CAP Terms Flashcards
(163 cards)
1
Q
5 Whys
A
iterative process of discovery through repetitively asking ‘why’; used to explore cause and effect relationships underlying and/or leading to problem (http:// en.wikipedia.org/wiki/5_Whys)
2
Q
5S
A
workplace organization method promoting efficiency and effectiveness; five terms based on Japanese words for: sorting, set in order, systematic cleaning, standardizing, and sustaining (http://en.wikipedia.org/ wiki/5S_(methodology)
3
Q
80/20 Rule
A
AKA the Pareto principle: roughly 80% of results come from 20% of effort
4
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Accuracy
A
quality or state of being correct or precise, or the degree to which the result of a measurement, calculation, or specification conforms to the correct value or standard (https://www.google.com/#q=accuracy)
5
Q
Activity-based costing
A
method of assigning costs to products or services on the resources that they consume (http://www.economist. com/node/13933812)
6
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Agent-based modeling
A
a class of computation models for simulating actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole (http:// en.wikipedia.org/wiki/Agent-based_model)
7
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Algorithm
A
set of specific steps to solve a problem
8
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Amortization
A
allocation of cost of an item or items over a time period such that the actual cost is recovered; often used to account for capital expenditures
9
Q
Analytics
A
scientific process of transforming data into insight for making better decisions (INFORMS)
10
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Analytics professional
A
person capable of making actionable decisions through the analytic process; also a person holding the Certified Analytics Professional (CAP®) credential
11
Q
ANCOVA
A
acronym for analysis of covariance
12
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ANOVA
A
acronym for analysis of variance
13
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Artificial Intelligence (AI)
A
branch of computer science that studies and develops intelligent machines and software (http://en.wikipedia. org/wiki/Artificial_intelligence)
14
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Artificial Neural Networks
A
computer-based models inspired by animal central nervous systems (https://www.google.com/#q=artificial+ neural+networks)
15
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Assemble-to-Order (ATO)
A
manufacturing process where products are assembled as they are ordered; characterized by rapid production and customization (http://www.investopedia.com/terms/a/ assemble-to-order.asp)
16
Q
Assignment problem
A
one of the fundamental combinatorial optimization problems in the branch of optimization or operations research in mathematics; consists of finding a maximum-weight matching in a weighted bipartite graph (http:// en.wikipedia.org/wiki/Assignment_problem)
17
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Automation
A
use of mechanical means to perform work previously done by human effort
18
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Average
A
sum of a range of values divided by the number of values to arrive at a value characteristic of the midpoint of the range; see also, Mean
19
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Batch production
A
method of production where components are produced in groups rather than a continual stream of production; see also, Continuous production
20
Q
Benchmark problems
A
comparison of different algorithms using a large test set (http://www.cs.cmu.edu/afs/cs/project/jair/pub/ volume24/ortizboyer05a-html/node6.html)
21
Q
Benchmarking
A
act of comparison against a standard or the behavior of another in attempt to determine degree of conformity to standard or behavior
22
Q
Bias
A
a tendency for or against a thing, person, or group in a way as to appear unfair; in statistics, data calculated so that it is systematically different from the population parameter of interest (http://en.wikipedia.org/wiki/ Bias_(statistics)
23
Q
Big data
A
data sets too voluminous or too unstructured to be analyzed by traditional means
24
Q
Box-and-whisker plot
A
a simple way of representing statistical data on a plot in which a rectangle is drawn to represent the second and third quartiles, usually with a vertical line inside to indicate the median value. The lower and upper quartiles are shown as horizontal lines either side of the rectangle (http://oxforddictionaries.com/us/definition/ english/box-plot)
25
Branch-and-Bound
a general algorithm for finding optimal solutions of various optimization problems; consists of a system enumeration of all candidate solutions where large subsets of fruitless candidates are discarded en masse using upper and lower estimated bounds of the quantity being optimized (http://en.wikipedia.org/wiki/Branch_ and_bound)
26
Business analytics (BA)
refers to the skills, technologies, applications, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning; can be descriptive, prescriptive, or predictive; focuses on developing new insights and understanding of business performance based on data and statistical methods (http:// en.wikipedia.org/wiki/Business_analytics and www. informs.org)
27
Business case
reasoning underlying and supporting the estimates of business consequences of an action
28
Business intelligence (BI)
a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information (http://en.wikipedia.org/wiki/ Business_intelligence)
29
Business Process Modeling or Mapping (BPM)
act of representing processes of an enterprise so that the current process may be analyzed and improved; typically action performed by business analysis and managers seeking improved efficiency and quality (http:// en.wikipedia.org/wiki/Business_process_modeling)
30
Chief Analytics Officer (CAO)
possible title of one overseeing analytics for a company; may include mobilizing data, people, and systems for successful deployment, working with others to inject analytics into company strategy and decisions, supervising activities of analytical people, consulting with internal business functions and units so they may take advantage of analytics, contracting with external providers of analytics (Davenport, Enterprise Analytics, p. 173)
31
Chi-squared Automated Interaction Detection (CHAID)
a technique for performing decision tree analysis developed by Gordon V. Kass. CHAID is one of several commonly used techniques for decision trees and is based upon hypothesis testing using Bonferroni correction.
