How can you ensure your R&D team interprets data correctly?

It is essential to make certain that the team comprehends the potential implications of their findings and adheres unwaveringly to ethical standards in their work. Establishing a supportive environment that fosters precise data interpretation within the R&D team is pivotal. Consistent communication, continuous training, and a steadfast commitment to quality will collectively enhance the team's ability to interpret data accurately, thus significantly contributing to the overall success of research endeavors.

While defining goals is important, I believe asking the “right” questions and being open to any answer that presents itself through data are even more critical to successful interpretations. By “right” questions, I’m referring to unbiased, open ended, and objective. The “wrong” questions will inherently lead the team to inaccurate, biased, and potentially altogether incorrect interpretations. In many cases, it takes an outsider view or new eyes to truly help distinguish between right and wrong, biased and unbiased, objective and subjective. Seek fresh eyes!

1. Always keep in mind what the end customer needs and if the data you are using ultimately affects the way the product is used by them. It isneasy to get bogged down with details that do not matter in the overall purpose. 2. Ensure you have sufficient expert interpretation to incorporate user variability into your analysis. 3. Finally have a clear and representative training and validation set.

It is quite simple a) experimental design, b) using controls c) use relevant models. -experimental design is critical component that is to say the parameters one measures need to be relevant to your next decision inflex point Control needs to be robust relevant and above reproducible. Relevant models it is plural for a reason a single model rarely provides enough data to confirm further pursuit

Often the most difficult part of product development is coming up with the initial idea. Ideas can come from a variety of sources. Some ideas are for a brand-new product and some are for a line extension of an existing product. Once ideas have been generated, it is important to identify the target customer. Trends are followed closely to determine what is new and upcoming. Trends often spark ideas for new products. Trends change over time, so it is difficult to list current examples, but convenience products, comfort foods, and plant-based foods have been trending over the last few years.

Cependant, selon moi il ne faut jamais partir avec une idée conçue en tête du résultat de l’expérience afin de ne pas orienter de manière inconsciente l’expérience ou l’interprétation des résultats.

Data interpretation starts with a well-designed experiment, much like scientific research that relies on precisely defined hypotheses for success. When you have a clear understanding of your R&D objectives and questions, it becomes easier to choose the appropriate data type, format, and even the scale of the experiment. This clarity allows researchers to concentrate on relevant data and essential parameters, avoiding distractions from insignificant outcomes. Ultimately, a well-posed question leads to more definitive answers. Without a good question, we cannot expect a satisfactory answer.

The importance of data interpretation is evident, and this is why it needs to be done properly. Also, data integrity is significant and tends to be extremely subjective and that can be achieved through setting up right goals, deep dive into the problem statements, performing qualitative analysis, create diverse analytical methods to categorize, clear baselines while answering some critical questions, summarize, review, and create relevant conclusions for the critical data points. Also, a scale of measurement should be defined to summarize the findings to have right interpretations.

In our snake venom AI project, we tackle data like we're hosting a comedy wildlife show. Setting goals is as thrilling as revealing a hidden snake world – always keeping the team excited and giggling. Studying venom with AI is like a snack party, where every fact is a deliciously fun bite. When it's time to analyze, we turn into Sherlock Holmes in the jungle, humorously uncovering nature's secrets. Our team chats are like sharing funny animal tales, making complicated science easy and entertaining. Brainstorming? It's a wild safari of ideas, where creativity roams free and laughter echoes. This mix of humor, clarity, and sharp focus makes understanding snake venom not just accurate, but an adventurous and hilarious journey.

The great cycle of life! Congratulations on getting back to the things you love and do so well! I know you will lead this extra-ordinary company with your great wisdom and many years of experience. Looking forward to many more years of innovation!

Quality of the data is critical. One should know what are the sources of variation of the measurements and how they are controlled (if they are). Then take them into account in the analysis (blocking, random effects or covariates). This should be considered beforehand to the production of data, but during data collection all relevant observation impacting data quality must be collected as well. This will allow to explain variability and explain outliers.

Understanding the quality of the data and data collection methods is critical. Researchers often aren’t aware of all the sources of noise that are impacting their experiment. Confounding factors may indicate a response that would actually be attributed to a different factor or interaction. Additionally, measurement technique can have major impact on results and the uncertainty. A good rule of thumb is don’t start running costly experiments until you intimately understand your measurement system.

