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Embedding analytics into processes

In their latest book, Analytics at Work: Smarter Decisions, Better Results, Thomas Davenport, Jeanne Harris and Robert Morison show how companies apply analytics in their daily operations. This excerpt, 'Embedded Analytics in Action,' explores what to consider when infusing analytics into business processes.

We see examples of analytics at work within core processes in a variety of business areas. Statistical analysis has been a feature of supply chain and lo­gistics management for decades, start­ing with the techniques of statistical process control (SPC) and total quality management (TQM).

Real-time analytics are helping guide call center workers in their interactions with customers. And analytics are well established in the engineering and sim­ulation sides of product design.

Among business support functions, analytics are essential to many facets of finance, common in the management of technology operations, and rela­tively new to human resources (though of enormous potential there). In cor­porate development, key decisions – for example, regarding mergers and acquisitions – may benefit greatly from analytics, but few companies take a process approach to such activities.

Consider the example of UPS to what your appetite for embedding analytics in your core business processes. As a logistics company, UPS lives and breathes the “traveling salesman problem” – how to reach a variable series of destinations most efficiently with the right delivery capacity, and often in designated time windows, every day.

The solutions naturally demand very sophisticated and industrialized ana­lytics: for capacity planning of aircraft and truck fleets, for routing packages through its distribution network, and for scheduling and routing delivery trucks. For a company this steeped in analyti­cal applications, the frontier is moving closer to real-time, dynamic adjustments. For example, UPS is experimenting with algorithms to adjust the order of deliv­eries as conditions (e.g., road closures, extraordinary customer need) change.

Making processes analytical
The effects of analytics on the opera­tions of a process can be profound, and over time you may want to reengi­neer the overall business process and revamp its information systems to capitalize on the potential for analyt­ics-based improvement. But you can start embedding analytics without a major overhaul. For processes that rely extensively on enterprise systems, it may be possible to simply start taking advantage of the analytical capabilities that are already included in the soft­ware. However, many process analytics initiatives will require tools, techniques, and working relationships that are likely to be new and unfamiliar at first. We have found that implementing analytics­-enabled processes requires applying four major perspectives.

The first is process implementation. Occasionally a business may create a new analytically enabled process or rebuild a process from scratch, but most often you are adding capability to and altering an existing process. Espe­cially given the iterative nature of many analytical applications, it's essential to measure baseline process performance first and to run the enhanced process in parallel to the original (perhaps as a pilot or test) in order to refine the new process and measure its performance and value. In some cases, process simulation can yield insights about how the process might perform even before implementation.

Next, organizations should consider model implementation. Much of the distinctive work of process analytics centers on designing, developing and iteratively refining statistical algorithms and descriptive or predictive models or rule-based systems. If you are go­ing to industrialize important decision processes, it is important that the rules, assumptions and algorithms in your model are correct. Analytical projects generally require different tools and development methodologies from those employed in more traditional sys­tems development. And, of course, this work is performed by business analysts and programmers with special skills in statistical methods and modeling.

Third is systems implementation. The analytical system must be incorporated into the set of systems and technolo­gies supporting the business process. In building these interfaces, it helps to employ process-oriented technologies, including capabilities of ERP systems, workflow and document management systems. And integrating and testing the new systems and interfaces is criti­cal given analytics' reliance on a broad range of quality data and the fact that analytics-based decisions may dramat­ically change process flow.

Human implementation is the fourth perspective. Often the greatest imple­mentation challenge, especially when analytics is new to the process and the people performing it, is on the human side. Only people can tell if an embed­ded application is resulting in good decisions, so be sure to involve them in developing, managing and monitor­ing the assumptions and results of any embedded model. Another important factor is developing the right mix of automated and human decision making and enabling process performers to trust and use their new analytical infor­mation and sometimes tools.

All four perspectives must mesh: pro­cess flow and decisions are enabled or controlled by analytical models, other information systems interface with the models and provide clean data feeds, and people perform the process better with the help of embedded analytics. If you lack clear business goals, specifi­cations or momentum, be prepared to demo or pilot the concept, to work with stakeholders to define targets and set ambitions, and to make the business case for investing in prerequisite assets, often starting with data.

IT's role in embedding analytics into business processes 
Technology is an integral part of most business processes today. So the best route to embedding analytics into pro­cesses is often through the technolo­gies and applications that employees routinely use to do their jobs. Embed­ding analytics into processes starts with a robust analytical architecture that provides an accurate, timely, standard­ized, integrated, secure and reliable information management environment. Scorecards and applications that moni­tor and alert based on predetermined thresholds are the norm these days, but too many remain as standalone applications. An industrial-strength IT architecture makes it vastly easier to weave analytics into ongoing work processes in three ways:

1. Automated decision applications.
These sense online data or conditions, apply codified knowledge or logic, and make decisions – all with minimal human intervention. Technology is best suited to automate decisions that must be made frequently and rapidly, using any kind of information (data, text, images) that is available electronically. The knowledge and decision criteria used in these systems need to be highly structured.

The factors that must be taken into account (the business problem's dimensions, conditions and decision factors) must be clearly understood and not subject to rapid obsolescence. The conditions are ripe for automating the decision when experts can readily codify the decision rules, a production system automates the surrounding process and high-quality data exists in electronic form. Business activities that benefit from automated decision-making applications include fraud detection, solution configuration, yield optimization, recommendation/real­time offers, dynamic forecasting and operational control (like monitoring and adjusting temperature).

2. Business applications for opera­tional and tactical decision making. 
Analytical managers rely on analytical applications (whether custom devel­oped or from third parties) that are integrated directly into Web applica­tions or enterprise systems for tasks such as supply chain optimization, sales forecasting and advertising effectiveness/planning. Recom­mendation, planning and "what-if" applications can incorporate near real-time information and multiple models to dynamically optimize a solution while factoring in conflicting goals like profitability and customer satisfaction. Analytical business applications are best suited to well-defined, periodic tasks in which most of the information needed is predict­able and available electronically. Since the data, knowledge and deci­sion criteria are typically less defined and/or more fluid than those of a fully automated application, they require industry and functional expertise.

3. Information workflow, project management, collaboration and personal productivity tools. 
Most information work is done through personal productivity tools like Microsoft Office. As vendors in­crease the analytical quotient of their collaboration and productivity tools, analytics become more accessible to analytical amateurs throughout the enterprise. One consumer prod­ucts company found that its elaborate modeling tool was ignored by nearly everyone until the findings were distilled into a monthly deck of ten PowerPoint slides and e-mailed directly to the sales force. As platform vendors align their products to work together more seam­lessly, a manager needn't know that his Excel spreadsheet is using the company's ERP system to prepare his forecast. These tools and applications work best for less structured information with less defined decision criteria.

To address the growing need to embed analytics into processes, both specialty applications vendors and the major platform vendors are building more analytical functionality directly into their tools and applications.

Software companies are building more industry-specific, process-driven applications. Major platform providers like Oracle are embedding analytics into their products by building statistical functions directly into their enterprise data warehouse products. ERP vendors, which are including more sophisticated analytical features, remain a powerful way to integrate industry best practices into business processes. And Microsoft, Oracle, SAP and SAS continue to quiet­ly embed more sophisticated analytics and business intelligence capabilities into their applications and tools.

*Reprinted by permission of Harvard Business Press. Excerpted from Analytics at Work: Smarter Decisions, Better Results by Thomas Davenport, Jeanne Harris and Robert Morison. All rights reserved.

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