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Business applications


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Business applications


Integrate data-driven, predictive business applications into everyday processes for smooth and effective decision-making.

Within a company, hundreds or even thousands of critical decisions are made every day. These decisions affect the company's ability to generate revenue, the size of costs and the level of risk. SPSS business applications can help you make these decisions as smoothly and efficiently as possible.
Companies in every industry around the world are turning their data into information to align their operations with their future goals. By integrating predictive business applications into everyday business processes, organisations can take control of decisions to successfully meet their business goals. This is achieved by using past information to analyse the current situation and then predict the future. They use historical data to optimise, manage and automate their decision-making processes. As a result, they can increase cross-selling, reduce marketing costs, detect suspected fraud, or increase response rates to campaigns. The aim is to do all this in an automated way.

As predictive analytics become a key business process in the life of an organization, the organization will need an application that integrates these predictive models into its day-to-day operational processes. SPSS Collaboration and Deployment Services (CADS) automates and manages the complex analytical processes and makes the results easy to deploy.
With these capabilities, it is an essential tool for companies with large volumes of data who need to make decisions to improve their business results based in large part on the analysis of their data, and to do so as efficiently as possible and with the least investment of resources.
With SPSS Collaboration and Deployment Services, you can develop an analytical enterprise, organize and operationalize your analytical processes, and effectively apply the results of these ad-hoc and manual analyses to other areas of your business.
The CADS framework brings a wealth of benefits both to the individual areas of the organisation and to the business as a whole through collaboration between units. Thanks to the CADS architecture and its user and rights management capabilities, storage, resources, and functionalities can be used by multiple areas simultaneously, but also separately or collaboratively as required.

The central element of the CADS framework is the so-called Repository, a central repository, that stores the created models, streams, analyses, SPSS syntaxes, SAS jobs, SQL procedures, according to version and privileges. From the elements stored in the Repository, analysts can build complete data management processes, called jobs, in the system's visual user interface.
In a job, error handling is also possible: if a process or a part of it (even a specific stream) does not run successfully, error correction procedures can be built in according to the nature of the error.
The completed job can be scheduled on demand, the status of the process can be monitored and notifications can be sent to the specified people about their status.

By automating processes to this level, human resources can be saved, allowing analysts to focus on updating models and building new ones.

Automatic model monitoring
Keeping models up to date and ensuring their accuracy is essential if business decisions are to be based on the results.
Pre-defined model evaluation processes in the CADS framework help ensure that results are always reliable:
- monitor the performance of the predictive models and its changes;
- automate the process and scheduled execution of model updates.
The system is capable of automatically evaluating data mining models, with appropriate parameterisation, to continuously monitor their performance and accuracy, and to identify the necessary intervention and update points.

Real-time analysis
The Scoring component of the CADS framework enables the integration of analytical results into business processes, even in real-time. As the system can handle multiple data sources simultaneously, both historical and real-time data can be used to generate results.

What industries can it be used in?

Financial services
Energy services