32
Classification
assortment of items or entities into predetermined categories
33
Cleansing
AKA cleaning or scrubbing: the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database; may also involve harmonization of data, and standardization of data (http://en.wikipedia.org/wiki/Data_cleansing)
34
Clustering
grouping of a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups or clusters (http:// en.wikipedia.org/wiki/Cluster_analysis)
35
Combinatorial optimization
a topic that consists of finding an optimal object from a finite series of objects; used in applied mathematics and theoretical computer science (http://en.wikipedia.org/ wiki/Combinatorial_optimization)
36
Confidence interval
a type of interval estimate of a population parameter used to indicate the reliability of an estimate. It is an observed interval (i.e., it is calculated from the observations), in principle different from sample to sample, that frequently includes the parameter of interest if the experiment is repeated (http:// en.wikipedia.org/wiki/Confidence_interval)
37
Confidence level
if confidence intervals are constructed across many separate data analyses of repeated (and possibly different) experiments, the proportion of such intervals that contain the true value of the parameter will match the confidence level (http://www.usablestats.com/ lessons/ConfidenceLevel)
38
Conjoint analysis
allows calculation of relative importance of varying features and attributes to customers
39
Constraint
a condition that a solution to an optimization problem is required by the problem itself to satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints (http://en.wikipedia.org/wiki/Constraint_ (mathematics))
40
Constraint programming
a programming paradigm wherein relations between variables are stated in the form of constraints (http:// en.wikipedia.org/wiki/Constraint_programming)
41
Continuous production
method of production where components are produced in a continuous stream; see also, Batch production
42
Correlation
a broad class of statistical relationships involving dependence (http://en.wikipedia.org/wiki/Correlation_ and_dependence)
43
Cost of capital
the cost of funds used for financing a business. Cost of capital depends on the mode of financing used—it refers to the cost of equity if the business is financed solely through equity, or to the cost of debt if it is financed solely through debt (www.investopedia.com)
44
Cube
see OLAP cube
45
Cumulative density function
probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x; used to specify the distribution of multivariate random variables (http://en.wikipedia.org/ wiki/Cumulative_distribution_function)
46
Cutting stock problem
optimization or integer linear programming problem arising from applications in industry where high production problems exist (http://en.wikipedia.org/wiki/ Cutting_stock_problem)
47
Data
(plural form of datum) values of qualitative or quantitative variables, belonging to a set of items; represented in a structure, often tabular (represented by rows and columns), a tree (a set of nodes with parent-children relationship), or a graph structure (a set of interconnected nodes); typically the results of measurements (http://en.wikipedia.org/wiki/Data)
48
Data mining
relatively young and interdisciplinary field of computer science; the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems; see also, KDD (Davenport, Enterprise Analytics, p. 14)
49
Data warehouse
a central repository of data that is created by integrating data from one or more disparate sources; used for reporting and data analysis (http://en.wikipedia.org/wiki/ Data_warehouse)
50
Database
an organized collection of data organized to model relevant aspects of reality to support processes requiring this information (http://en.wikipedia.org/wiki/Database)
51
Decision tree
graphic illustration of how data leads to decision when branches of the tree are followed to their conclusion; different branches may lead to different decisions
52
Decision variables
a decision variable represents a problem entity for which a choice must be made. For instance, a decision variable might represent the position of a queen on a chessboard, for which there are 100 different possibilities (choices) on a 10x10 chessboard or the start time of an activity in a scheduling problem. Each possible choice is represented by a value, hence the set of possible choices constitutes the domain that is associated with a variable (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
53
Descriptive analytics
prepares and analyzes historical data to identify patterns for reporting trends (http://www.informs.org/ Community/Analytics/About-Us)
54
Design of experiments
design of any information gathering exercise where variation is present, whether under the control of the experimenter or not; see also, Experimental design (http://en.wikipedia.org/wiki/Design_of_experiments)
55
Discrete event simulation
models the operation of a system as a discrete sequence of events in time; between events, no change in the system is assumed thus a simulation can move in time from one event to the next (http://en.wikipedia.org/wiki/ Discrete_event_simulation)
