How Much Data is Enough for Analysis into Various Fields?

3 min readJun 20, 2019

Data analytics is kind of analysis process of huge amount of data for specific purpose. And data analysis using the data analytics is done for industries like healthcare, travel, gaming and energy management. Data analytics helps to understand the trend and insights about the particular industry. But the question is right here how much data do you need for useful data analysis.

Actually, there are many factors determine how much data you required for useful data analysis. Sometimes smaller amount of data is enough to analysis the data but sometimes large quantity of data is not sufficient to analysis certain things.

Data analysis involves classification and summarization of data to make it understandable for particular needs. The analysis process helps to get the insight about the particular industry and there are various ways to calculate and analyze the data and get the useful information.

Data Analysis for Business Research

If data is analyzed for business research it is not necessary data should be in large quantity, as data taken as sample from different locations as a survey would be enough to analyze the trend of sentiments of people about a particular thing. Scientist doing research also needs maximum data to analyze and find out a conclusion while solving a hypothesis question. While doing surveys and research huge amount of data is not required instead a sample is taken.

Data Analysis for Machine Learning and AI

Here for developing a successful AI model you need huge amount of training data for AI and machine learning. In AI model developments deep learning process with right algorithms is followed here to train the model learn about the maximum types of patterns as a inputs and use the same while predicting when AI model is used in real-world scenario.

Hence, here you can use maximum amount of data for analysis and make it usable for AI-based machine learning projects. The maximum the data used on machine learning training, the model prediction will be accurate and precise. So, while working with AI models don’t set your data limits for analysis, instead utilize the maximum amount of data for analysis.

Data Quantity vs Quality for Data Analysis

While analyzing the data, quality or quantity, which one is more important? Sometimes your data is not sufficient, as there is no useful information available or some key values are missing or formated which can create confusion among the people.

The quality is important when you are working on data sensitive projects that can give you an accurate result. Big data for deep learning is one the best example that could be enough for you to train your AI-backed model and make the best use of data analysis. And for AI-based projects quality is more important than quantity so be careful while choosing the data.

Cogito is one the well-known company providing the AI training data with proper labeling and annotation of images, videos or texts with best accuracy. Cogito is also dedicatedly involved in data collection and classification with data labeling service to make such data recognizable for computer vision. It is providing the data with best accuracy while ensuring the quality at each level helping machine learning hire data scientist to make best use of such data.





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