The key difference between Data Analytics and Data Analysis.
Looks can be deceiving. The term “analytics” and “analysis” may look the same, but there is ever so slight difference between them. In today’s data-driven world, a lack of understanding between two related terms can affect the ability to leverage the data to their absolute potential.
While both of them are useful in gaining new insights, the key difference between them is their approach to finding insights, and the type of insights they find from the data. One gives insight into the past while other the future. Let us look into the key points that make Analytics differ from the Analysis.
Data Analytics refers to applying statistical algorithms to the dataset for finding new insights. With the data, it helps the organization to find new opportunities, or outcomes by predicting the probable outcome. The analytics done with data is to find more about the future.
Data Analytics is a detailed method in analyzing the raw data to get predictions about future performance. It can be used in better decision making, understand how a new product could perform, reduce the business cost moving forward, etc in an organization. Data Analytics is extensive in its scope, where it includes Analysis as a sub-component which we’ll see later.
Eg: Say you have 1PB of business performance data for the current year. Using data analytics, one can find what will be the performance of the business in the coming year.
A good real-time example for data analytics is Amazon’s product recommendation system, which uses an item to item collaborative filtering techniques based on the existing purchase.
There are several tools available for data analytics like R & Python programming language, Apache spark, excel, etc.
Data Analysis takes the historical data to find “what had happened?”. It deals with the past. It finds some meaningful insights from the dataset such as correlation or patterns to improve the performance of the business. First, the dataset is extracted, cleansed, transformed, and modeled to find useful information.
The information from the modeled dataset can be used to assess business and product performance, identify risks, build a business case, etc in an organization. Analyzing the historical data will give useful insights, and based on the insights, a business can improve its performance.
Eg: Say you have 1PB of business performance data. With data analysis, you take this data and measure how the business has performed over a year, where it failed, where there are risks, etc. With these insights, the business can improve its performance coming year.
A real-world example is applying data analysis to remote sensing data by Geostaticians to recover useful insights about a geographical place.
Some of the tools available for data analysis are Wolfram Alpha, Tableau, Google fusion table, KNIME, etc.
Analytics and Analysis are both used in the realm of data science interchangeably because they are both used in converting raw data into useful insights for an organization. Though they both seem similar, they are quite different and understanding the difference is a key to improve your business.