Advances in BI

1
Advances in BI

• Why Data Mining?
• 2. Expert Systems A Tool for Sifting
  Through Mountains of Data- Case Example Ocean
  Spray Cranberries
• 3. Data Mining Models- Association,
  Sequential Patterns, Classification, Clustering
  and Predictive Models
• 4. Data Mining Techniques- Decision Trees,
  Rules Induction, Regression Neural Networks
• 5. Text Mining for Unstructured Data
• 6. Business Activity Monitoring A Priority
  Today

2
Why Data Mining ?
Now that we have gathered so much data, what do
we do with it? The datasets are of little
direct value themselves. What is of value is the
knowledge that can be inferred from the data and
put to use.

• Data volumes are TOO BIG for traditional DSS
  Query/ Reporting and OLAP tools.
• Organizations have to get value from the huge
  investments of time and money made in building
  data warehouses.

3
Discover the Diamonds in Your Data Warehouse

• Maximize your ROI on data warehousing data
  marts by enabling your decision makers to exploit
  your customer data for competitive advantage
• This web-enabled, point-and-click approach lets
  you employ OLAP, neutral networks, churn
  analysis, and many other visualizations and
  analytical techniques to improve
• Customer retention
• Target key prospect
• Profile market segments
• Detect fraud
• Analyze customer response, and much more
• Source Ads of BI vendors

Without BI, your DW is.. .. Well, a warehouse
full of data
4
The Economics of Attention

• A wealth of information creates a poverty of
  attention.
• - Nobel prize- winning economist, Herbert
  Simon
• Problem NOT Information Access
• BUT Information Overload
• Challenge Locating , Filtering Communicating
• What is useful to the user

5
Why is Data Mining a Hot Topic Today?

1. Implementation of ERP, CRM SCM systems have
   resulted in vast stores of operational data.
2. Emergence of global competition has put the
   pressure on companies to be data- driven
   i.e., make informed decisions based on facts and
   not hunches.
3. The speed of change in the marketplace demands
   that the pearls of actionable information have to
   be found faster in the ocean of data, for
   companies to be one step ahead of competition.
4. The hardware needed to store and process a ton
   of data was prohibitively expensive until
   recently You would have had to have NASA at
   your disposal. Today, the technology makes it
   feasible to apply complex models to ferret out
   patterns previously left to rot in data jails.

6
The Payoff from Data Mining- Two Examples

• Farmers Insurance
• Based on traditional data analysis, drivers of
  sports cars were determined to be at higher risk
  for collisions than drivers of safe cars such
  as Volvos
• Hence charged them more for car insurance
• Data mining discovered a pattern that changed the
  pricing policy.
• .. As long as the sports car was not
  the only car in the household, the driver fit the
  profile of the safe family car driver, not the
  risky sports car driver.
• Walgreen (A large Retailer)
• In the past, success of promotional offers such
  as 2-for-1 sales was measured primarily by
  product sales..
• .. With data mining, Walgreen can see
  what other items are selling with its promotional
  offers
• .. Tuned its programs to put things on
  sale that people tend to buy in tandem with
  high-margin items.

7
What are Expert Systems?

• A technology that enables expertise to be
  distributed throughout a firm without the
  presence of the human expert
• Rule-Based System
• If This, Then That
• Rules are determined from expert knowledge and
  programmed in the software
• An HR Application
• Screening a large number of resumes for
  relatively low-level positions with well-defined
  and precise skill requirements
• - e.g., Call Center Agents
• Expert System can weed out applicants who do not
  meet the requirements

8
Applying Expert Systems To Extract News from
Scanner Data

• The Promise Better Data for Tracking Market
  Shares
• Compared to Retail Store Audits
• Frequency Weekly vs. Bimonthly
• Level of Detail UPCs vs. Brands
• Scope Top 50 Markets vs. Regions
• The Problem Too Much Data
• At least 100 times more data
• The Result Impossible to Use the Quality
  Data

