BI and Decision Support

7/29/2019 BI and Decision Support

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Business Intelligence

and Decision Support

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Changing Business Environment

Companies are moving aggressively tocomputerized support of theiroperations => Business Intelligence

Business PressuresResponsesSupportModel

Business pressures result of today'scompetitive business climate

Responses to counter the pressures

Support to better facilitate the process2


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Business PressuresResponses

Support Model

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The Business Environment

The environment in whichorganizations operate today isbecoming more and more complex,creating:

opportunities, and

problems

Example: globalization

Business environment factors:

markets, consumer demands, technology,

and societal 4


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Business Environment Factors

FACTOR DESCRIPTIONMarkets Strong competition

Expanding global markets

Blooming electronic markets on the Internet

Innovative marketing methods

Opportunities for outsourcing with IT support

Need for real-time, on-demand transactions

Consumer Desire for customizationdemand Desire for quality, diversity of products, and speed of delivery

Customers getting powerful and less loyal

Technology More innovations, new products, and new services

Increasing obsolescence rate

Increasing information overload

Social networking, Web 2.0 and beyondSocietal Growing government regulations and deregulation

Workforce more diversified, older, and composed of more women

Prime concerns of homeland security and terrorist attacks

Necessity of Sarbanes-Oxley Act and other reporting-related legislation- corporate accountability & penalties for acts of wrongdoing

Increasing social responsibility of companies

Greater emphasis on sustainability 5


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Organizational Responses

Be Reactive, Anticipative, Adaptive,and Proactive

Managers may take actions, such as Employ strategic planning

Restructure business processes

Participate in business alliances

Improve partnership relationships Encourage innovation and creativity cont>

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Managers actions, continued

Improve customer service and relationships

Move to make-to-order production and on-demandmanufacturing and services

Respond quickly to competitors' actions (e.g., inpricing, promotions, new products and services)

Automate certain decision processes

Improve decision making by employing analytics

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Closing the Strategy Gap

One of the major objectives ofcomputerized decision support is tofacilitate closing the gap between thecurrent performance of an organizationand its desired performance, asexpressed in its mission, objectives, and

goals, and the strategy to achieve them

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Managerial Decision Making

Management is a process by whichorganizational goals are achieved byusing resources

Inputs: resources

Output: attainment of goals

Measure of success: outputs / inputs

Management Decision Making Decision making: selecting the best

solution from two or more alternatives9


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Mintzberg's 10 Managerial Roles

Interpersonal1. Figurehead

2. Leader

3. Liaison

Informational

4. Monitor5. Disseminator

6. Spokesperson

Decisional7. Entrepreneur8. Disturbance handler

9. Resource allocator

10. Negotiator

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A Decision Support Framework

Degree of Structuredness (Simon, 1977)

Decision are classified as

Highly structured (a.k.a. programmed)

Semi-structured Highly unstructured (i.e., non-programmed)

Types of Control (Anthony, 1965)

Strategic planning (top-level, long-range) Management control (tactical planning)

Operational control

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Computer Support for Structured

Decisions

Structured problems: encounteredrepeatedly, have a high level of structure

It is possible to abstract, analyze, andclassify them into specific categories e.g., make-or-buy decisions, capital

budgeting, resource allocation, distribution,

procurement, and inventory control For each category a solution approach is

developed => Management Science

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Management Science Approach

Also referred to as Operation Research

In solving problems, managers shouldfollow the five-step MS approach1. Define the problem

2. Classify the problem into a standard category (*)

3. Construct a model that describes the real-worldproblem

4. Identify possible solutions to the modeled problemand evaluate the solutions

5. Compare, choose, and recommend a potentialsolution to the problem

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Automated Decision Making

Applies to highly structured decisions

Automated decision systems (ADS)

(or decision automation systems)

An ADS is a rule-based system thatprovides a solution to a repetitivemanagerial problem in a specific area

e.g., simple-loan approval system

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Automated Decision Making

ADS initially appeared in the airlineindustry called revenue (or yield)management (or revenue optimization)

systems dynamically price tickets based on actual

demand

Today, many service industries usesimilar pricing models

ADS are driven by business rules!

