Data analytics company is defined as “analyzing the acquisition, storage and analysis of data in order to find trends or associations within it.” Within this article, you will find a breakdown on what those  features are.

  1. Data Analytics Starts with Understanding Data

An understanding of data is the foundation of all data analytics projects and while a project’s objective must be clear, it also needs to be understood in a way that allows it to be realized. For example, if the objective is to improve customer satisfaction with reporting analytics, then what you mean by that needs to be explained; how do you define customer satisfaction? 

  1. Data Analytics Is a Process

Data analytics is not something done once and forgotten. The process never ends and as requirements change, so does the business intelligence landscape. Business intelligence, therefore, is not something you should try for one project and then forget about.

  1. Data Analytics Is Connected

Data analytics is not only dependent upon having valid data for analysis, it is also dependent upon having the proper connection to other data sources. From a business process perspective, the ability to understand what the data means becomes ever more important as new requirements change and business processes evolve. These connections need to be understood in order to develop tests and apply them where they are needed, when they are needed.

  1. Data Analytics is Personal

A single database developer cannot solve an entire organization’s problems alone … or can he? The answer is actually yes with the right mindset and training. Business intelligence is not independent of the people who use it, the people who define it or the people who make it happen. The reality is that these factors need to be considered when discussing business intelligence because they are all part of the same process.

  1. Data Analytics Has a Methodology

Similar to data analytics being a process, there is also an established methodology for data analytics. Like most methodologies, its purpose is to provide a framework for structure and consistency and includes activities like:

  1. Data Analytics Is Iterative

Data analytics does not begin and end in one effort; it is iterative by nature. From the beginning of a project, you need to know whether or not your proposed solution is going to work. Once defined, you will be able to use that feedback to either refine the question, redefine it or abandon it.

  1. Data Analytics Is an Exploration

The reality is that you cannot have all the information at once! What typically happens is we find what we need and then we decide if there is more information that might be useful and dive back in again. This iterative process continues throughout the analysis process and one of the most important keys is to know when you have enough information (or don’t) in order to make decisions.

Conclusion

Business intelligence is more than a collection of data and it takes a strong understanding of what that data means and how best to apply them. When you are looking to build your business intelligence, you want to make sure that the right questions are being asked, not just any question.