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The analysts are grouped in most organizations, depending on the market domains. Unfortunately, the research is shared with the top executives and so the findings are not easily transmitted to the market users for whom the greatest value is given.

Since analyst-prefabricated solutions aren't especially difficult to implement; they can be very expensive and the ROI isn't immediate. Such analytical models are, by default, ready to increase accuracy over time, but it is a complicated model that requires commitment to implement the solution. Since business users do not immediately see the results expected, they lose interest, resulting in loss of confidence as a result of which models fail.

Implementation of business analyst solutions fails because either the data is not available, the data sources are too complicated or they are poorly designed.

Complying with government legislation is another thorny problem for big analytics efforts. Most of the information in the big data stores of businesses is confidential or personal, which means that the organisation will need to ensure that while processing and storing the data, they follow market norms or government requirements. Many organisations that have been around for a very long time have siloed data throughout their ecosystems across a number of different applications and systems. Integrating all these various sources of data and transferring information where it needs to be often adds to the time and cost of dealing with big data.

Another significant problem for organisations is the IT infrastructure required to sustain projects in the area of big data analysis. Buying and maintaining storage space to house the data, networking bandwidth to move it to and from analytics systems, and computing resources to perform those analytics are all costly. Through using cloud-based analytics, some organisations can offset this issue, but that typically doesn't completely eliminate the technology issues.

Reference:

Books on Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking- Book by Tom Fawcett and Business Analytics: Data Analysis & Decision Making-Book by S. Christian Albright and Wayne L. Winston