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Accelerating the analytics payoff

​​​​Article by Puni Rajah

The pursuit of analytics-driven innovation has become a critical goal for life sciences research teams. The ability to increase efficiencies and effectiveness of decisions from source to submission is the promise from the long-term vision of combining study data, real world evidence (RWE),  and health records. However, control over  process and data, the ability to streamline  at source to reduce variability yet allow access, and control and traceability all present complex challenges. Consequently, many teams struggle with the apparently never-ending loop of IT integration. We invited Philip Henrik Puls, Life Sciences Subject Matter Expert of NNIT, to demonstrate how the value of analytics can be unleashed, even as the underlying data integration remains daunting.

Why have integration and governance, which are not new concepts, been such  a challenge?

Two reasons. The scope of integration to maximize the analytics opportunities means previously disparate parts of the research process now have to come together. This often requires harmonizing work processes, using common vocabulary, and synchronizing data taxonomy. Standards, traceability, and control are renegotiated. The integration project, by default, becomes the place where organizational issues get resolved.  At the same time, technology itself is changing fast and may open up opportunities to transform entire processes. And the volume, velocity, and variety of data, including RWE, demand more aggressive management. Taken individually, these are not monumental
issues. However, as so much needs to change simultaneously, the program  needs a lot more help.

How have you helped customers overcome these issues?

We have taken a holistic approach.  Our foundation is an integrated clinical environment (ICE) that allows research  teams to maintain control. By using  standards, whether they are industry or company specific, data and data exchange processes are inherently more efficient.  This enables faster statistical analysis, and data teams can respond more efficiently  to demands for information. From this  foundation, we have built an entire off-theshelf solution, based on a standard tool  that includes best practices distilled from  our past ICE engagements.

What have you learned from engagements thus far?

We have been encouraged by how effectively agile methods work to build consensus across diverse stakeholder groups. Most teams are used to fixing the scope and getting flexible with price and schedule.  By turning the approach on its head, and enforcing fixed resource and schedule, we have been able to drive focus around scope. We emphasize the need for the design to be intuitive. The task at hand is complex, and people respond better when the decisions put before them are simplified. We also recognize and respect the impact of duality; participants from lines of business need to balance day-to-day operational priorities with transformation engagements.

Isn’t taking a flexible approach to scope potentially dangerous?

Normally, yes. We mitigate this risk with strong expectation management. We draw from our library of use cases to help stakeholders imagine their best-case outcomes. We then work with them to apply design discipline to prioritize the top functionalities required. It is also worth remembering  that, often, a few months have lapsed since the original requirements were drawn up.  The market moves fast. It would have been  necessary to review the scope anyway.  But critical to managing scope flexibility  is NNIT’s reference assets that range from design blueprints, configuration templates, use case documentation, and system  set-up scripts.

Is your best practice toolkit critical here?

Absolutely. Implementing ICE is one of the most complex programs our customers will undertake. We use our methodology and supporting assets to contain risk for our customers. In addition to insight and guidance on how best to structure and organize the implementation, we are able to draw from our technology partnerships with
companies like Entimo, Oracle, and SAS to simplify the technical rollouts. The magic on top of these normal assets is the techniques we can share about making good decisions fast. We have done a lot of the heavy work  to compartmentalize decisions, so that even for the largest life sciences firms, implementing an ICE within an annual cycle is comfortably feasible.

What additional benefits have your customers realized?

Most customers have been driven to ICE  by pressure to increase speed to market. Revenue growth targets, profit goals, and competitors’ successes all motivate research teams to do more, faster, often with less.  And ICE has delivered against these goals. And there has been more. The confidence to achieve more has been buoyed by easier and faster analytics, facilitating deeper insights. The ability to use clean data from multiple sources has fueled curiosity and unconventional approaches. This additional sense of collective “can-do” is the icing on the cake, well beyond quantified objectives.






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