56
Dynamic programming
based on the Principle of Optimality, this was originally concerned with optimal decisions over time. For continuous time, it addresses problems in variational calculus. For discrete time, each period is sometimes called a stage, and the DP is called a multistage decision process. Here is the Fundamental Recurrence Equation for an additive process: F(t, s) = Opt{r(t, s, x) + aF(t’, s’): x in X(t, s) and s’=T(t, s, x)}, (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
57
Effective domain
the domain of a function for which its value is finite (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
58
Efficiency
the comparison of what is actually produced or performed with what can be achieved with the same consumption of resources (money, time, labor, etc.). It is an important factor in determination of productivity (www.businessdictionary.com)
59
Engagement
an estimate of the depth of visitor interaction against a clearly defined set of goals; may be measured through analytical models (Davenport, Enterprise Analytics, p. 73-74)
60
Enterprise resource planning (ERP)
a cross-functional enterprise system driven by an integrated suite of software modules that supports the basic internal business processes of a company (http:// en.wikipedia.org/wiki/Enterprise_resource_planning)
61
ETL (extract, transform, load)
refers to three separate functions combined into a single programming tool. First, the extract function reads data from a specified source database and extracts a desired subset of data. Next, the transform function works with the acquired data—using rules or lookup tables, or creating combinations with other data—to convert it to the desired state. Finally, the load function is used to write the resulting data (either all of the subset or just the changes) to a target database, which may or may not previously exist (http://searchdatamanagement. techtarget.com/definition/extract-transform-load)
62
Experimental design
in quality management, a written plan that describes the specifics for conducting an experiment, such as which conditions, factors, responses, tools, and treatments are to be included or used; see also, Design of experiments (www.businessdictionary.com)
63
Expert systems
a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program (http://searchcio-midmarket. techtarget.com/definition/expert-system)
64
Factor analysis
a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis searches for such joint variations in response to unobserved latent variables (http:// en.wikipedia.org/wiki/Factor_analysis)
65
Failure Mode and Effects Analysis (FMEA)
a systematic, proactive method for evaluating a process to identify where and how it might fail, and to assess the relative impact of different failures to identify the parts of the process that are most in need of change (http://intranet.uchicago.edu/quality/ FailureModesandEffectsAnalysis_FMEA_1.pdf)
66
Fixed cost
a cost that is some value, say C, regardless of the level as long as the level is positive; otherwise the fixed charge is zero. This is represented by Cv, where v is a binary variable. When v = 0, the fixed charge is 0; when v = 1, the fixed charge is C. An example is whether to open a plant (v = 1) or not (v = 0). To apply this fixed charge to the non-negative variable x, the constraint x 0), x = 0 is forced by the upper bound constraint. If v = 1 (e.g., plant is open), x
67
Forecasting
the use of historic data to determine the direction of future trends (http://www.investopedia.com/terms/f/ forecasting.asp)
68
Fuzzy logic
a form of mathematical logic in which truth can assume a continuum of values between 0 and 1 (http:// wordnetweb.princeton.edu/perl/webwn?s=fuzzy logic)
69
Game Theory
in general, a (mathematical) game can be played by one player, such as a puzzle, but its main connection with mathematical programming is when there are at least two players, and they are in conflict. Each player chooses a strategy that maximizes his payoff. When there are exactly two players and one player’s loss is the other’s gain, the game is called zero sum. In this case, a payoff matrix A is given where Aij is the payoff to player 1, and the loss to player 2, when player 1 uses strategy i and player 2 uses strategy j. In this representation each row of A corresponds to a strategy of player 1, and each column corresponds to a strategy of player 2. If A is m × n, this means player 1 has m strategies, and player 2 has n strategies (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
70
Genetic algorithms
a class of algorithms inspired by the mechanisms of genetics, which has been applied to global optimization (especially for combinatorial programs). It requires the specification of three operations (each is typically probabilistic) on objects, called “strings” (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society. informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.) Originally authored by Harvey J. Greenberg, 1999-2006.)
