Successfully Automating Decisions with Business Analytics: How to identify high-value opportunities for embedding analytics into your business processes

Successfully Automating Decisions with Business Analytics: How to identify high-value opportunities for embedding analytics into your business processes provided by Successfully Automating Decisions with Business Analytics Table of Contents Introduction ................................................................................................................................... 1 The goal: evolve from “craft” to “industrial” analytics ......................... 2 Where to start: identify the opportunities .....................................................3 Taking a Process View .......................................................................................................... 5 “Big Picture” Frameworks ................................................................................................ 6 Systematic Inventory............................................................................................................ 9 Setting Ambition....................................................................................................................11 Qualify the opportunities ................................................................................................13 DELTA-Readiness .................................................................................................................14 Motivation and Momentum ...........................................................................................14 Experimentation ...................................................................................................................16 Summary ....................................................................................................................................... 17 About nGenera ........................................................................................................................ 18 About Intel ................................................................................................................................... 18 About SAS .................................................................................................................................... 18 i Successfully Automating Decisions with Business Analytics This white paper is excerpted from “Managing Business Processes Analytically,” a research study conducted by nGenera Corporation in partnership with Dr.Thomas Davenport of Babson College. ii Successfully Automating Decisions with Business Analytics Introduction Business analytics has been a rather ad hoc activity in most corporations, requiring an interested decision-maker, a skilled analyst, and typically some unstructured activities for pulling together the right data and constructing the right analytical model. There is normally a high degree of interaction and iteration between analyst and decision- maker as the model is refined and the results are understood. This approach can serve individual decisions and initiatives (e.g., marketing campaigns) well, but it has several shortcomings: • It is time-consuming and labor-intensive, making it infeasible for decisions that must be made often and quickly. • It requires a positive interpersonal relationship between the decision-maker and the analyst, which may well affect the decision outcome. • The process steps and cycle time for ad hoc analytical decisions are unpredictable, making it difficult to respond quickly to business change and customer demand. • Because the means of making the decision are typically not saved or embedded in an ongoing business process, execution of the decision may be inconsistent, and the next similar decision requires that the entire cycle be repeated. As a result of these shortcomings, corporations are getting much more interested in how analytical decisions can be embedded into business processes on an ongoing, repeatable basis. Such analytical decisions will either be automated or clearly specified as to when human intervention is appropriate. Analytics may be used in the execution of the process, or as a means to measure and monitor process performance and process improvement. The goal of such efforts is to make analytical decision-making faster, more reliable, more consistent, and more aligned to the operational activities of the enterprise. The problem is that a process orientation is still not well established in many corporations. They don’t have agreed-upon maps of major processes or owners responsible for those processes. Even those that have mapped the activity and information flows of their processes have yet to inventory the major decision points and the potential for more analytical decision making. So how do you determine which of your business processes would benefit the most from efforts to embed analytics into them? Where would you likely get the biggest return on your investment of time and money? The purpose of this paper is to provide practical advice on how to identify the best opportunities for making your processes more analytical. 1 Successfully Automating Decisions with Business Analytics The goal: evolve from “craft” to “industrial” analytics Business decision-making has been predominantly a “craft.” Each decision is its own ad hoc effort, including decisions supported by extensive data and analysis. The contemporary sequence runs like this: The company stores data from transaction systems in data warehouses. A decision-maker defines the decision to be made and communicates this to an analyst. The analyst reviews the data available, selects the data wanted, extracts the data (sometimes with the help of IT), analyzes it with the help of statistical software, and communicates the results to the decision-maker. The decision- maker then cogitates, and the definition-extraction-analysis sequence may iterate, until the decision-maker makes the decision. The approach is highly customized and often skilled labor-intensive and time-consuming. As businesses learn to marry the disciplines of analytics and process management, however, many decisions can become more automated, embedded in the process flow, or, as we call it, “industrialized.” Here the decision-maker decides to automate a decision with the right attributes (e.g., it is structured and repetitive) and works with the analyst to define parameters, information needs, and decision rules. The analyst works with IT to build an automated decision system, often incorporating a statistical model, and tests and refines the system until it works well. Once implemented, the decision system receives data from transaction systems in real or near-real time, makes decisions automatically and instantaneously, and thus determines subsequent process flow and triggers new transactions. Often the system is designed to “kick out” the exceptional cases for the decision-maker to handle. Over time, the decision- maker and analyst work to refine and optimize the decision system. The approach requires lots of skilled effort up front, but thereafter the decisions are automated with the flexibility to tune the system and adapt to changes as needed. Figure 1 contrasts the “craft” and “industrial” approaches to employing business analytics. Craft is a one-time effort, inherently slow. Industrial takes more time and effort up front, but then the decisions can be instantaneous. With craft, the analysis is often discarded or forgotten after use. With industrial, it is maintained, improved, and available for reuse – thanks to investment in ongoing model management. Why automate decisions in this way? Corporations have begun to “industrialize” knowledge-intensive processes in response to: • Customer expectations – for real-time response and consistent decisions and actions. • Competitive pressures – for increased agility and a faster “insight to action” loop. • Talent shortages – the need to reduce dependence on costly, aging, or scarce human resources.