SAS architecture for business analytics
Content
WHITE PAPER
SAS Architecture for Business Analytics
®
SAS® delivers solution building blocks for empowering strategic business decisions within your existing technology environment
SAS® Architecture for BuSineSS AnAlyticS
Table of Contents
Introduction .....................................................................................................1 SAS® Technical Reference Model for Business Analytics .............................2 Data Management .......................................................................................4 Analytics .....................................................................................................4 Reporting .....................................................................................................5
Compute Services ..........................................................................................6 Metadata Services ..........................................................................................7 Security Services ............................................................................................8 Integration Services.........................................................................................8 Management Services ....................................................................................9 Workflow Services ..........................................................................................9
Five Styles of Business Analytics, One SAS® Architecture ..........................10 1) Classic Business Analytics ....................................................................10 2) Classic Business Analytics with Data Quality ........................................10 3) Business Analytics with Feedback Loops ..............................................11 4) Real-Time Business Analytics ...............................................................11 5) Business Activity Monitoring .................................................................12 One SAS® Architecture ...............................................................................12 Conclusion ....................................................................................................13
i
SAS® Architecture for BuSineSS AnAlyticS
Content for this paper, SAS® Architecture for Business Analytics, was provided by Diane Hatcher, Solutions Architect in the SAS Technology Practice, Cary, NC.
ii
SAS® Architecture for BuSineSS AnAlyticS
Introduction
SAS has developed a business analytics architecture that can support many different architectural styles and business uses. At the core of the business analytics architecture are sourcing, discovery and sharing activities. Along with proper data collection and verifiable data quality this leads to better analysis, which means more compelling information can be shared in a variety of ways with stakeholders, depending on the situational context. The details of what these activities involve vary across organizations, within organizations and even from day to day. Only SAS provides an architecture flexible enough to support organization-specific requirements without sacrificing performance while providing a platform that can evolve.
Architecture PlAnning Business Analytics Activity Source
• Data capture • Data cleansing • Data manipulations • Master data management Provide consistent data across the enterprise • IT data management • Operational systems • Data access tools • Data quality algorithms • Customer transformations • Job flow management and scheduling
Discover
• Query • Statistical models • Scoring • Visualization Uncover opportunities • Business decision makers • Decision support systems • SQL support • Analytical modeling • Custom development • Interactivity
Share
• Publishing • Interaction • Integration • Reusability Distribute actionable intelligence • Business interfaces • Business processes • Content management • Web-based access • “Push” techniques • Services orientation
WHAT Functionality WHY Value WHERE Stakeholders
HOW Technology
Figure 1. Architecture Planning Framework for Business Analytics
The diagram above (Figure 1) shows various attributes to consider when planning business analytics architecture. All elements and capabilities may not be required within each element, but the relevant ones should be linked and work together to provide a seamless solution. As the usage of business analytics grows over time, additional capabilities can be added. SAS provides a single framework to meet all of your business analytics requirements, without the need to continually install new components. The SAS architecture provides a core set of capabilities that work together out of the box and can be easily extended to add more functionality or integrated with other parts of your IT infrastructure.
1
SAS® Architecture for BuSineSS AnAlyticS
SAS® Technical Reference Model for Business Analytics
There are four key qualities that underlie and drive the SAS®9 architecture for business analytics: • Scalability – the ability to meet performance demands across the organization. Different stakeholders or interlinked business processes will have different resource usage characteristics. The SAS architecture has the flexibility to meet those diverse requirements. • Manageability – the ability to administer the SAS environment. As SAS becomes more ingrained in your organization, it becomes critical to manage and monitor usage to ensure optimal availability. SAS provides tools and techniques to manage the environment, either independently or within an existing environment. • Interoperability – the ability to use SAS capabilities from outside the SAS environment. SAS provides an open architecture built to work with your existing IT infrastructure (Figure 2).
ENTERPRISE CLIENTS
Custom Applications Enterprise Portals SharePoint Web-Based Application StandardsBased Apps Excel PowerPoint PDF Outlook Mobile
SAS®
ENTERPRISE DATA AND SYSTEMS
Enterprise Applications
SAP Oracle Peoplesoft Siebel Teradata DB2 Oracle
Operational Data
Netezza HP Neoview AsterData Greenplum Sybase SQL Server MySQL IMS-DL/1 JDBC ODBC OLEDB PC Files
OPERATING SYSTEMS
z/OS AIX HP-UX Solaris Windows Linux
Figure 2. Interoperability with Existing IT Infrastructure
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SAS® Architecture for BuSineSS AnAlyticS
From supporting existing hardware and IT system standards to using virtually any database management system to integrating with enterprise communication channels, SAS has remained focused on delivering decision support wherever and whenever needed. • Reliability – the ability to deliver results at the expected time. SAS delivers reliable results with powerful data access capabilities, industry-leading analytics and diverse tools for sharing content via multiple channels. SAS delivers reliable solutions, supporting multiple configurations to provide highly available systems. Consistency of performance in both results and delivery are integral to the SAS architecture.
