Data Warehousing Version 6.0 - 04/18/2000

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Data WarehousingVersion 6.0 - 04/18/2000
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Can your database answer questions like these?

• What is the cost of staff to break into a new
  line of business?
• What are the travel routes of my competitions
  inventory?
• At what velocity is my competitor moving toward a
  common goal?
• How will a transaction on a certain date be
  affected by currency exchange rates?
• Is a foreign labor source likely to produce a
  higher quality product?
• Which 20 of the problem creates 80 of the
  problems?

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Can your database answer questions like these?

• By product and location, how can we regain a lost
  customer base?
• Which skill and staff levels are most likely to
  accept the voluntary layoff package?

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Data is Difficult to Manage

• Amount of data increases exponentially past data
  must be kept for long times new data are added
  rapidly.
• Data are scattered throughout organizations
  collected by many individuals using different
  methods and devices.
• Only small portions of an organizations data are
  relevant for specific decisions.
• An ever-increasing amount of external data needs
  to be considered in making organizational
  decisions.

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Data is Difficult to Manage - contd.

• Raw data may be stored in different computing
  systems, formats, and human and computer
  languages.
• Legal requirements relating to data differ among
  countries and change frequently.
• Selecting data management tools can be difficult
  because of the large number of tools available.
• Data security, quality, and integrity are
  critical, yet easily jeopardized.

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Data and Knowledge Managementment

• Businesses do not run on data. They run on
  information and their knowledge of how to put
  that information to use successfully.

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Some Information Concepts

• Data Unorganized facts and figures. (raw
  material)
• Information Data that has been processed into a
  form that is meaningful to the recipient and is
  of real of perceived value in current or
  prospective actions or decisions.
• Information
• adds to a representation
• corrects or confirms previous information
• has surprise value in that it tells us
  something we did not know, or could not predict.
• What is a finished product to one, may be raw
  materials to someone else.

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Definitions Information vs. Knowledge

• Knowledge a combination of instincts, ideas,
  rules, and procedures that guide actions and
  decisions.
• Helping to provide the best available knowledge
  to decision-making is another role of information
  systems

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Relationship Between Data, Information, and
Knowledge

• The difference between data and information is
  easy to remember.
• It is often cited as the reason why systems that
  collect large amounts of information fail to meet
  managements information needs.
• There are many methods of converting data into
  information for decision making.
• Managers take action based on information about a
  current situation plus their accumulated
  knowledge. Actions taken feed the process of
  accumulating more knowledge (experience).
• Example How do medical students become competent
  physicians?

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Relationship Between Data, Information, and
Knowledge
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Attributes of Quality Information

• Timeliness
• Completeness
• Conciseness
• Relevance
• Accuracy
• Precision
• Appropriateness of Form

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Special Characteristics of Information

• Usefulness - depends on combination of
  quality,accessibility,and presentation.
• One persons information may be another persons
  noise.
• Soft data may be as important as hard data.
• Ownership of information may be hard to maintain.
• More information is not always better
  (information overload).
• Politics can often hide or distort information.

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Sources of Data

• Internal Data
• Data about people, products, services, processes.
• Often stored in corporate data bases (e.g. Sales
  or HR).
• Some data may be disparate in different regions,
  but accessible by networks.
• Personal Data
• Individuals document expertise by creating
  personal data - subjective estimates. Some is
  kept in heads, or mental models.
• Can be store on PCs, or available on the Web.
• External Data
• Many sources.

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Some Sources of Business External Data

• Federal Publications
• Survey of Current Business
• Monthly Labor Review
• Federal Reserve Bulletin
• Employment and Earnings
• Commerce Business Daily
• Census Bureau
• Other
• International Monetary Fund
• Moodys
• Standard Poors
• Advertising Age
• Dialog and Lexis/Nexis

• Other-contd.
• ABI/Inform
• Annual Editor Publisher Market Guide
• Thomas Register On-line
• Indexes
• Encyclopedia of Business Information Sources

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What is a data warehouse?

• A data warehouse is a pool of data organized in a
  format that enables users to interpret data and
  convert it into useful information to gain
  knowledge from this interpretation.
• It is a single place that contains complete and
  consistent data from multiple sources.
• Data warehousing is the act of a business person
  extracting business value from the data stored in
  the data warehouse.

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Collecting Raw Data

• Is not Easy
• collect in the field
• elicit from people
• collect manually, electronically, or by sensors.
• Data collection technology has not kept pace with
  advances of data storage technology.
• Data collection from external sources is not easy
  either.
• Bottom Line Garbage IN, Garbage OUT - GIGO.
• Data Quality is an Important Issue.

