Data analysis is an approach towards examining, modifying, and modelling data with the prospect of uncovering information, drawing inferences, and supporting decision-making. It refers to the subjective and quantitative style used in business-to-customer application to enhance productivity and business gain. Data is basically derived and classified for analysis to study the business trend and plan the future approach. At the crown of data distillation process lies what is referred as "analytics". The Analytics can be divided into four key categories- Descriptive, Diagnostic, Predictive and Prescriptive.
Descriptive analytics are outlined to provide the basic information about who, what, when, where, how many. It gives a view of key metrics and measures to the analyst for the business. They also provide crucial entities for discovering patterns that offer insight. Effective visualisation tools can be used to enhance the data provided by descriptive analytics. It consists of Canned Reports and Ad Hoc Reporting. A canned report contains the basic information to get an overview of the subject whereas an ad hoc report is designed by the receiver, consisting of more questions from your side. Descriptive analytics can be applied efficiently in the field of sales and one the best example of this type of analytics are the results, that a user fetches from the web server through Google Analytics tools as the inferences were drawn explains the trend of success and failure towards a particular topic.
It is the next step of complexity in data analytics which is used for discoveries and helps you to bring into the reach the reason of why something happened or what went wrong. Queries and drilldowns provide with more in-depth study related to a report which can help providing you various values for some study and conclusions drawn. It can also help you to draw out the root-cause of the problem. For example, for a social media marketing campaign, you can apply diagnostic analytics for the assessment of the number of posts, mentions, followers, fans, page views, etc and plan accordingly.
Predictive analytics is about forecasting and predicting. It assists you in identifying trends in relationships between variables, direct the strength of their correlation, and hypothesize causality. This helps in finalizing and working towards a realistic goal for the business and effective planning. There are few algorithms which collect data and for the missing data, it provides a prediction. Then the data is pooled with existing data in the CRM systems, POS Systems, ERP and HR systems which work on identification of data patterns and relationships. In a world of variability, it helps to predict and allows one to make better decisions.
Prescriptive analytics is where artificial intelligence and big data come into action. It advises user based on all outcomes and results in actions which are expected to maximise business metrics. It basically uses simulation and optimization to get into the depth of "What should a business do?” It is a concept of advanced analytics consisting of both internal and external data based on Optimization which focuses on achieving the best outcome and Stochastic optimization which provide us with the way to achieve the best outcome. A basic example is a traffic application which could help you decide the best route to home considering all factors.
Data analysis helps in structuring the findings and discoveries from various sources of data; it does the task of breaking a problem into smaller parts and act as a filter for acquiring meaning from the data set and much more. The better analysis provides a better exposure and healthy business strategies. It is one of the most important factors according to the way market has been expanding, presently there is no business which can survive without analysing the available data.
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