data warehousing with olap - tu braunschweig

1/28/2011

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Data Warehousing

& Data Mining Wolf-Tilo Balke

Silviu Homoceanu

Institut für Informationssysteme

Technische Universität Braunschweig

http://www.ifis.cs.tu-bs.de

• Last week:

– Clustering

• Flat: K-means

• Hierarchical: Agglomerative, Divisive

– Clustering high-dimensional data

• CLIQUE

• This week..

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 2

Summary

12. Decision Support Systems (DSS)

DSS Applications:

12.1 Marketing Models

12.2 Supply Chain Management

DW & DM – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 3

12. Decision Support Systems

• Decision-making is the process of making choices. It includes:

– Assessing the problem

– Collecting and verifying information

– Identifying alternatives

– Anticipating consequences of decisions

– Making the choice using sound and logical judgment based on available information

– Informing others of decision and rationale

– Evaluating decisions


12.0 DSS - Introduction

• Decision problem

• What kind of decisions are there?


12.0 Decisions

options

(alternatives)

goals

• FIND the option that best satisfies the goals

• RANK options according to the goals

• ANALYSE, JUSTIFY, EXPLAIN, …, the decision

• Types of decisions

– Easy (routine, everyday)

vs. difficult (complex)

– One-time vs. recurring

– One-stage vs. sequential

– Single objective vs. multiple objectives

– Operational, tactical, strategic

– …

• DSS address complex decisions


12.0 Decisions

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• Characteristics of complex decisions

– Novelty

• There was no prior similar decision

– Unclearness

• Incomplete knowledge about the problem

– Uncertainty

• Outside events that cannot be controlled

– Multiple objectives (possibly conflicting)

• Maximize economic benefits vs. minimize environmental costs

– Important consequences of the decision

– Limited resources


12.0 Complex Decisions


12.0 Decision-Making

• Decision making can be difficult for people.

Can we help decision makers make better

decisions?

– Decision Support: Provides methods and tools for

supporting people in making complex decisions.

How?


12.0 Decision Support


12.0 Decision Support

• Decision support systems (DSS)…

– are interactive, computer-based information systems

– developed for improving the decision-making

process

• Characteristics

– DSS incorporate both data and models

– They support rather the replace managerial

judgment

– Their objective is to improve the quality and

effectiveness rather then efficiency of decisions



• Types of DSS

– Data-driven, emphasizes access to and manipulation

of data e.g., time-series

– Document-driven, manages, retrieves and

manipulates unstructured information stored in

electronic formats

– Knowledge-driven, provides problem solving

expertise stored as rules or procedures

– Model-driven, make use of statistical or financial

models and simulations



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• Technologies DSS rely on

– Data mining

– Data warehousing and OLAP

– Traditional approaches



• Data Mining

– Association rule mining

– Sequence patterns and time series

– Regression analysis

– Classification

– Clustering



• Data Warehousing

– As support for OLAP

– Online Analytical Processing (OLAP)

• Traditional approaches

– Common mathematical modeling e.g., what-if-analysis

– Non-rigorous modeling

– Rule-based systems (RBS)



• DSS capabilities should offer…

– support for problem-solving phases

• Gather intelligence, identify and design the options, make

the choice, implement it, monitor for feedback

– support for different decision frequencies

• Ad hoc DSS: decisions that come up once in every 5 years

(e.g., where should a company open a new distribution

center?)

• Institutional DSS: decisions that repeat (e.g., what should

the company invest in?)



– support for different problem structures

• Highly structured problems: known facts and relationships

• Semi-structured problems: facts unknown or ambiguous, relations vague – E.g., which person to promote/hire for a position?

