Analytics is a critical success factor for the Health & Life Science (H&LS) industry particularly in the sales and operations area, especially since so many organisations are heavily reliant on customer profiling and market segmentation activities. Having this accurate data to hand, allows them to better target their customers/organise their sales team structure and also provides insight on which brands to promote. The quality of this data and therefore the insights generated by them depends on the quality of data that is fed into organisation’s management information and reporting systems. As this data resides in multiple disparate systems, each with its own data source, quality standards and maintenance processes, it means defining and implementing enterprise wide data quality standards is a challenging task.
Why is it an issue?
- The rising threat of generics can only be countered by strengthening customer loyalty. High quality data provides reliable insights into customer behaviour and improved understanding of customers’ requirements and aspirations, which can be exploited to enhance customer loyalty
- Areas like clinical trials and medical investigation capture and process vast amounts of data but unless the quality of this data is up to the mark, its usability remains questionable
- Lack of data quality standards results in incomplete or incorrect data capture, severely limiting its usability in exceptional scenarios like product recall
- There is an increased focus on establishing single views of customers, products and vendors. These initiatives require consistent data quality across the enterprise to successfully bring together data from different source systems and create a single version of truth
- There is an ever increasing regulatory scrutiny of total sales spend and expenses incurred in marketing activities. Effective reporting and auditing requires consistent data quality across enterprise systems.
Deloitte’s enterprise data management provides an integrated approach by helping H&LS organisations establish and implement a data quality framework that would support their analytics strategy:
- Data quality audit: provides an assessment of the quality of data residing in enterprise systems. The audit involves a gap analysis between existing data/systems and where the organisation wants to be. It also establishes if data is fit for purpose and the steps that need to be taken to enhance the quality of data.
- Data governance: lays down data quality standards that have to be enforced across the enterprise. Also defines an ownership matrix identifying the individuals responsible for maintaining data quality in each area, function and system.