Inside an organisation, adequate data quality is vital for transactional and operational processes. Data quality may be affected by the way in which data is entered, handled and maintained. This means that quality always depends on the context in which it is used, leading to the conclusion that there is no absolute valid quality benchmark.
PwC’s Enterprise Data Quality (EDQ) framework offers an inside-out perspective to gauge the key data quality solutions of an organisation and design a comprehensive data quality roadmap to fast track the adoption of key quality initiatives:
Define Data Quality Scope and Approach
Define Data Quality Organisation
Define Data Quality Process
Develop Data Quality Technical Infrastructure
Rollout Data Quality Programme
The Bank collects customer information from various sources like: core banking, credit cards, loans, demat accounts and Third Party MF etc.
So the primary concern was a single customer view in the existing DW after de-duping/clustering. Apart from that, house-holding, standardisation and some augmentation were also part of the solution.
PwC team introduced a Data Quality Solution using SAS technology through which customer information needs to pass before stored in the data warehouse. Approach included:
The organisation wishes to review customer accessing and targeting processes and systems across its business lines (AMC, Insurance and Distribution) with a goal towards revenue upliftment and enabling operational efficiencies, especially in front-line processes, i.e. sales, services and marketing.
PwC was engaged to define functional requirements, analyse gaps w.r.t. current application systems, design technical & operational architecture and recommend tools/solutions (BI, ETL, MDM, CRM and Portal) for implementation in a phased manner.
Client was looking to set up Enterprise Data Quality framework that will focus on building 100% accurate picture of the data as data profiling promotes good data governance. They wanted to set up comprehensive data quality model which would eventually provide accurate metadata and complete metrics for understanding the data as it actually is – rather than how it was designed years ago.
There were a number of challenges while building the strategy for formulating this framework:
PwC utilised the PwC Enterprise Data Governance Framework 2.0 (EDG) that helped the client drive the vision for Data Profiling and Data Quality checks in order to examine the characteristics of data. The following approach was undertaken:
The healthcare client distributes health care systems, medical supplies and pharmaceutical products. Additionally, it provides extensive network infrastructure for the healthcare industry.
The client has actively used master data such as vendor, customer and material masters in various ERP and CRM systems for their master data management operations. The client was facing issues related to incorrect details, data inaccuracy, duplicate data, inconsistent data and issues related to integration of various database interfaces.
Client is a United States-based refiner, transporter and marketer of transportation fuels, lubricants, petrochemicals and other industrial products. Client was seeking profiling of customer data to obtain data quality baseline for critical fields, identify data quality issues, and validate relationships between relevant tables.
The client had undertaken a massive data transformation project. The objective of this project was to migrate data from the numerous (close to 200) existing Billing and Customer systems to Seibel CRM application and Kenan billing application.
In the process, the customer, service, product and provisioning information had to be cleansed and a master data set had to be maintained.