Scalability
Infrastructure Blocks Analytics User Interfaces
Data Management
Manageability
Reliability
Reporting
Framework Services
Interoperability
Figure 3. SAS®9 Technical Reference Model
Depicted above (Figure 3) is a high-level technical reference model for the SAS®9 architecture for business analytics. This represents a taxonomy of the core entities, without stating a specific relationship between them. The SAS architecture is made up of building blocks that encompass key characteristics of a service-oriented architecture. They are reusable services available across the suite of SAS tools and solutions to meet specific functionality requirements. Reuse allows SAS to provide service consistency and common integration points to fit within existing technology architecture. Framework services make up the application platform that delivers common core capabilities across the breadth of the SAS architecture. Data management, analytics and reporting entities provide activity-specific building blocks for sourcing, discovery and sharing. These blocks are a combination of targeted functionality and user interfaces designed to meet the needs of the specific activity.
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SAS® Architecture for BuSineSS AnAlyticS
Data Management
• Do you have the need to access data across multiple databases and systems? • Are you confident that your data is clean and consistent? • Do you know what your data looks like? The data management building block supports the business activity of sourcing. This could include prebuilt, high-performance capabilities for connectivity, quality, extraction, transformation and loading, migration, synchronization and federation of data. Data management means having a way to fully use all the data flowing into the organization. This includes profiling capabilities and the ability to incorporate data quality business rules across data sources and platforms. If you can automatically integrate data quality into data integration processes, you can ensure that data is current and accurate.
SAS® Data Management
Analytics
• Do you need industry-leading statistical algorithms and visualization techniques? • Can you optimize and manage the portfolio of analytical models across the enterprise? The SAS Analytics building block supports discovery. The core of a business analytics architecture is the framework support for an analytics engine, but it is how that analytics engine is surfaced to the end user that determines its value. It is important that technologies are in place to support a robust analytics development process. This includes defining the business problem, developing and deploying the appropriate models and tracking performance. Each of these steps requires due diligence and appropriate methodologies to deliver results to the organization. Analytics must be supported in a disciplined, managed environment to provide the maximum value.
SAS® Analytics
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SAS® Architecture for BuSineSS AnAlyticS
Reporting
• Can all end users ask questions and get a usable answer? • Do you have the ability to present the information in the appropriate format? • Can you use multiple channels to share reports? Reporting should not be treated as a standalone activity. It is part of a seamless approach for creating and sharing intelligence. Reporting is more than querying. It includes properly presenting the information and sharing the information across the appropriate channels. The reporting building block is critical for the business processes of preparing and sharing information. This information must be presented in a way that is viable and delivered to the right person at the right time. Only when this is done can information become intelligence.
SAS® Reporting
5
SAS® Architecture for BuSineSS AnAlyticS
SAS® Framework Services
Framework services (Figure 4) represent the application platform that delivers standard capabilities across the breadth of the SAS Business Analytics architecture. Data management, analytics and reporting address the components required for the core activities around sourcing, discovery and preparation, but they use a single infrastructure, representing the nervous system of the SAS architecture.
Figure 4. SAS Framework Services
These services are used by all SAS architecture building blocks to provide consistency of service and performance.
compute Services
• Does the engine you are using handle more than SQL processing? • Can you create customized analytical models beyond what’s provided out of the box? • Can the analytics engine access data from all existing data warehouses and systems? At the heart of the SAS architecture are the compute services. Essentially, this is the Base SAS software that has been the engine of SAS Analytics for more than 30 years. The SAS engine remains the power of the platform, executing queries and models to deliver intelligence. SAS has always been scalable vertically and horizontally, but we have also engineered SAS to be scalable across the enterprise for all types of users requiring both batch and interactive processing.
