In my 12 years consulting for PwC and subsequently IBM, I estimate that I participated in life sciences strategic consulting projects totaling in the tens of millions of dollars in revenue. The vast majority of these projects were commercial IT and data architecture assessments to address key business pain points, or to determine how best to support new requirements in an ever changing life sciences landscape. Each architecture was inevitably complex and resulted in unhealthy complications including low ROI, unmet business requirements, long times to value, and ultimately significant tensions between business and IT.
Developing a good IT vision and implementing the governance and enforcement needed to support it is a major challenge for life sciences companies. Usually, the architectures evolve organically based on point solutions to satisfied business requirements. These are the typical dynamics due to corporate budget cycles, lack of experience, the need to deliver business value quickly, and frankly office politics that do not favor successful governance or changes in direction.
This is why modern data management and architectures are now focusing on the data first. Then subsequently enabling data-driven applications to be rapidly created to solve any business problem. The ability to bring in data sources of any kind into a simple low cost, big data, cloud platform to manage the information, understand it, query it, share it, and make decisions from it isn’t trivial. The quality and latency of access to reliable data is often "the" bone of contention between IT and business, as well documented in the post “Bridging the Gap between IT and Business” by Dr. Tom Redman (aka the data doc himself).
So what will help life sciences companies cut to the chase, and truly allow them to have data-driven applications that both IT and business can agree on? I passionately believe that Data as a Service (DaaS) is a key component in this equation.
What is DaaS? Simply put, it’s the ability for data to be delivered regardless of geographic or organizational separation of provider and consumer. Today data from third party providers is still often delivered through batch files, IT dependent ETL uploads, laborious comparisons of what data has changed between updates, difficulty gauging the quality and value of the data provided, and slow manual communication back to data provider about corrections, uncovered by field teams that could be applied to improve data quality.
@Reltio we believe that data-as-a-service integrated into a modern data management foundation, and fully accessible by data-driven business applications are a game changer. But just sourcing and having on demand access to data is not enough. Data has to be converted into reliable information, by seamlessly cleansing and blending together related data from third party vendors, and social and public data sources, but also across departments within the enterprise. As a bonus it should also be flexible enough to be used as the backbone technology for data monetization.
This is why I joined Reltio, and why I’m focused on recruiting and enabling leading data providers through Reltio DaaS. In life sciences we are fortunate to have companies such as MedPro, DarkMatter2BD, Healthcare Management Systems and Enclarity (now LexisNexis) as data partners through our Delivered by Reltio program. Reltio DaaS benefits both pharma companies and data providers providing a two way on demand consumption of data, and closed feedback loop that can reduce the cost of improving data quality and delivery.
This is truly an exciting time, and I believe fully integrated DaaS is an integral offering that will enable life sciences companies to achieve an agile and data-driven architecture they need to succeed.