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Computer Support for

Unstructured Decisions

Unstructured problems can be onlypartially supported by standardcomputerized quantitative methods

They often require customized solutions They benefit from data and information

Intuition and judgment may play a role

Computerized communication andcollaboration technologies along withknowledge management is often used

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Computer Support for

Semi-structured Problems

Solving semi-structured problems mayinvolve a combination of standardsolution procedures and human

judgment MS handles the structured parts while

DSS deals with the unstructured parts

With proper data and information, arange of alternative solutions, along withtheir potential impacts

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Concept of Decision Support

Systems

Classical Definitions of DSS

Interactive computer-based systems, which helpdecision makers utilize data and models to solve

unstructured problems" - Gorry and Scott-Morton, 1971

Decision support systems couple the intellectualresources of individuals with the capabilities of the

computer to improve the quality of decisions. It is acomputer-based support system for managementdecision makers who deal with semistructuredproblems - Keen and Scott-Morton, 1978

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DSS as an Umbrella Term

The term DSS can be used as an umbrellaterm to describe any computerizedsystem that supports decision making in

an organization E.g., an organization wide knowledge

management system; a decision support

system specific to an organizational function(marketing, finance, accounting,manufacturing, planning, SCM, etc.)

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Business Intelligence (BI)

BI is an umbrella term that combinesarchitectures, tools, databases, analyticaltools, applications, and methodologies

BI's major objective is to enable easy access todata (and models) to provide businessmanagers with the ability to conduct analysis

BI helps transform data, to information (andknowledge), to decisions and finally to action( cf ETL later)

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A Brief History of BI

The term BI was coined by the GartnerGroup in the mid-1990s

However, the concept is much older 1970s - MIS reporting - static/periodic reports

1980s - Executive Information Systems (EIS)

1990s - OLAP, dynamic, multidimensional, ad-hocreporting -> coining of the term BI

2005+ Inclusion of AI and Data/Text Miningcapabilities; Web-based Portals/Dashboards

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Components in a BI Architecture

The Data Warehouse is a large repository ofwell-organized historical data

Business analytics are the tools that allow

transformation of data into information andknowledge

Business performance management (BPM)allows monitoring, measuring, and comparing

key performance indicators

User interface (e.g., dashboards) allows accessand easy manipulation of other BI components

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Business Analytics

Business analytics is the use of models anddata to improve an organization's performanceand/or competitive advantage

Business analytics makes extensive use of data,statistical and quantitative analysis,explanatory and predictive modeling,and fact-based management to drive decision making

Analytics may be used as input for humandecisions or may drive fully automateddecisions

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Business Analytics

OLAP and other database tools can answerquestions such as what happened(description), how many, how often, where the

problem is, and what actions are needed Business analytics can answer questions such

as why is this happening, what if these trendscontinue, what will happen next (prediction),

what is the best that can happen and how canwe get there (optimization)

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Descriptive Analytics

The most traditional of business analytics isdescriptive analytics, and it accounts for themajority of all current business analytics

Descriptive analytics looks at past performanceand understands that performance by mininghistorical data to look for the reasons behindpast success or failure Almost all management reporting such as sales, marketing,

operations, and finance, uses this type of post-mortemanalysis

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Predictive Analytics

The next phase is predictive analytics This is when historical performance data is combined with

rules, algorithms, and occasionally external data to determinethe probable future outcome of an event or a likelihood of a

situation occurring Predictive Models in banking industry is widely developed to

bring certainty across the risk scores for individual customers.Credit Scores are built to predict individuals behaviour andalso scores are widely used to evaluate the credit worthiness

of each applicant and rated while processing loan applications Used in health care to determine which patients are at risk of

developing certain conditions, like diabetes, asthma, heartdisease, and other lifetime illnesses

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Prescriptive Analytics

The final phase is Prescriptive Analytics

Prescriptive analytics goes beyond predicting futureoutcomes by also suggesting actions to benefit from thepredictions and showing the decision maker the

implications of each decision option Prescriptive analytics not only anticipates what will happen

and when it will happen, but also why it will happen

Prescriptive analytics can suggest decision options on howto take advantage of a future opportunity or mitigate a

future risk and illustrate the implication of each decisionoption

prescriptive analytics can continually and automaticallyprocess new data to improve prediction accuracy and

