How can you balance speed and accuracy when analyzing data?

On my experience, data by itself will show only what it has or what it was intended for. The most important thing is to identify what is really what you are looking for, that will save you time. Now, in order to be concise and accurate, the iterations need to be shared with others to ensure that the data is not biased based on your assumptions.

When i deal with big data there is somethings or steps i do and avoid : 1- clearly define the objectives of your data analysis. Understand what insights you hope to gain and how they will contribute to your business or research goals. 2- Data Integration Issues: If data from different sources is not integrated properly, it can create discrepancies and hinder the holistic understanding of insights. 3- data analysis is not just a Data visualization (power bi - tablaeu) It's more than it like NoSQL Databases: Non-relational databases like MongoDB, Cassandra, and Couchbase are well-suited for handling unstructured and semi-structured Big Data. They offer scalability and flexibility for various data types.

Absolutely true about planning data analysis. Make sure all data sets are available. Know your research objectives and let your analysis model match your research objectives

Before going into any analysis, it's wise to take the time and plan it out. By creating a structured plan, every time you completed a step, you'll find a sense of motivation and progress. Taking the time to make the process more structured and adaptable is what can make your analysis successful.

Planning your analysis is one of the best ways to start. I find that writing it down with pen and paper to decide a pattern and then looking at the data that way first helps a lot. I write about my passions like literature and even there you have to critically analyse content. Writing points down and then breaking them into smaller pieces ultimately leads to a great analysis. And the analysis needs to be prioritised not just according to goals and deadlines but also according to what content is most relevant leading down to the least relevant. I hope this helps.

The simplest way to validate your data is by creating tools that will correlate past, current and future data into the model that you have. If your model is able to predict accurately and what you were looking for, it is a good validation. Nevertheless, error % will vary based on the data, the trend should always follow the real pattern.

Without data validation, you can lose credibility and trust in your team. Taking the time to validate the data is a necessity so you can provide insightful yet accurate analysis. Specific tools, like Data Validation and Conditional Formatting in Excel, are great ways to validate your data to ensure you can provide the best analysis possible.

As a person who writes a lot of critical and subjective analysis, it is always important to validate what you have said by justifying it in some manner, whether it is through another article, essay, content from the original source, etc. For data that is based on factual evidence, this becomes even more important because you don't want to be caught off guard when you present numbers, figures or statistics. Stay relevant and keep ensuring that the data presented is accurate and up to date.

When it comes to analyzing data, you want to think analytically and logically. Understanding the best way to analyze and show your data is a key aspect of any analysis. For example, choosing the right kind of chart can make or break your presentation to your stakeholders. Take the time to assess the data and determine the best way to represent it.

When analyzing data, various techniques can be applied depending on the nature of the data, the objectives of the analysis, and the insights you seek to derive. Here are some commonly used data analysis techniques: Descriptive Statistics: This involves summarizing and describing the main features of a dataset, such as mean, median, mode, standard deviation, and percentiles. Data Visualization: Representing data visually using charts, graphs, and plots (e.g., bar charts, line charts, scatter plots) to identify patterns, trends, and relationships. Exploratory Data Analysis (EDA): EDA involves investigating the data to understand its distribution, identify outliers, and discover hidden patterns through visualizations and summary statistics.

The main thing about presentation of data is that it needs to be understood by everyone you are showing it to. There are various styles of data presentation but always remember, it is the audience that matters. A text heavy presentation will have people comatose in no time. Keep it short and explain your points. Too much colour, too many fonts and too much data in a slide only makes it unintelligible. This goes for any type of audience, from students to board members. Keep it short. Keep it clean. Other forms of data presentations include reports, flyers, pamphlets, etc. Choose these media according to whom you're approaching. A flyer with relevant data in an attractive, simple manner would be perfect if you're handing it out to a busy CEO.

This is actually one of the best decisions you can take as a data analyst. I remembered sending one project to my direct supervisor without reviewing the results, it was later I discovered an error when my supervisor called me to verify some claims; apparently, my excel miscalculated. I had to do manual calculations and reconfigured my excel. Reviewing results have benefits and will help you correct your mistakes.

Always go back and review what you create. There may be something missing or, worse, something incorrect in your analysis. Asking peers and supervisors to go through your work is one of the best ways to ensure your data remains Rock solid, because they will be seeing it with fresh eyes. Once you've gone through your own work again and again, you tend to miss things. Always ask for a second or a third opinion.

Being able to accept feedback is one of the most important parts of being in data. Not everyone will agree with your analysis every time. Make sure you use the feedback as a way to make changes or adjustments to your future approach. Getting feedback from different types of end users can give you different perspectives, which is always recommended.

Feedback, when constructive, is invaluable. Never fail to take it into account. If you have the means and time to correct your work based on the feedback given, that's great. If not, store it in your memory for the next time you need to analyse content and data.

As mentioned, one can work with the data on a silo and produce a “perfect” report, but that will be a biased approach. Feedback and challenges to the data, models and results, are the best to have improvements in the final results.

While planning and validation are required for any data project, I don't believe the article so far addresses the original question on balancing speed and accuracy. For instance in the validation section, we can discuss the potential to increase accuracy at the cost of speed by possibly using cross-validation. Digging further, we can examine optimizing the number of folds and parallelizing iterations as a method to increase accuracy and still balance speed. This is just a single example, but I believe each section would need to go beyond generic concepts to address the original, and very valuable, question.

Size Matters If this is the first time working with a particular dataset, reduce the size as far as possible to a timeline that would still make logical sense during analysis for example; single year portions, and compare your values with previous years (taking time values as the standard here) before proceeding to work with larger datasets eg a 10-year forecast. This will help you avoid overwhelming yourself and to identify patterns in the data to help with cleaning for the subsequent datasets. Also consider automating your data loads once you have identified what a clean set looks like to you, you can use low-code tools for a start such as Automation 360 or data lakes for the the more experienced with SQL functions