Master data management and addressing the forthcoming ISO IDMP standards
SummaryMaster data management and addressing the forthcoming ISO IDMP standards
AMPLEXOR’s Sonia Monahan looks at what is involved in practice and how can life sciences firms ensure the investment pays off ISO IDMP compliance is more than just another regulatory hurdle for life sciences firms to straddle. It also promotes the kind of structure and discipline needed if organisations want to break new ground and take their businesses forward. It all begins with data though, and with assumptions about its quality, completeness, and reliability as an accurate record of operations.
As EMA’s IDMP requirements near finalisation, the scale of the standards’ impact on managing regulatory data becomes more apparent. The transition from the current xEVMPD submission requirement to the more extensive and rounded demands of IDMP will involve extensive work. Data is extracted from a wider range of sources than just regulatory affairs (i.e. also across chemistry, manufacturing, and controls; clinical; pharmacovigilance; and manufacturing). The same high levels of data integrity and data quality need to extend across all of these business areas, so that the combined data can be relied upon as a definitive reflection of product reality.
Progressing from initial data analysis
After the initial IDMP data analysis the next stage should move to a broader plan for ‘master data management’ (MDM), that will set the company in good stead for wider transformation. EMA’s own ambition for ISO IDMP supports this effort to improve the data’s quality and integrity, thereby increasing its value. This requires getting the underlying data (the master data) in order, using agreed upon standards.
Companies can enhance and add to this source data for their own internal purposes. The idea is that building on the right foundations and using agreed upon terminology will make the complete data set more meaningful and easier to repurpose.
Beyond compliance requirements, companies should be striving for a 360-degree view of product data. This includes a global, integrated view of product information, which supports business processes throughout the product lifecycle and provides a definitive master data set servicing multiple applications.
The journey to MDM
What might MDM best practice look like? A single, finished product takes three forms from an IDMP perspective, including: the pharmaceutical product as administered; the authorised medical product; and the packaged product that ships to market. This is just one indication of the complexity that systems need to be able to cope with to keep data correct and in sync.
The journey to MDM should be viewed as an evolutionary one, though the scale of the transition could be construed as daunting. The important thing is that companies start somewhere and treat developments as a continuum – with people, processes, and technology brought on in parallel.
The importance of good governance
The starting point should be data governance, so with this in mind, it is important to establish early on how quality and consistency will be managed, who owns the data, and who is accountable for its quality and integrity.
Data policies and processes should then provide the documented guidelines, procedures, and tasks to direct data stewards and other stakeholders, enabling them to ensure the integrity, consistency, and sharing of enterprise data resources.
Data stewardship will be critical in extracting value from MDM and IDMP investments. This involves proactive management and oversight of an organisation's data assets. Operationally, the remit can be broken down into a number of clear steps, from initial data profiling/discovery/scoping, and data modelling, to data cleansing, profiling, enriching, matching, consolidating, and relating.
IDMP compliance will require solid data governance and use of MDM principles and processes for data stewardship, irrespective of whether an organisation plans to implement MDM technology to support IDMP or not. Ultimately, ISO IDMP’s main focus is master data. As such, it makes business sense to harness this master data for maximum effect.