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Efficient R&D Teams Use Data-Based Decision Making

By August 5, 2020No Comments

Using data, or rather, insights drawn from data, to make top-level decisions is a fairly recent phenomenon in business. Between 2005 and 2010, the share of manufacturing plants utilizing data-based decision making grew from 11% to 30%.

Data-based decisions, which require not only data collection but analysis as well, rely on a combination of human input, automation, and artificial intelligence to uncover insights. Leaning on data as part of the decision-making process eliminates some human biases (whether known or unknown) and reliance on observations or gut feelings alone. A commitment to data-based decisions can also, over time, result in significant ROI. A 10% increase in data accessibility could lead to more than $65 million additional net income for a typical Fortune 1000 company. 

Innovation teams collect a lot of data through research, experimentation, feedback, and numerous other methods. This information informs many aspects of research and development. The potential company-wide impact of data accessibility and processing is mirrored in research and development departments. 

Opportunities for R&D Efficiencies

Data collected from within an R&D team and its collaborators can help eliminate inefficient workflows, as well as identify opportunities for automation. Utilizing data to make research and development decisions can help engineers and scientists prioritize their innovation efforts and complement other departments as well. For example, our Technology Vitality Report (TVR) ranks ideas based on novelty, allowing R&D teams to focus efforts on investment-worthy innovations. Using data, including predictive analytics, to determine customer needs or new product pricing can also inform research and development’s role within an organization.

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