9
"CoverStory"- An Expert System Replaced the
Human Analyst

• Before . . .
• ? Companies circulated top-line reports,
  including tables and charts from the retail store
  audit data. An analyst prepared the cover
  memo highlighting important news in the data.
• Now. . .
• Not feasible to have an army of analysts to
  sift through the mountain of scanner data.
  Instead, "CoverStory" automatically writes this
  memo!
• a model-imbedded expert system extracts the news
• includes a built-in thesaurus to eliminate
  repetitious wording

10
Case ExampleOcean Spray Cranberries

• A 1 billion grower-owned agricultural
  cooperative
• Lean IS staff
• Only one marketing professional for analyzing the
  tracking data
• Scanner data for juices is imposing
• -- 400 M numbers covering up to 100 data
  measures, 10,000 products, 125 weeks and 50
  geographic markets
• -- Grows by 10 million new numbers every four
  weeks

11
Impact of CoverStory

• Enables a department of one to alert all Ocean
  Spray marketing and sales managers to key
  problems and opportunities and provide
  problem-solving information
• Being done across 4 business units handling
  scores of company products in dozens of markets
  representing hundreds of millions of dollars of
  sales
• System is totally integrated into business
  operations because it delivers information of
  competitive value in running the business

12
Tools to Get Value from Data Warehouses

• Business Intelligence Tools
• To enable users without programming skills to
  analyze the raw data in the data warehouse.
• Ad Hoc Query / Reporting
• OLAP Tools to slice and dice data.
• Data Mining Tools
• Automate the detection of patterns in the data
  warehouse
• Build models to predict behavior through
  statistical and machine-learning techniques.

13
Data Mining Not Limited to Discovery

• i.e., finding an existing nugget of gold in
  the mountain of data,
• Data Mining used for Prediction also
• Telling you not just where the gold is today,
  but where the gold might be tomorrow
• Predict what is going to happen next based on
  what we have found.
• From the moment I signed up for my Total
  Rewards card in the casino lobby and filled in my
  name, address, date of birth and drivers license
  number, Harrahs had a pretty good hunch that my
  long term potential was already low I was a 32-
  year old man from the distant state of Montana
  did not fit the profile of a high- value
  customer!
• Age, gender and distance from the casino were
  identified through data mining as critical
  predictors of frequency of visiting casinos.

14
Knowledge Discovery in Databases- Steps in KDD
process
Data Warehouse
Selection
Target Data
Cleaning
Pre - processed Data
Data reduction
Transformed Data
DATA MINING
Patterns
Evaluation Interpretation
Knowledge
Source Communications of the ACM, 1996
15
Data Mining is One Step in the KDD Process

• Determine patterns from observed data to solve a
  business problem.
• Step 1 Identify the Business Problem
• - e.g., Who are good customers?
• Which customers are likely to leave?
• Step 2 Choose Model or Goal for Data Mining
• - Some models are better for predictions while
  others are better for describing behavior
• Step 3 Choose Technology to Build Model
• Step 4 Apply the Algorithm (Computation process)
  to Data. Review the results and
  refine the Model
• Step 5 Validate the Model on New Data (the
  hold-out dataset)

16
Data Mining Models

• Association
• If customer buys spaghetti, also buys red wine in
  70 of cases
• Sequential Patterns time or event based
• A customer orders new sheets and pillow cases
  followed by drapes in 75 of the cases
• Classification
• Opera ticket buyers are usually young urban
  professionals with high income while country
  music concert ticket purchasers are typically
  blue collar workers
• Clustering
• Discovers different groups in the data whose
  members are very similar
• Predictive Models
• Relate behavior of customers (dependent
  variable) to predictors (independent variables
  felt to be responsible for the dependent one)

17
Association Models for MarketBased Analysis

• Model finds items that occur together in a given
  event or record
• Discovers rules of the form
• If item A is part of an event, then X of the
  time (confidence factor), Item B is part of the
  event.
• Used to discover patterns of items bought
  together from the mountain of scanner data
• Example
• If a customer buys corn chips, then 65 of the
  time, also buys cola
• Unless there is a promotion, in which case buys
  cola 85 of the time.