Even before understanding where your data comes from, you need to to establish the 'Class' of data required. Very simplistically you can categorise them into 3 classes:- Class 1 - Observable not easily collectible - e.g information on fairly upset customers at a hotel concierge who do not send written complaints. Some 'ground-level' human sensing is required. Class 2 - non-exhaustively collected data - e.g survey data that typically don't go in as input into the Machine Learning models you develop Class 3 - those that are available in digital format Once you develop a feel for the 'Class' of data that you require then you can think about how 'data representable' your problem is and subsequently where you data sources come from!

It is so important to remember the basics! Two decades ago, we embarked on HTS journey under the premises that with enough data would self-assemble and give us the answer to a challenging problem. But what we really needed to do was develop a hypothesis, test it ; observe the data and then draw a conclusion. Sometimes the simplest principle eludes us because they seem inconvenient.

When one has to deep dive into some search, that search has to be initiated with a public domain website. as public domain sites such as PubMed, Cochrain Review, etc. have access to all the full articles and have the maximum number of published articles. Keywords for the search play an essential role, as the right search will fetch you the right information. The articles that have limited access or data can be used for cross-referencing. The number of references in the same articles can give the right direction for a search.

This comes back to cooperation between testing teams. People in charge of setting up the test need to be provided clear requirements regarding data aquisition (measured values, sampling freq., precision, measurement points) and they need to comunicate back the actual setup with this data and weather it fulfils the request.

Este es el motivo por el que todos los avances en Medicina se construyen con la metodología del ensayo clínico, un tipo de estudio prospectivo que permite ver con la menor variabilidad posible el efecto del tratamiento experimental. La primera descripción de la metodología proviene de Sir Austin Bradford Hill: HILL AB. The clinical trial. Br Med Bull. 1951;7(4):278-82. doi: 10.1093/oxfordjournals.bmb.a073919. PMID: 14879025.

Well, we are living in an era when there is a magnificent flow of information, with a huge volume of data, at an incredible speed and vector. I would argue that this contemporary moment of time could be described as the 3V era (related to the velocity, volume and vector) of data, information, and decisions. Hence, it is very important for any data scientist or analyst to take a deep breath, stop his watch and delve deep into the authenticity, correctness, and unbiased nature of any data before hand. Personally, I value those data-experts who make sure to verify data sources, validate data, and monitor data collection and entry processes and techniques to ensure making non-biased decisions and avoid ethical issues related with data analysis.

This point is so important especially when you are working in lab research. Data must be collected properly and you need to write everything that happen during your experiment as miscelanious information. You need to understand deeply about your experiment and choose the right instrument to help you analyse so that you can interpret your findings. Dont forget to always run different scenarios while working on your experiment.any gap findings must be filled and understandable by running another set of experiment and test. Always try to connect your data from different intsrument test.

Data Quality is another important factors during data analysis for R&D activities. Data with less quality may lead to bias analysis and wrong decision making.

Data is like a cloud in front of us. In order to be transformed into understandable and meaningful information, it needs to be acquired by the right measurement techniques and devices. Therefore, initially, how data will be collected is crucial. Moreover, if the data has been collected previously, how it will be interpreted becomes another important issue. We actually name this process as analysis. Meaningful expressions and interpretations can be derived from data that has been collected or analyzed in this way. Finally, working with skillful people who are experts in their fields is another of the most important criteria to achieve correct results by implementing the right methods and approaches.

Incorporation of Internal/external negative and/or positive control ensures the robustness of the techniques used for data analysis.

Test data analysis is a very deep subject, and in my expirience, for a good test execution, people dooing the test should be given a very clear and standardized checking tool - that provides a "fail"/"pass" answer - with a "gray area in the midle. Tests taht fall into thos category shoud then be avaluayed by an expert for that particular testing subject.

Choosing appropriate method is very critical as - It may directly impact on your results. -Wrong results can mislead you and the final goal cannot be achieved. - Using proper method and techniques can save the valuable money as well time.

Match methods to questions: Don't apply fancy algorithms to simple questions. Choose methods that address your specific research goals. Seek expert guidance: Consult statisticians or data scientists for complex analyses, ensuring proper tool selection and interpretation. Validate your choices: Test and compare different analysis methods to ensure the chosen approach yields reliable results.

