A leading bio-technology company was unable to meet process deadlines and lacked actionable insights due to inadequate data management
WNS partnered with the company to set up an automation- and analytics-led center of excellence
The solution enabled the bio-technology company to transform its data governance practice
Pharmaceutical companies possess humungous volumes of data encompassing physicians, prescriptions and patients. However, tracking, managing and governing this data across siloed databases is a challenge. Frequent pain points include inconsistent definitions on who can access the data, inadequate documentation, ill-defined processes and an overall lack of business alignment with the data strategy. A clearly defined data governance framework, underpinned by analytics and automation, can result in data integration and generate actionable insights to drive excellence in business outcomes.
Gaps in data quality and governance were resulting in missed process deadlines, inability to generate actionable insights and low trust among internal stakeholders leveraging the data. Every month, more than 1500 reports and dashboards were generated pertaining to physicians, patients and prescriptions, based on large volumes of data sets. However, as the data was updated manually, the reports were error-prone and the Turnaround Time (TAT) for issue resolution was high.
For example, the Customer Relationship Management (CRM) database contained the addresses of healthcare practitioners and hospitals. Every month, there were more than 1000 requests from field representatives to change / alter the data, which had to be done manually.
The governance around Extract, Transform, Load (ETL) was not well-defined. The process documentation, timelines, Key Performance Indicators (KPIs) and quality of data were also not along expected lines.
WNS leveraged its domain knowledge, and analytics and process automation expertise, to offer a holistic solution for the bio-technology company.
A data analytics Center of Excellence (CoE) was set up and a data governance framework was designed to formalize roles and drive quality indicators, such as accountability, compliance, security, privacy, protection and quality.
Key aspects included:
Deploying frequent checks to ensure data accuracy
Creating process documentation and ensuring future documentation, including:
Data ingestion framework
Data dictionary from source
Data integration including business rules and data model
Data quality including process flow and business rules
Data quality framework
Creating transparency by developing a system of timely updates to stakeholders about refresh, success, failure and final confirmation
Designing a dashboard for senior executives on data health pertaining to indicators such as completeness, accuracy, uniqueness, timeliness and consistency
Defining ownership and access pertaining to data, documentation, dashboard and reports
Building a KPI repository consisting of the most relevant KPIs based on user feedback
The CRM address updation process was automated. Automation enabled the data to be cross-verified across the CRM and national identification provider data, and highlight discrepancies. Analytics was deployed to generate actionable insights on areas such as project optimization and patient analytics.
That as the efficacy of data increases, the quality of business decisions improves. The solution enabled the bio-technology company to transform its data governance practice. Key benefits delivered include:
60 percent TAT improvement in issue resolution
TAT for weekly deliveries reduced from 2 days to 2 hours due to automation; enabled savings of ~USD 100,000 per year
25 percent increase in user acceptance of the dashboards and analytical reports
Streamlined data governance led to faster TAT in downstream activities
Timely publishing of reports and dashboards enabled quick and effective decision-making
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16 October 2019
03 December 2021