In the vast majority of talks with clients and prospects about Big Data, we soon realized an astonishing gap between the business itself and the expectations of Data Analytics projects. Therefore, we carried out a research to respond the following questions:
- What are the main business sectors that already use Big Data?
- What are the most common Big Data results per sector?
- What is the minimum dataset to reach the results per sector?
The summary is organized in the table below.
- The table presents a summary for easy understanding of the subject. However, for each business there are many more variables, opportunities and of course, risks. It is highly recommended to use multivariate analysis algorithms to help you prioritize the data and reduce project’s cost and complexity.
- There are many more sectors in which excellent results have been derived from Big Data and data science methodology initiatives. However we believe that these can serve as examples for the many other types of similar businesses willing to use Big Data.
- Common to all sectors, Big Data projects need to have relevant and clear input data; therefore it is important to have a good understanding of these datasets and the business model itself. We’ve noticed that currently many businesses haven’t been yet collecting the right data in their systems, which suggests the need pre-Big Data projects. (We will write about this soon).
- One obstacle for Big Data projects is the great effort to collect, organize, and clean the input data. This can surely cause overall frustration on stakeholders.
- At least as far as we are concerned, plug & play Big Data solutions that automatically get the data and bring the analysis immediately still don’t exist. In 100% of the cases, all team members (technical and business) need to cooperate, creating hypothesis, selecting data samples, calibrating parameters, validating results and then drawing conclusions. In this way, an advanced scientific based methodology must be used to take into account business as well as technical aspects of the problem.