Setting up an Effective Data Quality Platform for Pharma
Setting up an Effective Data Quality Platform for Pharma https://datazymes.com/wp-content/uploads/2020/12/CaseStudy_DZLumen.jpg 874 600 Vivek g https://secure.gravatar.com/avatar/927c001dd3a13b6e015a946097c9aa5c?s=96&d=mm&r=gDZLumen TM, a complete Data Quality Platform purpose built for Pharma has helped clients improve Data Quality.
Is your Data Accurate? Most companies cannot answer this question with confidence since they do not have a framework and process to measure quality of data across the Data Continuum. While syndicated data is assumed to be clean, there are multiple data sources that companies deal with, which are not accurate, complete, and timely. This problem is compounded in Pharma, since most data sets are evolving, there are few benchmarks to compare the data against, and IT process maturity is vastly different between organization. Pharma companies continue to spend millions of dollars on various initiatives to improve data quality processes. Despite this, the problem is far from solved. The constantly changing Pharma landscape is bringing in newer, more complex, and larger datasets. Business users are evolving strategies and analytical methodologies to succeed in an ever-increasing competitive landscape.
There is a huge shift in the industry where Pharma Commercial IT is working closely with business teams to enable them with accurate, timely and consistent data. Similarly, new BI platforms and CRMs are making information dissemination faster and with better reach. Data Scientists are looking to get higher volumes and greater complexity of data. In such a demanding scenario, Commercial IT Teams need a robust, automated, and comprehensive Data Quality Framework, that can ensure accurate data to business users, without creating too much additional processing time or effort on current systems and processes. The Data Quality Framework also needs to cater to the ever-evolving business and data needs – meaning extensible and configurable.
Most companies are at a level 2 (Reactive) stage of the Data Quality Maturity Index. We built DZLumen – a purpose-built Data Quality Framework with over 200 Pre-Built, Configurable, Dynamic checks cutting across the Data Continuum and customized to each Pharma data source. DataZymes has brought together its deep domain and data expertise, bringing to build DZLumen. The framework not only enables clients to measure their current data quality maturity levels, but to identify improvement opportunities, and to continue measuring the value of those improvements on Data Quality.
Challenge
A global Pharmaceutical company (one of top 20) had challenges with their data. The brand was performing extremely well in terms of sales, but the Brand and Analytics teams were unable to measure the growth, identify the key growth drivers accurately and replicate them in all regions. Being a specialty product, the challenges were compounded with inaccurate Customer masters, inconsistent and inaccurate data from Specialty Distributors and Re-distributors. Moreover, merging these two data sources led to double counting of vials, resulting in erroneous Incentive Compensation (IC) payouts. In many cases, the company was overpaying the IC, and sales reps were never clear of their performance payouts.
DataZymes approached this problem by leveraging its DZLumen Data Quality Framework.
Approach
Phase 1: Explore
A comprehensive Data Audit covering people, data, processes, technology, and rules was conducted. We identified and mapped current processes, customer pain points, expected outcomes, gap analysis, technology landscape and process map. The inputs included over 30 stakeholder interviews and 500+ emails that were parsed and analyzed.
Phase 2: Customize
DZLumen has over 200 Data Quality pre-built data quality checks that were configured across the data systems. DZLumen’s data quality checks measure data quality across the ETL layer, as well as business level checks in the output layer. The Data Quality checks are built to capture variance, deviation, nulls, incorrect entries and many more such commonly occurring errors across sources such as sales, call activity, patient, hub, access, claims, etc., Once configured, the data quality measurement was a simple task of reviewing the inbuilt comprehensive and intuitive dashboard. Commercial IT and Business Teams had a clear visibility of Data Quality for each source data from each vendor/source and across the various data processing steps. Using this information, DataZymes identified the key areas of concern and improvement opportunities.
Phase 3: Improve
Once the key areas of concern were identified, DataZymes created a plan of potential improvements that could address these areas of concern. The total list of improvement opportunities exceeded 20 in number. DataZymes hence created a complexity – benefit analysis, where the idea was to identify quick wins which provided high impact. 4 key opportunities were identified as quick wins. The rest of the opportunities were presented in a Data Quality roadmap, that the client could use to improve their processes to reach Level 5 – ‘Effective’ Level of Data Quality Maturity.
Result
