advanced analytics

Copyright 2015, Compsim LLC, All Rights Reserved 1

Advanced Analytics:

What is Missing? Or

What “Was” Missing? A Compsim Whitepaper

Abstract

If your analytics tools are so good, why don’t they actually MAKE the decisions?

Organizations are always looking for ways to improve. Improvement can come from many directions; reducing costs, improving operational capabilities, improving products or services. For the past decade Information technology suppliers have hyped “Business Intelligence” (BI) tools and “Big data”. Business intelligence tools have focused on collecting, interpreting, and organizing information so their clients (the decision makers within the organizations) can make better business decisions. In other words, the tools were: “decision aids”. Big Data has been the term used to define the location and distribution of available structured and unstructured data that is commonly referred to as “data in the cloud” today.

The two faces of organizations: First, they are viewed by their clients as operational entities. It is almost as if the organizations have a mind of their own and the organizations are the purveyors of the products and services. They do not consider that it is the people within the organizations that are using manpower and tools to deliver the products and services in the name of the


organization. The ultimate consumers of the products and services of an organization view the organization in this abstract way. So, for example, GM delivers cars, and Google delivers search capabilities. The second face: the organizations are viewed as a human hierarchy. Named individuals within the organizations make the decisions, build the products, and/or deliver the services. Purveyors of BI tools and cloud services target the second face. It is this same human face that investment groups validate when they assign value to the organization. It is this group that also makes the errors; they apply personal biases, get tired, cut corners, violate regulations, procrastinate, fail to respond in a timely manner, form inappropriate relationships, violate laws, give preferential services contrary to stated policies, etc. Every human has drivers that are contrary to the stated goals of the business.

This paper suggests that technology exists today to take the next step, which is the active interpretation of the data and the active decision-making (adaptive command and control) that can accelerate the decision-making process and remove (or at least expose) human biases that may not be appropriate for public, commercial or governmental organizations. Core to this capability is the necessity to make decisions and actions that are 100% explainable and auditable, so that the policies driving the decisions can be tuned / adjusted when necessary.

The Problem

Many organizations have a Mission Statement that presents an internal and external view of the boundaries of the organization. They may even have a Vision statement that suggests how they will apply their capabilities to meet a need that will exist in the future. These statements (if they exist) target human consumption. We suggest that, in most organizations, employees have never studied their company’s mission or vision statements. Therefore they have never considered how those statements and goals should impact their behavior.


Most organizations talk about continuous improvement. They hire consultants and/or form teams to search for areas of improvement. Over time, the “low hanging fruit” will have been picked. They invest in new tools based on the topic of the day. Over the past decade BI and Big Data have gotten a lot of exposure and much of the hype. At a lower level, the focus has been coding solutions using distributed IF THEN ELSE processing. This can almost be viewed as outsourced processing, or moving processing to where it can be managed cheaper.

The target problems (addressed by BI and the cloud) are those that Dr. Horst Rittel (UC, Berkeley) labeled “Wicked Problems” in the 1970s. Dr. Rittel differentiated wicked problems from “Tame Problems” by suggesting that tame problems are those where you can obtain a correct answer by writing a formula to describe how the components of the problem are combined to get a correct answer. Wicked problems are those where you are looking for the “best response”; where it would not be economically feasible (maybe impossible) to develop a formula to get a correct answer. He was a city planner. He suggested that running a city could not be accomplished by writing formulas.

The Common Solution

Solutions that are commonly proposed today suggest that (as Dr. Rittel suggested) if you can accumulate all information associated with a problem (all the positions, with all the pros and all the cons of each) that a “reasonable person” should be able to make the best decision. (Paraphrased from Dr. Rittel’s paper1)

Much of today’s work has focused on the interpretation of unstructured information using text analytics tools. BI tools collect, organize, and present information for human consumption. This ultimately leaves the final decision-

1 Rittel, Horst; “Issues as Elements of Information Systems”, Working Paper No. 131, July 1970, University of California Berkeley


making up to the humans. Unfortunately those humans have other drivers that may be unrelated to the stated business objectives. With the present analytical tools, there is no way to isolate the human from the actual decisions, unless one can remove the human from the decision-making process.

The Challenges

So, this leads to the following questions:

If the analytical tools are so good, why don’t they actually make the best decisions (without depending on the human-in-the-loop)?

Why don’t the systems package their decision-making rational in a format that the humans can audit (human-on-the-loop rather than human-in-the-loop)?

Why can’t organizations explain their value system?

• How important is profit? • How much is at risk? • How important is a new product in achieving the business objectives?

Why aren’t all decisions and actions explained with mathematical precision, thus avoiding problems associated with the English language that may have only general levels of compatibility from one person to the next?

• How warm is warm? • How much stress? • How much need? • How much value? • How important? • How much risk?

Why can’t an organization provide a mathematically explicit explanation of “what was considered” in any decision or action? If this explanation was


available, they would also be able to see what was not considered in the decisions and actions.

The Evolution of Technology

Man created machines to increase speed, increase strength, and to increase quality; essentially to do more with less in order to amplify human capability. Today many products and services cannot be delivered without the use of machines. Computers are the machines for processing information. Computers manipulate information by integrating and reformatting that information to satisfy some need. Information is sensed, transformed, moved, integrated and sometimes interpreted. We are moving towards a time when some information will never be touched or observed by any humans. While there are those that will reject this concept, it is happening. At the present time, much of this information is lost, but as its value is recognized, technology will expand to fill the gap. All information, even incorrect information, has value. If incorrect information can be detected, the problem can be fixed.

