Data collection is an essential part of data science, but it is often misunderstood or overlooked by aspiring or novice data professionals. In this article, we will debunk some of the most common misconceptions about data collection careers and help you gain a clearer picture of what this role entails and how it can benefit your data science journey.
One of the biggest myths about data collection is that it is a dull and monotonous task that involves scraping, cleaning, and organizing data without much creativity or challenge. However, this is far from the truth. Data collection is a dynamic and diverse process that requires a lot of problem-solving, analytical, and communication skills. You need to identify the right sources, methods, and tools for collecting data that matches your research questions and objectives. You also need to ensure the quality, validity, and reliability of the data you collect and deal with any ethical, legal, or technical issues that may arise. Data collection is not a one-size-fits-all activity, but rather a customized and contextualized one that varies depending on the domain, project, and goal.
Contrary to the misconception, data collection is like detective work, not a dull routine. In my experience, it's a dynamic puzzle where every piece matters. Identifying sources, solving quality puzzles, and navigating ethical landscapes make it an engaging journey. It's not a robotic task; it's about customizing approaches for each unique project. Data collection, far from monotonous, is a nuanced craft demanding problem-solving flair and adaptability.
Another common misconception about data collection is that it is a simple and straightforward task that does not require much expertise or training. However, this is also false. Data collection is a complex and nuanced process that involves a lot of knowledge and skills in different areas, such as data engineering, data analysis, data visualization, data ethics, and data governance. You need to understand the data lifecycle, the data ecosystem, and the data stakeholders. You also need to master various data collection tools and techniques, such as web scraping, APIs, surveys, interviews, experiments, and sensors. Data collection is not a trivial or trivialized task, but rather a crucial and valued one that impacts the quality and usefulness of the data you work with.
A third common misconception about data collection is that it is a distinct and independent function that does not interact or collaborate with other data functions or roles. However, this is also incorrect. Data collection is an integral and interrelated function that connects and coordinates with other data functions and roles, such as data analysis, data modeling, data visualization, data storytelling, and data management. You need to align your data collection goals and strategies with the overall data project or product objectives and requirements. You also need to communicate and cooperate with other data team members and stakeholders, such as data analysts, data scientists, data engineers, data managers, data users, and data customers. Data collection is not a separate or isolated function, but rather a connected and collaborative one that contributes to the data value chain.
A fourth common misconception about data collection is that it is a one-time and fixed activity that happens at the beginning of the data project or product lifecycle and does not change or evolve over time. However, this is also inaccurate. Data collection is a continuous and flexible activity that adapts and responds to the changing needs and demands of the data project or product lifecycle and the data environment. You need to monitor and evaluate your data collection performance and outcomes and make adjustments and improvements as needed. You also need to explore and experiment with new data sources, methods, and tools and learn from your data collection experiences and feedback. Data collection is not a one-time or fixed activity, but rather a continuous and flexible one that grows and develops over time.
A fifth common misconception about data collection is that it is a low-level and low-value skill that does not require much intelligence or creativity and does not contribute much to the data science field or the data-driven world. However, this is also untrue. Data collection is a high-level and high-value skill that demands a lot of intelligence and creativity and plays a vital role in the data science field and the data-driven world. You need to apply critical thinking, logical reasoning, and ethical judgment to your data collection decisions and actions. You also need to use your imagination, innovation, and intuition to find and collect data that is relevant, reliable, and useful for your data projects or products. Data collection is not a low-level or low-value skill, but rather a high-level and high-value skill that enables and empowers data science and data-driven solutions.
The misconception that data collection is a low-value skill is a misjudgment of its true significance. In my journey, I've witnessed it as the cornerstone of insightful decisions. It's not about mindlessly gathering data; it demands a blend of intelligence, creativity, and ethical judgment. Data collection is the art of sculpting meaningful insights, requiring critical thinking and intuition. Far from low-value, it's the powerhouse fueling the engine of impactful data science and driving innovation in the data-driven realm.
Pinpointing the data that is needed and the schema/structure/form in which is needed is really important. There has to be a long term consideration when collecting information. In most cases it would be costly (time and resourcewise) to revisit the same datapoint again to extract other bits of information from it. Model is only as good as your data. Hence it is very important to collect and organize data very meticulously and label it appropriately!