Data Stewardship

HighPoint provides data stewardship for life science & healthcare clients wishing to outsource the time-consuming daily operations involved with this function. Our data management services are designed to deliver clean, consistent and complete information in a timely manner.

The success of data stewardship requires organizations to move toward a culture that views data as a competitive asset and define clear goals for data-quality improvement. HighPoint views data quality as more than an IT related function — it’s a business issue that requires an organization to not only take responsibility for this asset, but to drive improvements.  Appointing data quality stewards help organizations achieve data quality improvement goals. HighPoint’s data stewards have industry experience in life science and healthcare. Our specialists are trained to have responsibility for ensuring relevant information adheres to the corporate data quality standards. Our stewards manage daily queues of hundreds of records to backlogs of grey-area records numbering in the hundreds of thousands.

In addition, HighPoint’s stewards are trained to help with an effective governance strategy for data quality, since governance must cascade across the entire organization to ensure that appropriate accountability is enacted and enforced.

 

The governance duties of stewards are to:

 

  • Ensure the consistency and accuracy of data as it flows from one application to the next
  • Implement governance tasks and achieve data quality metrics pertaining to the accuracy and completeness of information in their domain
  • Be responsible for the elements that support data sharing and master data management objectives (such as official product hierarchies, valuation models, customer segmentation profiles and preferred suppliers)
  • Manage Standard Business Naming Standards, entity definitions, rules specification, data quality analyses, sources of data for the data warehouse, data security specification
  • Support ongoing profiling activities and identify issues with source systems (such as calculation routines and missing values)
  • Create or update document taxonomies and actively participate in the reconciliation of data models.