1 categories of data operational and very short-term decision making data current, short-term...

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Categories of data

Operational and very short-term decision making data

Current, short-term decision making, related to financial transactions, detailed data are stored, not structured for decision making.

Historical and long-term decision making dataSaved for a pre-determined period of time, usually related to long-term decision making, structured for decision making.

Contains data that will support decisions of strategic importance.

Referred to as a “data warehouse”.

Archival dataSaved for a pre-determined period of time, used to track transactions for audit, not structured for decision making.

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Webflix data storage requirements

Operational needs.

What are examples of questions management needs to be able to answer to handle daily operations effectively?

Decision support needs.

What are examples of questions management needs to be able to answer to manage the organization effectively on a short and long-term basis?

Governmental, legal or auditing needs.

What types of questions might be relevant for this type of organization?

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Operational data

Includes:Master data (also called reference data): Customer, employee, video, distribution center, critic, keyword.

Transaction data: Queue, Copy, Customer Contract.

Must store both master and transaction data.

Must store changes to both master and transaction data.

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Problems with operational data

May not be integrated.

May not be of good quality:

Incomplete.

Not accurate.

Inconsistent.

The meaning of the data is not fully defined and/or understood by all stakeholders.

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Archival data

Examples of archived data:Emergency dispatch calls.

Credit card transactions.

Accounts payable transactions.

Tax-related data.

Does not usually have to be accessed quickly.

Must have procedures for extracting, transforming and loading (ETL) data as necessary.

Archive database design is usually a copy of the transaction database design.

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Topics about Data Warehouses

What is a data warehouse?

How does a data warehouse differ from a transaction processing database?

What are the characteristics of a data warehouse?

What are the components of a data warehousing system?

How is a data warehouse created?

How is a data warehouse accessed?

Compare and Contrast TPS and DSS

Issue TPS/MIS DSS

Definition Systems to support day-to-day operations.

Systems to support ad-hoc decision making.

Users clerks, data entry, low-level supervisors.

managers, analysts, support staff, researchers.

Design goal Performance. Flexibility, ease of use, ease of access.

Transaction Type

Updates. Queries.

Query Activity

low; few joins. high; many joins.

We use data to answer management questions

TPS Questions

How many customers currently have “Skyfall” in the queue?

How many copies of “Skyfall” are in inventory in Sacramento?

How many customers do we have in Nevada City?

When is “Cloud Atlas” going to be released?

Data Warehouse Questions

How long does a customer usually keep a video?

Which customers return videos within 2 days of receiving them?

Which city has the most customers who return videos within 2 days of receiving them?

What is the most popular genre for customers in Reno?

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Operational vs. Data Warehouse databases

Issue Operational database

Data Warehouse

Content Internal data, process-oriented.

Internal and external data.

Subject-oriented.

Data currency

Real time.

Current.

Volatile.

Batch.

Historical.

Non-volatile.

Summary level

Details of transactions; no (or very little) derived data.

Summarized; many aggregation levels.

Volume Megabytes to gigabytes.

Gigabytes to terabytes.

Design Normalized to prevent anomalies.

Denormalized to enhance query performance.

So, can one database support both transaction processing and decision

support applications?Yes?? No??

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Historical Data

Historical Data

A Business Intelligence “System”

A business intelligence system encompasses all processes, hardware and software necessary to extract data, transform it, integrate it, store it, and provide information. The information is then made effective and accessible to users to support decision making.

Sounds like just another information system...

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So what makes it different?

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Big Data!

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DataSources

ERP

Legacy

POS

OtherOLTP/wEB

External data

Select

Transform

Extract

Integrate

Load

ETL Process

EnterpriseData warehouse

Metadata

Replication

A P

I

/ M

iddl

ewar

e Data/text mining

Custom builtapplications

OLAP,Dashboard,Web

RoutineBusinessReporting

Applications(Visualization)

Data mart(Engineering)

Data mart(Marketing)

Data mart(Finance)

Data mart(...)

Access

No data marts option

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Components of a business intelligence/data warehousing system

Data store.

Extraction/transformation/loading processes.

Analysis tools – both end-user and IT professional.

Visualization tools – primarily end-user.

What is a data warehouse (data store)?

A data warehouse is a database designed to support a decision support system.

A data warehouse is:

Integrated: It is a centralized, consolidated database integrating data from an entire organization.

Subject-oriented: Data warehouse data are organized around key subjects. The data are usually arranged by topic, such as customers, products, suppliers, etc.

Time-variant: Data in the warehouse contain a time dimension so that they may be used as a historical aggregation.

Non-volatile: Once data enter, they seldom leave. Data are appended rather than overwritten. Data are updated in batches.

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Issues in creating a data warehouse

How to get accurate and complete data?

How to consolidate data?

Differing data meanings.

Differing storage mechanisms.

Differing data formats.

CustomerTransactionDatabase

ProductTransactionDatabase

OrderTransactionDatabase

DataScrubbing

DataScrubbing

DataScrubbing

DataExtraction

DataExtraction

DataExtraction

DataIntegration

Sales DataWarehouse

Creating aData

Warehouse

Data mart extraction data warehouse

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Operationaldatabase

Operationaldatabase

External data source

User departments

Data mart

Data mart

Data mart

Extract, Transform and Load Processes

Two-tier data warehouse architecture

Data warehouse

Operationaldatabase

Operationaldatabase

Externaldata source

EDM

Summarizeddata

Transformationprocess

Data warehouseserver

User departments

Three-tier data warehouse architecture

Data warehouse

Operationaldatabase

Operationaldatabase

Externaldata source

EDM

Summarizeddata

Transformationprocess

Data warehouseserver

Userdepartments

Data mart

Data mart

Data mart tier

Extractionprocess

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Issues in designing a data warehouse

Must have a predefined subject focus.

Has the potential to be very large – must define the “grain” or granularity level of storage.

Will always have a dimension of time.

May contain derived data.

May be a summary of data, rather than each detailed transaction.

Does not always adhere to standard normalization rules.

Analysis tools

Standard old queries

Online Analytical Processing

Data Mining

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Online analytical processing

Provides multi-dimensional data analysis techniques.

Works primarily with data aggregation.

Provides advanced statistical analysis.

Supports access to very large databases.

Provides enhanced query optimization algorithms.

Lots of acronyms: OLAP, ROLAP, MOLAP, HOLAP.

Can be add-ons to existing products, example is Excel. Can have their own user interfaces.

OLAP vs. Data Mining questionsOLAP Data Mining

Which customers spent the most with us in the past year?

Which types of customers are likely to spend the most with us in the coming year?

How much did the bank lose from loan defaulters within the past two years?

What are the characteristics of the customers most likely to default on their loans before the year is over?

What were the highest selling fashion items in our London stores?

What additional products are most likely to be sold to customers who buy shorts?

Which store/ location made the highest sales in the past year?

In which area whould we open a new store next year?

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Data mining

Data mining tools:

analyze the data;

uncover patterns hidden in the data;

form computer models based on the findings; and

use the models to predict business behavior.

Proactive tools.

Based on artificial intelligence software such as decision trees, neural networks, fuzzy logic systems, inductive nets and classification networking.

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Visualization tools

Graphical.

Spreadsheet format - usually Excel look-and-feel.

Beyond the spreadsheet using discovery tools. Example: http://www.gapminder.org/

Dashboard. Examples: http://www.dundas.com/dashboard/online-examples/

Web-based.