Data Analytics vs. Data Analysis

 Two words dominated the conversation concerning data strategy in this data-driven world: data analytics and data analysis. They sound similar or even overlap at some points; however, they function quite differently. So what does each of them do and why does it make any difference?

Data Analytics is the big picture. It means the whole process of gathering, transforming, and processing raw data in order to derive more pervasive insight. This is a treasure hunt where data scientists go through mountains of data, just for the diamonds-invaluable insights for major business strategies. It's all about analytics-the big questions: what trends are emerging, and where should we pay further attention? It is an expansive approach in a way, using diversified tools-from cleaning and organizing data to all the sophisticated algorithms of machine learning-to support strategic decisions.

Data Analysis is much more focused and targeted. This is the statistical microscope where prepared, formatted data zooms in on something that might answer some very specific question, while the statistical truth lies at the interpretation of numbers and statistics that point to a relationship between them.


Analysts use regression, visualization, and trend analysis so they can drill down deep in the data and can trace the "why" and "how" regarding a business outcome. They thereby help organizations understand the information that data really implies-its clarity to pinpoint for the precise decision-making process. So, what matters is from data to insight, that's all. Data analysis is the realization of the depth of the insight. Analytics lays the map, and analysis would explore the detail within that map. So, mastery of both-it is this that will help turn raw data into impactful action strategies.

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