Faced with data collection challenges, how will you navigate R&D decision-making?

When data quality is in question, it's a bit like navigating in the dark. You need to turn on the lights before moving forward. Start by scrutinizing the data you have—clean up errors, fill in gaps, and ensure it's reliable. If the data still doesn't measure up, don't hesitate to hit pause and gather new, more accurate information. It might feel like taking a step back, but ensuring you’re working with trustworthy data is essential for making sound R&D decisions. In the long run, a strong foundation of accurate data will save you from costly mistakes and misguided strategies.

Cleansing the doubtful data, going back to black board,revisiting data, alternate source of the data etc ...all can be avoided if a complete process of data collection, analysis, involvement of domain expert R&D & its outcomes, ... ....everything is well discussed ,agreed documented & followed up. Budgetary control & risk assement are vital .Human elements need to be well taken care of : internal project experts, their ambitions ,professional jealousy, people behavioural patterns ,their willingness to support & share data with external domain experts. Total R&D Process should be full proof.

Understanding market potential in the foodservice channel is particularly challenging. Syndicated data is usually survey-based with questionable accuracy, and competitive pricing, if available at all, may be 2X the actual price after discounts and rebates. Early customer engagement is key: collaborative brainstorming, competitive product tastings and early prototype demos can all be opportunities to glean information on pricing and size of market through customer conversations.

When the data found under question mark, then the basic source of data need to be evaluate for quality of data. If there is something misguide, the source, instructions to data collection from supervisors need to evaluate. Also experience level is too much important factor to evaluate the data and focus on the target goals

Yes, data quality plays a very vital role in any descision making process. Specially in today's AI world data driven approach are very usefull in making business decisions. Data is new gold today...

Its great challenge , and from my opinion the alternative should be aware from the beginning and make your self for all changeable things happens

By tapping into alternative sources, you can often find insights that others overlook. This approach not only fills in the gaps but can also lead to fresh perspectives that might just give your R&D decisions an edge. Sometimes, the best solutions come from the most unlikely places.

When primary data sources are compromised, it's crucial to broaden your search to alternative and unconventional data sources. Exploring public datasets, industry reports, and social media trends can reveal unique insights and provide valuable information, helping you navigate challenges and make more informed R&D decisions.

The successful minds working in R&D must always look for important information in different sources. This is the way to obtain relevant insights and valuable informations. Nowadays leaving out the different sources of information that can be accessed could be a huge mistake. Assess social media, ethnic sources and market reports, can bring wonderful answers. However, this information must be checked and not be considered more important than scientific information.

La investigación interdisciplinaria es muy importante en la construcción de un comocimiento colectivo para el desarrollo en el campo de la producción, la ciencia, la educación y la cultura.

In the field of data analysis one very famous quote is there “If you torture the data long enough, it will confess to anything you want” Data collection and analysis process can be very much exhaustive for those who are not familiar with it due to involvement of humongous amount of data (which indeed is required to get best results through data analysis) and unfamiliar processes of its analysis. This can lead to taking short cuts to arrive at the solution just for the sake of it which is not intended. So, in these situations, taking help of experts of the field will help to great extent and R&D person can support the data analysis expert as domain expert to get the best results out of it.

When data collection hits a wall, it’s a smart move to call in the experts. Bringing in a data scientist or industry specialist can shine a light on things you might be missing. They can offer fresh insights, suggest new approaches, and help you sidestep common pitfalls. Sometimes, a new perspective is all you need to turn a challenge into an opportunity. Their expertise can help you navigate the complexities, ensuring your R&D decisions are backed by solid, well-analyzed data.

Yes, data is so complex that it becomes literally impossible to understand the data without domain expert, domain expert is a person who possess an in-depth knowledge about the domain and can help people understand the data that would be helpfull in finding further insight of the data

Consulting with experts in data science or industry specialists can be highly beneficial when facing data collection challenges. Their fresh perspectives and advanced methodologies can uncover blind spots and offer innovative solutions to enhance your data collection and analysis processes.

