Your product development team is struggling with data analysis. How can you help them get back on track?

In my experience, another crucial aspect is fostering a culture of continuous learning within the team. Encouraging a mindset that values experimentation and iteration in data analysis can lead to more innovative solutions.

Uma coisa que acho muito útil no processo de desenvolvimento de produtos é o compartilhamento de boas e não tão boas experiencias durante o desenvolvimento, lições aprendidas ajudam a evitar erros, encurtar caminhos e minimizar custos evitando retrabalhos. A expertise dos fornecedores também é muito bem vinda, já que eles tem um spam muito grande de uso dos seus insumos.

Begin by understanding the specific requirements and goals of your product development team. Conduct a thorough assessment to identify the type of data needed, the sources available, and the desired outcomes. This step involves collaboration between the data team and product developers to ensure alignment with project objectives.

As a PM, I follow below steps in this situation: - Understand product goals and user requirements or pain points - Check the required data elements - Identify relevant metrics and KPIs aligned with product goals - Evaluate data sources by making sure that they capture necessary insights by keeping data quality intact - Partner and collaborate with cross-functional teams to prioritize data needs to establish clear ownership - Implement tools and processes for data sourcing, collection, storage analyze and present to refine data integration strategy Iterate on above steps in a frequent basis to adapt to evolving user needs or requirements and market dynamics

1. Training: Invest in training sessions to enhance the team's data analysis skills. 2. Tools: Provide the right tools such as Python, R, or visualization software for efficient data analysis. 3. Governance: Establish clear data governance policies to ensure data quality and consistency.

Finding a right product position which performance vs price. Bring the competiton offerings to the same positioning and under thier performance vs price. USPs need to added into the products.

Apply the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) to set realistic and focused goals. This ensures your data analysis efforts are well-defined and aligned with overall project objectives.

To help the product development team with data analysis challenges, I would initiate targeted training sessions to enhance their analytical skills. Provide access to relevant tools and resources, encouraging hands-on practice. Foster collaboration with data experts within or outside the team. Set clear objectives and expectations for data analysis tasks, ensuring alignment with overall project goals. Establish regular check-ins to address concerns and provide guidance. By empowering the team with knowledge, tools, and support, we can improve their data analysis capabilities and get the product development efforts back on track.

What I found useful is a clear objective and data that available to you. Clear objectives guide data analysis efforts, focusing on relevant insights. And the data that available to you is important. Identifying and understanding data sources, quality, and relevance is crucial.

Couple of pre-requisites to follow for choosing the data tools but I want to list out couple of steps: - Once the product goals are analyzed, identify the goal of test strategy - Ensure access to relevant tools and resources, and plan training sessions to enhance analytical skills - Encourage partner and collaborators between team members to share insights and problem-solving frameworks - Implement feedback or iterative loops to improve data analysis process on a continuous basis - Leverage external expertise or tools if necessary to augment internal capabilities and address specific data analysis needs efficiently.

Choose the best data tools to help your product development team analyze data. Choose tools that match your team's skill level and data analysis needs. Choose software that's user-friendly, has the functionality you need for data processing and analysis, and interfaces well with other team tools. By giving your team the correct tools, you may improve their data analysis efficiency and accuracy, helping them solve their challenges and make better product development decisions.

Selecting the right tools for data analysis is important. This involves considering what tools are your team capable to use, and how well they align with your analysis needs.

Data analysis tool to be selected based on the desired outcome. Trained the team and use the specific tool which can satisfy the intended outcome.

Algumas ferramentas - raiz são sempre úteis e funcionam como um check list complementar. Rastrear os 4Ps + 1 é sempre produtivo, são eles: Produto, Preço, Praça, Propaganda e "Package". Desse pente fino sairão conceitos, definições, limitações, posicionamento de preço, público alvos, especificações, regiões para distribuição, canais e mídias de comunicação e definições sobre as embalagens.