71
Global optimal
refers to mathematical programming without convexity assumptions, which are NP-hard. In general, there could be a local optimum that is not a global optimum. Some authors use this term to imply the stronger condition there are multiple local optima. Some solution strategies are given as heuristic search methods (including those that guarantee global convergence, such as branch and bound). As a process associated with algorithm design, some regard this simply as attempts to assure convergence to a global optimum (unlike a purely local optimization procedure, like steepest ascent). (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006. See the supplement by J.D. Pintér.)
72
Goodness of fit
degree of assurance or confidence to which the results of a sample survey or test can be relied upon for making dependable projections. Described as the degree of linear correlation of variables, it is computed with the statistical methods such as chi-square test or coefficient of determination (www.businessdictionary.com)
73
Graphical User Interface (GUI)
a human–computer interface (i.e., a way for humans to interact with computers) that uses windows, icons, and menus, and that can be manipulated by a mouse (and often to a limited extent by a keyboard as well) (http:// www.linfo.org/gui.html)
74
Greedy heuristics
an algorithm that follows the problem-solving heuristic of making the locally-optimal choice at each stage with the hope of finding a global optimum (http:// en.wikipedia.org/wiki/Greedy_heuristic)
75
Heuristic
in mathematical programming, this usually means a procedure that seeks an optimal solution but does not guarantee it will find one, even if one exists. It is often used in contrast to an algorithm, so branch and bound would not be considered a heuristic in this sense. In AI, however, a heuristic is an algorithm (with some guarantees) that uses a heuristic function to estimate the “cost” of branching from a given node to a leaf of the search tree (Also, in AI, the usual rules of node selection in branch and bound can be determined by the choice of heuristic function: best-first, breadth-first, or depth-first search) (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
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Histogram
graphic depiction of data using columns to represent relative size/importance of data grouping
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Hypothesis testing
the theory, methods, and practice of testing a hypothesis by comparing it with the null hypothesis. The null hypothesis is only rejected if its probability falls below a predetermined significance level, in which case the hypothesis being tested is said to have that level of significance (https://www.google.com/#psj=1&q=hypoth esis+testing+definition)
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Influence diagram
depicts structure of decision process and notes the data needed to make the decision
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INFORMS
the largest professional society in the world for professionals in the field of operations research (OR), management science, and analytics (www.informs.org/ About)
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Innovative Applications in Analytics Award
award administered by the Analytics Section of INFORMS to recognize creative and unique developments, applications, or combinations of analytical techniques. The prize promotes the awareness of the value of analytics techniques in unusual applications, or in creative combination to provide unique insights and/or business value (http:// www.informs.org/Community/Analytics/News-Events2/ Innovative-Applications-in-Analytics-Award)
81
Integer program
the variables are required to be integer-valued. Historically, this term implied the mathematical program was otherwise linear, so one often qualifies a nonlinear integer program versus a linear IP (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society. informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
82
Integrity
the measure of the trust that can be placed in the correctness of the information supplied by a navigation system (http://www.navipedia.net/index.php/Integrity; http://www.genengnews.com/gen-articles/preserving-the-integrity-of-statistics/3081/)
83
Internal rate of return (IRR)
the rate of growth that a project or investment is expected to create, expressed as a percentage, over a specified term. IRR is, in essence, the theoretical interest rate earned by the project (http://www.askjim.biz/ answers/internal-rate-of-return-irr-definition_4754.php)
84
KDD
acronym for knowledge discovery in databases process; see also, Data mining (Davenport, Enterprise Analytics, p. 14)
85
Knapsack problem
an integer program of the form, Max{cx: x in Zn+ and ax 0. The original problem models the maximum value of a knapsack that is limited by volume or weight (b), where x_j = number of items of type j put into the knapsack at unit return c_j, that uses a_j units per item (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
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Lead time
time between the initial phase of a process and the emergence of results, as between the planning and completed manufacture of a product (http://www. thefreedictionary.com/lead+time)
87
Lean production
