How to save millions on R&D budget?

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Your R&D team should research but where it actually spends a lot of its time on is gathering information.

Let’s begin our brief session together with a simple question: How often you find desired information on a single page and at a first attempt? My best guess is once in a blue moon. With information scattered across the web, it’s difficult to find full information at a first go.

An R&D team, in an ideal scenario, should get the right information for their research but what they get is partially incomplete or many a time incorrect information. At present, information gathering workflows of many R&D teams take too much time away from them or their workflows involve performing ad hoc tasks and work around to get the desired results.

An access to right information at the right time separates a winning R&D team from a losing one. Yet among many problems, the information flow/information gathering is still one of the major. It leads to delayed projects and waste of resources which hit the research productivity.

Is there a solution?  Well, yes there is. However, before we discuss that, let’s first find how big a hole can this “not finding the right information on time” create on budget of an R&D team.

What is the cost of not finding the right information on time?

Outsell Inc, a research, and advisory firm, in its 2007 report in which it surveyed 6300 R&D staff members found that a corporate R&D professional (scientists and engineers), per week, spends on average 5.5 hours in information retrieval and further 4.7 hours in analyzing and using it.

Let’s assume out of the 10 hours, 3 hours get wasted in sifting through and analyzing irrelevant information. Thus, in a year, approximately, a single R&D professional spends 156 hours on irrelevant information.

Let’s infuse life into these stats. Let’s consider an organization has 500 R&D Engineers with an annual salary of $100k. As per my calculations, such a company will lose $2888000 (two million eight hundred eighty-eight thousand) to the wasted hours. Here is the breakdown of my calculation:

In my calculations above, I used only one scenario under which I assumed few criteria to calculate the cost of finding relevant information after sifting through a lot of irrelevant information. A whitepaper by IDC considered another scenario where the analysts also took opportunity costs of investing time in sifting through a lot of irrelevant information into account.The analysts found the opportunity costs for an organization having 1000 knowledge workers with $500,000 revenue per employee calculated to be around  $15 million per year.

How Can North Star Help Here?

We are researcher also and we faced the similar problem though in a different manner and we built a tool called GreyFox for internal use which eliminates irrelevant results from the search.

After working with R&D teams of many fortune 500 companies for more than a decade, it dawned on us that we can solve their problem as well and it led us to develop North Star for R&D teams where they get the curated information they need in a customized dashboard tailored for their specific industry.

To give you an example, if you are from battery domain, say from Li-Ion, you will receive a dashboard where you will find all the problems listed in an intuitive dashboard which the industry is solving viz capacitor deterioration, short circuit, thermal runaway.

Let’s say your team is focusing on solving the thermal runaway issues and you want to know what all is happening in the domain, then clicking on Thermal Runaway will open curated solutions disclosed for the problem. This will save you and your team from investing time in information collection and filtering irrelevant information. You can jump to the analysis of information part quickly.

Next possible question you may have how we get relevant information for you? Well, to arm your R&D team with right information, we first use AI algorithms to fetch data of a problem of your tech domain. After that, our team of researchers– consists of engineers and PhDs, run a manual analysis to remove irrelevant information. This combination of machine and human intelligence increases the signal to noise ratio by multifold.

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