18
Sequential Patterns

• Similar to Association Models, except that the
  relationships among items are spread over time.
• Sequences are associations in which events are
  linked by time
• Require data on the identity of the transactors
  in addition to details of each transaction.
• Example
• If surgical procedure X is performed, then 45
  of the time infection Y occurs within 5 days
• But after 5 days, the likelihood of infection Y
  drops to 4

19
Classification Models - Most Common Data Mining
Model

• Describe the group that a member belongs to by
  examining existing cases that already have been
  classified, and inferring a set of rules
• These IF-THEN rules are often depicted in a tree
  like structure
• Examples
• - What are the characteristics of customers who
  are likely to switch to a rival telecom service
  provider?
• - Which kinds of promotions have been effective
  in keeping which types of customers so that you
  can target the right promotion to the right
  customer?

20
Clustering Models

• Segment a database into different groups whose
  members are very similar
• - Similar to Classification except that no
  groups have yet been defined
• The Clustering model discovers groupings within
  the data
• You do not know what the clusters will be when
  you start, or on what attributes the data will be
  clustered.
• Hence, a user who is knowledgeable in the
  business needs to interpret the clusters.
• Example
• Xerox has developed predictive models using
  clusters for analyzing usage profile history,
  maintenance data, and representations of
  knowledge from field engineers to predict
  photocopy component failure.
• An email is sent to the repair staff to schedule
  maintenance PRIOR to the breakdown
• Root Cause Analysis enables a prescription
  for what to do about a problem

21
Predictive Models

• Combine predictors (or independent variables)
  in a model relating them to the variable to be
  predicted (dependent or predictive variable)
  using historical data on the predictors and the
  predictive variable training data set
• Resulting model is used to predict the value for
  new data that does not include the predictive
  variable.
• Example 1 Predefined Predictors
• If the customer is rural and her monthly usage is
  high, then the customer will probably renew.
• If the customer is urban and new feature
  exploration is high, then the customer will
  probably not renew.
• Example 2 Customer Profiling
• We can tell the profile of someone who is about
  to have a baby by what purchases they make
• We can then compare that profile with those of
  others who are moving into baby space to
  predict needs. For instance, such a customer may
  be a good target for a life insurance sales
  pitch.

22
Data Mining Techniques- Decision Trees

• Derives rules from patterns in data to create a
  hierarchy of IF-THEN statements, called a
  Decision Tree, to classify the data.
• Segments the original data set
• Each segment is one of the leaves of the tree
• Records in each segment are similar with regard
  to the variable of interest
• Example Classification of Credit Risks

23
Pros Cons of Decision Trees

• How to handle continuous sets of data, like age
  or sales?
• Ranges have to be created such as 25-34 years,
  35-44 years, etc.
• This grouping of ages could inadvertently hide
  patterns
• e.g., a significant break at 30 could be
  concealed
• Crux of the Tree- Growing Process
• What is the best possible question to ask at each
  branch point of the tree?
• e.g., The question are you over 35? may not
  distinguish between churners and those who are
  not if the spilt of people over 35 is 40 for
  churners 60 for others. The goal is to get a
  90-10 (10- 90) spilt in the segment of people
  over 35 years.
• The algorithms look at all possible
  distinguishing questions and the sequence of
  asking them that could break up the training
  data set into segments that are nearly
  homogeneous with respect to the variable to be
  predicted. They stop growing the tree when the
  improvement is not substantial to warrant asking
  the question.

