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Five styles of business analytics

Whether you’re using one, two or all five business analytics approaches, the technologies are capable of much more than delivery of reports.

~ Co-authored by Diane Hatcher, Solutions Architect, SAS ~

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, consider the other styles when planning architectural changes to meet future business and IT needs. These styles can affect your needs at the business, application, data and technical levels. Assessing your required styles of business analytics will be valuable when making any changes to your architecture.

1) Classic Business Analytics

Provisioning a system to support query and reporting is classic business analytics. This basic level of data sourcing can include a data mart, warehouse, or predictive analytics surfaced via reporting interfaces. It provides information via data exploration or predictive analytics techniques and shares generated reports to users at various organizational levels. Traditional report-driven BI processes leaves the user to make sense of the situational context and the information’s effect on the business process, meaning execution lies in the hands of the receiver and how he or she interprets the information. Such reporting helps decision making, particularly if it provides backward-looking and forward-looking information.

The processes for generating the information have stayed the same, but the distribution methods have diversified based on users’ preferences. Now, information must be communicated across more channels (e.g., e-mail, portals dashboards and mobile devices) and should be self-service and interactive. This means providing dynamic access to information with the ability to refine questions, drill into more detail or visualize in a different manner.

Architecturally, this means having architecture building blocks of data integration, reporting and analytics that work together when deployed with a broad range of capabilities.

2) Classic Business Analytics with Data Quality

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. Driven by the need to increase the trust in information delivered to end users, another motivating factor to adopting this style can be regulatory requirements demanding cleansed and standardized data.

Architecturally, it still requires the building blocks of data integration, analytics and reporting, but you need to embed data quality as part of your data integration capabilities as well.

3) Business Analytics with Feedback Loops

Using business analytics for better decision making can also require the delivery of information to everyday operational applications for use within workflows at specific points in the business process.

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. During process development, basic reporting might be needed to check the result, and once the application is performing correctly, specific monitoring reports can be added.

In this closed-loop style, the business analytics engine sends back results to the underlying operational systems and processes them in the normal flow of business operations. It is possible to automate some of the workflow decisions by defining business rules; but in general, business stakeholders will use the information as recommendations to help them with their decision making. A key feature of this style is that information delivery always occurs at a precise point in a business process, and the information does not require a report format.

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, 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. You could also use the recommendations to drive automated procurement processes with business rules to flag potential outliers requiring human attention.

Adding these feedback mechanisms changes the way IT must provide 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.

4) Real-Time Business Analytics

There are also situations when a specific service might need to be performed quickly to gather information, requiring real-time business analytics.
Real-time business analytics can aid decision making when operational systems support users in customer-facing situations and the customer is looking for a quick response. In this instance, technology does not replace human interaction. It supports the user through automated processes and by providing consistency. That technology can be a point-of-sale system, a customer service application or a kiosk. Each customer touch point represents an opportunity to make a real-time decision to create additional sales or reinforce behavior.

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.

Examples of real-time business analytics include the real-time scoring of bank customers to see if they qualify for a loan, checking inventory levels in real time, or monitoring flight arrivals in real time.

The hallmark of this style is that a user or system triggers the service after the collection of some information. The information could be gathered via a batch process or streamed back during the course of operations. In order to implement this style, the solution building blocks can be used individually or together to provide a well-defined service. The business analytics architecture should incorporate data sourcing, analytics and reporting as reusable services that can be integrated into manual human-driven processes or other more automated business processes.

5) Business Activity Monitoring

Many companies today 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. One approach is to improve operational systems by monitoring key information and creating sets of business rules and triggers to send alerts that can drive downstream action. 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 different workflows.

Take, for example, the scenario of detecting credit card fraud. Rules exist to determine whether a transaction is approved, rejected or routed to a service agent to 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.

This style requires a services-oriented architecture to provide capabilities that can be plugged in as needed across various systems. Each element of your business analytics solution – within source, discover and share – should be a standalone service of its own and able to integrate with other services, and with other operational systems. Business rules determine which capability is needed and when. The goal is to deploy business analytics directly into the operational systems.

Read the white paper: Architecture for Business Analytics: A Conceptual Viewpoint

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