a Japanese approach to management that focuses on cutting out waste while ensuring quality. This approach can be applied to all aspects of a business – from design through production to distribution (http://www. tutor2u.net/business/production/introduction-to-lean-production.html)
88
Lift/lift curve
a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model; lift charts consisting of lift curve and a baseline are visuals aids for measuring model performance (http://www2.cs.uregina.ca/~dbd/ cs831/notes/lift_chart/lift_chart.html)
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Linear program
opt{cx: Ax = b, x >= 0}. (Other forms of the constraints are possible, such as Ax
90
Little’s Law
queuing theory where numerator and denominator are halved so queues are roughly equivalent no matter how many are in line; the long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, ?, multiplied by the (Palm) average time a customer spends in the system, W; or expressed algebraically: L = ?W. The relationship is not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else. (http://en.wikipedia.org/wiki/Little’s_law )
91
Local optimal
a solution that is optimal (either maximal or minimal) within a neighboring set of candidate solutions (http:// en.wikipedia.org/wiki/Local_optimum)
92
Logistic regression
a type of probabilistic classification model [1] used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features). Logistic regression can be binomial or multinomial. Binomial or binary logistic regression deals with situations in which the observed outcome for a dependent variable can have only two possible types (for example, “dead” versus “alive”). Multinomial logistic regression deals with situations where the outcome can have three or more possible types (e.g., “better” versus “no change” versus “worse”) (http://en.wikipedia.org/wiki/Logistic_ regression)
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Machine learning
an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation, and experience (http://www.techopedia.com/definition/8181/ machine-learning)
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MANOVA
acronym for multivariate analysis of variance for use with multiple independent variables
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Mean
the arithmetic average of a set of values or distribution; however, for skewed distributions, the mean is not necessarily the same as the middle value (median), or the most likely (mode); see also, Average (http:// en.wikipedia.org/wiki/Mean)
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Mean squared error (MSE)
the unbiased estimator of population variance. MSE divides by the error degrees of freedom, e.g., if only the mean is estimated, MSE divides by N-1, if four parameters are estimated, MSE divides by N-4, and so on (http://en.wikipedia.org/wiki/Mean_squared_error )
97
Mean time between failures (MTBF)
a measure of how reliable a hardware product or component is. For most components, the measure is typically in thousands or even tens of thousands of hours between failures (http://whatis.techtarget.com/ definition/MTBF-mean-time-between-failures)
98
Median
the value such that the number of terms having values greater than or equal to it is the same as the number of terms having values less than or equal to it (http:// searchdatacenter.techtarget.com/definition/statistical-mean-median-mode-and-range)
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Metaheuristics
a general framework for heuristics in solving hard problems. The idea of ``meta’’ is that of level. An analogy is the use of a metalanguage to explain a language. For computer languages, we use symbols, like brackets, in the metalanguage to denote properties of the language being described, such as parameters that are optional. Examples of metaheuristics are: Ant Colony Optimization, Genetic Algorithms, Memetic Algorithms, Neural networks, etc. (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
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Mode
value of the term that occurs the most often (http:// searchdatacenter.techtarget.com/definition/statistical-mean-median-mode-and-range)
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Monte Carlo simulation
a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil and gas, transportation, and the environment (http://www. palisade.com/risk/monte_carlo_simulation.asp)
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Net present value
value in today’s currency of an item or service (Davenport, Enterprise Analytics, p. 22)
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Network optimization
the process of striking the best possible balance between network performance and network costs, in consideration of grade of service requirements (www. yourdictionary.com)
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Next best offer (NBO)
a targeted offer or proposed action for customers based on analyses of past history and behavior, other customer preferences, purchasing context, attributes of the produces, or services from which they can choose (Davenport, Enterprise Analytics, p. 83)
105
Nominal group technique (NGT)
a structured method for group brainstorming that encourages contributions from everyone (http://asq. org/learn-about-quality/idea-creation-tools/overview/ nominal-group.html)
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Normalization
splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes (http://en.wikipedia.org/ wiki/Snowflake_schema)
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Objective function
the (real-valued) function to be optimized. In a mathematical program in standard form, this is denoted f (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
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OLAP
an abbreviation for “Online Analysis and Processing”; a type of database technology that has long been used by the business community to analyze and interactively explore large financial data sets. The basic idea is that data sets are viewed as cubes with hierarchies along each axis (http://biolap.sourceforge.net/whitepaper.pdf)
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OLAP cube
an array of data understood in terms of its zero or more dimensions; each cell of the cube holds a number that represents some measure of the business, such as sales, profits, expenses, budget, and forecast (http:// en.wikipedia.org/wiki/OLAP_cube)
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Operations management
deals with the design and management of products, processes, services, and supply chains. It considers the acquisition, development, and utilization of resources that firms need to deliver the goods and services their clients want (http://mitsloan.mit.edu/omg/om-definition. php)
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Operations Research
a discipline that deals with the application of advanced analytical methods to help make better decisions (http:// en.wikipedia.org/wiki/Operations_research)
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Opportunity cost
the cost of an alternative that must be forgone to pursue a certain action (http://www.investopedia.com/terms/o/ opportunitycost.asp)
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Optimization
procedure or procedures used to make a system or design as effective or functional as possible, especially the mathematical techniques involved (http://www. thefreedictionary.com/optimization)
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Pareto concept
See, 80/20 rule
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Pattern recognition
in machine learning, pattern recognition is the assignment of a label to a given input value (http:// en.wikipedia.org/wiki/Pattern_recognition)
116
Payback
the length of time required to recover the cost of an investment (http://www.investopedia.com/terms/p/ paybackperiod.asp)
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Pie chart
graphic depiction of data using a pie with different ‘slices’ to represent the relative size of different groupings of data points to the size of the whole
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Precision
the degree to which repeated measurements under unchanged conditions show the same results (http:// en.wikipedia.org/wiki/Accuracy_and_precision)
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Predictive analytics
any approach to data mining with four attributes: an emphasis on prediction (rather than description, classification, or clustering), rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining), an emphasis on the business relevance of the resulting insights (no ivory tower analyses), and (increasingly) an emphasis on ease of use, thus making the tools accessible to business users (http://www.gartner.com/it-glossary/predictive-analytics)
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Prescriptive analytics
evaluates and determines new ways of operating targeting business objective and balancing all constraints (http://www.informs.org/Community/ Analytics/About-Us)
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Pricing
a tactic in the simplex method, by which each variable is evaluated for its potential to improve the value of the objective function. Let p = c_B[B^-1], where B is a basis, and c_B is a vector of costs associated with the basic variables. The vector p is sometimes called a dual solution, though it is not feasible in the dual before termination; p is also called a simplex multiplier or pricing vector. The price of the jth variable is c_j - pA_j. The first term is its direct cost (c_j) and the second term is an indirect cost, using the pricing vector to determine the cost of inputs and outputs in the activity’s column (A_j). The net result is called the reduced cost, and its value determines whether this activity could improve the objective value (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
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Principal Component Analysis (PCA)
a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set (ftp://statgen. ncsu.edu/pub/thorne/molevoclass/AtchleyOct19.pdf)
123
Probability density function
the equation used to describe a continuous probability distribution (http://stattrek.com/statistics/dictionary. aspx?definition=Continuous_probability_distribution)
124
Problem assessment/ framing
initial step in the analytics process; involves buy in from all parties involved on what the problem is before a solution can be found
125
Project management
the application of knowledge, skills, and techniques to execute projects effectively and efficiently. A strategic competency for organizations, enabling them to tie project results to business goals (http://www.pmi.org/ About-Us/About-Us-What-is-Project-Management.aspx)
