This is a reference architecture for data and analytics. This will allow you to build solutions that gather data from any type of source, including web and social. With those solutions, you can store, analyze, and report on data by using analytic engines to drive actionable insights and visualization.

Shown below is a data and analytics reference architecture.

 

Step 1: Data is collected from data sources through edge services. Data sources include sensors or devices, weather, and social media such as Twitter and Facebook.

Step 2: f there is a need for enterprise data such as customer data and transaction data, it can be accessed from the enterprise network through a secured gateway such as a VPN.

Step 3: Real-time data can be collected and analyzed by streaming computing.

Step 4: Batch data is prepared and integrated for intended users. Various cognitive technologies can be used here to semi-automate the data ingestion, preparation, and integration. Data is transformed and augmented as it moves through the processing chain.

Step 5: Data repositories provide staging areas for the different types of data.

Step 6: Data can be used for analytics discovery and exploration by data scientists, citizen analysts, or both. New analytics models can be created or existing models can be enhanced. Machine learning or other cognitive technologies can be applied here.

Step 7: The new analytics models are executed and their outcome is provided for use as actionable insight. Machine learning or other cognitive technologies can be applied here.

Step 8: Actionable insight can augment enterprise applications and SaaS applications on the provider cloud.

Step 9: Actionable insight can also be pushed to users on a public network or mobile applications.

Step 10: Information governance, security subsystems, and systems management encompass each processing phase to ensure that regulations and policies for all data are defined and enabled across the system. Compliance is tracked to ensure that controls are delivering expected results. Security covers all elements including users, all data and analytics, and other applications.

Step 11: Users are broadly classified in two ways: enterprise and third party. Enterprise users access resources on premises or through a secure virtual private network (VPN). Data is available both directly and through applications that provide reports and analytics. Transformation and connectivity gateways assist by preparing information for use by enterprise applications and for use on different devices, including mobile, web browsers, and desktop systems.

Step 12: Third party users gain access to the provider cloud or the enterprise network through edge services that secure access to users with proper credentials. Access to other resources may be further restricted as dictated by corporate policy.