Compute
6
Metadata
Framework Services
Security
Integration
Management
Work ow
SAS® Architecture for BuSineSS AnAlyticS
Feeding the engine is the SAS procedural language. This powerful language provides the ability to analyze data using hundreds of analytical algorithms – including simple queries, regressions, neural networks, time-based forecasting, scoring and quantitative analytics. This is a huge advantage for users, because they are not limited to analytics delivered through a predefined user interface. Every organization is different, with different processes and different analytical needs. With user interfaces that are familiar to most organizations, SAS can support 80 percent of those needs. The remaining 20 percent can be customized to your organization to provide unmatched analytics. With SAS software’s multivendor architecture (MVA), SAS code can be written once and run on any supported operating system. Analytics and data go hand in hand. The more data that is available, the better the analytical results. One of SAS software’s core strengths has always been data access, and SAS is continuing to innovate with high-speed analytical data storage options, in-database processing support, and our commitment to continuous evolution of analytics techniques and user interfaces.
Metadata Services
• Have you implemented a central metadata layer across the breadth of analytic activities? • Does the metadata encompass both technical metadata and business metadata? Metadata services connect all the business processes in the platform – including resource management, security and sharing of content. Integrated metadata (information about data sources, how data is derived, business rules and access authorizations) is crucial for producing accurate, consistent information. SAS stores technical metadata and business metadata in an open, centralized and integrated repository. Data changes only need to be documented in one place. There are fewer systems to support and business users can count on high-quality information. A single version of the truth is available to all. Better use of staff time lowers the total cost of ownership for IT infrastructures. SAS integrated metadata also provides an auditable, repeatable and secure environment from which to derive business intelligence. Content can be organized and controlled from a central location, ensuring that enterprise users can only view the information they are allowed to see.
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SAS® Architecture for BuSineSS AnAlyticS
Security Services
SAS security services provide additional measures to complement your existing security infrastructure and conform to your existing standards for authentication, authorization and secure communications across the network. User authentication can be on host-based authentication systems or use LDAP or Active Directory mechanisms. Single sign-on is supported from desktop applications running on Windows and through Web applications using trusted authentication. Metadata-based authorizations can be used to add an additional layer of access controls beyond what is supported on the file system. Authorizations to SAScontrolled content and resources can be centrally managed via the metadata server. The implementation is flexible to control both access to content as well as role-based access to application functionality. For additional network security, SAS supports a number of mechanisms for encrypting network traffic. SAS/SECURE™ provides industry-standard encryption algorithms, including Triple-DES (168-bit encryption) and AES (256-bit encryption). These algorithms can be used to encrypt only credentials or all network traffic between SAS clients and servers. For Web applications, secure socket layer (SSL) is supported.
integration Services
• Can business analytics activities and results be integrated with external operating systems? • Can custom applications be developed that leverage analytical capabilities to address a specific business problem for your organization? • Is it possible to surface analytical models to be used in existing production environments? Integration services consist of technology components that facilitate integration of business analytics with other systems. The results of analytics can be used directly by individuals or can be automated into systematic decision processes. Integration services consist of technology components that facilitate SAS integration with other systems – either by invoking SAS algorithms or SAS calls to an external component. SAS algorithms can be surfaced across SAS itself, using the stored process interfaces. This allows analytical modules to be reused across the SAS architecture in an encapsulated manner – delivering consistent results. These stored processes can also be defined as Web services, allowing external applications to invoke SAS for real-time scoring or other analyses.
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SAS® Architecture for BuSineSS AnAlyticS
External systems can be operational systems or custom applications. SAS provides a library of APIs to support access to SAS functionality from custom applications, and provides an Eclipse-based development environment to streamline creation of custom applications. Language interfaces allow SAS code to be easily integrated with external systems – whether they’re based on other standards (e.g., PMML) or other platforms (e.g., Java or .Net).
Management Services
Management services consist of technical components that monitor and optimize the use of business analytics across the enterprise environment. The SAS Business Analytics infrastructure is able to operate and integrate into existing IT enterprise system management facilities such as: • System monitoring for monitoring the health of the environment. • Event management for assessing the status of applications via logging, monitoring and implementing methods to correlate events, errors and warnings. • System administration for the automation of start, stop and restart semantics. Management services include a technical component that provides the ability to log business analytics activity. The logs can be used to audit artifacts use, track performance and monitor changes made to metadata security settings and other content.
Workflow Services
Workflow services define the technical components supporting process management. These components are used by SAS solutions to provide the ability to define business rules, trigger events and send notifications when appropriate. Notifications can be alerts via e-mail, invoking another process or triggering a Web service. Workflow services allow for extension of workflows for greater integration with enterprise business processes. It is possible to generate multiple-level approval and review processes. With standard APIs for accessing workflow status for integration with business applications, analytics process flows become more manageable, visible and documented.
