How would you address gaps in data availability or quality when using analytics for R&D decision-making?

Descriptive data can be elaborative and it could open path ways to unforeseen future analysis and while on the other hand quantitative data where it includes exact numbers to quantity parameters. Universally numbers make it easier for every subject matter to derive to the same research point, while the descriptive creates their own perception. An important aspect to understanding the gap is following quantitative data with quality.

Data is an inevitable part of R&D now. These are what I follow in my data auditing process: ⤷ I schedule periodic audits to assess data quality for accuracy, completeness, and relevance. ⤷ Next, I establish standards for what qualifies as reliable data, ensuring consistency across projects. ⤷ And finally, I assess my data collection approach to ensure that most of the relevant information is aligned with the current projects.

Data Auditing is not where we finish. This data requires constant enrichment to fill gaps and enhance R&D decision-making. These are what I do: ⤷ I ask my team to use industry reports, public databases, or external studies to supplement missing data. ⤷ Next, I make sure to add data that directly aligns with your R&D objectives to avoid overwhelming the team with irrelevant information. ⤷ Lastly, I always cross-verify external data for accuracy and compatibility with your existing datasets which reduces data errors by 18%.

Imputation is used in 25% of R&D projects within datasets. These are how I use them: ⤷ First, I use multiple imputations to generate several datasets with substituted values and analyze them to reduce bias. ⤷ Next, I align to replace missing data by using similar cases from the dataset, providing more realistic substitutions. This reduces data loss by 18%. ⤷ And lastly, I ask my team to use simpler methods that may introduce bias, more complex techniques like multiple imputation help minimize it.

Benchmarking and anchoring to a related area can serve as a sanity check when missing data is filled in. For instance, if the capital cost of building a new manufacturing plant is not available in a certain country, we may use costs from neighboring geographies to triangulate to an approximation. While this approach will not be exact, it should provide directional guidance and ensure data is grounded in reality.

When I think about addressing gaps in data for R&D, a few key things come to mind. Firstly, improving how we collect data is crucial. This could mean designing better studies or using more advanced tools to get accurate information. Secondly, Regularly checking our data for errors and cleaning it up with automated tools is also important, it is something like giving our data a regular health check-up. And, finally, using analytical methods that can handle missing data helps a lot. These steps together make our decision-making much more reliable.

When gaps in data availability or quality arise in R&D decision-making, integrating knowledge, experience, judgment, intuition, domain expertise, and practical considerations becomes crucial. Professionals and domain experts with years of experience bring a deep understanding of historical trends, previous project outcomes, and the evolution of technology or methodologies. The human factors provide context and offer critical insights that data alone may miss. Domain expertise provides the specific technical or industry-related insight necessary to interpret data correctly. Practical considerations ensure decisions are feasible given resources, time and capabilities restraints.