How can you make data collection user-friendly for all stakeholders?

My role basically has been to tell where our product and engineering teams can do better. In short, show them the mirror. A lot of data analysis and decision making goes in the background. Now what eases all of these? - Ensure you go with the right requirement request to the stakeholders - Do all the homework so that you can wrap up your analysis in max 2-3 iterations - Present in a format which is easy to consume and not easy for you to present

What I thought is that before diving into data collection, having a well-defined plan is crucial. It's like charting a course before a journey. Clearly outlining your goals and data needs prevents wasting time and resources on irrelevant information. Equally important is sharing your plan with stakeholders and getting their input and approval. This ensures everyone's aligned on expectations, avoiding missteps. This strategic approach not only saves resources but also maximizes the data's value. It's about setting a clear direction and making data collection purposeful and efficient.

Other than technical aspects, what is crucial in my experience is to take into account who my stakeholders are. You need to know them, their habits. From example, one of the CEO that I know wanted to have every rapport printed, in table, and it needed to fill in in only 1 page. Can you imagine what Data Analyst must've gotten through to meet the requirements? :)

In my role as a Project Manager on a Document Management project, before embarking on a data collection initiative to streamline document retrieval, we defined our goals clearly. We aimed to reduce retrieval time by 30% and enhance search accuracy. To achieve this, we assessed the existing document landscape, identified critical data points, and set a target sample size for testing. This approach helped us focus our resources efficiently. Moreover, we engaged stakeholders early on, including end-users and department heads. We showcased the potential benefits, gathered their insights, and received unanimous approval.

How I approach learning:I dug deep into the facts, comprehending its types and structure.I was able to comprehend why we were even gathering all of that data as well as how we were gathering it, storing it, and processing it before storing it.I have to sit down with other operationally involved stakeholders in order to do this.I gained knowledge on how to manage data and make it flow efficiently.I gained knowledge on how to clean the data, process it as much as I wanted, and plot it in whatever manner I could.It took me hours upon hours just to plot the data to explore how different plots may present the data differently from one another.I experimented with several plotting programs,including PSP, R,and SQL in the tableau & power automate.

Majority of the companies still holds its analytics on Excel. And it's often being choosen as a data viz tool, because people already know it. That's something you need to avoid - prefering one tool or method over another, simply because you know it better.

As a Solution Architect on an Intranet project, data collection was pivotal for informed decision-making. We employed a multi-faceted approach, leveraging both qualitative and quantitative methods. Surveys were invaluable for gathering user feedback on interface preferences and functionality. We also conducted in-depth interviews with key stakeholders to understand their needs and pain points.

Implementation of automation and convenient user interface is of immense importance if you want to collect good quality, reliable data. The automation will guarantee that the data collection process is as painless as possible and usually the result of automations lends it self to efficiency in the data analysis steps. Here, all stakeholders, from the people who are creating the data to the people analyzing/using that data, would have all the necessary things to perform at their best!

I think that selecting data collection methods and tools should be driven by project goals. Web analytics, among surveys, interviews, sensors, and APIs, is a user-friendly choice. It yields insights into online user behavior, suitable for various applications. I prefer Web analytics tools because they are cost-effective, user-friendly, and provide real-time data, aiding swift decision-making. They particularly benefit businesses aiming to enhance their online presence. However, the method's suitability depends on context, so choose wisely to match your specific data needs and limitations.

Selecting the right data collection and analysis methods is crucial. Start by defining project objectives. Assess data sources (structured, unstructured, online) and choose appropriate methods (web scraping, APIs, surveys). Use tools aligned with your chosen methods, ensuring scalability and features. Post-collection, employ Python or SQL for data processing and cleaning. For analysis, opt for R or Python with statistical libraries and tools like Tableau or Power BI. Make informed data storage decisions (databases, data warehouses, or cloud solutions).

In my role as Project Manager on a Document Management project, we prioritised adherence to ethical and legal standards. We implemented stringent data privacy measures, ensuring that only authorized personnel had access to sensitive information. We also obtained explicit consent from users before collecting personal data, reinforcing trust. Additionally, robust governance mechanisms were established to monitor and control data throughout its lifecycle. Regular audits ensured that our practices remained aligned with evolving regulatory landscapes. This approach not only mitigated risks but also showcased our commitment to ethical data handling, instilling confidence in stakeholders.

?? Collecting data ethically is crucial for building trust and ensuring the integrity of your project. Always obtain informed consent from participants and be transparent about how their data will be used. Limit the data you collect to what's necessary for your goals, and protect it using strong security measures. Ensure you're in compliance with relevant laws and regulations, and have methods for auditing your data collection processes to maintain ethical standards.

