Data analytics is a method for individuals and organizations to ensure that their data is working in an age where statistics and information are becoming more crucial. Through a variety tools and methods, raw numbers can be converted into useful and educational insights that guide decision-making and thoughtful management.
Data analysis is the systematic analysis of data sets in order to discover significant patterns and trends. These patterns are used as the basis for business decisions as well as to enhance performance. It also involves presenting the results of the analysis to other employees and other stakeholders in the business.
If a data-analysis is to be efficient, it should begin with a clearly defined goal and end with concrete, actionable suggestions to the company. This will ensure the work is focused and can be effectively translated to real business outcomes. One example of this is the Netflix recommendations for watching, which are built on extensive data collection about what people watch frequently, how often they watch it, and how much they enjoy specific content. This has led Netflix to develop personalized recommendations that have a profound impact on how viewers view content and generate higher revenue.
Once a clear business issue or problem has been identified, it’s the time to gather the information. This usually involves pulling structured data from internal sources, like a customer relationship management system (CRM) or external sources, such as government records or social media application programming interfaces. The data is cleaned before analysis. This may include getting rid of duplicate entries or irregular entries or inconsistencies, resolving them, standardizing the structure and format of data, and addressing syntax and white spaces.