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Building Automated Decision Making into BI System Design – A Methodology Overview May 18, 2007

Posted by Cyril Brookes in BI Requirements Definition, Decision Automation, General, Issues in building BI reporting systems.
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Automated decision making for business is about flavor of the month. Most emphasis has been on automating business analytics, say, underwriting in the insurance industry and stock market program trading. But there are ample opportunities for incorporating automation in more conventional BI systems, especially corporate performance management, where there has been, so far, little discussion.

Tom Davenport’s recent work on business analytics has been widely reported and commented. The consultants and software marketers are circling the wagons.

To highlight opportunities and stimulate discussion among BI analysts this post explores how relevant BI system targets for automation might be identified.

Most BI analysts see their role as designers of systems to support management decision making through effective presentation of information. That is, of course, commendable and important. But is that all there is? That focus doesn’t preclude building automated decision making systems if the context is suitable. It’s just that it isn’t done often. We seem to be reluctant to try and replace managers, maybe it’s because they are our bread and butter?

There are three generally accepted classes of decisions in business; operational, tactical and strategic. It’s pretty obvious that automatic decision making is almost always associated with operational, and perhaps some tactical, contexts. If it’s strategic, then forget it. Since many BI environments serve a mix of strategic and operational users, the prevailing focus is almost always on information presentation, rather than active replacement of human decision makers.

This discussion reminds me of a 25 year prediction from a long forgotten business journal article of the 1960s “Boards of Directors will be retained for sentimental reasons; computers will make all the decisions….”. Didn’t happen, and won’t. A similar, but contrary, forecast in the HBR of June 1966 “A manager in the year 1985 or so will sit in his paperless, peopleless office with his computer terminal and make decisions based on information and analyses displayed on a screen…” There still seem to be a lot of executive assistants around!

My intention with this post is to suggest a methodology or process which demonstrates how BI analysts can effectively and efficiently identify opportunities beyond the passive aim of information presentation. Even if the resulting design only partially automates decision making, it is likely to be a better, more effective solution than its passive counterpart, simply because it will be the result of a more creative and challenging design process.

In the current spate of articles there are many examples of apparently successful automated business process systems. While these may whet the appetite of a designer they are not, in my view, useful guides when the task of synthesising a BI system incorporating is being undertaken. When your child is given his/her first bicycle, showing someone cycling down the street isn’t going to be much help in teaching how to ride. Hands-on synthesis is needed. Big pictures may create envy, but don’t instruct much.

I suggest that it will be worthwhile for a BI analyst and executive team to review the corporate BI environment, existing and planned, and assess the potential for including automated decision making in the BI systems supporting each business segment.

Further, such a review should use a project planning method which segments activities into several bite sized Phases. Here’s a suggested outline, with more detail on each Phase to follow.

Phase 1: Identify the controllable business variables in the target businesses, ignoring specific business processes

Most articles on automated decision making start with the business process and BPM analyses. I think this is the wrong initial focus. To me, the optimal review starting point is to identify the control parameters of typical business processes that are amenable to automatic adjustment. The number of business process control “levers” available to management is finite, quite small in fact, and the number that might be controlled automatically, with profit, is even smaller. Examples include: Automatic pricing adjustment, dynamic production scheduling, staff re-assignment.

A more complete discussion on identifying control variables follows in a later post. It is, I believe, the most important part of project selection and specification. Get this wrong and you will certainly miss out on the best opportunities.

Phase 2: Identify potential business processes, existing or planned, that utilize one or more of these candidate control parameters and may benefit from automation

The same control variables are likely to appear in multiple business processes. For example, automatic price adjustment could impact BI systems supporting Order Entry, Production Scheduling, CRM, Inventory Management, etc.

Phase 3: Identify components of the candidate BI systems that may profitably incorporate automated decision making

Management 101, since Herbert Simon’s day, tells us that there is a defined decision making process, with several component steps between becoming aware of a problem or opportunity, and deciding what action to take. Automating the decision process clearly requires that one or more of these steps should be performed without reference to a human.

It is relatively easy to consider each of these decision process components in turn, to determine the extent to which it/they can be automated. My later post will give more detail if you are interested, Dear Reader.

Phase 4: Design the business analytics; business rules, predictive analysis, time series analysis wherever Phase 3 indicates potential utility

This is the fun part. The software tools for business rules management are much improved since I first started playing with IF…AND…THEN….ELSE statements as the basis for automation, as are the forecasting and statistical analysis packages.

I leave it to you to work out the details, as they are always application dependent. But always be aware that rules change, sometimes quickly, so dynamic management, or decision making agility if you will, is important. Enjoy.

Also, note that Phase 4 will be an iterative process, with frequent Phase 5 reviews to ensure that business sense prevails, limiting the scope for white elephant projects; even though they can be fun.

Phase 5: Evaluation and feasibility reviews of the costs and benefits of automated decision making components within the BI system

Try not to let the excitement of creating rules and embedding predictive analytics in a BI system carry you away; well only a little bit anyway! To me, this is one of the most interesting and absorbing roles of being a BI analyst and designer; certainly it beats specifying reports.

Building automation into BI is highly recommended, especially if you are looking for a challenge!

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Comments»

1. James Taylor - May 22, 2007

Nice post. I find the identification and description of micro-decisions (http://www.edmblog.com/weblog/2007/02/think_micro_not.html) particularly important in this. Thinking about decision services (http://www.edmblog.com/weblog/2007/03/what_you_need_t.html) that can automate these works really well.
JT

2. Decision Automation in BI - Selecting Decisions to Automate « Cyril on Business Intelligence - May 25, 2007

[…] post follows on from that of May 17/18. I proposed a set of five project selection and implementation Phases for decision automation […]

3. Decision Automation in BI: Design Guidelines for Business Analytics and Rules « Cyril on Business Intelligence - June 18, 2007

[…] incorporated should follow five steps as below. Earlier posts have considered the overall issue (here) and Phases 1 through 3 […]

4. Implementing Decision Automation BI Projects « Cyril on Business Intelligence - July 13, 2007

[…] This post summarizes the major issues that received favorable comment and then deals briefly with profitability, feasibility and implementation techniques for these systems. It concludes the series of (now) four posts on decision automation for BI that commenced here. […]

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