As a Project Manager, I oversaw a Document Management project for a large corporation. We maintained a detailed record of our data collection methods, including the tools and software used for data extraction. This proved invaluable when a new team took over the project, providing a clear roadmap for them to continue the work seamlessly. By validating and documenting our data, we not only improved the quality of our insights but also facilitated knowledge transfer within the team. This experience reinforced my belief in the importance of these steps in any data-driven project.

Absolutely, data validation and documentation are two pivotal steps that often don't get the attention they deserve. While ethical collection lays the foundation, validating the data ensures its accuracy and reliability. This is particularly important when you're working with existing datasets, which might not have been collected with your specific needs in mind. In these cases, you may have to make do with what you have, making validation even more crucial. Additionally, thorough documentation serves as a roadmap for anyone who interacts with the data later—be it your future self, team members, or external partners. It provides context and makes the data actionable, further solidifying the trustworthiness of your data-driven endeavors.

as an economist : wich " user-friendly, standardized tools" do you recomend precisely? I read that excel sheets are adaptable, reliable, known by everyone, but less "user friendly". What are the tools we can recommend to clients?

In my role as Project Manager for a Document Management project, we measured the impact of our data collection efforts. One memorable instance was discovering that a document category was in high demand but wasn't initially prioritized. Thanks to our vigilant monitoring, we swiftly adjusted our strategy, resulting in a more seamless user experience and higher overall satisfaction. This experience emphasized the importance of continuous data collection in ensuring our project's success and delivering maximum value to our stakeholders.

Oh, data collection is like babysitting a hyperactive toddler—turn your back for one second, and chaos ensues! Just when you think you've got it all figured out, the data throws a curveball, demanding constant monitoring and evaluation. You're not just ticking off boxes; you're playing an endless game of "Whack-a-Mole" with variables and metrics. So keep your eyes peeled and your tools ready. Who knows? Your next "aha moment" could be lurking in that unmonitored corner! ????

Before you begin data collection you need to know your data source ,formats and limitations of collecting the required data ,define the columns and rows with the logics. Once you get the data sets do sample validation through appropriate tools such as sheets, excell, looker ,tableau or power BI and visualise same with appropriate methods, always be willing to experiment and refine to optimize the data collection. That will help you decide the next best action.

According to a study by NewVantage Partners, 92% of executives emphasize the importance of a data-driven culture, and that starts with stakeholder engagement and communication. By involving stakeholders at every step, you're not just collecting data; you're collecting diverse perspectives that can enrich the quality and applicability of your data. In turn, this builds trust and rapport, two invaluable assets that no amount of raw data can provide. It's like turning data collection into a community garden—everyone has a stake, everyone contributes, and everyone benefits from the harvest. ????

Streamlining data collection by gathering only necessary information reduces the burden on stakeholders. Transparency in how collected data will be used builds trust and compliance. Actively seeking feedback from users helps refine the process, ensuring it remains relevant, efficient, and user-friendly, thereby promoting a positive data collection experience.

In my experience working in the marketing strategy and decision sciences teams, I follow the following rule of 3 to ensure seamless and timely data collection which is easy for all stakeholders: 1. Define clear objectives and goals for data collection. 2. Use user-friendly, standardized tools with mobile capabilities. 3. Provide training, support, and feedback mechanisms for data collection improvement.

Assessment fatigue is real for those responsible for data collection in any domain. In public health, several officials can spend all year simply responding to surveys or tools or forms (especially in MS Excel). Data usually is not easily transferred from one database to another, so silos can exist at every level, and persist. Data in these ??tools?? are used only once, for that one purpose. This leads to ??Single-Use Data?? that is used once and then essentially thrown into an Excel black hole. Digitalization, or transforming information into structured data that can be queried ad infinitum, is the way to ensure Data Sustainability and Reusabilty, and the key to trend analysis and predictive analysis. So, be a Data Ecologist!

According to a Salesforce report, 84% of customers say being treated like a person, not a number, is very important to winning their business. Similarly, making data collection user-friendly is the difference between hosting a memorable party and throwing a dud. Make it so delightful that stakeholders can't wait for the next "data party" invite! ????

Facilitate better infra for data collection by stakeholders ~ Establish huge data storage which could hold data for months and years, without compromising data security. ~ Provide an intuitive process to access data instantly so that analysts spend less time on collection and more on analysis. ~ Avoid movement of data and try to store it in a common location and allow tools to augment collected data to present different perspectives.