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SAS® Architecture for BuSineSS AnAlyticS
Five Styles of Business Analytics, One SAS® Architecture
The SAS architecture is designed to be flexible enough to support the five styles of business analytics that are used by all organizations. Each style has its own set of architecture requirements. Multiple styles may be used at the same time within an organization. Understanding these styles can help you assess what architecture building blocks you should consider. For more details about these five styles of business analytics, please read Architecture for Business Analytics: A Conceptual Viewpoint, available at www.sas.com/reg/wp/corp/17871.
1) Classic Business Analytics
Classic business analytics is the basic process of data sourcing and may include the creation of a data mart or warehouse, information discovery – via data exploration or predictive analytics techniques – and sharing generated reports or information at different levels of an organization. Traditional report-driven BI processes put information into the hands of users, leaving them to interpret the situational context and how it affects the business process. What is changing is how the information is distributed. Business stakeholders want information via e-mail, portals, dashboards and mobile devices. SAS Business Intelligence solutions can provide dynamic access to information, regardless of the interface, with the ability to refine questions, drill into more detail or visualize in a different manner.
2) Classic Business Analytics with Data Quality
In many ways this style is similar to classic business analytics except that it recognizes the need to cleanse and standardize the sourced data, so it integrates data quality into the data sourcing process. This could be critical to your organization to improve trust in the data or to meet regulatory requirements. SAS Enterprise Data Integration and DataFlux® dfPower® Studio provide prebuilt transformations and data quality algorithms to build data warehouses with standardized data. SAS can help you manage the entire data life cycle to ensure that your data is clean, relevant and can be acted upon.
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SAS® Architecture for BuSineSS AnAlyticS
3) Business Analytics with Feedback Loops
This style supports cyclical business processes with a defined time window, where specific information is needed to finalize decisions. For example, you might want to provide specific recommendations into a procurement workflow. On a regular schedule, you may need to forecast sales to determine what you need to order to replenish your inventory. Data is extracted and cleansed using SAS Data Integration Studio, analyzed using advanced forecasting techniques with SAS Forecast Studio, and then specific information on recommended purchase amounts is written back to the operational system via message queues. Procurement system users can then access the recommendations to better guide their decisions.
4) Real-Time Business Analytics
There are instances when it is unknown exactly when a specific service might need to be executed to gather information because it is triggered when specific behavior is observed. These situations benefit from analytical enrichment by combining both contextual and historical data with very quick responses. This leads to a need for real-time business analytics. This style of business analytics reflects the need to trigger analytics or data collection and delivery in real time from an operational application. Contextual data is combined with existing historical data and analyzed. The results are sent back to the original touch point for a person to make a decision or for business rules to drive basic, automated decision making. One example is the real-time scoring of customers in a bank to see if they qualify for a loan. SAS® Enterprise Miner™ scoring models can be called via Web services, and the resulting score can be sent back via another Web service to the calling application to be used in the loan application process.
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SAS® Architecture for BuSineSS AnAlyticS
5) Business Activity Monitoring
Automated decision making is becoming the standard with technology-dependent operations, when human monitoring and decision making is generally not possible or timely. This requires monitoring key information, defining business rules and triggering alerts or other events that can drive downstream action. A services-oriented architecture is needed to provide sourcing, discovery and sharing to deploy business analytics directly into operational systems. SAS solutions, such as SAS Enterprise GRC, have begun to take advantage of workflow capabilities in the SAS application platform to support activity monitoring capabilities.
One SAS® Architecture
The SAS architecture is unique because it supports all five styles of business analytics from one deployment. Because of the service-oriented approach, capabilities are encapsulated, extended and integrated to deliver the required business analytics. SAS architecture building blocks work together out of the box, supporting the ability to deploy some solution blocks first and add additional modules later. The SAS application platform – SAS framework services – allows the infrastructure of the SAS architecture to be managed separately but delivered as part of any SAS solution. This provides flexibility in delivering targeted building blocks to support your business analytics activities. There is no need to reinvent the functionality provided by the framework services when deploying different SAS solutions.
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SAS® Architecture for BuSineSS AnAlyticS
Conclusion
Regardless of the size, industry or goals of your organization, SAS provides an architecture for business analytics that can meet your needs today and into the future. Key activities of sourcing the data, discovering what the data is telling you and sharing the information are supported with an integrated application platform and flexible building blocks. The SAS architecture building blocks are linked together to deliver consistent results. The success of your business analytics architecture depends on having: • The appropriate building blocks to deliver a business analytics architecture. • The analytical capabilities you need to support the best decision making. • A means to integrate analytic activities to maximize their value to your organization. The SAS Business Analytics infrastructure provides an effective way to: • Manage the growing appetite for intelligence. • Gain greater ROI from your existing IT investments. • Support sustainable growth through innovative use of technology and information.