– support for various decision-making levels

• Operational level – Daily decisions

• Tactical level – Planning and control

• Strategic level – Long-term decisions


12.0 DSS Capabilities

• DSS architecture



GUI

Analytical engine

Model Management

DW

Database

Management

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• The database management subsystem

– Purpose:

• Handles personal and unofficial data

so that users can

experiment with

alternative

solutions based

on their own

judgment -

- sandbox like

• Tracks data use

within the DSS


12.0 DSS Architecture

• The model management subsystem (MMS)

– Strategic models: non routine mergers, impact analysis, capital budgeting

– Tactical Models: sales promotion planning

– Operational Models: routine-day-to-day production scheduling, inventory control, quality control



• Major functions of the model manager

– Creates models either from scratch or from existing

models

– Allows users to manipulate models so that they can

conduct experiments and sensitive analysis e.g.,

what-if or goal seeking analysis

– Manages and maintains the model base e.g.,

• Store, access, run, update, link, catalog and query


12.0 MMS

• The analytical engine or knowledge based

subsystem

– Component of more advanced DSS

– Provides expertise in solving complex

unstructured and semi-structured problems

• Expertise is provided for example by an expert system

– Analytical engines are usually based on OLAP, data

mining, or expert systems



• The user interface

– Interactive, dialogue oriented

– Intuitive, graphical, symbolic

– Intelligent, context aware

– Customizable

• For the non-technical user, the user interface is

the system



• Applications of DSS

– Marketing Models

– Supply Chain Management



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• Marketing decision processes are characterized

by a high level of complexity

– Simultaneous presence of multiple objectives

– Countless alternative actions resulting from the

combination of the major choice options

• Massive sales transactions data are available

making DSS a important tool for reaching

marketing intelligence



• Marketing intelligence comprises 2 prominent

topics

– Relational marketing (RM)

– Sales force management (SFM)



• Relational marketing as DSS application

– Designed to create, maintain, and enhance strong

relationships with customers

– Application of predictive models to support

relational marketing strategies

– E.g.:

• An insurance company wishes to select the most promising

market segment to target for a new type of policy

• A mobile phone provider wishes to identify those

customers with the highest probability of churning



• Why is RM important?

– It costs five times as much to attract a new

customer as it does to keep a current one satisfied

• Advertising doesn’t come cheap at all!

– It is claimed that a 5% improvement in customer

retention can cause an increase in profitability of

between 25-85% depending on the industry

– Likewise, it is easier to deliver additional products and

services to an existing customer than to a first-time

buyer


12.1 Relational Marketing

• RM strategies revolve around the following

choices



Relational marketing

Sales processes

Distribution channels

Products Services

Segments

Prices Promotion channels

• How do we implement RM?

– E.g., using pattern recognition and machine

learning models on a company’s DW

• Derive different segmentations of the customers which are

then used to

design and

target marke-

ting actions



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• Cycle of RM analysis, phases: 1. Exploration of the data available for each customer

2. Identify market segments by using inductive learning models

3. Knowledge of customer profiles is then used to design marketing actions

4. The designed actions are translated into promotional campaigns which generate in turn new information for subsequent analyses



Collect information on

customers

Plan actions based on knowledge

Identify segments and needs

Perform optimized and targeted

actions

• General statistics show…

– The average business never hears from 96% of its

unhappy customers

• 91% never come back

• Dissatisfied customers may tell 9-10 people about their

experience

– Every positive experience is told to 4-5 people

– For every complaint received the average business in

fact has 26 customers with a similar concern


12.1 Customer Relations

– Of the customers who register a complaint, as many

as 70% will do business again with your organization, if

the complaint is resolved effectively

• This figure goes up to 95% if the complaint has been

resolved quickly

– 40% of complaints are the result from customer

mistakes or incorrect expectations

– A complaint that is handled efficiently is

actually better than no complaint at all

• Customers who complain and get satisfactory results are

8% more loyal than the others



• Important part of RM is customer relationship

management (CRM)

• CRM

– The software tools which allow tracking and

analysis of each customer's purchases, preferences,

activities, tastes, likes, dislikes, and complaints

– Enterprise vendors/products

• Oracle/Siebel, Salesforce.com, Amdocs, Microsoft Dynamics

– Open source tools

• Opentaps, XRMS, SugarCRM



• E.g., XRMS

– Contact

information

screen



• Aspects of CRM systems

– Operational

– Collaborative

– Analytical



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• Operational CRM

– Provides support to "front office" business processes, including sales, marketing and service

– Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database when necessary

– Main benefits is that customers can interact with different people in a company over time without having to describe the history of their interaction each time


12.1 CRM

• Collaborative CRM

– Covers aspects of a company's dealings with customers

that are handled by various departments within a company

• E.g., sales, technical support and marketing

– Staff members from different departments can share

information collected when interacting with customers

• E.g., feedback received by customer support agents can provide

other staff members with information on the services and

features requested by customers

– Goal of collaborative CRM is to use information collected

by all departments to improve the quality of services

provided by the company


12.1 CRM

• Analytical CRM

– Analyzes customer data for a variety of purposes:

• Design and execution of targeted marketing campaigns to optimize marketing effectiveness

• Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention

• Analysis of customer behavior to aid product and service decision making e.g., pricing, new product development

• Management decisions, e.g. financial forecasting and customer profitability analysis

• Prediction of the probability of customer defection (churn)

• Acquisition? Cross-selling? Up-selling? Retention? Churn? Let’s see the lifetime of a customer


12.1 CRM

• Lifetime of a customer

– Lost proposal

• Before becoming a customer, an individual may receive

repeated proposals from the enterprise to win him/her

as a customer

– Acquisition

• The individual

becomes customer



– Cross/up-selling:

getting more business from current customers by

selling them additional or complementary

services

– Retention:

the continuous attempt to satisfy and keep current

customers actively involved in conducting business

• Highly satisfied customers are

– Less price sensitive

– More likely to talk favorably about you

– More likely to refer you to others

– Remain loyal for longer


12.1 Lifetime of a customer

– Churn (defection):

the percentage of customers who leave a business in

one year

– Interruption:

customers leaving a business. Possible reasons are that

they:

• Die

• Move away

• Leave for competitive reasons

• Are dissatisfied

• Quit because of an attitude of indifference



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– Dissatisfied?

• United Airlines Brakes Guitars



• Sales force management (SFM)

– Management of the whole set of people and roles

that are involved with different tasks and

responsibilities in the sales process

• Why SFM?

– It plays a critical role in:

• The profitability of an enterprise

• The implementation of the relational marketing strategy



• Designing the sales network and planning agents activities involve complex decision making tasks

– Remaining activities are operational and sales force automation (SFA) software can be used

• SFM decision-making process can be grouped in 3 components each interacting with each other

– Design

– Planning

– Assessment


12.1 Sales force management

Sales force management

Assessment & control

Planning Design

• Design

– During start-up phase or during restructuring

– Includes 3 types of decisions

• Organizational structure

• Sizing

• Sales territories



– Organizational structure

• May take different forms corresponding to hierarchical

agglomerations of agents by group, products, brand or

geographical area

• In order to determine the organizational structure it is

necessary to analyze the complexity of customers products

and sales activities

– Decide whether and to what extent the agents should be

specialized


12.1 Design

– Sizing

• Decide the number of agents that should operate in the

selected structure

• Depends on several factors

– Number of customers, prospects, sales area coverage, estimated

time for each call, the agents traveling time, etc.

• Conflicting goals

– Reduction in costs due to decreasing sales force size is often

followed by a reduction in sales and revenues


12.1 Design

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– Sales territories

• Deciding on assigning territories to

agents

• Depends on factors such as

– The sales potential of the geographical areas

– The time required to travel from an area to

another

– The availability time of each agent

• Purpose of assignment is to determine a balanced situation

between sales opportunities in each territory to avoid

disparities among agents


12.1 Design

• Planning

– Decision-making process involving the assignment of

sales resources structured and sized during design

phase, to market entities

• E.g., sales resources

– Work time, budget

• E.g., market entities

– Products

– Market segments

– Distribution channels

– Customers



• Assessment

– Measure the effectiveness and efficiency of the

individuals in order to decide incentives and

remuneration schemes

• Define adequate evaluation criteria that take into

account the personal contribution of each agent having

removed effects due to area or product characteristics



• Sales Force Automation software

– Most CRM tools include SFA functionality

– Enterprise vendors/products

• Oracle/Siebel, SAP, Salesforce.com, Microsoft Dynamics,

Netsuite

– Open source tools

• XRMS, SugarCRM, Vtiger



• For producing industries, another field of business operation is of great importance:

