Agile Data Quality for Pharma
Pharma and biotech are always in demand of deeper innovation to overcome the health challenges of humankind, while fulfilling strict regulations and being swift enough to remain competitive. How to address the Data Quality challenge in this context? Texelia shows the way…
At some points in time, due to mergers and acquisitions among others, all large companies have to deal with fragmented business processes and a lagging IT landscape. This situation leads to Data Quality issues and, as a consequence, to the lack of a fully reliable company vision at a global scale; to the loss of potential business opportunities; and to an important operational overhead many times not foreseen…
For Data Quality improvement, Texelia’s triple helix holistic approach allows to address at the same time the triple perspective of data, processes and tempo.
Understanding the data is key to design the business rules aimed at mapping the data from different sources. Data Quality of the applications concerned has to be improved from various perspectives (completeness, conformity, accuracy, consistency, uniqueness and integrity).
Also, in order to support a Data Quality initiative, it is necessary to setup appropriate and dedicated business processes. Understanding these processes allows to address the definition of the roles and responsibilities in data management.
Finally, Texelia promotes an iterative implementation of Data Quality initiatives. This agile approach is an important factor in getting top management’s sponsorship, required to support transversal and enterprise-wide initiatives; and in prompting business users acceptance.
For a large international Pharma Company, Texelia was involved in a Data Quality initiative aimed at improving the product-related master data. These data are maintained, in parallel and at different granularity levels, within several applications (mainly the Enterprise Resource Planning system, the regulatory information tracking system, the change control system, and the document management system).
The selected approach was to setup a Data Quality platform in order to progressively implement data consistency checks within and across these data sources. The inconsistencies detected by those Data Quality checks, performed on a periodical basis, are automatically routed to the appropriate business users for review, and eventual update, in the source systems. The Data Quality platform enables also the capacity to collect the feedback of the business users (actions taken and comments).
The main outcomes of this initiative, complementary to other existing Data Quality approaches, were:
- its positive acceptance by the business users as they had to deal only with the inconsistencies there were in charge of;
- the important amount of corrections in the source systems it has been triggering;
- its contribution to the growth of the Data Quality maturity level within the company.