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A
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SAS Architecture for Business Analytics
®
SAS® delivers solution building blocks for empowering strategic business decisions within your existing technology environment
SAS® Architecture for BuSineSS AnAlyticS
Table of Contents
Introduction .....................................................................................................1 SAS® Technical Reference Model for Business Analytics .............................2 Data Management .......................................................................................4 Analytics .....................................................................................................4 Reporting .....................................................................................................5
Compute Services ..........................................................................................6 Metadata Services ..........................................................................................7 Security Services ............................................................................................8 Integration Services.........................................................................................8 Management Services ....................................................................................9 Workflow Services ..........................................................................................9
Five Styles of Business Analytics, One SAS® Architecture ..........................10 1) Classic Business Analytics ....................................................................10 2) Classic Business Analytics with Data Quality ........................................10 3) Business Analytics with Feedback Loops ..............................................11 4) Real-Time Business Analytics ...............................................................11 5) Business Activity Monitoring .................................................................12 One SAS® Architecture ...............................................................................12 Conclusion ....................................................................................................13
i
SAS® Architecture for BuSineSS AnAlyticS
Content for this paper, SAS® Architecture for Business Analytics, was provided by Diane Hatcher, Solutions Architect in the SAS Technology Practice, Cary, NC.
ii
SAS® Architecture for BuSineSS AnAlyticS
Introduction
SAS has developed a business analytics architecture that can support many different architectural styles and business uses. At the core of the business analytics architecture are sourcing, discovery and sharing activities. Along with proper data collection and verifiable data quality this leads to better analysis, which means more compelling information can be shared in a variety of ways with stakeholders, depending on the situational context. The details of what these activities involve vary across organizations, within organizations and even from day to day. Only SAS provides an architecture flexible enough to support organization-specific requirements without sacrificing performance while providing a platform that can evolve.
Architecture PlAnning Business Analytics Activity Source
• Data capture • Data cleansing • Data manipulations • Master data management Provide consistent data across the enterprise • IT data management • Operational systems • Data access tools • Data quality algorithms • Customer transformations • Job flow management and scheduling
Discover
• Query • Statistical models • Scoring • Visualization Uncover opportunities • Business decision makers • Decision support systems • SQL support • Analytical modeling • Custom development • Interactivity
Share
• Publishing • Interaction • Integration • Reusability Distribute actionable intelligence • Business interfaces • Business processes • Content management • Web-based access • “Push” techniques • Services orientation
WHAT Functionality WHY Value WHERE Stakeholders
HOW Technology
Figure 1. Architecture Planning Framework for Business Analytics
The diagram above (Figure 1) shows various attributes to consider when planning business analytics architecture. All elements and capabilities may not be required within each element, but the relevant ones should be linked and work together to provide a seamless solution. As the usage of business analytics grows over time, additional capabilities can be added. SAS provides a single framework to meet all of your business analytics requirements, without the need to continually install new components. The SAS architecture provides a core set of capabilities that work together out of the box and can be easily extended to add more functionality or integrated with other parts of your IT infrastructure.
1
SAS® Architecture for BuSineSS AnAlyticS
SAS® Technical Reference Model for Business Analytics
There are four key qualities that underlie and drive the SAS®9 architecture for business analytics: • Scalability – the ability to meet performance demands across the organization. Different stakeholders or interlinked business processes will have different resource usage characteristics. The SAS architecture has the flexibility to meet those diverse requirements. • Manageability – the ability to administer the SAS environment. As SAS becomes more ingrained in your organization, it becomes critical to manage and monitor usage to ensure optimal availability. SAS provides tools and techniques to manage the environment, either independently or within an existing environment. • Interoperability – the ability to use SAS capabilities from outside the SAS environment. SAS provides an open architecture built to work with your existing IT infrastructure (Figure 2).
ENTERPRISE CLIENTS
Custom Applications Enterprise Portals SharePoint Web-Based Application StandardsBased Apps Excel PowerPoint PDF Outlook Mobile
SAS®
ENTERPRISE DATA AND SYSTEMS
Enterprise Applications
SAP Oracle Peoplesoft Siebel Teradata DB2 Oracle
Operational Data
Netezza HP Neoview AsterData Greenplum Sybase SQL Server MySQL IMS-DL/1 JDBC ODBC OLEDB PC Files
OPERATING SYSTEMS
z/OS AIX HP-UX Solaris Windows Linux
Figure 2. Interoperability with Existing IT Infrastructure
2
SAS® Architecture for BuSineSS AnAlyticS
From supporting existing hardware and IT system standards to using virtually any database management system to integrating with enterprise communication channels, SAS has remained focused on delivering decision support wherever and whenever needed. • Reliability – the ability to deliver results at the expected time. SAS delivers reliable results with powerful data access capabilities, industry-leading analytics and diverse tools for sharing content via multiple channels. SAS delivers reliable solutions, supporting multiple configurations to provide highly available systems. Consistency of performance in both results and delivery are integral to the SAS architecture.