24
CART Classification and Regression Trees- A
Popular Statistical Package for Decision Trees

• CART begins by trying all the questions for
  grouping the population and picks the best one
  that splits the data into two or more organized
  segments that decrease the disorder of the
  original population as much as possible.
• Then, CART repeats the process on each of these
  new segments individually.
• The algorithm not only discovers the optimally
  generated tree but also has the validation of the
  model on new test data (holdout sample) built in.
• The most complex tree rarely fares the best on
  the holdout sample because it has been
  over-fitted to the training data set. The tree is
  pruned back based on the performance of the
  various pruned versions on the test data.

25
CHAID Another Statistical Tool for Decision Trees

• Chi-Square Automatic Interaction Detector
• Relies on the Chi-Square test used in
  contingency tables obtained by cross-tabulating
  the data on say, churners and non-churners by
  predictors, which have to be categorical such
  as age groups
• Less than 20, 20-29, 30-39, etc.
• It determines which categorical predictor is
  furthest from independence with the prediction
  values of churners and non-churners.
• Problem Continuous variables such as age have to
  be coerced into a categorical form how many
  categories? where should the splits be?

26
Decision Tree for Segmenting Customers- Who
Responded to a Marketing Campaign
Overall 7 of Customers Responded
Segment of Customers Who Rent with High Family
Income and No Savings A/c 45 response Target
this segment for Future Direct Marketing Campaign
27
Data Mining Techniques- Rule Induction

• Most common form of knowledge discovery in
  unsupervised learning systems
• Rule IF this and this and this, THEN that-
  Accuracy or Confidence How often is this rule
  correct?- Coverage How many records does this
  rule apply toHigh Coverage means that the rule
  can be used often and is less likely to be an
  idiosyncrasy of the data set
• Examples
• Rule Accuracy Coverage
• If cereal purchased, Then milk is purchased
• If bread, Then Swiss Cheese
• If 40-45 yrs and purchased, pretzels and peanuts,
• Then beer purchased
• Left Side of Rule (before THEN) Antecedent
  (Can Have Multiple Conditions)Right Side of Rule
  (after THEN) Consequent (Only ONE Condition)

85
20
15
6
95
0.01
28
Rule Coverage vs Accuracy
Coverage High Accuracy Low Rule is rarely correct, BUT can be used often Accuracy High Rule is often correct AND can be used often
Coverage Low Rule is rarely correctAND can only rarely be used Rule is often correctBUT can only rarely be used
Total of baskets in database 100 with eggs
30 with milk 40 with both eggs and milk
20 Rule IF Milk, THEN Eggs Rule IF Eggs,
THEN Milk Accuracy 20/40 50 Accuracy
20/30 67 Coverage 40/100 40 Coverage
30/100 30
29
What To Do With A Rule?

1. Target the Antecedent- All rules with a certain
   value for the antecedent, e.g., nails, bolts and
   screws, are presented to a retailer- Would
   discontinuing the sale of these low-margin items
   have any effect on sales of higher margin
   products, e.g., expensive hammers?- ExampleA
   British supermarket was about to discontinue a
   line of expensive French Cheeses which were not
   selling well.But data mining showed that the few
   people who were buying the cheeses were among the
   supermarkets most profitable customers so it
   was worth keeping the cheese to retain them.
2. Target the Consequent- Understand what affects
   the consequent, say, purchase of coffee- Put
   those items near the coffee on the store shelves
   to increase sales of coffee and those items-
   ExampleSales of diapers and beer were found to
   be highly correlated in shopping transactions
   between 5pm and 7pm young fathers dropped in at
   the stores to pick up diapers, and decided to
   stock up the latter at the same time hence put
   the beer display near the diapers

30
Rule Induction vs. Decision Trees

• Decision Trees One AND ONLY One Rule for a
  Record- All records in training data set will be
  mutually exclusive (non-overlapping) segments-
  Supervised learning where the outcome is known
  for each record in the training data set. e.g.,
  Was the person a good risk or a bad risk?-
  Process trains the algorithm to recognize key
  variables and values that will be used for
  predictions with new data.
• Rule Induction May be Many Rules for a Record-
  Not guaranteed that a rule will exist for every
  possible record in the training data set- Will
  not partition the data into mutually exclusive
  segments a particular record may match any
  number of rules, including no rules at all- More
  commonly used for knowledge discovery in
  unsupervised learning than prediction- Rules are
  generally created by taking a simple high-level
  rule, and then adding new constraints to it until
  the coverage gets so small that it is not
  meaningful