The tools are useless if the team use them just as if they were playing a video game, when it comes to software. About methodologies such as scrum, many projects fail because the manager focuses only in using the method according to the standard. But this has a low win rate if we don’t combine methodologies and aren’t flexible to succeed with the real goals of the project.

La elección de los métodos y herramientas de análisis de datos adecuados es una tarea importante que puede afectar significativamente los resultados. Hay una serie de factores a tener en cuenta al elegir los métodos y herramientas de análisis de datos, incluyendo: Los objetivos y preguntas de la investigación. Los métodos y herramientas de análisis de datos deben ser seleccionados para abordarlos; El tipo de datos; Los recursos disponibles. Los métodos y herramientas de análisis de datos deben ser factibles según el tiempo, el presupuesto y las habilidades disponibles.

Dependendo dos objetivos e hipóteses a serem testadas e validadas com os dados, as técnicas e métodos precisam ser específicos. Conhecimento multidisciplinar é fundamental o que pode ser conseguido também com uma equipe multidisciplinar.

'One-size(statistical test)-fits-all' could have detrimental effects, especially in medical research. A typical example is the 't-test'. Many published scientific papers use t-test inappropriately. A seasoned biostatistician can help researchers choose the correct statistical test.

Selecting the right data analysis method involves considering data type and research objectives. For quantitative data, statistical tools like SPSS, Excel, or R aid in descriptive and inferential analysis, enabling trend identification and hypothesis testing. Qualitative data benefits from software like NVivo or MAXQDA, facilitating coding, thematic analysis, and pattern recognition. Choose tools aligning with the research's complexity, ensuring accuracy and efficiency in handling the dataset. Assess usability, compatibility, and resources required for the selected tools, ensuring they meet the research needs effectively for insightful analysis and meaningful interpretation of data.

Visualisation and communication of findings is important because it can save you from big troubles that can come if the findings are not shared within time.

Visualisation is the first step of data analysis. Even before cleaning. When on of our researchers come to me with data we start by having a look at the data with graphic tools. When it comes to presenting results it is important to keep in mind that the statistician must keep factual and not biaised the outcomes with representations that influence the interpretation (zooming with axis settings, no error bars).

Visualisation = communication Therefore good data visualisation starts with understanding the public. * What knowledge do they have of various types of visualisations? And what knowledge do they have of the data? * Are there e.g. societal factors about colors with specific associations? * Use an visualisation that is the simplest representation of your message and test this with your public. All extra information that attracts attention, weakens your main message. And last, but not least: Make your visualisations digital accessible for all users, by using correct colors, contrasts with clear descriptions.

Clarity over complexity: Prioritize clear and concise visualizations that effectively communicate key insights to diverse audiences. Storytelling with data: Don't just show numbers; weave a narrative, highlighting trends, patterns, and actionable takeaways. Context matters: Provide context for visualizations, including data limitations, error bars, and clear labeling for informed interpretation.

We have two eyes and ears for a reason. Visualization the most important part of sharing the data such that your team will understand and use the data effectively. The presentation and verbal explanation of the data is critical to the digestion of the information by your colleagues. When presenting data, I rarely have anything more than a title accompanying my visual data results. I want my colleagues to see the visual results while listening to me explain them. The visual connection to the verbal explanation is far more successful than someone reading the explanation beside the visual.

Me ha resultado muy útil una guía de visualización de datos de Generalitat de Catalunya. Resume muy bien cómo sacarle el máximo provecho a las visualizaciones a través de tres acciones: explorar, analizar y explicar. Las dos primeras me parecen ideales para la investigación empresarial y académica, para manejar hipótesis y propuestas de mejora continua. Para la última, explicar, se abre un abanico de posibilidades para divulgar la información a diversos públicos, y así generar espacios de comunicación personalizados.

Na verdade senti um pouco de falta de uma explicação sobre “como interpretar os dados”. Outro ponto importante para lembrar é que na etapa de visualização e comunicação das descobertas é importante retornar às perguntas originais e checar se você conseguiu respondê-las parcialmente ou em sua totalidade, endereçando assim seu objetivo

To ensure your R&D team interprets data correctly, create clear communication channels, provide comprehensive documentation, offer regular training, encourage collaboration, and foster a culture of questioning and validation within the team. Regularly review and discuss findings to promote a shared understanding of the data.

