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The Best Practices of Operational Analytics

Operational Analytics is a mix of disciplines that support the seamless flow from initial analytic discovery to embedding predictive analytics into business operations, applications, and machines. The impacts of this analytics are then measured, monitored, and further analyzed to circle back to new analytic discoveries in a continuous loop.
However, the analytics field has not seen this type of industrial rigor around moving analytics into business operations. Organizations that wish to achieve competitive advantage through analytics need to cross the chasm between traditional ad-hoc analytics and Operational Analytics. Here are the best practices of Operational Analytics

Analytic Discovery: The Operational Analytics ecosystem starts with business discovery projects that are evaluated, planned, and executed with eventual production in mind. Without purposeful review and planning, analytics e orts can stall and fail to deliver on the promise of the insights they derive.

In a mature analytics environment, Analytic Discovery is supported by IT through “discovery zones,” which harness all forms of data and provide tools that enable rapid integration of data for analytics into IT platforms. The discovery zone also enables data manipulation for exploration and derivation of new attributes for analytic modeling. By providing an IT-hosted discovery zone, the business gets the power and flexibility it needs, while IT maintains security and traceability of company data.

Analytic Production & Management: Operational Analytics supports a more seamless bridge between Analytic Discovery and Analytic Production using common data, metadata, and tools between the two domains. This includes defining governance processes that ease the transition from discovery to production; managing models in a model-management application that supports versioning, documentation, and performance tracking; and centralizing models, leveraging them across a variety of business applications to create consistent tools supporting the business strategy.

Consistent hosting of models lets multiple applications leverage these models. This provides scalability and reuse of analytics and also leads to a consistent execution of intelligence across the organization touch points. Once these models are used in production, they are formally tracked, measured, and maintained to ensure their ongoing effectiveness.

Decision Management: Organizations need a clear understanding of the decision process and “fit-for-use” data available to support that process. Creation of a formal Decision Management and business rules framework streamlines the decision process and enables governance of proper business rules to the analytic processes. It also ensures repeatability and traceability of decisions—and that model delivers the proper decision support at the right time and in a consistent way across the enterprise. Use of Decision Management software can automate decisions enabling greater scale, consistency, and speed with which decisions are made.

Application Integration: The core best practice of Application Integration is ensuring your organization has the right tools mix with the appropriate level of integration, whilst simplifying business applications.

To meet scalability and modularity requirements on demand, one of the key best practices is to incorporate service-oriented architecture (SOA). This ensures changing business scenarios and varied requirements of business applications from analytical engines are well handled.

The best practitioners also centralize some of their key analytical models with common solution frameworks, enabling open integration with multiple applications and platforms, and an enterprise-level deployment of analytical models.

Information Delivery: While analytic information must be delivered through rich analytic tools and powerful data analysis applications, the demands and expectations of today’s information consumers require rethinking how information should be delivered. Among the key capabilities that are needed: The ability to take action quickly based on delivered information puts the user in control, enabling the user to convey a meaningful data-driven story. Simple and actionable information delivered to connected devices provides the right information, at the right time, in a mobile interface. New interfaces for requesting and querying information—for example, voice and intelligent search. More intuitive forms of information delivery quickly convey meaning through visualizations and infographic interfaces. Role-based design and delivery, along with a clear understanding of decision-making processes, leads to more active information delivery methods

These capabilities will create higher demand for a well-designed user interface (UI) and other human factors related to Information Delivery, beyond the feature-rich and complex interfaces that will still be required by power users.

These put together to make up the best practices of Operational Analytics.

Sources:

“Operational Analytics 10 Key Process Areas to Exploit Analytics for Business Results.” Operational Analytics: 10 Key Process Areas to Exploit Analytics for Business Results – Business White Paper (Business White Paper/4AA6-3046ENW.pdf). HP, n.d. Web. 13 May 2017. 

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