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Data Quality Issues

• Intrinsic DQ
• accuracy, objectivity, believability, reputation.
• Accessibility DQ
• Accessibility and access security
• Contextual DQ
• relevancy, value-added, timeliness,completeness,
  amount of data.
• Representation DQ
• Interpretability, ease of understanding,concise
  representation, consistent representation

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Why Data Warehousing?

• Managers do not make decisions that are good or
  bad, they make decisions on the basis of good
  or bad information.
• Management information
• a. the right information
• b. in the right form
• c. at the right time.
• Most transaction-based information systems have
  difficulty delivering this information.

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Why Data Warehousing - 2

• Not the right information
• data not easily accessible
• meaning is subtly (or significantly different)
  from the question context.
• Information is presented with too much or too
  little detail, covers the wrong time spans, or is
  in the wrong intervals.

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Why Data Warehousing - 3

• Not the right time
• Getting this information may require the efforts
  of highly skilled professionals who are not
  generally available at the whim of business
  managers.
• Data comes from a variety of different systems
  which are resident on a variety of different
  technology platforms.

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Why Data Warehousing - 4

• Not the right format
• If data is extracted, merged, and converted into
  a meaningful information, often it is not in a
  usable format.
• Users will want it loadable into a particular PC
  tool or spreadsheet with which he/she is
  familiar.
• Printouts weighing 10 pounds are not in the right
  format.
• a diskette with a COBOL file description is not
  in the right format.

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The Dilemma for Corporate IT

• How to control scarce IT resources consumed by
  insatiable user demand for ad-hoc reports.
• Each ad-hoc report generated by IT and analyzed
  by the user generates three more reports to
  further illuminate the insights gleamed in the
  first.
• Often the extract programs have few reusable
  components.
• The user is on a voyage of discovery in a sea of
  data.

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The Response of Corporate IT

• New methodologies Align the IT systems with the
  business goals and requirements.
• These techniques concentrate on business process
  requirements, not decision support requirements.
• Transaction systems must be rigorously specified
  in advance. The are an intersection between the
  organization and the customer.
• These systems should not be a voyage of
  discovery for either.

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Transaction Systems vs. Analytical Support Systems

• Transaction Systems
• Insert an order for 300 baseballs
• Update this passengers airline reservation.
• close-out accounts payable records for this
  vendor.
• What is the current checking account balance for
  this customer?
• Analytical Support Systems
• Did the sales promotion last quarter do better
  than the same promotion last year?
• Is the five-day moving average for this security
  leading or trailing actual prices?
• Which product line sells best in middle-America
  and how does this correlate to demographic data.

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Analytical Processing

• Analytical Processing today includes what in the
  past have been called
• DSS (Decision Support Systems)
• EIS (Executive Information Systems)
• ESS (Executive Support Systems)
• It is an evolution of End-User Computing
• Placing strategic data access in the hands of
  decision makers aids productivity and enables
  them to be better decision makers.

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Key Difference OLTP vs. OLAP

• OLTP Processing specific functions
• OLAP providing flexibility for undetermined
  analysis.

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A Multidimensional database
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Data for Decision Support

• The data must be integrated - requires data from
  many separate internal corporate databases.
• The data must be enriched - through integration
  with other external data.
• The data must be available - and not constrained
  by machine resources.

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Sources of Data

• Internal Data
• Financial Systems
• Logistics Systems
• Sales Systems
• Production Systems
• Personnel Systems
• Billing Systems
• Information Systems
• External Data Needs
• to recognize opportunities
• to detect threats
• to identify synergies

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Sources of Data - 2

• External Data Categories
• Competitor Data
• Economic Data
• Industry Data
• Credit Data
• Commodity Data
• Econometric Data
• Psychometric Data
• Meteorological Data
• Demographic Data
• Sales Marketing Data

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Operational Control vs. Operational Strategy

• Data is a source not just of operational control,
  but of operational strategy.
• Operational strategy is an attempt to describe
  the need, in a competitive and turbulent market,
  to continually innovate and re-align strategy
  with time scales too short to be comprehended by
  strategic planning in the conventional corporate
  sense.

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Comparison of Control and Strategy Data

• Operational Data
• short-lived, rapidly changing
• requires record-level access
• repetitive standard transactions and access
  patterns
• updated in real-time
• event-driven process generates data
• Strategic Data
• long-living, static
• data aggregated into sets (which is why warehouse
  data is friendly to RDBMS).
• ad-hoc queries with some periodic reporting
• updated periodically with mass loads
• data-driven data governs process.