31
When to Use What?

• Decision Trees- Create the smallest possible
  set of rules for a predictive model - work from
  a prediction target downward in what is known as
  greedy search look for the best possible
  split on the next step, greedily picking the best
  one without looking any further than the next
  step- If there is overlap between two
  predictors, the better of the two would be
  picked. e.g., height might be used instead of
  shoe-size as a predictor whereas both could be
  used as antecedents in a rule induction system-
  Traditionally used for exploration to determine
  the useful predictors to be fed on the second
  pass of data mining into prediction models using
  statistical techniques or neural networks
• Rule Induction- Yields a variety of rules with
  different predictors even if some are
  redundant.- Even though height and shoe size are
  highly correlated, both could be preset as
  antecedents in two different rules in contrast,
  the decision tree would pick the better of the
  two predictors- Mainly used to discover
  interesting patterns in the data

32
Data Mining Techniques- Regression Models

• Statistical models which link predictors or
  independent variables to the variable to be
  predicted or dependent variable
• User has to select the predictors and define the
  structure of the linkage
• e.g., a linear model linking the predictor,
  Customers Annual Income (Y) to the variable to
  be predicted, Average Customer Bank Balance,
  (X)Y a bXThe constants, a and b in the
  above model, are called parameters that specify
  the shape of the line relating X and Y.
• The parameters are calculated so as to minimize
  the sum of squares of the forecast errors when
  the model is applied to the training or
  model-fitting data set of X values and
  corresponding actual Y values The least
  squares method uses calculus to derive the
  formulas for the parameters a and b.

33
Validation and Refinement of Regression Models

• R-Squared value is calculated to show the
  goodness of fit of the predicted Y values from
  the model to the actual Y values in the data
  set.e.g., a value of 0.87 means than 87 of the
  variation in y was explained by the model
• Acid test of the model is to apply the fitted
  model to new data not used to calculate the
  parameters (a and b) of the model the
  hold-out or validation data set
• Refine the model, if necessary, to make better
  predictions Add multiple predictors (multiple
  regression models) Transform predictors by
  squaring, taking logarithms etc (non-linear
  models) Combine predictors by multiplying or
  taking rations(e.g., ratio of annual household
  income to family size)
• If dependent variable is a response variable with
  just Yes/No or 0/1 values, a different model
  called logisitic regression model is used.

34
Data Mining Techniques- Neural Networks

• Based on the concept of the human brain in that
  it learns- originally developed for military
  applications to tell whether a speck on a screen
  is a bomber or a bird, and discriminate between
  decoys and genuine mistakes- now, the same
  technology can separate good customers from bad
  ones
• Network composed of a large number of neurons
  (or processing elements) tied together with
  weighted connections (synapses)- A collection of
  connected notes, each having an input and an
  output, and arranged in layers.- Between the
  visible Input Layer and final Output Layer, there
  could be a number of hidden processing layers

35
Structure of a Neural Network

• A neural network uses a training data set to
  produce outputs from inputs, which are then
  compared with the known output. A correction is
  then calculated for the discrepancy in the output
  and applied to the processing in the nodes in the
  network
• The process is repeated until its stopping
  condition such as deviations being less than a
  prescribed amount is reached

36
A Simple Example
No Default
vs Actual value of 0
0.47(0.7) 0.65(0.1) 0.39

• Link weights (0.7 0.1 in the above example) are
  adjusted to correct for the deviation between the
  output of the processing (0.39 in this case) and
  the actual value (0 in this case)
• Large errors are given greater attention in the
  correction than small errors