Leveraging AI to challenge your initial thesis and identify potential biases and gaps in your argumentation. AI can indeed offer a different perspective, especially when one's own biases might limit the scope of analysis. By using AI as an initial sounding board, you can refine your arguments before presenting them to experts, ensuring a more comprehensive and well-rounded discussion. However, it's important to note that while AI can be a valuable tool, it should complement rather than replace expert input. AI models are trained on vast datasets and can generate insightful counterarguments, but they might lack the depth of understanding and context that human experts provide. Testing it with experts is a balanced and effective strategy.

One of the most important and revolution approach in the industry which helps in gather early feedback from stakeholders and also helps in fail first approach which can be fixed early in the development lifecycle

When data collection gets tricky, adopting an agile approach can be a lifesaver. By breaking down your R&D into smaller, iterative steps, you can stay flexible and adjust on the fly. This way, you’re not waiting for perfect data to move forward—you’re testing, learning, and refining as you go. It’s about making progress with what you have, staying adaptable, and letting real-time feedback guide your next move. In a world of data uncertainty, agility keeps you ahead of the curve

Incorporating agile methodology into R&D can significantly enhance your approach to data uncertainties. By breaking projects into smaller, iterative phases, you can adapt quickly to new insights, test hypotheses effectively, and refine strategies based on real-time feedback. This flexibility allows for more informed decision-making and a more responsive development process.

that's write , huge projects lead a lot of trouble and high load with a lot of mistakes without concentration the better way to make groups along with team leader to control all process required for the RND project

R&D is all about closing gaps in knowledge. As we learn, we understand, and at the very point of understanding, that Eureka moment, that is time to stop, regroup, and decide the next direction of travel. As such, re-evaluate your data, its analysis, and application. Take new knowledge and apply this to your thinking and any prior uncertainty. This will realign your development projects and help you to decide the next move forward.

When data is shaky, a solid risk assessment becomes your safety net. It’s about weighing the potential fallout of making decisions with incomplete or unreliable data. By understanding what’s at stake, you can create backup plans and make choices that safeguard against worst-case scenarios. This way, you’re not just rolling the dice—you’re making informed, calculated moves that keep your R&D on track, even in uncertain times.

While transforming to data-driven decision-making strategy, there should always be risk assessment before going ahead. Firstly, data driven decision making process should be made full proof by taking care of all the critical aspects of the new concepts and secondly it should be supported by strong domain knowledge. If, in case, there is difference between understanding derived from data analysis and experienced based knowledge, very close monitoring should be in place while executing the decision so that any abnormality can be detected in time and corrective actions can be taken to revert.

Evaluating risks associated with incomplete or incorrect data is essential for sound R&D decision-making. By assessing these risks, you can craft effective contingency plans and mitigate potential negative impacts. This proactive stance not only enhances confidence in navigating uncertain data but also strengthens the overall decision-making process.

lets think about the definition of risk assessment which is a process used to identify potential hazards and analyse what could happen if a disaster or hazard occurs. This proactive approach allows you to navigate uncertain data landscapes with greater confidence

Evaluating risks is essential when planning a complex project. Doing so at the beginning of the project will help to understand the strenghts and opportunities of the work, being an excellent way to create a strategy to conduct the experiments. This includes assessing sampling and data collection. If the risk of having limited or questionable data is anticipated early on the project, it may be mitigated or even prevented, and the paths for each scenario can be defined with more time and care, helping to avoid greater impacts on the results.

Create clear criteria to evaluate the data you do have, set thresholds for making calls, and build in checkpoints to reassess as new information rolls in. This structure doesn’t just keep you grounded—it ensures your R&D moves are thoughtful, strategic, and adaptable, even when the data landscape is murky. It’s about staying clear-headed and focused, no matter how uncertain things get.

En investigación y desarrollo, enfrentarse a la incertidumbre de los datos es un desafío constante. Por eso, establecer marcos sólidos de toma de decisiones es esencial para garantizar que las decisiones sean informadas y estratégicas. Estos marcos deben incluir criterios claros para evaluar la validez de los datos, umbrales específicos para actuar y protocolos de revisión continua. Tener directrices estructuradas no solo mejora la calidad de las decisiones, sino que también aporta agilidad y adaptación a la evolución constante de los datos.