Research the data that’s pertinent and most suitable to your business and goals . Ensure it’s in line with your focus to deep dive into further organic and proactive opportunities. Thereafter explain the use of this as a roadmap for use , and an investment to assist the growth by providing an additional insight to your market .

Only have experience with power BI and of course … the popular excel . Nothing better than have a visual way to report data. Some times you can get lost with so many filters possibilities or pivot tables , how to select? Report? Inform? Each case is unique and one way can work one time and the next not: be flexible and learn on the daily job, practice.

Choosing the right data tools is important for product development. Python and R are good choices for data analysis, as they have many libraries and packages that can help with various data tasks. Tableau and Power BI are good choices for data visualization, as they have many features and options that can help with creating interactive and attractive dashboards.

To better data analysis, your product development team needs data skills training. You may organize seminars or online courses on the data analysis areas where your team struggles. By learning data management, visualization, and statistical methodologies, your team grows more confident in analyzing and utilizing data. Your staff needs regular training on the newest data analysis methods and technologies to solve problems and boost production.

I agree. But the engineers won’t have the time and priorities to learn data skills. Therefore, as a Product manager, we need to master data analysis tools and skills to help development teams to understand and simplify the data structure. So that they can focus on software development, and product managers provide the data analytics insight.

Data analysis is the basic tool for R&D. Learn on the job and train yourself by books or internet searching is also the necessary skill for R&D and technology staffs. On the other hand, combining data analysis with solid know-how of the product being tested is equally critical.

O mais importante na coleta de dados é o nível de veracidade das informações para sua equide de desenvolvimento de produtos, estar preparada para aderir ao mais novo produto da companhia, as tendências de mercado, concorrência, inovação e tecnologia que podem ser aplicados, ter tudo isso em uma plataforma de inteligência, traz rapidez aos processos decisórios e de desenvolvimento.

Even if the basic interpretation of data is correct, it is very typical to mess with the visualization and clear conclusions for people not knowing the substance and statistics. Nice graphical tools are available for visualization but often they move the focus to visual aesthetics so that in the end, the actual result is presented to be much stronger than it is with all the uncertainty. Show also the noise to reveal the nature of everything: things appear as pretty messy.

Just with reviewing the data collection necessary for product development, understanding the strengths and weaknesses within your own team in analysing such data, is key to ensuring products are developed correctly and efficiently. However, by having someone in your team who may not be as advanced as others doesn't expose a problem for the team as they may well have specific skills and contributions in other areas of development. Training such personnel is paramount to leaving team members engaged in their work, happy within their work and thus promotes a more unified mindset within a team creating more cost efficient outputs. I can't emphasise enough how spending time with your team to educate and train is critical to performance.

Based on my experience, it is highly recommended that the data analysis team undergoes a thorough induction into the theoretical underpinnings of the data they analyze. This will streamline the data curation process, identify stronger and more robust correlations, and lead to more useful conclusions for the company. From this perspective, I believe that continuous training in data skills, through workshops, courses, and podcasts, should be accompanied by a constant update on the state of the industry being analyzed.

As a product manager, it is my job to ensure that gaps in human resources when building a product are bridged. Therefore, I'd address our data analysis challenge by arranging targeted training sessions, possibly bringing in an expert for guidance. I'd collaborate closely with the team to identify specific obstacles and ensure they have the tools and resources needed. Ongoing check-ins and fostering a culture of continuous learning would be crucial for improving our data analysis capabilities. This approach though would vary depending on the nature of the team and other available resources.

Creating a culture of continuous learning within product development team is essential. Encouraging team members to share their learnings and insights, and providing opportunities for collaborative learning through group projects or discussions, can further enhance the effectiveness of the training efforts. In addition to that, hands-on practice with real or simulated data sets can help solidify the understanding of data analysis concepts and techniques.

Need to check regularly and validate time to time Therefore, you should establish clear and consistent data processes that define how you collect, store, access, analyze, share, and act on your data. You can use tools like data pipelines, data warehouses, data dashboards, data reports, and data feedback loops to streamline and automate your data processes.