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DescriptiveAnalytics(Past)

PredictiveAnalytics(Future)

PrescriptiveAnalytics(Decision)

Phases of Business Analytics:

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Web Analytics

Web analytics implies using business analytics on real-time Web information to assist in decision making;often related to e-Commerce

Allows marketers to collect session-level information

about interactions on a website The interactions provide the web analytics information

systems with the information to track the referrer,search keywords, IP address, and activities of the

visitor With this information, a marketer can improve the marketing

campaigns, site creative content, and information architecture

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Web Analytics


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Big Data

Big data is a collection of data sets so large andcomplex that it becomes difficult to process usingtraditional data processing tools and applications

capture, curation, storage, search, sharing, analysis,

and visualization The trend to larger data sets is due to the additional

information derivable from analysis of a single largeset of related data, as compared to separate smaller

sets with the same total amount of data, allowingcorrelations to be found to e.g. spot business trends,determine quality of research, prevent diseases,combat crime, and determine real-time roadway

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Big Data

Limits on the size of data sets that are feasible toprocess in a reasonable amount of time are of the orderof exabytes (1018 bytes) of data Walmart handles more than 1 million customer transactions every

hour, which is imported into databases estimated to contain morethan 2.5 petabytes of data the equivalent of 167 times theinformation contained in all the books in the US Library of Congress

Facebook handles 40 billion photos from its user base

FICO Falcon Credit Card Fraud Detection System protects 2.1 billionactive accounts world-wide

What is the covariance between two time series

What if these time series are stock prices and on all trading days forthe past 5 years, and it is wished to obtain such correlation for eachpairing of 3,000 stocks?

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The Benefits of BI

The ability to provide accurate informationwhen needed, including a real-time view ofthe corporate performance and its parts

A survey by Thompson (2004) Faster, more accurate reporting (81%)

Improved decision making (78%)

Improved customer service (56%)

Increased revenue (49%)

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Characteristics of Decision Making

Groupthink group members accept the solution without thinking

for themselves can lead to bad decisions

Evaluating what-if scenarios Experimentation with a real system!

Changes in the decision-making environmentmay occur continuously

Time pressure on the decision maker

Analyzing a problem takes time/money

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Characteristics of Decision Making

Better decisions Tradeoff: accuracy versus speed

Fast decision may be detrimental

Areas suffering most from fastdecisions personnel/human resources (27%)

budgeting/finance (24%) organizational structuring (22%)

quality/productivity (20%)

IT selection and installation (17%)

process improvement (17%) 35


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Decision Making

A process of choosing among two ormore alternative courses of action forthe purpose of attaining a goal(s)

e.g., Planning What should be done? When? Where?

Why? How? By whom?

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Decision Making and Problem

Solving

A problem occurs when a system

does not meet its established goals

does not yield the predicted results, or

does not work as planned

Problem is the difference between thedesired and actual outcome

Problem solving also involvesidentification of new opportunities

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Decision Making and Problem

Solving

Are problem solving and decision makingdifferent? Or, are they the same thing?

Consider the phases of the decision process

Phase (1)Intelligence

Phase (2) Design

Phase (3) Choice, and

Phase (4) Implementation (1)-(4): problem solving; (3): decision making

(1)-(3): decision making; (4): problem solving

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Decision Style

The manner by which decision makersthink and react to problems

perceive a problem

cognitive response

values and beliefs

When making decisions, people

follow different steps/sequence

give different emphasis, time allotment,and priority to each steps

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Decision Style

Decision-making styles

Heuristic versus Analytic

Autocratic versus Democratic

Consultative (with individuals or groups)

A successful computerized systemshould fit the decision style and thedecision situation

Should be flexible and adaptable todifferent users (individuals vs. groups)