37
How do Neural Networks Learn?
Compute Output
Desired Output Achieved?
Adjust Weights
No
Yes
Stop
38
Pros and Cons of Neural Nets

• Pros
• Data-driven
• Used when expertise is hard to codify, but good
  results are known
• Works well when the technique is customized for a
  well-defined problem such as- Credit Cards
  Fraud Detection (HNC Softwares Falcon System)-
  Direct Marketing Campaigning (ASAs ModelMAX)
• After the technique has proven to be successful,
  it can be used over and over again without a deep
  understanding of how it works
• Cons
• Hard to interpret weights and neuron
  relationships
• Not easy to use- All the predictors must have
  numeric values- Output is also numeric and needs
  to be translated if the final output variable is
  categorical such as the purchase of blue or white
  or black jeans

39
How to Evaluate a Data Mining Product

1. What kind of business problem does it address?
2. What technique does it use to model the data?
3. How does it handle categorical data and
   continuous data?
4. How sensitive is it to noise data?
5. How does it avoid the problem of overfitting
   the model?
6. Does it have a built-in process for validating
   the model on the holdout data?
7. Is the user interface easy to understand and use?
8. How long does it take to get useful answers from
   the data?
9. How clear are the results to interpret?
10. ABOVE ALL, TEST DRIVE THE PRODUCT ON YOUR DATA!

40
Text Mining An Imperative Today
We are drowning in information, but are
starving for knowledge

• Unstructured data, most of it in the form of
  text files, typically accounts for 85 of an
  organization's knowledge stores, but its not
  always easy to find, access, analyze or use.

41
New Generation of Text Mining Tools

• to extract key elements from large
  unstructured data sets, discover relationships
  and summarize the information
• Categorization
• Presents the search results in categories,
  rather than an undifferentiated mass.
• Clustering
• Grouping similar documents based on their
  content.
• Extraction
• Extracting relevant information from a document
• e.g., pulling out all the company names from a
  data set.

42
New Generation of Text Mining Tools

• Keyword Search
• Searching documents for the occurrence of a
  particular word or set of words.
• Natural-Language processing
• Determining the meaning of written words taking
  into account their context, grammar, etc.
• Visualization
• Graphically presenting the mined data as
  relationships are easier to spot and understand.

43
Case Example of Text Mining- Dow Chemicals BI
Center

• Using ClearResearch software to extract data from
  a centurys worth of chemical patent abstracts,
  published research papers and the companys own
  files.
• By eliminating the irrelevant, weve been able
  to reduce the time it takes for researchers to
  find what they need to read.
• ClearResearch uses a proprietary pattern-matching
  technology to search for information, categorize
  it and show its relationship to other data.
• The software can see, discover and extract
  concepts, not just words. It gives us a
  pictorial representation of the text in the
  document in an easy-to-understand chart

44
Case Example of Text Mining- Air Products
Chemicals Knowledge Management System

• Company has over 18,000 employees in 300
  countries, and more than 600 intranet and
  extranet sites.
• Its file servers contain 9TB of unstructured
  data, excluding email or anything stored on local
  drives.
• Using SmartDiscovery to generate a catalog and
  index of the data repository so that it can be
  more easily accessed by MS SharePoint Portal
  Document Management System.
• Also using the software for Sarbanes-Oxley
  compliance and e-learning since by correctly
  categorizing the data, business rules can be
  applied to a category of documents rather than to
  individual documents
• e.g., if a document relates to operations
  covered by SOX, then the appropriate
  data-retention policies are applied to it.