Scalability
Infrastructure Blocks Analytics User Interfaces
Data Management
Manageability
Reliability
Reporting
Framework Services
Interoperability
Figure 3. SAS®9 Technical Reference Model
Depicted above (Figure 3) is a high-level technical reference model for the SAS®9 architecture for business analytics. This represents a taxonomy of the core entities, without stating a specific relationship between them. The SAS architecture is made up of building blocks that encompass key characteristics of a service-oriented architecture. They are reusable services available across the suite of SAS tools and solutions to meet specific functionality requirements. Reuse allows SAS to provide service consistency and common integration points to fit within existing technology architecture. Framework services make up the application platform that delivers common core capabilities across the breadth of the SAS architecture. Data management, analytics and reporting entities provide activity-specific building blocks for sourcing, discovery and sharing. These blocks are a combination of targeted functionality and user interfaces designed to meet the needs of the specific activity.
3
SAS® Architecture for BuSineSS AnAlyticS
Data Management
• Do you have the need to access data across multiple databases and systems? • Are you confident that your data is clean and consistent? • Do you know what your data looks like? The data management building block supports the business activity of sourcing. This could include prebuilt, high-performance capabilities for connectivity, quality, extraction, transformation and loading, migration, synchronization and federation of data. Data management means having a way to fully use all the data flowing into the organization. This includes profiling capabilities and the ability to incorporate data quality business rules across data sources and platforms. If you can automatically integrate data quality into data integration processes, you can ensure that data is current and accurate.
SAS® Data Management
Analytics
• Do you need industry-leading statistical algorithms and visualization techniques? • Can you optimize and manage the portfolio of analytical models across the enterprise? The SAS Analytics building block supports discovery. The core of a business analytics architecture is the framework support for an analytics engine, but it is how that analytics engine is surfaced to the end user that determines its value. It is important that technologies are in place to support a robust analytics development process. This includes defining the business problem, developing and deploying the appropriate models and tracking performance. Each of these steps requires due diligence and appropriate methodologies to deliver results to the organization. Analytics must be supported in a disciplined, managed environment to provide the maximum value.
SAS® Analytics
4
SAS® Architecture for BuSineSS AnAlyticS
Reporting
• Can all end users ask questions and get a usable answer? • Do you have the ability to present the information in the appropriate format? • Can you use multiple channels to share reports? Reporting should not be treated as a standalone activity. It is part of a seamless approach for creating and sharing intelligence. Reporting is more than querying. It includes properly presenting the information and sharing the information across the appropriate channels. The reporting building block is critical for the business processes of preparing and sharing information. This information must be presented in a way that is viable and delivered to the right person at the right time. Only when this is done can information become intelligence.
SAS® Reporting
5
SAS® Architecture for BuSineSS AnAlyticS
SAS® Framework Services
Framework services (Figure 4) represent the application platform that delivers standard capabilities across the breadth of the SAS Business Analytics architecture. Data management, analytics and reporting address the components required for the core activities around sourcing, discovery and preparation, but they use a single infrastructure, representing the nervous system of the SAS architecture.
Figure 4. SAS Framework Services
These services are used by all SAS architecture building blocks to provide consistency of service and performance.
compute Services
• Does the engine you are using handle more than SQL processing? • Can you create customized analytical models beyond what’s provided out of the box? • Can the analytics engine access data from all existing data warehouses and systems? At the heart of the SAS architecture are the compute services. Essentially, this is the Base SAS software that has been the engine of SAS Analytics for more than 30 years. The SAS engine remains the power of the platform, executing queries and models to deliver intelligence. SAS has always been scalable vertically and horizontally, but we have also engineered SAS to be scalable across the enterprise for all types of users requiring both batch and interactive processing.