A Military Example

Perhaps a military example can help explain how advances in technology may translate from a military application into the commercial space.

Today everyone knows about drones. They are perceived by some as autonomous systems. At this time, in most cases they are simply remotely piloted vehicles that are driven by humans. But this picture will change in the near future. When you consider that policies are being developed for machines to make the ultimate decision about who and what to target, it is easy to understand that the complexity of this type of decision is comparable to (or even more complex than) the decisions associated with running a company.

First, it is not acceptable to allow an unmanned combat aerial vehicle (UCAV) to have a bad day, wait too long to make a decision, or to violate international


law. In combat where the fog of war complicates the decision-making process, humans are given some latitude. Errors are made. But when policies are executed by machines, far less latitude for human-like errors will be accepted. Also, when policies are created for machines to make life and death decisions, they will be mass-produced. Those policies must be correct and enforceable, because those responsible for the policies will be held responsible in a court of law. This means that for a machine to execute the policies they must be packaged in a mathematically explicit manner. Everything that the UCAV does must be reviewed.

The problems faced by the UCAV are not unlike those faced by employees of a company. Once you get past the elementary problems, human-like judgment and reasoning are required to interpret the complex, inter-related sets of information. The UCAV must make time critical decisions to 1) do something or refrain from doing something (shoot or don’t shoot), 2) choose from mutually exclusive options (select the most appropriate weapon, choose the most effective tactical maneuver, and 3) allocate resources (apply the right amount of power to each of the control surfaces, use the right amount of weapons balanced against evaluated collateral damage, balance the tactical value of the observed target with target validation, and mission importance). And, like the human decision-maker in an organization, the situation is likely to change. Decisions are not likely to be made in isolation. They will be based on a hierarchy of information sources, where each situation is changes.

In terms of speed the decisions, actions and reactions of a UCAV must be performed much faster than those made in the corporate executive office today (using today’s executive decision-making speed as a comparison). The complex, life and death decisions made by the UCAV must be made in milliseconds. This doesn’t mean that they are any less complex. What it does mean is that the public has a higher level of expectations from a machine than they do from a human.


Events over the past few years have highlighted the failure of human-in-the-loop systems where the response to adversarial actions have resulted in mass casualties. Militaries around the world are coming to the realization that they have to react faster to survive.

The Question

So the question remains: Why should the public expect more from its automated warriors than from corporate management?

The Answer

One would suggest it should not expect more! The public should know that companies behave ethically, make appropriate use of their resources, and make the best decisions for their customers and their shareholders.

So why haven’t all the organizational and operational decisions been automated by now? We have the BI tools and access to all the information in the cloud, yet industry has refrained from taking that next step.

One reason is that there is a general lack of trust in machines making decisions. At least that is what is stated, yet we trust in all the products that are made by machines. We eat food created by machines. We are transported by machines. We are entertained by machines. We are monitored by machines for our safety.

There is also resistance from decision makers. Making decisions is what they are compensated for. But what happens if their stock holders demand a better level of service? Will they relinquish their high paid jobs to a machine that could do the job better? Will they accept inputs from machines that can automatically analyze all the data continually and advise the decision maker of the optimal time to make the big decisions?


Perhaps there are some enlightened executives that want to lead their organizations into the future…?

The Suggestion

Compsim’s Knowledge Enhanced Electronic Logic (KEEL®) Technology2 enables the capture, test, packaging, explaining and auditing of human-like judgment and reasoning for deployment in microcontrollers. For example: KEEL Technology can be used in Unmanned Combat Aerial Vehicles and conventional computer systems, such as those executing BI tools. When human-like judgment and reasoning are packaged in a mathematically explicit manner, systems can make complex decisions, just as if the decisions and actions were made by the humans that created the policies. The added benefit is that the decisions and actions are easily interpreted by humans. The KEEL Dynamic Graphical Language exposes the value system used in the decision-making process. Automatically generated influence diagrams show exactly what was considered in making the command and control decisions.

Rather than subjective “shoot from the hip” decisions, effort is expended auditing, and expanding the decision-making model to take into account new knowledge and understanding of the market.

When an organization uses KEEL Technology, they are making organizational decision-making their core competency. They are packaging their decision-making models into reusable / automatable policies that can be continually extended as the business changes. Automated systems can monitor changes in technology, marketing practices, the economy, political situations, environmental considerations, and be prepared for change by capturing “how to think” rather than how to follow a set of rules. This allows systems to respond to surprise situations they have never been programmed to respond too.

2 KEEL Technology; http://www.compsim.com/About_KEEL.htm

http://www.compsim.com/About_KEEL.htm


Stock holders will grow to expect that executive decisions are exposed to audit and are not biased for personal gain.

Summary

Technology exists today to begin capturing organizational decision-making in a manner that can be automated. First it will be delivered as an executive augmented management function. Then, as organizations capture their decision-making skills in formal automatable policies, executive management models can be retained as a corporate competency. That competency can be extended over time to provide continuous improvement and avoid the delayed and biased decisions that crepe into organizations today. Eventually organizations will be transformed to the state where corporate policy engineers refine and extend the policies with better intelligence and a better understanding of the core competencies of the organization.

Bottom Line: Organizations (commercial and governmental) that fail to test the water now with augmented decision-making models now will be left behind and will be rejected by their customers (and by their stockholders who demand that the best products and services are offered in the most cost effective manner).

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