Innovatively communicate data findings by crafting compelling visualizations—infographics, charts, or interactive dashboards—capturing key insights succinctly. Embrace creativity in design to enhance clarity and engagement, ensuring easy comprehension for diverse audiences. Utilize storytelling techniques, weaving data into narratives that resonate, evoking curiosity and understanding. Tailor visualizations to align with the research's objectives, highlighting trends, patterns, and correlations effectively. Employ accessible language, avoiding jargon, fostering broader comprehension. Encourage interaction with the data through user-friendly interfaces, enabling stakeholders to explore findings dynamically.

Certainly, we researchers tend to get excited as soon as the first positive results appear. This can distort reality, especially because laboratory results cannot always be implemented at an industrial level.

Validate data interpretations by cross-referencing multiple sources, employing diverse analytical approaches, and conducting peer reviews within the R&D team. Challenge assumptions and conclusions by actively seeking contradictory evidence, encouraging debates, and exploring alternative explanations. Foster an environment that encourages critical thinking, inviting perspectives from multidisciplinary experts. Employ robust validation methods, such as sensitivity analysis or simulation, to test the reliability and robustness of interpretations. Emphasize the importance of rigorous scrutiny and openness to refining conclusions, ensuring a comprehensive and reliable understanding of the data for informed decision-making within the R&D team.

This is where many ignore the effectiveness of multiple experiments/tests (and reproducibility of same) for multiple data points before one can rely on the results to be interpreted. Interpretation of a 1-time fluke test result does more harm than good.

In the process of interpreting your data you should be able to validate and verify the out come. The method and tools you are using no matter how well known or popular they are you have to verify them according to your parameters and practice and run sone previously worked on data

I think, that before the Digital Transformation Era, validation of the data an its source was extremely crucial. Even nowadays, data cleaning is crucial to avoid GIGO (garbage in - garbage out) situation before doing the interpretation of the data collected. Thanks to ML and GPT-models scraping and formatting tasks become easier and much-much faster.

La forma de interpretar los datos debe considerar la implicancia que tiene cada resultado del análisis, y si es posible el alcance en aspectos técnicos económicos y ambientales. Por ejemplo la interpretación estadísticas de aspectos técnicos de una opción puede sugerir que una opción sea aceptada pero si esta opción es económicamente inviable entonces de seguro la decisión de adoptar es cuestionable

All findings from data analysis must be interpreted within a context. First, internal context : the measurement system, the experimental process (model, sample, volume of data). How these results compare with other results obtained ont the same facility in the same conditions ? Use of dashboards can be of interest here. Second, external context : what does say the literature, scientific experts? Then one need to be critics about its results with all these considerations in mind.

Take as basis that you are not right. Be critic with your results and put your ego in the closet, being the one that research the most in a topic don’t correlates directly with “you are right”. Try to see your results in a more holistic way introducing “crazy ones” to challenge your results

Exacto. Para que los datos de un experimento tengan validez éste debe ser reproducible; los resultados entre experimentos pueden presentar variaciones estadísticas por el simple azar, pero estas no deben superar los límites habitualmente admitidos para ese tipo de experimentos. En estadística se conocen como errores alfa y beta.

For an R&D section, this approach is essential. Continuous validation and scrutiny of data interpretations ensure the accuracy of findings. Seeking input from diverse perspectives helps uncover hidden insights and potential errors that could impact research outcomes. Any differences should be explored and explained, leading to a deeper understanding or the identification of new research paths.

Interlinking of dots is very important, reflect on past projects, assess the effectiveness of methodologies used, and identifying areas for improvement. Discussions on lessons learned from the past will always improve the progress of the current project.

Leverage data experiences to drive continuous improvement within the R&D team. Encourage reflection on past methodologies, successes, and failures, extracting lessons for enhanced future practices. Foster a culture of learning by documenting insights, establishing knowledge-sharing sessions, and integrating feedback loops for iterative advancements. Emphasize skill development in data analysis, interpretation, and application. Implement agile methodologies to adapt swiftly to emerging trends or challenges. Embrace innovation, experimenting with new techniques and technologies to refine data-driven strategies.