I call it the central nervous system for what we
are doing with knowledge management.
45
Text Mining Tools

• Come either as stand-alone products or
  embedded as part of a larger software system
• Database vendors Oracle, IBM,
• Incorporating pattern-matching algorithms into
  their database products
• Data Mining vendors SAS, SPSS,
• Added text mining to their portfolios.
• Enterprise Search Engine Vendors Autonomy,
  Verily,
• Specialized Text Mining Firms Inxight Software,
  Stratify

Installing SAS Text Miner is a simple process-
just needed to load 6 CDs on my workstation
Hard part Get meaningful results - Depends on
the skill and knowledge of user to properly
interrogate text repositories We are getting an
increasing understanding of what things are
possible with text mining. But there is a huge
skills problem in this area, which is why it
hasnt gotten much traction so far- Gartner
46
Dec 2003 Report of Gartner
Text Mining Will revolutionize CRM Strategies by
2008 Companies will retire older technologies
such as IVR, and redesign customer-facing
processes.

• Text Mining has not been well coupled with
  clearly recognized pain points in the
  organisation. Customer service has been mainly
  handled in call centers, with an emphasis on
  transaction processing and short interaction
  times. As a result, most firms have been missing
  valuable input from customers on how to improve
  their business processes. This has led to low
  levels of customer satisfaction, little long-term
  loyalty and an expensive, albeit necessary, way
  of resolving customer complaints
• Blended service delivery models using text
  mining, telephone and web services will enable
  companies to identify not only what the customer
  said, but also what was meant will be able to
  spot and resolve problems earlier improve their
  ability to prevent problems recurringimproved
  measurement of customer satisfaction over todays
  flawed survey methodology.

47
Business Activity Monitoring (BAM)

• Automated monitoring of business-related activity
  affecting an enterprise
• Report on activity in the current operational
  cycle, e.g., the current hour, day or week.
• Designed to spot problems early enough to head
  them off.
• BAM is not a new concept
• Credit Card companies have had real-time fraud
  monitors for years.
• Manufacturers have real-time error-detection
  software built into their assembly lines.
• Proactive or Reactive?
• The conventional wisdom has been to just take
  transactional data and move it to the data
  warehouse and then to the BI System. But these
  systems arent responsive
• Monitoring business activity after the fact is
  too late to head off a problem such as a missed
  deadline or the loss of a major customer.
• BAM systems pluck the data in real time from the
  applications where it originates - order entry,
  accounts receivable, call centers, etc. Output in
  variety of forms dashboards, e-mails, pager
  alerts,

48
GEs Real-Time Dashboard

• GEs aim is to monitor everything in real time,
  GEs CIO explains, calling up a special web page
  on his PC a digital dashboard. From a distance
  it looks like a Mondrian canvas in green, yellow
  and red. A closer look reveals that the colors
  signal the status of software applications
  critical to GEs business. If one of the programs
  stays red or even yellow for too long, he gets
  the system to e-mail the people in charge. He can
  also see when he had to intervene the last time,
  or how individual applications such as programs
  to manage book-keeping or orders have performed.
• As CIO, Mr. Reiner was the first in the firm to
  get a dashboard, in early 2001. Now most of GEs
  senior managers have such a constantly updated
  view of their enterprise. Their screens differ
  according to their particular business, but the
  principle is the same the dashboard compares how
  certain measurements, such as response times or
  sales or margins, perform against goals, and
  alerts managers if the deviation becomes large
  enough for them to have to take action.

49
BAM Case Example- Davis Controls Ltd. (Canada)

• Every afternoon, at 430 pm, a screen pops up on
  the CEOs PC with important news
• How many orders the company booked
• Names of customers who have gone past 90 days
  without paying
• Orders that have missed delivery promises
• PLUS 15 Daily E-mail Alerts, e.g.,
• Which salespeople have not logged in that day to
  download the latest data from a corporate
  database about the customers in their territories
  Sometimes those remote sales guys will just sit
  out there in never-never land, and as long as
  they think no one is watching, they will march to
  their own drummer.
• When a promised order-delivery is missed, one
  e-mail alert is generated for the responsible
  salesperson, one goes to a customer with an
  apology, and one goes to an expediter Different
  e-mails go to new customers, depending on the
  size of their initial orders.

