Enterprise Intelligence Quarter

Enterprise Intelligence Quarter (hereinafter, the Quarter) is the second of four lectures of Team Quadrivium (hereinafter, the Quadrivium):

• The Quarter is designed to introduce its learners to enterprise discovery, or, in other words, to concepts related to obtaining data needed to administer the enterprise effort; and
• The Quadrivium examines concepts of administering various types of enterprises known as enterprise administration as a whole.

The Quadrivium is the first of seven modules of Septem Artes Administrativi, which is a course designed to introduce its learners to general concepts in business administration, management, and organizational behavior.

Outline

The predecessor lecture is Bookkeeping Quarter.

Concepts

1. Enterprise intelligence.
   • Intelligence.
   • All-source intelligence. (1) Intelligence products and/or organizations and activities that incorporate all sources of information, most frequently including human resources intelligence, imagery intelligence, measurement and signature intelligence, signals intelligence, and open-source data in the production of finished intelligence; (2) In intelligence collection, a phrase that indicates that in the satisfaction of intelligence requirements, all collection, processing, exploitation, and reporting systems and resources are identified for possible use and those most capable are tasked.
2. Intelligence data.
   • Known known.
   • Known unknown.
   • Unknown known.
   • Unknown unknown.
3. Risk analysis. Controlling residual risks, identifying new risks, executing risk response plans, and evaluating their effectiveness throughout an enterprise effort.
   • Risk event. A discrete occurrence that may affect the project for better or worse.
   • Trigger. Triggers, sometimes called risk symptoms or warning signs, are indications that a risk has occurred or is about to occur. Triggers may be discovered in the risk identification process and watched in the risk monitoring and control process.
   • Risk category. A source of potential risk reflecting technical, project management, organizational, or external sources.
4. Risk. An uncertain event or condition that, if it occurs, has a positive or negative effect on an enterprise effort.
   • Secondary risk. A risk that arises as a direct result of implementing a risk response.
   • Residual risk. A risk that remains after risk responses have been implemented.
   • Identified risk.
5. Risk intelligence.
   • Countersurveillance.
   • Signals intelligence, imagery intelligence, measurement and signature intelligence
6. Artificial intelligence (AI). A field of computer science dedicated to the study of computer software making intelligent decisions, reasoning, and problem solving.
   • Strong AI. An area of AI development that is working toward the goal of making AI systems that are as useful and skilled as the human mind.
   • Weak AI. Also known as narrow AI, weak AI refers to a non-sentient computer system that operates within a predetermined range of skills and usually focuses on a singular task or small set of tasks. Most AI in use today is weak AI.
7. Algorithm. A formula or set of rules for performing a task. In AI, the algorithm tells the machine how to go about finding answers to a question or solutions to a problem.
   • Autonomous. Autonomy is the ability to act independently of a ruling body. In AI, a machine or vehicle is referred to as autonomous if it doesn't require input from a human operator to function properly.
   • Data mining. The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it.
   • Heuristics. These are rules drawn from experience used to solve a problem more quickly than traditional problem-solving methods in AI. While faster, a heuristic approach typically is less optimal than the classic methods it replaces.
   • Pruning. The use of a search algorithm to cut off undesirable solutions to a problem in an AI system. It reduces the number of decisions that can be made by the AI system.
8. Machine learning. A field of AI focused on getting machines to act without being programmed to do so. Machines "learn" from patterns they recognize and adjust their behavior accordingly.
   • Artificial neural network (ANN). A learning model based on the biological neural networks present in the brains of animals. Based on the activity of neurons, ANNs are used to solve tasks that would be too difficult for traditional methods of programming.
   • Deep learning. A subset of machine learning that uses specialized algorithms to model and understand complex structures and relationships among data and datasets.
   • Backpropagation. Short for "backward propagation of errors," backpropagation is a way of training neural networks based on a known, desired output for specific sample case.
   • Weights. The connection strength between units, or nodes, in a neural network. These weights can be adjusted in a process called learning.
9. Artificial reasoning.
   • Analogical reasoning. Solving problems by using analogies, by comparing to past experiences.
   • Case-based reasoning (CBR). An approach to knowledge-based problem solving that uses the solutions of a past, similar problem (case) to solve an existing problem.
   • Inductive reasoning. In AI, inductive reasoning uses evidence and data to create statements and rules.
10. Artificial chaining.
    • Backward chaining. A method in which machines work backward from the desired goal, or output, to determine if there is any data or evidence to support those goals or outputs.
    • Forward chaining. A situation where an AI system must work "forward" from a problem to find a solution. Using a rule-based system, the AI would determine which "if" rules it would apply to the problem.
11. AI application.
    • Natural language processing (NLP). The ability of computers to understand, or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition.
    • Planning. A branch of AI dealing with planned sequences or strategies to be performed by an AI-powered machine. Things such as actions to take, variable to account for, and duration of performance are accounted for.

Methods

1. Target-acquisition assessment. A three-step pattern used to evaluate risks. Risks are identified first, qualitatively analyzed second, and those, that are selected as the most important ones, quantitatively analyzed third.
   • Risk identification. Determining which risks might affect the project and documenting their characteristics. Tools used include brainstorming and checklists.
   • Qualitative analysis. Performing a qualitative analysis of risks and conditions to prioritize their effects on project objectives. It involves assessing the probability and impact of project risk(s) and using methods such as the probability and impact matrix to classify risks into categories of high, moderate, and low for prioritized risk response planning.
   • Quantitative analysis. Measuring the probability and consequences of risks and estimating their implications for project objectives. Risks are characterized by probability distributions of possible outcomes. This process uses quantitative techniques such as simulation and decision tree analysis.
2. Risk response technique.
   • Risk acceptance. This technique of the risk response planning process indicates that the project team has decided not to change the project plan to deal with a risk, or is unable to identify any other suitable response strategy.
   • Risk avoidance. Risk avoidance is changing the project plan to eliminate the risk or to protect the project objectives from its impact. It is a tool of the risk response planning process.
   • Risk mitigation. Risk mitigation seeks to reduce the probability and/or impact of a risk to below an acceptable threshold.
   • Risk transference. Risk transference is seeking to shift the impact of a risk to a third party together with ownership of the response.
3. Simulation. A simulation uses a project model that translates the uncertainties specified at a detailed level into their potential impact on objectives that are expressed at the level of the total project. Project simulations use computer models and estimates of risk at a detailed level, and are typically performed using the Monte Carlo method.
   • Monte Carlo method. A technique that performs a project simulation many times to calculate a distribution of likely results.
4. Turing test. A test developed by Alan Turing that tests the ability of a machine to mimic human behavior. The test involves a human evaluator who undertakes natural language conversations with another human and a machine and rates the conversations.

Instruments

1. Stakeholder-analysis tool.
   • Power-interest grid.
   • Influence-interest grid.
   • Power-interest-attitude grouping.
2. Probability and impact matrix. A common way to determine whether a risk is considered low, moderate, or high by combining the two dimensions of a risk, its probability of occurrence, and its impact on objectives if it occurs.

Results

1. Risk database. A repository that provides for collection, maintenance, and analysis of data gathered and used in the risk management processes. A lessons-learned program uses a risk database. This is an output of the risk monitoring and control process.

Practices

The successor lecture is Organizational Structure Quarter.