Compute
6
Metadata
Framework Services
Security
Integration
Management
Work ow
SAS® Architecture for BuSineSS AnAlyticS
Feeding the engine is the SAS procedural language. This powerful language provides the ability to analyze data using hundreds of analytical algorithms – including simple queries, regressions, neural networks, time-based forecasting, scoring and quantitative analytics. This is a huge advantage for users, because they are not limited to analytics delivered through a predefined user interface. Every organization is different, with different processes and different analytical needs. With user interfaces that are familiar to most organizations, SAS can support 80 percent of those needs. The remaining 20 percent can be customized to your organization to provide unmatched analytics. With SAS software’s multivendor architecture (MVA), SAS code can be written once and run on any supported operating system. Analytics and data go hand in hand. The more data that is available, the better the analytical results. One of SAS software’s core strengths has always been data access, and SAS is continuing to innovate with high-speed analytical data storage options, in-database processing support, and our commitment to continuous evolution of analytics techniques and user interfaces.
Metadata Services
• Have you implemented a central metadata layer across the breadth of analytic activities? • Does the metadata encompass both technical metadata and business metadata? Metadata services connect all the business processes in the platform – including resource management, security and sharing of content. Integrated metadata (information about data sources, how data is derived, business rules and access authorizations) is crucial for producing accurate, consistent information. SAS stores technical metadata and business metadata in an open, centralized and integrated repository. Data changes only need to be documented in one place. There are fewer systems to support and business users can count on high-quality information. A single version of the truth is available to all. Better use of staff time lowers the total cost of ownership for IT infrastructures. SAS integrated metadata also provides an auditable, repeatable and secure environment from which to derive business intelligence. Content can be organized and controlled from a central location, ensuring that enterprise users can only view the information they are allowed to see.
7
SAS® Architecture for BuSineSS AnAlyticS
Security Services
SAS security services provide additional measures to complement your existing security infrastructure and conform to your existing standards for authentication, authorization and secure communications across the network. User authentication can be on host-based authentication systems or use LDAP or Active Directory mechanisms. Single sign-on is supported from desktop applications running on Windows and through Web applications using trusted authentication. Metadata-based authorizations can be used to add an additional layer of access controls beyond what is supported on the file system. Authorizations to SAScontrolled content and resources can be centrally managed via the metadata server. The implementation is flexible to control both access to content as well as role-based access to application functionality. For additional network security, SAS supports a number of mechanisms for encrypting network traffic. SAS/SECURE™ provides industry-standard encryption algorithms, including Triple-DES (168-bit encryption) and AES (256-bit encryption). These algorithms can be used to encrypt only credentials or all network traffic between SAS clients and servers. For Web applications, secure socket layer (SSL) is supported.
integration Services
• Can business analytics activities and results be integrated with external operating systems? • Can custom applications be developed that leverage analytical capabilities to address a specific business problem for your organization? • Is it possible to surface analytical models to be used in existing production environments? Integration services consist of technology components that facilitate integration of business analytics with other systems. The results of analytics can be used directly by individuals or can be automated into systematic decision processes. Integration services consist of technology components that facilitate SAS integration with other systems – either by invoking SAS algorithms or SAS calls to an external component. SAS algorithms can be surfaced across SAS itself, using the stored process interfaces. This allows analytical modules to be reused across the SAS architecture in an encapsulated manner – delivering consistent results. These stored processes can also be defined as Web services, allowing external applications to invoke SAS for real-time scoring or other analyses.
8
SAS® Architecture for BuSineSS AnAlyticS
External systems can be operational systems or custom applications. SAS provides a library of APIs to support access to SAS functionality from custom applications, and provides an Eclipse-based development environment to streamline creation of custom applications. Language interfaces allow SAS code to be easily integrated with external systems – whether they’re based on other standards (e.g., PMML) or other platforms (e.g., Java or .Net).
Management Services
Management services consist of technical components that monitor and optimize the use of business analytics across the enterprise environment. The SAS Business Analytics infrastructure is able to operate and integrate into existing IT enterprise system management facilities such as: • System monitoring for monitoring the health of the environment. • Event management for assessing the status of applications via logging, monitoring and implementing methods to correlate events, errors and warnings. • System administration for the automation of start, stop and restart semantics. Management services include a technical component that provides the ability to log business analytics activity. The logs can be used to audit artifacts use, track performance and monitor changes made to metadata security settings and other content.
Workflow Services
Workflow services define the technical components supporting process management. These components are used by SAS solutions to provide the ability to define business rules, trigger events and send notifications when appropriate. Notifications can be alerts via e-mail, invoking another process or triggering a Web service. Workflow services allow for extension of workflows for greater integration with enterprise business processes. It is possible to generate multiple-level approval and review processes. With standard APIs for accessing workflow status for integration with business applications, analytics process flows become more manageable, visible and documented.
