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Featured - 4 items
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:
- How do we provide definitions and standards for our data?
- How do we define, measure and report data quality across transactional, reference and master data?
- How and where is data remediated?
- How do we provide proper context, relationships and lineage for data?
Our Services
- Identify and suggest areas of focus based on enterprise data quality using in-house tools and accelerators
- Help organisations to establish data quality framework fit for their business model and future goals - EDQ versus traditional DQ
- Assist in development of metrics, thresholds and policies, processes, people, roles and metrics needed to measure and track changes over a period of time
- Accelerated setup through tried and tested model to help an organisation start with data quality programmes faster using AI/ML driven automation
- Visualise data quality scorecards to measure data health and take faster actions through generated insights
Our Data Quality Framework
DQF1
Define Data Quality Scope and Approach
DQF2
Define Data Quality Organisation
DQF3
Define Data Quality Process
DQF4
Develop Data Quality Technical Infrastructure
DQF5
Provide Training
DQF6
Rollout Data Quality Programme
Case studies
Leading Bank in India – Data Quality solution implementation
- Challenge
- Approach
- Outcome
Challenge
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.
Approach
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:
- Fine tuning the quality knowledge base in the DataFlux(DQ Product) as per the data provided by the bank
- This QKB was applied to the overall solution frame implemented using SAS ETL studio
- DQ Framework used: DQF1, DQF3
Outcome
- Single view of customer and GUI for user defined field management
- Cleansed data ensuring single version of truth across customer information reporting
- Facilitating customer data analytics
Financial Services Group – Data Quality tool evaluation and EDW Roadmap
- Challenge
- Approach
- Outcome
Challenge
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.
Approach
- Scan – PwC scanned the business and system environment, captured management’s vision/objective and gathered functional requirements from users in each line of business.
- Focus – PwC studied current systems, perfumed gap analysis against requirements and identified feature gaps.
- Act – PwC segregated requirements which could be catered to by customisations to existing systems, developed operational and technical architecture and recommended implementation blueprint for new applications and solution areas.
- PwC was further involved in business case (ROI – cost benefit analysis) along with expected hard/soft benefit for the proposed implementation
- DQ Framework used: DQF1, DQF4
Outcome
- Scalable technology platform for growing financial services business
- Standardise deliveries to channel partners and customers
- Measure and improve productivity of teams and distribution
- Differentiate offerings from competition
Insights
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