Data Collection

1. What is Data Collection? 

Big data is an important information asset. When used properly, you can quickly and efficiently process large volumes of information to give users insights and aid in decision making. In order to properly utilize big data, it is also important to collect the right data format and quality data. When defining data collection, it can be viewed as storing data suitable for a purpose through an appropriate process, and the following four points should be noted.

 - Purpose of data collection
It is for the purpose of collecting and analyzing data. This is important because the data you need to collect varies depending on which service you use.

- Difficulty of data collection 
Much depends on whether the data to be collected is internal or external. If it is internal data, it is likely to be structured data in the form of a table, and the cost or difficulty of collecting it will be lower than when collecting external data.

 - Periodicity of data collection
First of all, you need to decide whether data collection will be necessary periodically or on a one-off basis. If you need to collect data periodically, you need to set the frequency of how often to collect it.

 - Storage format of data collection
How to store the collected data is also an important issue. Depending on the collected data, the collection technology is different and the type of collection is different, so when designing the data storage, you need to determine the type of file system (excel, pdf, etc.) and database (DB).

2. Precautions for Data Collection Process

When collecting data, keep in mind the following:
 ✓ Consistency
You need to have consistent rules. This is because if the collection method changes during the process, the quality of the results cannot be guaranteed. Therefore, data must be obtained according to the priorly established criteria.
  ✓ Flexibility
But being consistent doesn't mean you shouldn't have flexibility. Data collection can be stopped if the required data collection is completed faster than the planned deadline, and it can be continued if the volume of data is insufficient even after the planned period has ended.
 ✓ Randomness
It is not always possible to do complete enumeration survey. In most cases, you will need to select a sample and collect data on that sample. There must be randomness in the selection of samples, but analysis of the population can be done.

3. Data Collection with CLICK AI

CLICK AI prepares predictive modeling by working with multiple organizations that support data collection, storage and transformation. Once you have collected and prepared the right data for your specific business problem, it can be easily imported into the CLICK AI automated machine learning platform, no matter where it is stored. Then CLICK AI automatically generates new AIs and builds and evaluates hundreds of machine learning models that can be immediately deployed to production.

Reference
http://www.hellodatascience.com/?p=282

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