9
SAS® Architecture for BuSineSS AnAlyticS
Five Styles of Business Analytics, One SAS® Architecture
The SAS architecture is designed to be flexible enough to support the five styles of business analytics that are used by all organizations. Each style has its own set of architecture requirements. Multiple styles may be used at the same time within an organization. Understanding these styles can help you assess what architecture building blocks you should consider. For more details about these five styles of business analytics, please read Architecture for Business Analytics: A Conceptual Viewpoint, available at www.sas.com/reg/wp/corp/17871.
1) Classic Business Analytics
Classic business analytics is the basic process of data sourcing and may include the creation of a data mart or warehouse, information discovery – via data exploration or predictive analytics techniques – and sharing generated reports or information at different levels of an organization. Traditional report-driven BI processes put information into the hands of users, leaving them to interpret the situational context and how it affects the business process. What is changing is how the information is distributed. Business stakeholders want information via e-mail, portals, dashboards and mobile devices. SAS Business Intelligence solutions can provide dynamic access to information, regardless of the interface, with the ability to refine questions, drill into more detail or visualize in a different manner.
2) Classic Business Analytics with Data Quality
In many ways this style is similar to classic business analytics except that it recognizes the need to cleanse and standardize the sourced data, so it integrates data quality into the data sourcing process. This could be critical to your organization to improve trust in the data or to meet regulatory requirements. SAS Enterprise Data Integration and DataFlux® dfPower® Studio provide prebuilt transformations and data quality algorithms to build data warehouses with standardized data. SAS can help you manage the entire data life cycle to ensure that your data is clean, relevant and can be acted upon.
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SAS® Architecture for BuSineSS AnAlyticS
3) Business Analytics with Feedback Loops
This style supports cyclical business processes with a defined time window, where specific information is needed to finalize decisions. For example, you might want to provide specific recommendations into a procurement workflow. On a regular schedule, you may need to forecast sales to determine what you need to order to replenish your inventory. Data is extracted and cleansed using SAS Data Integration Studio, analyzed using advanced forecasting techniques with SAS Forecast Studio, and then specific information on recommended purchase amounts is written back to the operational system via message queues. Procurement system users can then access the recommendations to better guide their decisions.
4) Real-Time Business Analytics
There are instances when it is unknown exactly when a specific service might need to be executed to gather information because it is triggered when specific behavior is observed. These situations benefit from analytical enrichment by combining both contextual and historical data with very quick responses. This leads to a need for real-time business analytics. This style of business analytics reflects the need to trigger analytics or data collection and delivery in real time from an operational application. Contextual data is combined with existing historical data and analyzed. The results are sent back to the original touch point for a person to make a decision or for business rules to drive basic, automated decision making. One example is the real-time scoring of customers in a bank to see if they qualify for a loan. SAS® Enterprise Miner™ scoring models can be called via Web services, and the resulting score can be sent back via another Web service to the calling application to be used in the loan application process.
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SAS® Architecture for BuSineSS AnAlyticS
5) Business Activity Monitoring
Automated decision making is becoming the standard with technology-dependent operations, when human monitoring and decision making is generally not possible or timely. This requires monitoring key information, defining business rules and triggering alerts or other events that can drive downstream action. A services-oriented architecture is needed to provide sourcing, discovery and sharing to deploy business analytics directly into operational systems. SAS solutions, such as SAS Enterprise GRC, have begun to take advantage of workflow capabilities in the SAS application platform to support activity monitoring capabilities.
One SAS® Architecture
The SAS architecture is unique because it supports all five styles of business analytics from one deployment. Because of the service-oriented approach, capabilities are encapsulated, extended and integrated to deliver the required business analytics. SAS architecture building blocks work together out of the box, supporting the ability to deploy some solution blocks first and add additional modules later. The SAS application platform – SAS framework services – allows the infrastructure of the SAS architecture to be managed separately but delivered as part of any SAS solution. This provides flexibility in delivering targeted building blocks to support your business analytics activities. There is no need to reinvent the functionality provided by the framework services when deploying different SAS solutions.
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SAS® Architecture for BuSineSS AnAlyticS
Conclusion
Regardless of the size, industry or goals of your organization, SAS provides an architecture for business analytics that can meet your needs today and into the future. Key activities of sourcing the data, discovering what the data is telling you and sharing the information are supported with an integrated application platform and flexible building blocks. The SAS architecture building blocks are linked together to deliver consistent results. The success of your business analytics architecture depends on having: • The appropriate building blocks to deliver a business analytics architecture. • The analytical capabilities you need to support the best decision making. • A means to integrate analytic activities to maximize their value to your organization. The SAS Business Analytics infrastructure provides an effective way to: • Manage the growing appetite for intelligence. • Gain greater ROI from your existing IT investments. • Support sustainable growth through innovative use of technology and information.
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