Your data culture is growing! But if the amount of data at your disposal is exploding, then you may find it difficult to handle these colossal volumes of information. From then on, you will have to work on the basis of a sample that is as representative as possible. This is where Data Sampling comes in.
As the range of your data expands and your data assets become more massive, you may one day be faced with a volume of data that will make it impossible for your query to succeed. The reason: insufficient memory and computing processing. A paradox when all the efforts made up to now have been to guarantee excellence in the collection of voluminous data.
But don’t be discouraged! At this point, you will need to resort to Data Sampling. Data Sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points. This technique allows you to identify patterns and trends in the larger data set.
Data Sampling: How it Works
Data Sampling enables data scientists, predictive modelers and other data analysts to work with a small, manageable amount of data on a statistical population.
The goal: to build and run analytical models faster, while producing accurate results. The principle: refocus analyses on a smaller sample to be more agile, fast and efficient in processing queries.
The subtlety of data sampling lies in the representativeness of the sample. Indeed, it is essential to apply the most suitable method to reduce the volume of data to be taken into consideration in the analysis, without degrading the relevance of the results obtained.
Sampling is a method that will allow you to obtain information based on the statistics of a subset of the population, without having to investigate each individual. Because it allows you to work on subsets, Data Sampling saves you valuable time because it does not analyze the entire volume of data available. This time saving translates into cost savings and therefore a faster ROI.
Finally, thanks to Data Sampling, you make your data project more agile, and can then consider a more frequent recourse to the analysis of your data.
The different methods of data sampling
The first step in the sampling process is to clearly define the target population. There are two main types of sampling: probability sampling and non-probability sampling.
Probability sampling is based on the principle that each element of the data population has an equal chance of being selected. This results in a high degree of representativeness of the population. On the other hand, data scientists can opt for non-probability sampling. In this case, some data points will have a better chance of being included in the sample than others. Within these two main families, there are different types of sampling.
Among the most common techniques in the probability method, simple random sampling is one example. In this case, each individual is chosen at random, and each member of the population or group has an equal chance of being selected.
With systematic sampling, on the other hand, the first individual is selected at random, while the others are selected using a fixed sampling interval. Therefore, a sample is created by defining an interval that derives the data from the larger data population.
Stratified sampling consists of dividing the elements of the data population into different subgroups (called strata), linked by similarities or common factors. The major advantage of this method is that it is very precise with respect to the object of study.
Finally, the last type of probability sampling is cluster sampling, which divides a large set of data into groups or sections according to a determining factor, such as a geographic indicator.
In all cases, whether you choose probabilistic or non-probabilistic methods, keep in mind that in order to achieve its full potential, data sampling must be based on sufficiently large samples! The larger the sample size, the more accurate your inference about the population would be. So, ready to get started?