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Decision Makers

Small organizations

Individuals

Conflicting objectives

Medium-to-large organizations

Groups

Different styles, backgrounds, expectations

Conflicting objectives

Consensus is often difficult to reach

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Model

A significant part of many DSS and BIsystems

A model is a simplified representation

(or abstraction) of reality Often, reality is too complex to describe

Much of the complexity is actually

irrelevant in solving a specific problem

Models can represent systems/problemsat various degrees of abstraction

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Types of Models

Models can be classified based on theirdegree of abstraction

Iconic models (scale models)

Analog models (e.g. organizational chart)

Mental Models

Mathematical (quantitative) models

Degre

eofabstraction

Less

More

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The Benefits of Models

Ease of manipulation

Compression of time

Lower cost of analysis on models

Cost of making mistakes on experiments

Inclusion of risk/uncertainty

Evaluation of many alternatives Reinforce learning and training

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Phases of Decision-Making Process

Humans consciously or sub consciouslyfollow a systematic decision-makingprocess - Simon (1977)

1) Intelligence

2) Design

3) Choice

4) Implementation

5) (?) Monitoring (a part of intelligence?)

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Simons Decision-Making Process

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Decision-Making: Intelligence Phase

Scan the environment, either intermittentlyor continuously

Identify problem situations or opportunities

Monitor the results of the implementation

Problem is the difference between whatpeople desire (or expect) and what is actually

occurring Symptom versus Problem

Timely identification of opportunities is asimportant as identification of problems

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Potential issues in data/informationcollection and estimation

Lack of data

Cost of data collection

Inaccurate and/or imprecise data

Data estimation is often subjective

Data may be insecure

Key data may be qualitative

Data change over time (time-dependence)

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Problem Classification

Classification of problems according to the degreeof structuredness

Problem Decomposition Often solving the simpler subproblems may help

in solving a complex problem

Information/data can improve the structuredness

of a problem situation Problem Ownership

Outcome of intelligence phase:A Formal ProblemStatement

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Decision-Making: The Design Phase

Finding/developing and analyzing possiblecourses of actions

A model of the decision-making problem is

constructed, tested, and validated Modeling: conceptualizing a problem and

abstracting it into a quantitative and/orqualitative form (i.e., using symbols/variables)

Abstraction: making assumptions for simplification

Tradeoff (cost/benefit): more or less abstraction

Modeling: both an art and a science

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Selection of a Principle of Choice

It is a criterion that describes theacceptability of a solution approach

Reflection of decision-making objective(s)

In a model, it is the result variable

Choosing and validating against

High-risk versus low-risk Optimize versus satisfice

Criterion is not a constraint

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Normative models (= optimization) the chosen alternative is demonstrably the

best of all possible alternatives

Assumptions of rational decision makers Humans are economic beings whose objective is

to maximize the attainment of goals

For a decision-making situation, all alternative

courses of action and consequences are known Decision makers have an order or preference

that enables them to rank the desirability of allconsequences

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Heuristic models (= suboptimization) the chosen alternative is the best of only a

subset of possible alternatives

Often, it is not feasible to optimize realistic(size/complexity) problems

Suboptimization may also help relaxunrealistic assumptions in models

Help reach a good enough solution faster

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Descriptive models describe things as they are or as they are

believed to be (mathematically based)

They do not provide a solution butinformation that may lead to a solution

Simulation - most common descriptivemodeling method (mathematical depictionof systems in a computer environment)

Allows experimentation with the descriptivemodel of a system

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Good Enough, or Satisficingsomething less than the best

A form of suboptimization

Seeking to achieving a desired level ofperformance as opposed to the best

Benefit: time saving

Simons idea of bounded rationality

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Bounded Rationality

Herbert Simon - Nobel Prize in EconomicSciences (1978) decision makers do not optimize their decisions

find feasible decisions that are good enough

Limitations of: available data

processing capability

methods

intelligence level of decision makers

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Developing (Generating) Alternatives In optimization models (such as linear

programming), the alternatives may be

generated automatically In most MSS situations, however, it is

necessary to generate alternatives manually

Use of GSS helps generating alternatives

Measuring/ranking the outcomes

Using the principle of choice

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Risk Lack of precise knowledge (uncertainty)

Risk can be measured with probability

Scenario (what-if case) A statement of assumptions about the

operating environment (variables) of a

particular system at a given time Possible scenarios: best, worst, most likely,

average (and custom intervals)

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Decision-Making: The Choice Phase

The actual decision and the commitment tofollow a certain course of action are made here

The boundary between the design and choice

is often unclear (partially overlapping phases) Generate alternatives while performing evaluations

Includes the search, evaluation, andrecommendation of an appropriate solution to

the model Solving the model versus solving the problem!

