Analytical testing

Report

Deliverables/Work products

• Data profiling results

• Electronic extracts of exceptional data records

Dependency

• Client to provide data extracts with checksum for profiling

• Client / Stakeholders to participate in management workshops on assessment findings

Note: The above approach is illustrative and needs to be co-developed with the client based on the client needs

8 | Data management

Our approachData migration review

• Understand the migration blueprint• Understand sample datasets• Understand source and target tables along with data mapping• Understand transformations and business rules applied

• Design customized scripts to compare source and target data• Perform electronic data comparison testing on key fields/tables as per the business

rules/migration blue print• Compare exceptions with planned list of exclusions from migration

• Report data gaps arising from migration

Understand

Analytical testing

Report

Deliverables

• Data migration review report with detailed findings

• Electronic extracts of exceptional data records

Dependency

• Client to provide field level data mapping and business rules and transformation involved during migration

• Client to participate in management workshops on review findings

Note: The above approach is illustrative and needs to be co-developed with the client based on the client needs

9Data management |

Data templates ScorecardsScripts Methodology

Data managementOur tools and accelerators

• Oracle and SAP templates for all major data objects

• 30+ script library to perform rapid data quality assessment and cleansing

• Scoring models and scorecards for data enrichment

• Tested methodology and procedures for various data activities and documentation

EY is well positioned to help organizations maintain and protect strategic data assets. Our in-house scripts for data will identify the following data anomalies for various masters across industries:

• Standardize names, addresses, and phone numbers

• Compare and identify mismatch between two systems

• Identify and review junk records

• Identify data inconsistencies and consolidation issues

• De-duplication, relationship discovery and merge

• Review of the defaults and standard business rules

• Automated row counts and statistics

• Audit error and detail report

10 | Data management

Key benefits

11Data management |

Key benefits

• Improve the alignment of data management activities with business objectives

• Increase the effectiveness of data governance and enhance the efficiency of operations and processes

• Provide an independent review on data quality and reliability of data conversion processes (if any)

• Master data enhancement and clean up

• Improving the return on investment on data and related technologies

12 | Data management

Selected credentials

13Data management |

Selected credentials

The client is a leading global pharmaceutical company, with its R&D, manufacturing and marketing offices in India, was in an endeavor to implement SAP across its global offices.

There were serious concerns about the quality of data existing in the legacy systems. These issues remained unaddressed and the client wanted to act on the data quality issues before migrating to SAP.

• Understood the current data sets and business processes and performed an assessment of “as-is” master data quality including Assets, AR, AP, GL, Vendors and Customers

• Identified data quality issues and analyzed the patterns in occurrence and causes for the same

• Developed a cleansing strategy roadmap for improving master data quality within the available timeframe for their finance and supply chain functions

• Managed the data quality improvement activities including setting up of data quality standards and data collection initiatives including inventory cataloguing

The client is a 16 billion dollar global provider of HR services with various lines of businesses such as temporary staffing, permanent placements, search & selection, HR solutions and in-house services

The client was migrating their financial system from Microsoft Dynamics (GP) to SAP. The client wanted to assess the data quality and perform data enrichment and review the data post migration.

• Assessed the data quality which included the duplicity and referential integrity tests and invoices to payments mapping

• Performed reconciliation between main ledger and sub ledgers– Fixed Assets, Accounts Payable, Salaries Payable, Accounts Receivable, Advance from Customers, Travel Advances and Trade Receivables

• Performed data mapping of chart of accounts in legacy system to SAP accounts

• Performed restructuring and reconciliation of revenue and cost of sales, based on profit centres, cost centres and business areas

Pharmaceuticals HR services

14 | Data management

Selected credentials (cont’d.)

The client is a government run non-profit health care provider in the Middle East with the biggest chain of hospitals and is implementing an integrated suite of Oracle applications namely Finance, Supply chain and HR.

The legacy systems were not integrated thereby causing a lot of discrepancies between the data maintained by different departments. The client wanted to assess and cleanse the data before migrating to oracle.

• Performed a data quality check on the existing data sources across multiple systems including HR, Payroll, Recruitment, Visa and Timesheet systems

• Developed data cleansing scripts and charted a roadmap for the data collection of missing elements like supervisor hierarchy, approval authority levels, SSN, etc.

• Performed a data mapping of the legacy elements to Oracle ERP– Cost centres, positions, allowance elements, organizations

• Understood the business rules and performed transformations that cater to the needs of the new ERP– payroll elements, leaves and compensation structures

The client is an international home products company that designs and sells furniture in 34 countries. The client HR and Payroll data migrated from their legacy system to unified industry standard platform.

The client wanted to mitigate risks on the data available in their system. The client’s HR and finance group wanted an independent evaluation of the quality and reliability of the data migrated.

• Analytical tests were carried out for all key fields. The tests checked for accuracy of data migration and completeness of data in the old and new system

• The pre-audit reviews were used for identifying design level issues, data completeness issues and data mismatches

• The issues identified in the pre-audits were analyzed with the client to determine and fix the root resulting in minimal issues during the actual migration

• Data analytical/Data conversion tests were designed on data from a single country and was replicated to data sets received from the various countries

Healthcare Home products

15Data management |

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