126
Proprietary data
data that no other organization possesses; produced by a company to enhance its competitive posture (Davenport, Enterprise Analytics, p. 37)
127
Queuing theory
mathematical study of waiting in lines; results are used when making business decisions about the resources needed to provide service; research begun by A. K. Erlang (http://en.wikipedia.org/wiki/Queuing_theory on 2/20/13)
128
Random
of or characterizing a process of selection in which each item of a set has an equal probability of being chosen (http://dictionary.reference.com/browse/random)
129
Range
the difference between the maximum and minimum observations providing an estimate of the spread of the data (http://explorable.com/range-in-statistics)
130
Regression
a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables) (http://www.investopedia.com/terms/r/regression.asp)
131
Regression analysis
statistical approach to forecasting change in a dependent variable (e.g., sales revenue) on the basis of change in one or more independent variables (e.g., population and income); AKA curve fitting or line fitting (www.businessdictionary.com)
132
Response surface methodology (RSM)
a surface in (n+1) dimensions that represents the variations in the expected value of a response variable (see, regression) as the values of n explanatory variables are varied. Usually the interest is in finding the combination that gives a global maximum (or minimum) (http://www.answers.com/topic/response-surface)
133
Return on investment (ROI)
calculations that provide a basis for comparison with other investment opportunities; typically calculated using ROI = ((Total value/benefits) – (total investment costs))/Total investment costs (Davenport; Enterprise Analytics, p. 20)
134
Revenue management
the science and art of enhancing revenues while selling essentially the same amount of product (http://www.ivey. uwo.ca/faculty/Peter_Bell/RM%20Ahmedabad%202005. pdf)
135
RFM
data related to customer relationship management; refers to recency, frequency, and monetary value of purchases (Davenport, Enterprise Analytics, p. 49)
136
Risk
the potential of loss (an undesirable outcome, however not necessarily so) resulting from a given action, activity, and/or inaction (http://en.wikipedia.org/wiki/Risk)
137
Robust optimization
a term given to an approach to deal with uncertainty, similar to the recourse model of stochastic programming, except that feasibility for all possible realizations (called scenarios) is replaced by a penalty function in the objective. As such, the approach integrates goal programming with a scenario-based description of problem data (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society. informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
138
Scatter plot
graphic depiction of data, used to show/identify relationship between independent variables
139
Scenario analysis
a process of analyzing possible future events by considering alternative possible outcomes (scenarios). The analysis is designed to allow improved decision making by allowing more complete consideration of outcomes and their implications (http://www. investordictionary.com/definition/scenario-analysis#sthash.f03iNGP9.dpuf)
140
Scheduling
a schedule for a sequence of jobs, say j1,...,jn, is a specification of start times, say t1,...,tn, such that certain constraints are met. A schedule is sought that minimizes cost and/or some measure of time, like the overall project completion time (when the last job is finished) or the tardy time (amount by which the completion time exceeds a given deadline). There are precedence constraints, such as in the construction industry, where a wall cannot be erected until the foundation is laid (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
141
Sensitivity analysis
the concern with how the solution changes if some changes are made in either the data or in some of the solution values (by fixing their value). Marginal analysis is concerned with the effects of small perturbations, maybe measurable by derivatives. Parametric analysis is concerned with larger changes in parameter values that affect the data in the mathematical program, such as a cost coefficient or resource limit (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society. informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
142
Shadow price
an economic term to denote the rate at which the optimal value changes with respect to a change in some right-hand side that represents a resource supply or demand requirement (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
143
Simulated annealing
an algorithm for solving hard problems, notably combinatorial programs, based on the metaphor of how annealing works: reach a minimum energy state upon cooling a substance, but not too quickly in order to avoid reaching an undesirable final state. As a heuristic search, it allows a nonimproving move to a neighbor with a probability that decreases over time. The rate of this decrease is determined by the cooling schedule, often just a parameter used in an exponential decay (in keeping with the thermodynamic metaphor). With some (mild) assumptions about the cooling schedule, this will converge in probability to a global optimum (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary.computing.society. informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