Fostering a culture of collaboration and communication around data is equally important. Encouraging cross-functional teams to work together on data-related projects, sharing insights and best practices, and providing visibility into the data analysis process will enhance the overall effectiveness of data-driven decision-making.

There's 3 main steps in data processes. First you have to guarantee reliable and secure data collection, working hand in hand with IT and PCD. Than you must define a standard way of treating and visualizing the information, in order to be able to compare, for example, breweries from all over the world all in the same basis. And finally you must foster positive comparison and collaboration between those "breweries" so they can boost efficiency and results together!

Clear and effective data procedures help your product development team analyze data. This requires an organized strategy to data collection, storage, processing, and analysis. Make these procedures well-documented and available to team members. Standardized methods help your team prevent misunderstanding and mistakes, resulting in more accurate data analysis. This organized method simplifies workflow and helps discover and fix data analysis pipeline bottlenecks.

Once you’ve understood the data and have defined what you need it to show, the development of a data analysis tool should be sustainable. I recommend the data be stored in a single place where it’s easily accessible, but if not possible and you need to gather it from different inputs, you can always manage that through the data source name. Working with raw data and transforming it in the tool of choice is also better so you won’t have to work on the data and then again on your tool, just have it be a straight input and establish a treating process for the tool to manage. For the means of updates, automate. Create workflows and automate the most that you can in your process to move away from human error.

One thing I've found helpful is forging a solid foundation for your product development team by setting precise data objectives, ensuring the collection of pertinent, clean and filtered data, selecting optimal storage and analysis tools, and documenting the entire process. Clearly define goals and secure reliable data sources, implementing a rigorous cleaning process to uphold accuracy. Other thing is choosing storage solutions and tools with scalability in mind. Establish a culture of continuous improvement through feedback loops and invest in ongoing training to keep your team at the forefront of data analysis techniques.

In my experience ask questions as what the goal or outcome is, what the current challenges are, what key metrics or indicators need to be measured, and who the stakeholders are.

Os relatórios de dados em uma empresa comercial, importadora e distribuidora como a nossa, elevam a assertividade do processo de escolha de que produtos devem ser importados e para qual público alvo, teremos mais êxito em atender, faz parte dessa pesquisa de campo, captar todas as informações e trazer para o time comercial e estratégico, definir as tendências de mercado e como utilizar isso a favor da empresa e das vendas , como um todo.

Validation data results one from most important steps to be done through product development cycle. Once you validate your product as per consumer requirements as well as applicator needs you will be confident about your product performance. Validation of products in real site could help to build more valuable insights through products development journey.

When we trained the AI model for smart cities application, sometimes the Moldova returned some errors, we always need to set up a process and system to validate and verify the data outcome and result.

Validate your data findings to help your product development team analyze data. This requires frequently verifying that the data being analyzed is accurate, dependable, and relevant to your project objectives. Implement cross-verification, where various team members evaluate and validate outcomes. This stage checks for flaws, ensures analytical integrity, and boosts trust in data-driven choices. Validating data outputs ensures that your team's efforts provide valuable insights and aid progress.

That’s the way!! It’s an important step.. Just verify the relevance of the validation process being adopted… once done you are heading towards a great creation…

It's a sensible approach to superimpose confidence and sigma levels upon a validated data set. This will enable you to more confidently dismiss random chance or at least know the standard deviation and statistical errors associated with the data set you are studying. You can potentially have a validated data set, but it can produce meaningless results or unfounded conclusions. The nature of your sample and its size is important.

Validar os dados captados do campo de trabalho, pelos profissionais que buscam , informações detalhadas são fundamentais para trazer todos esses insights aos times de marketing, desenvolvimento e produção, para alimentar o ecossistema interna e gerar tranquilidade na tomada de decisões, ter o time preparado para checar esses dados novamente e tomar a melhor decisão , com confiança, traz o time confiabilidade em exercer seu papel de desenvolvedores e acreditar que o produto e serviço criados, são de suma importância ao mercado e consumidor final deste novo lançamento da empresa.

