Harnessing sophisticated advanced analytics has become essential in understanding and making complex customer-facing decisions. Whether it is analyzing customer sentiments, determining pricing strategies, forecasting demand, understanding sales propensity or performing dozens of other marketing processes, exploiting big data is the key to gaining new advantages, new revenue and increased loyalty. To succeed, analytics environments must be enabled quickly, leverage the most comprehensive and timely information and empower analysts and data scientist with the tools to explore, analyze and collaborate.
Most analytics environments present problems with set-up times, completeness of functionality, enabling collaboration, and data usability. Research and development (R&D) activities are often performed in silos, which leads to teams building analytics solutions from scratch each time. Often there are too few readily available modules or pre-built functions. The lack of a unified platform makes it difficult to scale up advanced analytics and makes collaboration very difficult.
Understanding these issues, Fractal Analytics has developed the Centralized Analytics Environment (CAE). CAE provides:
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CAE is a unified, web-enabled and open-source business analytics platform that delivers a PCI-DSS certified analytics environment pre-built to easily integrate with data. CAE provides a comprehensive set of pre-built, proven tools and modules to address dozens of customer- and market-facing business decisions. Features of the CAE system include:
Build on the fly: Users can build new models or enhance existing ones quickly and intuitively without the intensive need of new coding, integration or custom development.
Open-source: CAE uses open-source standards to enable quick and painless integration with any data source and provides the ability to integrate popular third-party applications as well as proprietary or in-house systems.
Web-based/cloud-enabled: CAE is a hosted cloud solution and delivers functionality to data scientists and business users in an intuitive web-based interface.
Traditional or machine learning techniques: CAE supports a number of traditional and machine learning techniques and models, enabling models to improve over time.
Data processing: CAE can acquire, process and improve data from a variety of sources, harmonizing the data for optimal use in analytics applications.
Validation checks: CAE uses validation checks on the statistical model outputs using rules to confirm the validity of the outputs.
Collaboration: CAE enables collaboration among different user types by centralizing and sharing tools and outputs.
Role - and project-based secured workspaces: Multiple users and teams can establish a variety of different role- and project-based workspaces to keep analytics activities separate across different users, teams, projects and timeframes.
Visualization and presentation: CAE can harness visualization and presentation tools like Tableau to provide intuitive, colorful displays of data for business users to consume and understand business insights.
The CAE platform can be leveraged using the following two delivery models:
CAE is the analytics platform of choice for the following companies:
Fortune 100 consumer goods and pharmaceutical company: With the help of CAE, the company has set up an automated demand forecasting process for 90 countries globally.
Fortune 10 technology major: With the help of CAE, the company has set up a marketing mix modeling process to help analyze the media mix for one of their leading brands.
Fortune 100 global consumer goods company: With the help of CAE within their secure infrastructure, we are running their market-facing analytics such as Integrated Business Drivers analysis and Demand Forecasting.
A pharmaceutical manufacturer is leveraging CAE to successfully deliver a market size forecasting solution across 450+ country-category combinations in 90 countries.
The business challenge
The global analytics team of a large manufacturer of medical devices, pharmaceuticals and consumer packaged goods was developing global scale country-category forecasts using syndicated data sources. They wanted to build automated forecasting solutions for generating forecast numbers across 450 country-category combinations of value and volume. To get a holistic view of the numbers, country, regional, and GPO heads were to consume the output of such forecasts through dashboards and presentations.
To fulfill the requirement, Fractal implemented the following CAE modules:
CAE provided the following insights and forecasts for the next two years for all global markets across regions:
The client also benefited from using these technologies:
CAE uses the white box approach to integrate data sources and algorithm modules to create sequential workflows and end-to-end advanced analytics solutions.
CAE leverages technologies, including artificial intelligence and machine learning techniques, for gaining business insights and making collaborative decisions. CAE is able to pull data from sources, including big data sets, and push it to different sources. Its core functions include nodes to process data, advanced models and algorithms to analyze data, and big data integration to manage huge volumes of data.
CAE can be delivered as an on-premises solution or on the cloud. It is easy to deploy and can operationalize analytics rapidly in an organization. It can blend with any technology infrastructure and can be scaled up easily.