50
BAM Case Example- Davis Controls Ltd. (Canada)

• Use Macola Enterprise Suite, an ERP package from
  Exact Software, a subsidiary of a Dutch Company
• Includes the Exact Event Manager, a BAM product
  that triggers alerts and reports on activity and
  non-activity, both inside and outside the ERP
  system.
• BAM enables me to manage the Company more
  proactively. Before, Id have to wait until a
  customer called with a complaint or the month-end
  financial reports to really get a feel for how
  the business was doing.

51
BAM Case Example- A Fortune 100 Financial
Services Firm

• Uses SeeRun Platform, a suite of products from
  SeeRun Corp. in San Francisco
• To monitor some 50,000 cases per year where the
  firm has signed contracts with its clients
  guaranteeing performance against operational
  metrics relating to dozens of milestones in the
  contracts.
• If a task is supposed to be completed within 24
  hours but isnt, an alert is generated for the
  appropriate manager.
• Even more helpful is receiving live
  activity-tracking along the way at 6 hours, 12
  hours, 18 hours and so on.
• Benefits
• Improved Performance Reduced Expenses
• Serves also as a marketing tool to show
  prospective clients
• Biggest Challenge What To Do With All the Data
• You can actually over engineer something like
  this. If you get too many stakeholders involved,
  everyone wants their own particular metric. We
  have been able to keep it focused and simple.

52
BAM Case Example- The Albuquerque City Government

• Uses NoticeCast from Cognos
• To proactively push e-mail notices of important
  events, in near real time, to city employees,
  residents vendors
• NoticeCast sits outside the citys firewall on an
  extranet and monitors events by periodically
  querying Oracle tables populated by municipal
  systems.
• Vendors
• Sends an e-mail to each vendor that was issued an
  electronic payment during the night.
• Directs the vendor to a Website on the extranet
  where it can get a remittance report
• Residents
• Sends an e-mail to each residents for whom a
  water-bill was produced with all the pertinent
  billing info
• Directs the resident to a Website where he may
  pay his bill online
• City Employees
• Once-a-day e-mails to certain employees letting
  them know of all online payments made to the city
  during the past 24 hours gt whenever a candidate
  files a contribution report, NoticeCast sends an
  e-mail to city employees responsible for tracking
  campaign law compliance

53
Whats Next for BAM?

• Will become tightly coupled to Business Process
  Management (BPM) systems
• Send Alerts in a publish/subscribe model to lots
  of BPM systems throughout the enterprise.
• Events go in and alerts come out, but those
  alerts just become events in other applications
• Example
• A BAM system could generate an alert that the
  estimated date of a package delivery had slipped.
• A CRM system and a BPM system might each
  subscribe to such package due-date change
  alerts, extending the usefulness of the alerts.

54
Whats Next for BAM?

• More sophisticated rules of logic will be
  included in BAM capable of finding hidden
  patterns in current business activity by doing
  on-the-fly analyses of historical data.
• If a process is beginning to go South, the early
  birds of that are hard to see. Eventually, well
  see BI BAM married at the level of using
  historically recorded data to identify problems
  much earlier.
• Even further out lies the Holy Grail of BAM When
  a system not only sees a problem coming but also
  goes beyond alerts to actually fixing the
  problem.
• e.g., automatically reordering a part when it
  sees that a shipment has been lost an example
  of autonomic response, a self-learning system.

55
An Example of Autonomic Response

• 10 years ago If you were a good customer, FedEx
  shipped you a PC and allowed you to dial into
  their network
• 5 years ago You could get the shipping
  information from any browser
• Customers now want shipping information on their
  order status screen
• Tomorrow's Scenario

FedEx plane containing your package is snowed in
Cincinnati
FedEx system knows your package will not arrive
in the morning
A Web service can send you early notice of a
non-delivery through the CRM system
Business process for supply chain looks for an
alternate supplier, if you cannot wait for the
package