In the real world scenario at present, the challenges of dealing with big data can be grouped into three major dimensions, namely process, data, and management. Let us delve into the ins and outs of these big data challange one by one.

Big Data

Process Challenges

In this specific context, the biggest challenge is how to analyze. According to business experts, it can consume tremendous paramount exploration to figure out the right model for analysis and to iterate very rapidly by means of numerous models at scale. In nutshell, process challenges can be broken into the following points:

  • Aligning data from numerous sources
  • Capturing data
  • Grasping the output, sharing and visualizing results, and considering the process of presenting complex analytics on a mobile device
  • Altering the data into a form apt for analysis

Data Challenges

The data challenges associated with big data can be pointed down as:

  • Variety: Uniting multiple sets of data in which the real challenge is to handle the multiplicity of types, formats, and sources.
  • Data discovery: Here, the challenge is to find high quality data from the colossal collections of data present out there over the internet.
  • Scalability: This includes methodologies like social graph analysis for leveraging social network to create enhanced user experience.
  • Quality and relevance: It poses the test of determining the quality of data sets together with relevance pertaining to specific issues.
  • Velocity: The challenge is to determine the right way of reacting to the flood of data within the stipulated time.
  • Volume: The major challenge is to deal with the size of copious data.
  • Veracity: Here, the focus is on knowing how to cope with uncertainty, missing values, imprecision, and misstatements. In addition, finding out how good is the data, how sound is the sampling resolution, how extensive is the coverage, how timely are the readings, and how well comprehended are the sampling biases.

Management Challenges

The prime management challenges are associated with data security, privacy, governance, and ethical problems. Here, the core lies in ensuring the correctness of data, which means following the intended usage and relevant laws of the data. Also, tracking the way in which the data is utilized, derived, transformed, and managed.

Apparently, numerous data warehouses comprise sensitive data, for instance personal and confidential data. There are ethical and legal concerns attached to the access of such kind of data. Thus, the data must be access controlled, secured, and logged for audits.


Big data has clearly hit the spot beyond the realm of buzzword status. It is emerging as an innovation carrying a huge potential for value creation. Along with colossal opportunities, such as location related data, social data, manufacturing or retail data, and healthcare, there are challenges, such as data volume, data capturing, data quality, and data management.

As more and more data becomes less expensive and technology becomes more advanced in terms of analysis and acquisition, the opportunity to render actionable information would augment. This can be termed as the common great challenge for big data.

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