Validate the data actions starting as early as possible, to make corrective actions if needed. Confirm that the data that you plan to get and use is in accordance with your need (questions) and tools for working with it. Ask for advice. Have somebody with statistical understanding close to you. In the worst case, you have the data "in your pants" when you are being told that it cannot be used for what you should be doing.

Validate your data results is most important before you make decisions. The results value % can support you take actions efficiency.

It is crucial to consider the latency of the continuous improvement process when incorporating data analysis. Both data collection and the improvement process have intrinsic response times that are independent of data analysis. It is important to be realistic about the time it takes for the improvement cycle to show results in the KPIs after data analysis is carried out. Sometimes it is necessary to analyse behaviour over several cycles to understand its true impact of data analysis.

As we know , in any project the most is the human resource,in this case of development product team,we must know the individual skills .. strengths, weaknesses of each player for to manage data analysis, know how etc.. then after defined the focus the working team give the support regarding to obtain the common goal .

Regularly review the outcomes of your data analysis in collaboration with the product development team. Assess the impact of data-driven insights on decision-making and product development milestones. Continuous feedback loops help refine analysis methods and improve the relevance of data-driven recommendations.

Data analysis is like a syndrome. One tends to analyze anything and everything. This gets aggravated when digital skills are easily accessible. Hence, the key aspect is to identify bottleneck "process or operation or pool of resources" and analyze data related to those first to identify irregularities. This will provide quick insight or might provide quick solution as well.

Data Detox: Reboot Your Product Team's Analysis Mojo! Data Detective Challenge: Become Sherlock Holmes and solve the mystery of missing metrics! Learn analysis skills through a fun, teamwork-building game. Visualization Showdown: Unleash creativity! Turn data into masterpieces with wild visualization formats – from emoji timelines to mind maps. Gamified Learning Dash: Level up your data skills with bite-sized challenges! Earn points, unlock new abilities, and make learning fun and rewarding. Real-Time Data Playground: Build interactive dashboards, design live alerts, and see data's impact in action! Put learning into practice with a real-world lab.

Don’t underestimate the time to be invested in creating the mindset which should be based on a data driven decision. Understand that starting with habits of collecting and leveraging data is not an overnight task

In my experience, I consider adopting unconventional yet effective approaches like gamification of data analysis tasks, fostering a culture of data-driven storytelling, and encouraging collaborative hackathons with cross-functional teams. Also, introducing AI-driven tools like automated data cleansing and enrichment solutions can streamline the workflow.

Help them to select right tool which make there data analysis as per there requirements and train them for that and try to set culture to take decisions data base . Set KPI which automatically make team to get there work driven with data proper analysis.

Besides de database it is recommended to do open end customer interviews and/or end customer panels in order to understand what are the explicits and implicites real requirements concerning your product development process. The results are really surprising!!!

I'd like to reinforce the importance of people in the process. We are moving more and more to automation, machine learning and AI, but we work to, with and for PEOPLE. So it's key to make sure all team involved in data processes know WHY we are collecting data and what's the RESULT expected. I always try to tell how beer is made to colleagues from Finance, Procurement, IT, because we all work data to deliver a good quality, tasteful beer to our consumers!

To improve your product development team's data analysis, consider organizing targeted training sessions, providing access to relevant tools, fostering collaboration with data experts, and establishing clear objectives for analysis projects. Regular feedback and communication channels can also enhance their understanding and performance.

It can be very beneficial for the development team to learn, and perform their own data analysis. On the surface it may seem like it is more efficient for data analysis support, it impedes the ability of the developer to gain insight they may not otherwise achieve without doing their own analysis. It can be beneficial for the developer to collect their own data from time to time as well. Can be very impactful, but I digress.