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Information Requirements by Management
Level(Source Gorry and Scott Morton)
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Dimensional Modeling

• Dimensional Modeling gives us a way to visualize
  data.
• The CEOs perspective
• We sell products in various markets, and we
  measure our performance over time.
• From the data warehouse designers perspective,
  we hear three dimensions
• We sell Products
• in various Markets
• and measure performance over time.

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Dimensional Modeling - contd.

• Management may be interested in examining sales
  figures in a certain city by product, by time
  period, by salesperson, and by store.
• The more dimensions involved, the more difficult
  it is to represent in a single table or graph.
• The ability to add and modify the dimensions used
  in a table or graph is often known as slicing
  and dicing the data.

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Dimensional Model of the Business
e
m
i
T
M a r k e t
Product
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Data Dependencies Model of a Business
Product
Ship To
Shipper
Ship Type
Product Line
District Credit
Contact Location
Order Item
Sales Order
Customer Location
Product Group
Contact
Contract
Customer
Contract Type
Sales Rep
Sales District
Sales Region
Sales Division
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Transaction Processing

• The Relation Model was full of promises for equal
  access to data.
• In the early 1980s the relational model was a
  dream. Typical transaction rates were one per
  second.
• Today the SABRE system typically processes 4,000
  transactions per second, with peak bursts of
  13,000 per second.
• OLTP - (On-line Transaction Processing) The point
  is to get data in to the database.

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Segregating Operational and Warehouse Data

• In the past, data administrators were constantly
  told to build data sharing, normalization, and
  non-redundant corporate databases.
• Early attempts at data warehousing tried to share
  the data with transaction-based systems. This
  resulted in LONG response times for complex
  queries.
• The idea today is to keep the two separate.
• Separate databases, and perhaps separate DBMS
  products and processor platforms are used.
• Controlled and practical redundancy is better
  than out-of-control theoretical purity.

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Fundamental Obstacles With Traditional Systems

• Systems Integration - Disintegration grew slowly
  from islands of automation.
• ownership, planning, economic, organizational
  development issues all contribute.
• Hardware Architecture
• Inconsistent Data
• Data Pollution
• Bad Application Design (semantic and syntactical
  differences).
• Ownership
• Data Entry Conventions

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The Data Warehouse

• Active, tactical, and current events flow from
  the operational systems to the data warehouse to
  become static, strategic, and historical data.
• The data warehouse becomes a middle ground
  where a large number of disparate and
  incompatible legacy systems are tied to an
  equally diverse collection of end-user
  workstations.
• Legacy systems usually comprise a hodge-podge of
  assorted hardware, software, and operational
  systems accumulated over many decades, are by
  nature, incompatible with one another and unique
  to each organization.

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Practical Facts About the Warehouse

• The chances are remote that any single vendor
  will be able to develop a product that can
  interface with all legacy systems painlessly
  and seamlessly and at the same time, combine
  data from modern platforms and data external to
  the organization.
• Instead warehouse product vendors develop
  specialized capabilities to work with various
  environments.

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Typical Dimensional Model
Product Dimension
Sales Fact
Product_key description brand category
Time_key product_key store_key dollars_sold units_
sold dollars_cost
Time Dimension
Time_key day-of-week month quarter year holiday_fl
ag
Store Dimension
Store_key store_name address floor_plan_type
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Fact Table

• Fact Table is where numerical measurements of the
  business are stored.
• Each measurement is taken at the intersection of
  all the dimensions.
• The best facts are numeric, continuously valued
  and additive.
• For every query made against the fact table may
  use hundreds of thousands of individual records
  to construct an answer set.

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Dimension Tables

• Dimension tables are where textual descriptions
  of the business are stored.
• Each textual description helps to describe a
  member of the dimension.
• Example each member in the product dimension is
  a specific product. The product dimension
  database has many attributes to describe the
  product. A key role of the dimension table
  attribute is to serve as the source of
  constraints in a query.

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Example
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Example Query

• Find all product brands that were sold in the
  first quarter of 1995 and present the total
  dollar sales as well as the number of units.
• Brand is a collection of individual products.
• To construct
• A. Drag attribute brand from product dimension.
  Place as Row Header.
• B. Drag Dollar Sales and Units Sold from the
  Fact Table, and place to the right of the Brand
  row header.
• C. Specify row constraint 1st Q 1995 on the
  quarter attribute in the Time Dimension Table.

