Back in 2003, when R&D Logic was a young and proud survivor of the dot-com bust, and a pioneer in cloud-based software solutions (eventually dubbed Software as a Service, or SaaS), The Economist published an article (http://www.economist.com/node/1747329) which drew insight from Carlota Perez's book “Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages” (Edward Elgar, 2002).
We are all a bit tired of hearing "big data" is the big deal of our time. And we all know that there's nothing really big about data unless you can convert it into information, then gather insight, and create knowledge.
If you have asked yourself whether automating FP&A in R&D when your business model keeps evolving is a contradiction in terms, you are not alone. How can you automate FP&A business processes within an ever-changing context?
In an R&D-intensive environment, roles are highly interdependent. Specialization of knowledge—both business knowledge and scientific knowledge—leads to specialization of language. But high performance arises from fostering and leveraging the interdependence. So the fundamental need is to foster collaboration. What does that have to do with tracking time in R&D?
You can only measure what you can see
Visibility of your resources is about knowing at all times who is doing what. As we mentioned in our last blog entry, tracking time in R&D is really tracking people's time and tracking project time. In an R&D-intensive environment, that means seeing what's going on both across departments and among active projects in your portfolio. But measurement goes one step further than who and what: you have to gauge how much. Effort to projects is where you begin to create records and, from those records, patterns that you can leverage to recognize your strengths, weaknesses, opportunities and threats.