Common Methods of Collecting Big Data

Collecting Big Data

The volume of big data has grown tremendously in the last decade. Therefore, it’s essential to understand the following questions to use this data to its fullest potential:

  • What qualifies as big data.
  • How it’s collected.
  • How to best protect it from unauthorized access.

It will only continue to grow as we become more technologically advanced and reliant on interconnected systems that collect and store our personal information.

About Data Collection

What is data collection? The term data collection refers to a procedure in which data is extracted from raw sources and prepared into a format that you can analyze. Depending on your industry, different data collection methods will provide varying results. You should consider several factors before beginning any kind of data collection process: volume, predefined formats, variety and velocity (VoV), integration and customization options, security policies/privacy rights.

Steps in the Data Collection Process

Steps in data collection vary from project to project. Still, they typically include extracting information from predefined formats. They include Excel, SPSS, or Tableau and transforming that data into a form that is usable for analysis. The data management phase can be one of the more labor-intensive parts of a big data project. It usually requires additional resources and planning on top of what’s necessary for just collecting raw data. According to professionals at Egnyte, “Primary data collection methods gather information directly, so it is source data. Secondary data collection methods pull information from existing repositories.”

Challenges in Big Data Collection

The fact that big data is collected in a variety of formats and from many sources poses additional challenges. Due to its size, big data has to be identified and managed in predefined formats. It requires improved access methods to allow all required data sets to be accessed internally and externally while allowing internal and external data silos to remain intact. Therefore, getting hold of all required information becomes increasingly challenging because multiple platforms are often involved.

You must maintain the quality of big data at a good level, and you must perform various ETL tasks concerning unstructured and structured data. Unfortunately, companies often face the challenge of not having enough skilled talent on board. This makes it difficult for them to handle all types of ETL activities while building reports and doing other work related to big data analytics.

Big Data Security and Privacy Issues

PII data may consist of Social Security numbers, personal health information, financial information, and more. You should take steps to collect and analyze big data to mitigate security risks. Further, access controls are essential for identifying what types of users should have access to various parts of your big data warehouse. You have to establish business policies on how authorized employees will access data (such as separation of duties). Also, methodically identify if any local or federal laws/regulations apply regarding subject matter and purpose for collecting big data.

Create a strategy to mitigate security risks and design systems to enforce governance rules and protocols. You can build business confidence that your firm will handle all big data responsibly by putting policies in place. This can make your company more competitive by allowing it to easily add value to its products and services, as well as by attracting talented employees.

Choosing a Method for collecting data can be tricky. It all depends on what kind of data you are trying to collect and what you will use it for.

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