This is our story of transforming data governance for a global pharma leader, ensuring the on-demand availability of relevant and high-quality data for strategic decision-making.
As we know…
Data accessibility is a formidable challenge for businesses. While companies are amassing and generating more data than ever, only a few can harness its full potential. A notable dichotomy prevails as most data producers and technicians focus on metadata, data quality and tracing data lineage. Simultaneously, the demand from data consumers for readily accessible information to address pressing business issues and drive decision-making intensifies. Organizations equipped with high-quality data must have this valuable resource at their fingertips to react swiftly to market changes, capitalize on emerging trends and gain a competitive edge.
The challenge for our client was…
Extracting meaningful business insights from its datasets scattered across multiple platforms. The absence of a centralized data repository and glossary hindered the timely retrieval of relevant information. Redundancies and duplications proliferated, with identical data appearing in diverse formats. Moreover, the lack of a mechanism for purging non-relevant and obsolete data led to an increase in storage and management costs.
In response to these challenges, the firm sought to implement a searchable, centralized and cost-efficient knowledge base to improve transparency and facilitate the ready availability of organizational knowledge.
Stepping in as a consulting and end-to-end data, analytics and AI partner…
WNS Analytics (WNS' data, analytics and AI practice) mobilized a team of data engineering experts to drive a unique solution and service comprising Artificial Intelligence + Human Intelligence. This was delivered by bringing together data engineering proprietary frameworks, best practices and assets from our utility library and collaborating with the client on comprehensively re-engineering the firm’s data ecosystem. We began our journey by:
Assessing legacy complexities
by performing a complete inventory count of data assets and processes across platforms
Developing a roadmap
for the data governance project, ensuring its alignment with our client’s business goals
Ensuring full stakeholder buy-in
for effective data stewardship. This embedded transparent processes in data governance, improved data confidence and enabled continuous system improvement
Leveraging our extensive network of technology partnerships, WNS Analytics' data engineering experts implemented a holistic solution that entailed the following strategic measures:
Data Integration Platform
Implemented a robust data integration platform, dismantling the barriers of scattered, siloed data. In the process, we introduced data quality checks and cleansing procedures and established standards for data entry and maintenance.
Data Glossary
Built a comprehensive data glossary to standardize terms and definitions across the organization. This, in conjunction with a secure, automated data de-duplication tool, eliminated redundancies across the system.
Information Review System
Deployed an information review system, with the agreement of data owners, to periodically identify and eliminate non-relevant and obsolete records. These steps ensured robust data control, instilling greater confidence in decision-making.
Finally, we successfully delivered a robust data governance and data management program to plan and implement operational models. The program defined data strategy, standards and tools for enhanced data quality, compliance and measurable outcomes.
Strategic data integration and governance…
Transformed our client’s operations, ensuring timely availability of high-quality data while driving significant operational efficiencies and cost optimization.
By onboarding multiple datasets into a robust governance model, we enhanced data asset optimization and decision-making. Automation of numerous data quality rules saved substantial man-hours annually, reducing errors and increasing the precision of our analytical models.
Furthermore, the use of a unified platform helped identify redundant and interconnected data elements, leading to the development of more efficient storage solutions and substantial cost savings.
Key measurable outcomes included:
percent increase in operational efficiency by integrating 15 datasets into a structured and comprehensive governance model
man-hours saved annually across suppliers by automating 683 data quality rules