144
Six Sigma
a set of strategies, techniques, and tools for process improvement. It seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes (http:// en.wikipedia.org/wiki/Six_Sigma)
145
Spreadsheet analysis
the analysis of data using special computer software to anticipate marketing performance under a given set of circumstances (http://www.marketing-dictionary.com/s. php)
146
Standard deviation
measure of the unpredictability of a random variable, expressed as the average deviation of a set of data from its arithmetic mean and computed as the positive square root of the variance. Customarily represented by the lower-case Greek letter sigma (?), it is considered the most useful and important measure of dispersion that has all the essential properties of the variance plus the advantage of being determined in the same units as those of the original data. Also called root mean square (RMS) deviation (www.businessdictionary.com)
147
Statistical significance
probability of obtaining a test result that occurs by chance and not by systematic manipulation of data (www.businessdictionary.com)
148
Statistics
branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis of their quantifiable likelihood (probability). Statistics can interpret aggregates of data too large to be intelligible by ordinary observation because such data (unlike individual quantities) tend to behave in regular, predictable manner. It is subdivided into descriptive and inferential statistics (www.businessdictionary.com)
149
Stepwise regression
a semi-automated process of building a model by successively adding or removing variables based solely on the t-statistics of their estimated coefficients (http:// people.duke.edu/~rnau/regstep.htm)
150
Supply chain management
the active management of supply chain activities to maximize customer value and achieve a sustainable competitive advantage (http://scm.ncsu.edu/scm-articles/article/what-is-supply-chain-management)
151
System dynamics
a computer-aided approach to policy analysis and design. It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems (http://www.systemdynamics.org/what_is_ system_dynamics.html)
152
Tolerance
an approach to sensitivity analysis in linear programming that expresses the common range that parameters can change while preserving the character of the solution (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
153
Traveling salesman problem (TSP)
given n points and a cost matrix [cij], a tour is a permutation of the n points. The points can be cities, and the permutation the visitation of each city exactly once, then returning to the first city (called home). (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
154
Uncertainty
the estimated amount or percentage by which an observed or calculated value may differ from the true value (http://www.thefreedictionary.com/uncertainty)
155
Validation (of a model)
determining how well the model depicts the real-world situation it is describing (http://www.easterbrook.ca/ steve/2010/11/the-difference-between-verification-and-validation/)
156
Variability
describes how spread out or closely clustered a set of data is (http://en.wikipedia.org/wiki/Variability)
157
Variable cost
a periodic cost that varies in step with the output or the sales revenue of a company. Variable costs include raw material, energy usage, labor, distribution costs, etc. (http://www.businessdictionary.com/definition/variable-cost.html)
158
Variance
a parameter in a distribution that describes how far the values are spread apart. Variance is a characteristic of some probability distribution, which distinguishes the concept of variance from the ways to estimate it from sample data(http://en.wikipedia.org/wiki/Variance)
159
Variation reduction
reference to process variation where reduction leads to stable and predication process results (http://www. businessdictionary.com)
160
Vehicle routing problem (VRP)
finding optimal delivery routes from one or more depots to a set of geographically scattered points (e.g., population centers). A simple case is finding a route for snow removal, garbage collection, or street sweeping (without complications, this is akin to a shortest path problem). In its most complex form, the VRP is a generalization of the TSP, as it can include additional time and capacity constraints, precedence constraints, and more (A. Holder, editor. Mathematical Programming Glossary. INFORMS Computing Society, http://glossary. computing.society.informs.org/, 2006-08. Originally authored by Harvey J. Greenberg, 1999-2006.)
161
Verification (of a model)
includes all the activities associated with the producing high quality software: testing, inspection, design analysis, specification analysis (http://www.easterbrook. ca/steve/2010/11/the-difference-between-verification-and-validation/)
162
Web analytics
ability to use data generated through Internet-based activities; typically used to assess customer behaviors; see also, RFM (Davenport, Enterprise Analytics, p. 49-51)
163
Yield
percentage of ‘good’ product in a batch; has three main components: functional (defect driven), parametric (performance driven), and production efficiency/ equipment utilization (http://www-inst.eecs.berkeley. edu/~ee290h/fa05/Lectures/PDF/lecture%201%20 intro%20IC%20Yield.pdf)