All engineering or scientific work shall be defined with a clear and concise glossary of terms, and include any units and proper designation for a proper and uniform results evaluation

Continuous improvement in data interpretation is vital. Reflecting on past data experiences allows for recognizing successful approaches and areas needing improvement. Learning from mistakes and failures is crucial in research and development. They offer invaluable lessons to refine data skills and knowledge. Embracing these experiences as learning opportunities strengthens the team's capabilities in handling diverse datasets. Staying updated with the latest trends in data science and technology is key. For an R&D section, this perspective is gold.

Document data analysis processes: Maintain detailed records of methods, tools, and rationale for future reference and replication. Debrief and iterate: Regularly discuss what worked well and what could be improved in future data analysis practices. Stay updated: Encourage continuous learning of new data analysis techniques and tools to adapt to evolving needs and challenges.

One of the most fascinating situations in research is the feeling that what you have just discovered is insurmountable, that you have reached the ceiling in terms of your own ability and knowledge. After a short time, however, you realize that it can be improved and that there is still room for improvement. You can always improve.

I prefer to work in iterations time boxed better than huge planifications. Fail often and fast, learn from failure and the result will be better

Para mantenerse activo con las últimas tendencias, uno puede estar en contacto con publicaciones de referencias tecnológicas, académicas y empresariales. Considero que tanto LinkedIn como otras redes sociales han potencializado la manera de explorar distintas perspectivas de un mismo tema.

Para um projeto ser eficaz é necessário que sua interpretação de dados seja confiável. Por isso é importante o entendimento dos dados, saber qual relevância e impactos ele trará para o projeto. Uma busca por conhecimento e interpretação dos dados deve ser realizada, podendo adquirir uma sólida experiência e familiaridade com os mesmos. Após, inicia-se a próxima etapa do processo, chamada de Melhoria Contínua, que visa sempre a melhora do resultados nos dados obtidos.

One thing I have found helpful to ensure my team interpret data correct is to give them repat mission which they have done previously as new one or give similar mission to my people seperately (tell them it's a secret test) and see the results. Another way is to do some test by myself in absence of team and comparing the results. These steps doesn't mean lack of confidence on team but Quality assurance is required every stage of work.

Having people taht become experts on test evaluation is mandatory, but finding them is a big task, because they can not be brought in from another company and be effective, due to different softwares, testing methods and strategies. It is far better to "groom" someone with existing testing experience, that has shown interest in a particular subject. That person can become verry effective realtivly quickly and give mutch better results .

I find it useful when I assess data generated from previous experiments while deciphering possible confounding factors that could impact on the data. Data interpretation can be complex especially in generating hypothesis but can become a valuable tool in avoiding worthless chase of non useful projects. We must strive to save time and costs but it all boils down to clear interpretation of generated data!

Self-delusion is deadly. “You must not fool yourself, and you are the easiest person to fool.” (Feynman). Bounce off ideas with teammates, friends, aliens. Review, adjust, correct, and review some more with the above "personas". E.T. is perfect, as that creature always seem to be asking questions. Avoid people that pretend having read and carefully reviewed your stuff: "Yeah, yeah, it's all good". Enjoy the ride!

On peut apprendre plein de méthodes et outils, le meilleur moyen d'y arriver est d'apprendre aux équipes à prendre du recul, être curieux pour comprendre d'autres manières de penser. C'est peut être le plus dur mais probablement le plus efficace à terme.

In addition to what has been said consider: 1.-Peer review: Internal and external. 2.-Two independent teams or individuals to analyze the data and compare their results. 3.-continuous training where at least one has above average statistics knowledge (not only p values, but also skewness, statistical distributions, correlations, tests, etc) and completely describes in discussion and results sections. 4.-I give weight to sources of information i.e. no conflicts of interests reported. I also take with an extra precaution information which comes from vendors, or the first publication of only one author, or their PhD thesis. Might be important to know the author’s university, the journal impact factor (JIF), or the journal peer review process.

The opportunity to get the data peer reviewed either internally in your own organisation, a research partner or industry body. This provides through acceptance that the data is not only accurate but fit for purpose.

Para conseguir una investigación y desarrollo de calidad es imprescindible un buen liderazgo y un equipo robusto, estable y bien coordinado.

They don't. After all, they are only hunan beings, it's essential that non-human intelligence will revie the data. It't also optional to get 2nd opinion from AI