From descriptive analysis to predictive analysis: what are the advantages?
Descriptive analysis is a method for analyzing historical data which is based on aggregation and reorganization of them in order to describe the past and answer the question: What happened? This method is widely used in companies to quantitatively analyze internal events, such as management of goods, average customer spending in e-commerce, changes in sales levels in a given period and more broadly everything that can be quantitatively analyzed.
A further step: predictive analysis
Unlike the descriptive one, predictive analysis, which is still a method of data analysis, allows to make predictions about future events and answer the question: what could happen? This method, starting from historical data, tries to create a model to extrapolate information about the future using different statistical techniques or using the most recent Machine learning and data mining techniques, which allow to analyze large amounts of data and provide information that are as correct as possible and real, to better prepare for what will happen in the future
Historical data are extrapolated from different areas of a company, mixed together and enriched with variables that enhance the possibility to forecast the event.
Studying the data is therefore necessary in order to identify patterns within them (for example by analyzing ice cream sales, it is possible to identify the following pattern: low sales in winter, high sales in summer), hence allowing the application of machine learning statistical models and algorithms to capture their meaning, relations and extrapolating useful information to make authentic predictions about future events.
What is SAP’s response to this need?
SAP is the best known and most requested management software to understand the importance of collecting and analyzing data for a company.
SAP has integrated tools in its products in order to achieve predictive analysis, trying to automate the process as much as possible so that the user does not waste time developing codes in order to save time and obtain information faster.
SAP allows to set up analyses with the aim of classifying data, for example whether a customer will lose interest in the company or will continue to be loyal.
Regression models are also easily set up from the SAP platform and allow, for example, to predict the price of a product by analyzing variables such as transport costs or product taxes.
In addition, SAP allows to set up analyses focused on a historical series, thus bringing together other information that could influence the data under examination.
Example of predictive analysis in SAC
Furthermore, SAC leaves the possibility of developing custom algorithms using the R language, which allows to have more control and flexibility on the analysis currently underway. In this way, users will have access to the potential that R makes available, using all the statistical algorithms made available in the great deal of packages that can be installed within the language. Finally, SAP has developed a product, SAP Predictive Factory, which stands out for its user-friendly interfaces and which allows users, both business analysts and data scientists, to create, maintain and monitor predictive models in a process which is productive and safe.
ccelera, thanks to its experience, has the possibility to help you frame the necessary path to start a predictive analysis process; our partnership with SAP ensures a very in-depth knowledge of the offer, to guarantee you the best solution.
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