What's your business analytics style?
A guide for planning your business analytics architecture
Today's business analytics technologies are capable of so much more than simple reporting. Organizations are using predictive analytics to forecast outcomes, optimize processes and build what-if simulations. Automated or semi-automated decision making is built into business processes. Decision makers have self-service access to information that allows them to interactively navigate, visualize and find patterns in data.
Organizations are recognizing the value of business analytics, and the need to exploit the full range of capabilities is increasing. The challenge is melding the elements your organization needs into a cohesive business analytics architecture.
There are essentially five styles of business analytics that are being embraced by organizations in all industries. No matter which style(s) you use today, you need to consider the other styles when planning your architecture in order to meet future business and IT needs. Here’s a guide to help get you started:
Classic business analytics
A system that supports only query and reporting is classic business analytics. This basic level of data sourcing provides information via data exploration or predictive analytics techniques and shares information with users at various organizational levels using generated reports. This type of reporting provides important information that helps decision making, particularly if it provides backward-looking and forward-looking information.
But a change is occurring in the traditional information distribution method. Static reports are not enough to support decision making. Now, information must be communicated across more channels. Business stakeholders need mobile access to timely information within the context of their day-to-day operations – and it should be self-service information that they can drill down into for more detail.
Architecturally, this means having building blocks of data integration, reporting and analytics that work together when deployed with a broad range of capabilities.
How it works: At a large US insurance company, more than 150 users can use SAS to look at financial information, marketing activity and enrollment information. In a matter of seconds, they can view the monthly growth of a product over five years. They can view state population statistics and provider data down to the county level. Queries are point-and-click and can be easily downloaded into other applications, such as Excel, for analysis.
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 the sourced data. It integrates data quality into the data sourcing or data input process. The adoption of this style is driven by the need to increase the trust in information delivered to end users. Another motivating factor can be regulatory requirements demanding cleansed and standardized data.
Architecturally, it still requires the building blocks of data integration, analytics and reporting, but you also need to embed data quality as part of your data integration capabilities.
How it works: Two European steel manufacturers merged and faced challenges such as standardization, integration and rationalization of data. SAS technology solutions improved the quality and manageability of the data. Now, the IT staff can manage all parameters through the SAS server and consistent information is fed to the full range of reporting tools.
Business analytics with feedback loops
Business analytics with feedback loops supports cyclical business processes. For example, you might want to provide specific recommendations into a procurement workflow. On a weekly or biweekly basis, depending on the purchasing cycle, you would forecast sales of your inventory to determine what you need to replenish. In this scenario, data is extracted, cleansed and analyzed using advanced forecasting techniques, and then the business analytics engine feeds specific recommended purchase amounts back to the operational system. Business users can use the recommendations to better guide their procurement decisions.
The simplest way to embed business analytics into operational system workflows is to create a repeatable process that sources data, analyzes it and then feeds the results back into the operational data store. Adding these feedback mechanisms changes the way IT provides support as the business analytics process needs to be less linear in implementation. A closed-loop system should provide more circular support regarding sourcing, discovery and information sharing. Architecturally, all three must work together as a single composite service without the need for manual intervention or data movement.
How it works: A European shipbuilder developed a solution based on SAS to analyze operational projects and capacities. It also functions as an integrated information system through which ongoing processes can be observed. The system creates transparency in an environment that could not be monitored or managed holistically without the support of technology.
Real-time business analytics
Real-time business analytics can aid decision making in customer-facing situations. Each customer touch point represents an opportunity to make a real-time decision to create additional sales or reinforce behavior – for example, the real-time scoring of bank customers to see if they qualify for a loan, or in deciding what additional offers to make to a customer placing an order.
This approach requires triggering analytics or data collection and delivery in real time from an application. Data is collected, along with existing information when required, and analyzed. The results are sent to the original touch point for a decision or are put into an operational workflow with simple business rules for basic automated decision making.
To implement this style, the solution building blocks can be used individually or together to provide a well-defined service. The business analytics architecture needs to be deployed in a service-oriented fashion, incorporating data sourcing, analytics and reporting as reusable services that can be integrated into manual human-driven processes or other more automated business processes.
How it works: A government agency in Asia uses SAS to implement an advanced risk management system to improve the effectiveness of customs inspections. More than 400 staff members in the field are using the system. Upon a request for customs service, the personnel in charge of import inspection can immediately decide whether to inspect the cargo. They can detect potentially illegal attempts for customs clearances on the spot through know-how and data mining techniques.
Business activity monitoring
Many companies have a host of operational systems that are critical for day-to-day operations. Automation is the standard, as human monitoring and decision making is impossible given the large number of possible events or transactions. Take, for example, credit card fraud detection. Rules exist to determine whether a transaction is approved, rejected or routed to a service agent for follow up with the cardholder. It is not just the single transaction that is considered but the pattern of behavior linked to the transaction that is important.
The scenarios supported here are more complex than the previous style and use business analytics in real time in combination with business rules. The triggers might not be based on a single event or request but on a series of events. Based on the nuances of those events, rules could dictate different reactions and workflows.
This style requires a services-oriented architecture to provide capabilities that can be plugged in as needed across various systems. The key is that each element of your business analytics solution – within source, discover and share – should be a standalone service and able to integrate with other services, as well as with other operational systems. Business rules determine which capability is needed and when it is needed. The goal is to deploy business analytics directly into the operational systems.
How it works: With SAS, organizations are able to consolidate data from multiple sources to drive decisions that affect ongoing operations. Using integration via Web services and other interfaces, decisions can be automated to immediately adjust for critical changes in behavior to minimize operational risk or gain customer acceptance.
Building for better decisions
Regardless of the size, industry or goals of your organization, the architecture required for business analytics is very similar. The architectural building blocks you need are based on providing capabilities for data integration, analytics and reporting and should work together to provide consistency, reuse and self-sufficiency.
No matter which style, or styles, you choose, by sourcing the data, discovering what the data is telling you, and sharing the information with the people who need it, you’ll support better decision making in your organization.
Mark Torr is Director of the SAS Global Technology Practice. Diane Hatcher is a SAS Solutions Architect.