– Supply chain management (SCM)

• A supply chain summarizes the logistic and production processes of a single enterprise as well as a network of companies

– Covers the flow of materials and products from the raw material down to the end product at the customer

• Contains acquisition of raw materials, production, transportation, storage,



• Within a single company, internal supply chain

can be modeled and optimized

– Contain aspects of material purchase, production and

distribution

Relational Database Systems 1 – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 54


Internal Supply Chain

Purchasing Production Distribution Suppliers Customers

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• However, global supply chains may form

complex networks of various material flows

and costs



Main Plant

European Plant

Asian Plant Asian Suppliers

US Assembly

US Market

Asian Market

European Market

Recycling 1

Recycling 2

Asian Assembly

European Assembly

Kit Supplier

European Suppliers

US Suppliers

• Supply chain management is about managing

and optimizing those complex supply networks

– Eliminating excess inventory

– Improve on-time delivery performance

– Maximize the value of procurement

– Minimize transport costs

– Minimize storage costs

– Etc.



• Steps of SCM

– Plan (strategic portion of SCM) • Strategy for managing all the resources that go towards meeting

customer demand

• Developing a set of metrics to monitor the performance of the supply chain so that it is efficient, costs less and delivers high quality

– Source • Choose suppliers to deliver the goods and services

• Develop a set of pricing, delivery and payment processes with suppliers

• Create metrics for monitoring and improving the relationships

• Put together processes for managing goods and services inventory, including receiving and verifying shipments, transferring them to the manufacturing facilities and authorizing supplier payments



– Make (manufacturing step)

• Schedule the activities necessary for production, testing, packaging and preparation for delivery

• Most metric-intensive portion of the supply - measure quality levels, production output and worker productivity

– Deliver (the logistics part)

• Coordinate the receipt of orders, develop a network of warehouses, pick carriers to get products to customers and set up an invoicing system to receive payments

– Return

• Receive and manage defective or excess products

• Recycle used products





• For solving these tasks, SCM has to span across

most other enterprise management areas

– Thus, software

solutions are usually

very diverse and

customized

– Highly dependent

on data from

all branches of

business

Supply Chain Management

Supply Chain Strategy

Supply Chain Planning

Supply Chain Enterprise

Applications

Asset Management

Procurement

Product Lifecycle

Management

Logistics

• The traditional approach for optimizing supply chains was severely hampered by the unavailability of necessary data – Thus, usually only future demand was forecast as good

as possible, using statistical trending and “best fit” techniques – Trend Analysis and Trend Channels • Only high level data necessary (aggregated values from

OLAP cubes) – e.g. by weekly data by product category and customer group

• For dealing with unpredictability, security margins are added

• Based on the estimates, the supply chain could be optimized – Capacity Planning

– Bill of Material problems

– Network flow optimization

– etc.



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• However, due to improved data warehouse

strategies, more dynamic and fine-grained

optimizations are possible

– Forecasting at much finer-granularity (DW allows

for drilling into the data)

• e.g. calculate the best inventory level per article for each

store

• So called model stock



– Allows for new optimization techniques

• Simulation

• Stochastic models

– Include wider verity of metrics

• Stackability constraints

• Load and unloading rules

• Palletizing logic

• Warehouse efficiency

• “Shipping air” minimization



• Decision Support Systems

– Decision Making Process

– Decision Support

– Typical Applications

• Marketing Models

– Relational Marketing

– Sales force management

• Supply Chain Management

Data Warehousing & OLAP – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 63

Summary


Next week

• We have seen the theory, how about the praxis?

– Next week: practical problems in DW

– Guest: Toma Buchinsky, CEO Adastra,

Germany.

– Adastra Corporation specialized in

DW-based solutions and

Business Intelligence services.


The End

• I hope you enjoyed the lecture and learned at

least some interesting stuff…

– Next semester’s master courses:

Multimedia Databases, Information Retrieval,

Relational Databases 2

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 66

12 Thank You!

data warehousing with olap - tu braunschweig

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