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Multidimensionality

• Examples of dimensions
• products, salespeople, market segments, business
  units, geographical locations, distribution
  channels, country, industry
• Examples of Facts or Measures
• money, sales volume, head count, inventory,
  profit, actual vs. forecast.
• Examples of Time
• daily, weekly, monthly, quarterly, yearly

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Components of a Data Warehouse - 1

• Acquisition - The first component handles
  acquisition of data from legacy systems and
  outside sources.
• Data is identified, copied, formatted, and
  prepared for loading into a warehouse.
• Vendors provide tools for extraction and
  preparation.

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Components of a Data Warehouse - 2

• Storage Area - The second component is the
  storage area managed by relational databases,
  multi-dimensional databases, specialized hardware
  - symmetric multiprocessor (SMP) or massively
  parallel processors (MPP) machines - or by
  software.
• The storage component hold the data so that many
  different data mining, executive information and
  decision support systems can make use of it
  effectively.

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Components of a Data Warehouse - 3

• Access - The third component of the warehouse is
  the access area.
• Different end-user PCs and workstations draw data
  from the warehouse with the help of
  multi-dimensional analysis tools, neural
  networks, data discover tools, or analysis tools.
• These smart data-mining tools are the driving
  force behind the data warehouse concept.
• What good is it to store all the information
  without some way to understand it in new and
  different ways.

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Data Warehouse Access Tools

• Intelligent Agents and Agencies - tools work and
  think for user.
• Query Facilities and Managed Query environments.
• Statistical Analysis - One of the biggest
  surprises in the data warehousing marketplace is
  the resurgence of interest in traditional
  statistical analysis, and the concomitant
  resurrection of the popularity to products like
  SAS and SPSS.

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Data Warehouse Access Tools - 2

• Data Discovery - A large class of tools formerly
  classified as decision support, artificial
  intelligence and expert systems. They now make
  use of neural networks, fuzzy logic, decision
  trees, and other tools from advanced mathematics
  to allow a user to sift through massive amounts
  of raw data to discover new, interesting,
  insightful, and in many cases useful things about
  the organization, its operations, and its
  markets.
• Currently there are nearly 60 different data
  discovery tools/products on the market.

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Data Warehouse Access Tools - 3

• OLAP - On-line Analytical Processing often uses
  multi-dimensional spreadsheet tools allowing
  users to look at information from many different
  angles.
• Users are able to slice and dice reports and to
  look at the same kinds of information at
  different levels at the same time.
• Typical OLAP application might allow a product
  manager to view sales figures for a given product
  at the national level, see them broken down by
  division, drill down to see territories within a
  division, check sales numbers for each store
  within a territory, and then compare them against
  sales of stores from another territory.

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Data Warehouse Access Tools - 4

• Data Visualization - These tools turn ugly,
  boring numbers into exciting visual
  presentations.
• These tools bring graphical representation to new
  heights. Example Geographical information
  systems turn data about stores, individuals, or
  anything else into compelling, easy to
  understand, dynamic maps.
• PC-based Geographic Information systems have the
  ability to display spatial occurrences and the
  relationship between and among geographically
  specific variables.

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Developing the Data Warehouse

• The most expensive warehousing ventures involve
  major new hardware acquisitions and significant
  investments in training, analysis, and systems
  development costs.
• Typical startup projects allocate 60 of budget
  for hardware and software for creation of a
  powerful storage component. 30 on data mining
  and acquisition tools.
• Budgeting for Systems Analysis and Development
  has 50 of budget on acquisition capabilities,
  30 fund user solutions, 20 creation of
  databases in the storage component.

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Developing the Data Warehouse

• Clarify what you want to do with the Warehouse -
  How Will It be Used.
• Scrutinize the offerings of vendors and systems
  integrators. Make sure you understand which
  functions they provide, and which you must build.
• Most successful projects start as small, tightly
  defined tactical systems to solve pressing
  business needs, and develop into larger systems
  over time.

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DW Summary Key Concepts

• The DW is a collection of integrated,
  subject-oriented databases designed to support
  the decision support function where each unit of
  data is non-volatile and relevant to some moment
  in time (W.H. Inmon, 1992).
• Implicit Assumptions
• physically separate from operational systems
• hold aggregated data and transactional (atomic)
  data for management separate from those used for
  OLTP.

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DW Summary Characteristics

• Subject-orientation
• integrated
• non-volatile (i.e. not updated)
• time variant (kept for long periods, for
  forecasting and trend analysis)
• summarized
• large volume
• not normalized
• metadata
• data sources

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Data Mining