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Decision-Making: The Choice Phase

Search approaches Analytic techniques (solving with a formula)

Algorithms (step-by-step procedures)

Heuristics (rule of thumb) Blind search (truly random search)

Additional activities

Sensitivity analysis What-if analysis

Goal seeking

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Decision Making:


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Decision-Making:

The Implementation Phase

Nothing more difficult to carry out, normore doubtful of success, nor moredangerous to handle, than to initiate a

new order of things.- The Prince, Machiavelli 1500s

Solution to a problem = Change

Change management?

Implementation: putting arecommended solution to work

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How Decisions Are Supported

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Support for the Intelligence Phase Enabling continuous scanning of external

and internal information sources to

identify problems and/or opportunities Resources/technologies: Web; ES, OLAP,

data warehousing, data/text/Web mining,EIS/Dashboards, KMS, GSS, GIS,

Business activity monitoring (BAM) Business process management (BPM)

Product life-cycle management (PLM)

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Support for the Design Phase Enabling generating alternative courses of

action, determining the criteria for choice

Generating alternatives Structured/simple problems: standard and/or

special models

Unstructured/complex problems: human

experts, ES, KMS, brainstorming/GSS, OLAP,data/text mining

A good criteria for choice is critical!

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Support for the Choice Phase Enabling selection of the best alternative

given a complex constraint structure

Use sensitivity analyses, what-if analyses,goal seeking

Resources

KMS

CRM, ERP, and SCM

Simulation and other descriptive models

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Support for the Implementation Phase Enabling implementation/deployment of

the selected solution to the system

Decision communication, explanation andjustification to reduce resistance to change

Resources

Corporate portals, Web 2.0/Wikis

Brainstorming/GSS

KMS , ES

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DSS Description

An early definition of DSS A system intended to support managerial decision

makers in semistructured and unstructureddecision situations

meant to be adjuncts to decision makers(extending their capabilities but not replacingtheir judgment)

aimed at decisions that required judgment or at

decisions that could not be completely supportedby algorithms

would be computer based; operate interactively;and would have graphical output capabilities

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DSS Description

A DSS is typically built to support thesolution of a certain problem (or to evaluate aspecific opportunity). This is a key difference

between DSS and BI applications BI systems monitor situations and identify

problems and/or opportunities, using variety ofanalytic methods

The user generally must identify whether aparticular situation warrants attention

Reporting/data warehouse plays a major role inBI

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DSS Description

DSS is an approach (or methodology) forsupporting decision making

uses an interactive, flexible, adaptable computer-based information system (CBIS)

developed (by end user) for supporting the solutionto a specific nonstructured management problem

uses data, model and knowledge along with afriendly (often graphical; Web-based) user interface

incorporate the decision maker's own insights

supports all phases of decision making

can be used by a single user or by many people

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Components of DSS

Data Management Subsystem Includes the database that contains the data

Database management system (DBMS)

Can be connected to a data warehouse Model Management Subsystem

Model base management system (MBMS)

Knowledgebase Management Subsystem Organizational knowledge base

User Interface Subsystem70


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Data Management Subsystem

DSS database

DBMS

Data directory Query facility

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Model Management Subsystem

Model base

MBMS

Model directory

Model execution,integration, andcommandprocessor

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Knowledgebase Management


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Knowledgebase Management

System

Incorporation of intelligence and expertise Knowledge components:

Expert systems,

Knowledge management systems, Neural networks,

Intelligent agents,

Fuzzy logic,

Case-based reasoning systems, and so on Often used to better manage the other DSS

components

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User Interface Subsystem

User InterfaceManagement System

DSS User Interface

Graphical icons Dashboard

Colour coding

Interfacing withmobile devices

Documents

BI and Decision Support