What do you do if you're overwhelmed by large datasets and need valuable insights?

If you're overwhelmed by large datasets and need valuable insights, here's what you can do, Break Down the Problem, Use Sampling Techniques, Leverage Data Visualization, Apply Data Preprocessing, Utilize Summary Statistics, Employ Dimensionality Reduction Techniques, Apply Machine Learning Algorithms, Collaborate with Peers, Seek Automation Opportunities, Stay Organized and Documented By employing these strategies, you can effectively manage and analyze large datasets to derive valuable insights that support informed decision-making and drive business outcomes.

Firstly, define what insights you'd like to get from the data - that comes from being able to break down the problem at hand to its simplest form. Secondly, identify what you'll need from the data to resolve the problem. Thirdly, extract only what you need (often times we have huge datasets and don't even need half the columns/info), do the necessary cleaning, for example if there are nulls determine whether they have any intrinsic meaning or if they're just blank and how to deal with them in the context of the problem. Then it's analyzing, building pivots/charts, dashboards etc. Hopefully, at this point you're able to extract insights from the data, if not, go back to the problem and see if there is anything I may have missed.

As an Internal Auditor I often face this challenges sometimes. I try to segregate the data as per the requirement and then try to fit then in the avilable tools(Excel Charts or filtering) and always should focused on outcomes like what I need from this very specific set of datas. By this eventually anyone can get their desired results I think it's actually helpful.

Several ideas come to mind, and here are a select few: 1.Sample the data using methods that encompass the entire population. This will help us get the data into a more manageable format. 2.Apply dimensionality reduction methods to reduce the data's size and make it more easily transferable. 3.Transform the data into summary statistics (mean, median, mode, etc.) This will allow us to present the summarized data even to a non-technical audience.

Décomposer les données vous aide à mieux conna?tre la structure de vos données et à ma?triser les relations au sein de la grande base de données. Il est important en décomposant de tenir compte également du flux de chaque données (sources, périodicité), de sa redondance et des définitions

In my experience, I have noticed that when you’re overwhelmed by large data sets, it always helps to first clean the data thereafter breaking it into smaller groups for analysis. Clean data also gives one clear data analysis directions.

If facing overwhelming datasets, I would start by cleaning the data to remove irrelevant or inconsistent entries. Then, I would break it into manageable chunks based on key variables. Next, I would identify and address any missing or erroneous values through data validation techniques. This ensures the dataset is accurate and reliable for analysis.

Data cleanup should be prioritized over other steps when dealing with larger datasets. This initial cleaning helps us determine if the data is even relevant to the proposed problem. It also allows us to identify the fields that are highly impactful for our analysis, ultimately leading to better modeling decisions.

Data inconsistency across government applications is a major hurdle. Data analysts spend a significant amount of time (up to 80%) cleaning it. We address this by: Benchmarking: Comparing the same data points across applications. Statistical verification: Using standard techniques like standard deviation to assess data integrity. Data imputation: Filling missing values using specialized techniques to create a more complete and consistent dataset. This focus on data cleaning ensures datasets are unified and ready for analysis.

Consejo útil es tener siempre muy claro el objetivo del análisis y el resultado que estamos buscando, esto nos ayudará a identificar los datos que no necesitamos y que solo hacen ruido en el data set que estamos trabajando.

While selecting the right tool is important for data cleaning, I believe choosing a tool you are most comfortable using with higher/expert skill level will help you achieve results faster than using the right tool with a lower skill level which will take time away from the main task and increase time to completion.

Not all datasets require complex tools. Leverage your experience to choose the right technology for the job. Consider spreadsheets for smaller datasets or explore programming languages like Python or R for larger, more complex ones

Todo está en el alcance del trabajo en el que te encuentres y la cantidad de datos que tienes que trabajar. Si paquetería Office ya no es lo suficiente robusto para tu tarea hay herramientas como PANDAS y NumPy Incluso hay que ir volteando mas al machine learning.

Dependiendo de la empresa donde estemos trabajando estás herramientas se pueden limitar al paquete básico de Microsoft Office. Sin embargo, vivimos en la era de la información, tener el Excel básico no nos limita a hacer muchas cosas con cantidades de datos grandes. Cursos en línea, tutoriales e incluso la inteligencia artificial pueden ser grandes herramientas de apoyo para cuando nos encontremos en escenarios donde hayan cantidades grandes de datos.

I would begin by analyzing trends within the data. This involves identifying patterns, correlations, and anomalies using different methods and visualization tools like histograms or scatter plots. By examining trends over time or across different variables, I can uncover meaningful patterns that inform decision-making. This analytical approach helps in extracting actionable insights from complex datasets, enabling informed decision-making.

Once prepped, explore the data visually. Use charts and graphs to identify trends, correlations, and outliers. Look for patterns that might hold the key to your desired insights.

En escenarios donde nos piden un resultado o una respuesta en específico, hay que concentrarse en eso y buscar los argumentos dentro del análisis para respaldar los resultados, siempre enfocarse en el objetivo! Una vez hecho esto de la mejor manera, aplicando la proactividad se pueden agregar conjeturas que hayan salido en el proceso de análisis dando valor extra al trabajo realizado.

Formulate specific questions or predictions based on your initial observations. Use statistical tools and data mining techniques to test these hypotheses and validate your assumptions.

It is essential to not just be able to ask the right questions, but to have the ability to discern upon asking the right questions, what might be possible solutions to achieve the desired outcome. When I was a kid, I would have long pauses before compositions, for English (Language). My teacher told me “write something, the ideas will come.” Since I tried that, it works ever since. So I think it is essential to start of with a hypothesis, test it, fail to disprove it, or change it along the way. We simply can’t rely on intuition and/or speculation to get something right at once.

Data analysis is a journey, not a destination. As you uncover new insights, refine your questions and analysis methods. Be prepared to revisit previous steps and adjust your approach for a more complete picture.

Rather than starting with the data, I like to start by thinking about the ultimate audience for my work and what they care about--what questions are they trying to answer? what business problems are they trying to solve? From there it's usually easier to think about what information is needed and how it can be extracted from the available data using the steps outlined in this article (or some variation thereof).