The information technology research and advisory compan Gartner estimates one third of all business intelligence projects fail to deliver the ROI and evidence suggests that number rises significantly when internationally driven pharmaceutical projects are examined.
To rationalise and save costs, pharma has attempted large-scale global or regional customer relationship management (CRM) and business intelligence projects. In many cases, the centrally-driven CRM projects have tended to be successful; on the other hand, this success does not appear to have been replicated with business intelligence.
More granular data is becoming available all the time and there is a need to gain real value from pharma’s significant investments in both data and BI systems to unlock opportunities at the local, regional and global levels. However, frustration exists due to the failure to provide individuals – wherever they sit in the organisation – with accurate and relevant business intelligence, so that decisions can be made on a firm foundation. The hard truth is that the default approach to enterprise business intelligence has not worked.
Centralised data warehouses are not for everybody
With large-scale centrally driven data warehouses, standardisation tends to be a key goal. This implies harmonised data models, standard definitions and common approaches to business rules and structures.
However, business intelligence solutions are truly valued and well-used when the information is accurate and relevant to the individual. That individual in pharma can come from a myriad of different roles concerned with regional, national, local or account level information, each having their own needs regarding the insight the data provides. A UK specialist sales rep with brands promoted through a complex supply chain, involving for example homecare delivery, has different information needs from an Italian primary care brand manager where wholesaler data is the only source for competitive information.
When the above is overlaid with a centralised large-scale data warehouse approach the two worlds collide with the millions invested never turning into a solution that is widely used on an on-going basis. Too much tends to be ‘standardised’ needlessly, at great expense, while missing the whole point of what success truly looks like – a widely-embraced business intelligence solution that continually evolves to reflect accurately information relevant to the individual.
What does success look like?
In order for business intelligence solutions to be successfully implemented they must be:
- Relevant: reflecting the local conditions such as markets, supply chains, reimbursement statuses, business structures and other local rules and dynamics
- Accurate: providing the right data sources to provide the right insights
- Designed for change: providing a solution in which, within a service model, common elements are readily and easily changed
- Centrally and locally owned: when both central and local individuals perceive they are getting real value from the business intelligence solution then significant road-blocks to success are removed.
Can business intelligence really be successful when driven on an international scale? Undoubtedly, yes. A combined top-down and bottom-up approach has been proven to be successful. The key is to not ‘boil the ocean’ by trying to harmonise everything into one large-scale data warehouse – do only what is worthwhile when harmonising. A successful International business intelligence solution needs to do the right thing in the right place; international alignment and standardisation is important but only for the right elements, equally local flexibility is very important but for what really matters.
Commonality should exist for the platform infrastructure and approach where patterns can be successfully applied, for example in terms of hardware and software choice, visualisation style, quality controls and project governance, and a core of central shared data sources. However, this must be balanced with an architecture and methodology that has the flexibility to allow for local data sources, local business rules, local structures and individual insights to be integrated into business intelligence solutions pertinent to the right level – be it at a country, regional or global level.
